WO2020077281A1

WO2020077281A1 - Methods and systems for waste treatment management

Info

Publication number: WO2020077281A1
Application number: PCT/US2019/055961
Authority: WO
Inventors: Stephen D. Allen; Charles Howard CELLA
Original assignee: Water Solutions, Inc.
Priority date: 2018-10-12
Filing date: 2019-10-11
Publication date: 2020-04-16

Abstract

A system for treating a waste source a waste source, a management system structured to monitor and control aspects of the system, a treatment and separation system structured to receive a process condition from the management system and execute it on the waste source, and a collection system.

Description

METHODS AND SYSTEMS FOR WASTE TREATMENT MANAGEMENT

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims the benefit of U.S. Provisional Patent Application number 62/744,963, filed October 12, 2018, entitled Methods and Systems for Waste Treatment Management, which is hereby incorporated by reference in its entirety as if fully set forth herein.

BACKGROUND

[0002] Field:

[0003] The present disclosure relates to intelligent management of process for waste treatment.

[0004] Description of the Related Art:

[0005] Many agricultural and industrial operations generate large quantities of processing waste. Often, this processing waste can be further processed to extract valuable resources or commodities. For example, fruit or vegetable processing waste can be treated to extract carotenoids, which are compounds that have use in, at least, human health treatments. On the other hand, aqueous processing waste, such as hydraulic fracturing water or agricultural impound water, often contain contaminants and can be treated to remove such contaminants so that clean water can be safely discharged into the environment.

[0006] Carotenoids are lipids, i.e. fat soluble yellow to orange to red pigments, universally found in the photo synthetic tissue of higher plants, algae and photosynthetic bacteria. They are also found distributed in flowers, fruits, roots of higher plants and fungi and bacteria. Some well- known carotenoids include Beta-carotene (b, b-Carotene), lycopene (y, y-Carotene, C40H56), lutein or xanthophylls (b, e-Caro tene- 3 ,3 '-diol, C40H56O2) and zeaxanthin (b, b-6hΐΌΐ6h6-3,3'- diol, C40H56O2).

[0007] Other processes for extracting carotenoids from certain sources have been proposed, but each has certain disadvantages and limitations, such as low yield and concentrations of carotenoids, and instability of the extracted carotenoids, which are susceptible to oxidation which further decreases their yield. Therefore, there exists a need in the art for a process which efficiently and effectively extracts high concentration and yield levels of biologically active, near-pure carotenoids in a stable form.

[0008] Hydraulic fracturing or“fracking” is a common technique that is often used to increase the rate at which fluids, such as oil, can be extracted from an underground reservoir. The handling of contaminated water from oil wells generated during fracking has been a problem for years, and while the oil and gas exploration industry has been looking for a method and technology to treat contaminated water from oil wells and to be able to return the water for reuse in the wells, such contaminated water is currently often impounded or simply injected back into deep wells. Applicant has identified a need in the art for a process and system which removes contaminants from hydraulic fracturing water so that it can be re-used.

[0009] Agricultural impound water comprises drainage water or runoff water from any number of activities including agricultural activities. Impound water contains any number of

contaminants including organic compounds, minerals, heavy metals, and biological substances. Impound water typically has several minerals and organics present that incorporate selenium, arsenic and uranium as contaminants of the impounded water and are in several forms, both as metallurgical and as organic species, and are present in these forms to unacceptably high levels to prevent discharge as canal grade water. Accordingly, Applicant has identified that it would be an advancement in the art to provide systems and processes for treating contaminated impound water to render it acceptable for environmental discharge or industrial recycling.

[0010] While some conventional methods have been developed to treat agricultural impound water, these conventional methods are ineffective in removing exopolymeric substance (EPS). EPS fouls microfiltration membranes and leads to increased back pressures and lowered flow rates across the microfiltration membranes. In some cases, biofouling of the microfiltration membrane continues until microfiltration is completely impeded. Accordingly, Applicant has identified that it would be an improvement in the art to provide methods and systems for reducing biofouling of microfiltration membranes that are biofouled with biological substances during treatment of agricultural impound water.

SUMMARY

[0011] In an aspect, a method for extracting carotenoids from a carotenoid-containing source may include admixing the carotenoid-containing source with a first organic solvent and a surfactant to form a slurry; treating the slurry with a second organic solvent to form a carotenoid- surfactant combination; separating the treated slurry into a liquid fraction and a solid fraction; separating a first portion, which is a solution of the combination and the second organic solvent, from the liquid fraction; and processing the solid fraction in an anaerobic digester to produce methane. The solid fraction may be processed in an aerobic pre-treatment chamber prior to its being processed in the anaerobic digester.

[0012] In an aspect, a monitoring, testing, and control system for a carotenoid extraction process may include an input system feeding into a carotenoid extraction process system; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the carotenoid extraction process system; a data analysis circuit structured to analyze the plurality of detection values to determine a state of the carotenoid extraction process; and an analysis response circuit structured to control an aspect of the carotenoid extraction process system in response to the state. The plurality of sensors may be selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor. The detection value may be at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non-metal, wherein the metal, metal oxide, metalloid, or non-metal is selected from the group consisting of Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), and Silica (SiCL). The detection value may be at least one of an amount or a presence of a carotenoid, wherein the carotenoid is selected from the group consisting of a Beta-carotene (b, b- Carotene, a lycopene (y, y-Carotene, C₄₀H₅₆), a lutein or a xanthophylls (b, s-Carotene-3,3'-diol, C40H56O2), or a zeaxanthin (b, b-Carotene-3,3'-diol, C40H56O2). The detection value may be at least one of an amount or a presence of an exopolymeric substance, at least one of an amount or a presence of a biological material, at least one of an amount or a presence of water, a component presence, a component concentration, a pH, a temperature, a pressure, or a flow rate. The analysis response circuit may be structured to control an item, wherein the item is at least one of a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, or an enzyme. The analysis response circuit may be structured to control a separation process, a temperature setting, a pressure setting, a pH setting, a mixing period, a mixing speed, a filtration aspect, a filter pore size, and a filter diameter. The input system may feed a waste stream into the carotenoid extraction process system. The waste stream may include solids obtained from a waste treatment of the solids suspended in a wastewater from a fruit processing plant or a vegetable processing plant, a pumice or a rough cut grinding of an exterior of a fruit or a vegetable, at least one of a fine or a slice of a fruit or a vegetable present as a waste or as a disclaimed product, or a solid present from floor sleeping or a general maintenance of a fruit processing facility or vegetable processing facility. The system may further include a pre processing facility that processes an input to the input system, wherein it may mince the input, macerate the input, or employ a caustic peel process. The carotenoid extraction process may include admixing the carotenoid-containing source with a first organic solvent and a surfactant to form a slurry; treating the slurry with a second organic solvent to form a carotenoid-surfactant combination; separating the treated slurry into a liquid fraction and a solid fraction; and separating a first portion, which is a solution of the combination and the second organic solvent, from the liquid fraction. [0013] In an aspect, a system for extracting carotenoids from a carotenoid-containing source having a machine learning or artificial intelligence system for predicting a carotenoid extraction process outcome or state may include a data acquisition circuit structured to collect a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of a carotenoid extraction process system; and a machine learning data analysis circuit structured to receive the detection values and learn received detection value patterns predictive of at least one of an outcome and a state of a carotenoid extraction process, wherein the system is structured to determine if the detection values match a learned received detection value pattern. The machine learning data analysis circuit may be structured to learn received output detection value patterns by being seeded with a model, wherein the model is at least one of a prediction model, a physical model, an operational model, a system model, a neural network, a regression-based model, or a machine-learned model. The machine learning data analysis circuit may improve a prediction of the outcome or the state over time as the model used by the machine learning data analysis circuit is refined or trained by feedback or training data. Training of the model may be supervised, semi-supervised, or unsupervised. The feedback may be a set of circumstances that led to the prediction and an outcome related to a treatment or a contaminant component concentration and the outcome related to the treatment is a post-treatment contaminant concentration. The prediction model may be a treatment prediction model and receives the carotenoid-containing source properties and a treatment, and outputs one or more predictions regarding the treatment. The prediction may be at least one of a component yield, a component concentration, an output temperature, an output viscosity, and a flow rate. The carotenoid-containing source properties may include at least one of a temperature, a flow rate, and a component concentration. The treatment may be addition of at least one of a reagent, a chemical, a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, and an enzyme. The treatment may be a time reacted, a separation type, a pressure setting, a pH setting, a temperature setting, a mixing period, and a mixing speed. The model may have vectors corresponding to an outcome, one or more attributes of a treatment that resulted in the outcome and one or more attributes of the carotenoid-containing source. The machine learning system generates the prediction model based on the vectors or stores the prediction model in a model datastore. The machine learning data analysis circuit may be structured to learn received detection value patterns based on a technique selected from the group consisting of decision trees, k-nearest neighbor, linear regression, k-means clustering, deep learning neural networks, random forest, logistic regression, naive Bayes, learning vector quantization, support vector machines, linear discriminant analysis, boosting, principal component analysis, and a hybrid of k-means and linear regression. The plurality of sensors may be selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor. The detection value may be at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non- metal, wherein the metal, metal oxide, metalloid, or non-metal is selected from the group consisting of Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), and Silica (S1O₂). The detection value may be at least one of an amount or a presence of a carotenoid, wherein the carotenoid is selected from the group consisting of a Beta-carotene (b, b-Carotene, a lycopene (y, y-Carotene, C₄₀H₅₆), a lutein or a xanthophylls (b, s-Carotene-3,3'-diol, C₄₀H₅₆O₂), or a zeaxanthin (b, b^3toΐ6he-3,3'Mίo1, C₄₀H₅₆O₂). The detection value may be at least one of an amount or a presence of an exopolymeric substance, at least one of an amount or a presence of a biological material, at least one of an amount or a presence of water, a component presence, a component concentration, a pH, a temperature, a pressure, or a flow rate. The carotenoid extraction process may include admixing the carotenoid-containing source with a first organic solvent and a surfactant to form a slurry; treating the slurry with a second organic solvent to form a carotenoid-surfactant combination; separating the treated slurry into a liquid fraction and a solid fraction; and separating a first portion, which is a solution of the combination and the second organic solvent, from the liquid fraction.

[0014] In an aspect, a monitoring, testing, and control system for a hydraulic fracturing water treatment process may include an input system feeding into a hydraulic fracturing water treatment process system; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the hydraulic fracturing water treatment process system; a data analysis circuit structured to analyze the plurality of detection values to determine a state of the hydraulic fracturing water treatment process; and an analysis response circuit structured to control an aspect of the hydraulic fracturing water treatment process system in response to the determined state. The plurality of sensors may be selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor. The detection value may be at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non-metal, wherein the metal, metal oxide, metalloid, or non-metal is selected from the group consisting of Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), and Silica (Si0₂). The detection value may be at least one of an amount or a presence of a biological material, at least one of an amount or a presence of water, a component presence, a component concentration, a pH, a temperature, a pressure, or a flow rate. The analysis response circuit may be structured to control an item, wherein the item may be at least one of a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, or an enzyme. The analysis response circuit may be structured to control a separation process, a temperature setting, a pressure setting, a pH setting, a mixing period, a mixing speed, a filtration aspect, a filter pore size, or a filter diameter. The system may further include a pre processing facility that processes an input to the input system. The hydraulic fracturing water treatment process may include adjusting the pH, adding an inorganic coagulant and a polymer to the contaminated water to form particles, and removing the particles.

[0015] In an aspect, a system for treating hydraulic fracturing water having a machine learning or artificial intelligence system for predicting a treatment process outcome or state may include a data acquisition circuit structured to collect a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the system; and a machine learning data analysis circuit structured to receive the detection values and learn received detection value patterns predictive of at least one of an outcome and a state of the treatment process, wherein the system is structured to determine if the detection values match a learned received detection value pattern. The machine learning data analysis circuit may be structured to learn received output detection value patterns by being seeded with a model, wherein the model is at least one of a prediction model, a physical model, an operational model, a system model, a neural network, a regression-based model, or a machine-learned model. The machine learning data analysis circuit may improve a prediction of the outcome or the state over time as the model used by the machine learning data analysis circuit is refined or trained by feedback or training data. Training of the model may be supervised, semi-supervised, or unsupervised. The feedback may be a set of circumstances that led to the prediction and an outcome related to a treatment. The feedback may be a contaminant component concentration and the outcome related to the treatment is a post-treatment contaminant concentration. The prediction model may be a treatment prediction model and receives the carotenoid-containing source properties and a treatment, and outputs one or more predictions regarding the treatment. The prediction may be at least one of a component yield, a component concentration, an output temperature, an output viscosity, and a flow rate. The carotenoid-containing source properties may include at least one of a temperature, a flow rate, and a component concentration. The treatment may be an addition of at least one of a reagent, a chemical, a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, and an enzyme. The treatment may be a time reacted, a separation type, a pressure setting, a pH setting, a temperature setting, a mixing period, and a mixing speed. The model may have vectors corresponding to an outcome, one or more attributes of a treatment that resulted in the outcome and one or more attributes of the hydraulic fracturing water source. The machine learning system may generate the prediction model based on the vectors. The machine learning system may store the prediction model in a model datastore. The machine learning data analysis circuit may be structured to learn received detection value patterns based on a technique selected from the group consisting of decision trees, k-nearest neighbor, linear regression, k-means clustering, deep learning neural networks, random forest, logistic regression, naive Bayes, learning vector quantization, support vector machines, linear discriminant analysis, boosting, principal component analysis, and a hybrid of k-means and linear regression. The plurality of sensors may be selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor. The detection value may be at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non-metal, wherein the metal, metal oxide, metalloid, or non-metal is selected from the group consisting of Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), and Silica (SiCh). The detection value may be at least one of an amount or a presence of a biological material, at least one of an amount or a presence of water, a component presence, a component concentration, a pH, a temperature, a pressure, or a flow rate. The hydraulic fracturing water treatment process may include adjusting the pH, adding an inorganic coagulant and a polymer to the contaminated water to form particles, and removing the particles.

[0016] In an aspect, a monitoring, testing, and control system for a hydraulic fracturing water treatment process may include an input system feeding into a hydraulic fracturing water treatment process system; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the hydraulic fracturing water treatment process system; a data analysis circuit structured to analyze the plurality of detection values to determine a state of the hydraulic fracturing water treatment process; and an automatic cleaning and shut down system that is automatically activated in response to the determined state. The automatic cleaning and shut down system may perform a back flushing of a filter used in hydraulic fracturing water treatment process system or a resin stripping of an ion exchange media used in hydraulic fracturing water treatment process system. The hydraulic fracturing water treatment process may include adjusting the pH, adding an inorganic coagulant and a polymer to the contaminated water to form particles, and removing the particles.

[0017] In an aspect, a monitoring, testing, and control system for an impound water treatment process may include an input system feeding into an impound water treatment process system; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the impound water treatment process system; a data analysis circuit structured to analyze the plurality of detection values to determine a state of the impound water treatment process; and an analysis response circuit structured to control an aspect of the impound water treatment process system in response to the determined state. The plurality of sensors may be selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor. The detection value may be at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non-metal, wherein the metal, metal oxide, metalloid, or non-metal is selected from the group consisting of Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), and Silica (SiCL). The detection value is at least one of an amount or a presence of a biological material, at least one of an amount or a presence of water, a component presence, a component concentration, a pH, a temperature, a pressure, or a flow rate. The analysis response circuit may be structured to control an item, wherein the item is at least one of a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, or an enzyme. The analysis response circuit may be structured to control a separation process, a temperature setting, a pressure setting, a pH setting, a mixing period, a mixing speed, a filtration aspect, a filter pore size, or a filter diameter. The system may further include a pre-processing facility that processes an input to the input system. The impound water treatment process may include treating impound water with metal ion and organic species by treating the impound water with ferric iron ions, flowing the mixture through an upflow reactor filled with iron filings or steel wool, removing the ferrous-treated mixture from the upflow reactor and treating with an oxidant, adding an inorganic coagulant and cationic polymer to oxidized mixture to form particles, and microfiltering the mixture to remove particles.

[0018] In an aspect, a system for treating impound water having a machine learning or artificial intelligence system for predicting a treatment process outcome or state may include a data acquisition circuit structured to collect a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the system; and a machine learning data analysis circuit structured to receive the detection values and learn received detection value patterns predictive of at least one of an outcome and a state of the treatment process, wherein the system is structured to determine if the detection values match a learned received detection value pattern. The machine learning data analysis circuit may be structured to learn received output detection value patterns by being seeded with a model, wherein the model is at least one of a prediction model, a physical model, an operational model, a system model, a neural network, a regression-based model, or a machine-learned model. The machine learning data analysis circuit may improve a prediction of the outcome or the state over time as the model used by the machine learning data analysis circuit is refined or trained by feedback or training data. Training of the model may be supervised, semi-supervised, or unsupervised. The feedback may be a set of circumstances that led to the prediction and an outcome related to a treatment or a contaminant component concentration and the outcome related to the treatment is a post-treatment contaminant concentration. The prediction model may be a treatment prediction model and receives the carotenoid-containing source properties and a treatment, and outputs one or more predictions regarding the treatment or at least one of a component yield, a component concentration, an output temperature, an output viscosity, and a flow rate. The carotenoid-containing source properties comprise at least one of a temperature, a flow rate, and a component concentration. The treatment may be addition of at least one of a reagent, a chemical, a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, and an enzyme. The treatment may be a time reacted, a separation type, a pressure setting, a pH setting, a temperature setting, a mixing period, and a mixing speed. The model may have vectors corresponding to an outcome, one or more attributes of a treatment that resulted in the outcome and one or more attributes of the impound water source. The machine learning system may generate the prediction model based on the vectors. The machine learning system may store the prediction model in a model datastore. The machine learning data analysis circuit may be structured to learn received detection value patterns based on a technique selected from the group consisting of decision trees, k-nearest neighbor, linear regression, k-means clustering, deep learning neural networks, random forest, logistic regression, naive Bayes, learning vector quantization, support vector machines, linear discriminant analysis, boosting, principal component analysis, and a hybrid of k-means and linear regression. The plurality of sensors may be selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor. The detection value may be at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non-metal, wherein the metal, metal oxide, metalloid, or non-metal is selected from the group consisting of Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), and Silica (SiCh). The detection value may be at least one of an amount or a presence of a biological material, at least one of an amount or a presence of water, a component presence, a component concentration, a pH, a temperature, a pressure, or a flow rate. The impound water treatment process may include treating impound water with metal ion and organic species by treating the impound water with ferric iron ions, flowing the mixture through an upflow reactor filled with iron filings or steel wool, removing the ferrous-treated mixture from the upflow reactor and treating with an oxidant, adding an inorganic coagulant and cationic polymer to oxidized mixture to form particles, and micro filtering the mixture to remove particles.

[0019] In an aspect, a monitoring, testing, and control system for an impound water treatment process may include an input system feeding into an impound water treatment process system; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the impound water treatment process system; a data analysis circuit structured to analyze the plurality of detection values to determine a state of the impound water treatment process; and an automatic cleaning and shut down system that is automatically activated in response to the determined state. The automatic cleaning and shut down system may perform a back flushing of a filter used in the impound water treatment process system or a resin stripping of an ion exchange media used in the impound water treatment process system.

[0020] In an aspect, a system for treating a waste source may include a waste source; a management system structured to monitor and control aspects of the system; a treatment and separation system structured to receive a process condition from the management system and execute it on the waste source; and a collection system. The management system may test the waste source properties to determine the process condition to use in the treatment and separation system. The process condition may be the addition of at least one of a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, or an enzyme. The process condition may relate to a separation process, a temperature setting, a pressure setting, a pH setting, a mixing period, a mixing speed, or a filtration aspect. The management system may monitor waste source properties as the waste source moves through the system to update an upstream or a downstream process condition. The management system may test a post- treatment component concentration to determine if additional treatment is needed. The management system may include a monitoring, testing, and control system, an analytics system, a machine learning system, and an artificial intelligence system. The collection system may collect extracted outputs and waste outputs or clean water in a clean water reservoir. The collection system may discharge clean water into the environment.

[0021] In an aspect, a monitoring, testing, and control system for a waste treatment process may include an input system feeding into a waste treatment process system; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the waste treatment process system; a data analysis circuit structured to analyze the plurality of detection values to determine a state of the waste treatment process; and an analysis response circuit structured to control an aspect of the waste treatment process system in response to the state. The plurality of sensors may be selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor. The detection value may be at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non-metal, wherein the metal, metal oxide, metalloid, or non-metal is selected from the group consisting of Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), and Silica (SiCL). The detection value is at least one of an amount or a presence of a carotenoid, wherein the carotenoid is selected from the group consisting of a Beta-carotene (b, b-Carotene, a lycopene (y, y-Carotene, C₄₀H₅₆), a lutein or a xanthophylls (b, s-Carotene-3,3'-diol, C₄₀H₅₆O₂), or a zeaxanthin (b, b-Carotene-3,3'-diol, C₄₀H₅₆O₂). The detection value may be at least one of an amount or a presence of an

exopolymeric substance, at least one of an amount or a presence of a biological material, at least one of an amount or a presence of water, a component presence, a component concentration, a pH, a temperature, a pressure, or a flow rate. The analysis response circuit may be structured to control an item, wherein the item is at least one of a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, or an enzyme. The analysis response circuit is structured to control a separation process, a temperature setting, a pressure setting, a pH setting, a mixing period, a mixing speed, a filtration aspect, a filter pore size, a filter diameter. The input system may feed a waste stream into the waste treatment process system. The waste stream may include solids obtained from a waste treatment of the solids suspended in a wastewater from a fruit processing plant or a vegetable processing plant, a pumice or a rough cut grinding of an exterior of a fruit or a vegetable, at least one of a fine or a slice of a fruit or a vegetable present as a waste or as a disclaimed product, or a solid present from floor sleeping or a general maintenance of a fruit processing facility or vegetable processing facility. The system may further include a pre-processing facility that processes an input to the input system, wherein the pre-processing facility minces the input, macerates the input, or employs a caustic peel process. The waste treatment process may include admixing the waste source with a first organic solvent and a surfactant to form a slurry; treating the slurry with a second organic solvent to form a waste component- surfactant combination; separating the treated slurry into a liquid fraction and a solid fraction; and separating a first portion, which is a solution of the combination and the second organic solvent, from the liquid fraction. The waste treatment process may include adjusting the pH, adding an inorganic coagulant and a polymer to waste to form particles, and removing the particles. The waste treatment process may include treating the waste source with metal ion and organic species by treating the waste source with ferric iron ions, flowing the mixture through an upflow reactor filled with iron filings or steel wool, removing the ferrous-treated mixture from the upflow reactor and treating with an oxidant, adding an inorganic coagulant and cationic polymer to the oxidized mixture to form particles, and microfiltering the mixture to remove particles.

[0022] In an aspect, a system for treating a waste source having a machine learning or artificial intelligence system for predicting a carotenoid extraction process outcome or state may include a data acquisition circuit structured to collect a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of a waste treatment process system; and a machine learning data analysis circuit structured to receive the detection values and learn received detection value patterns predictive of at least one of an outcome and a state of a carotenoid extraction process, wherein the system is structured to determine if the detection values match a learned received detection value pattern. The machine learning data analysis circuit may be structured to learn received output detection value patterns by being seeded with a model, wherein the model is at least one of a prediction model, a physical model, an operational model, a system model, a neural network, a regression-based model, or a machine- learned model. The machine learning data analysis circuit may improve a prediction of the outcome or the state over time as the model used by the machine learning data analysis circuit is refined or trained by feedback or training data. Training of the model may be supervised, semi- supervised, or unsupervised. The feedback may be a set of circumstances that led to the prediction and an outcome related to a treatment. The feedback may be a contaminant component concentration and the outcome related to the treatment is a post-treatment contaminant concentration. The prediction model may be a treatment prediction model and receives waste source properties and a treatment, and outputs one or more predictions regarding the treatment. The prediction may be at least one of a component yield, a component concentration, an output temperature, an output viscosity, and a flow rate. The waste source properties may include at least one of a temperature, a flow rate, and a component concentration. The treatment may be addition of at least one of a reagent, a chemical, a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, and an enzyme. The treatment may be a time reacted, a separation type, a pressure setting, a pH setting, a temperature setting, a mixing period, and a mixing speed. The model may have vectors corresponding to an outcome, one or more attributes of a treatment that resulted in the outcome and one or more attributes of the waste source. The machine learning system may generate the prediction model based on the vectors or store the prediction model in a model datastore. The machine learning data analysis circuit may be structured to learn received detection value patterns based on a technique selected from the group consisting of decision trees, k-nearest neighbor, linear regression, k-means clustering, deep learning neural networks, random forest, logistic regression, naive Bayes, learning vector quantization, support vector machines, linear discriminant analysis, boosting, principal component analysis, and a hybrid of k-means and linear regression. The plurality of sensors may be selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor. The detection value may be at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non- metal, wherein the metal, metal oxide, metalloid, or non-metal is selected from the group consisting of Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), and Silica (SiCL). The detection value may be at least one of an amount or a presence of a carotenoid, wherein the carotenoid may be selected from the group consisting of a Beta-carotene (b, b-Carotene, a lycopene (y, y-Carotene, C₄₀H₅₆), a lutein or a xanthophylls (b, e-Caro tene- 3 ,3 '-diol, C₄₀H₅₆O₂), or a zeaxanthin (b, b-Carotene-3,3'-diol, C₄₀H₅₆O₂). The detection value may be at least one of an amount or a presence of an exopolymeric substance, at least one of an amount or a presence of a biological material, at least one of an amount or a presence of water, a component presence, a component concentration, a pH, a temperature, a pressure, or a flow rate. The waste treatment process may include admixing the waste source with a first organic solvent and a surfactant to form a slurry; treating the slurry with a second organic solvent to form a waste component- surfactant combination; separating the treated slurry into a liquid fraction and a solid fraction; and separating a first portion, which is a solution of the combination and the second organic solvent, from the liquid fraction. The waste treatment process may include adjusting the pH, adding an inorganic coagulant and a polymer to waste to form particles, and removing the particles. The waste treatment process may include treating the waste source with metal ion and organic species by treating the waste source with ferric iron ions, flowing the mixture through an upflow reactor filled with iron filings or steel wool, removing the ferrous-treated mixture from the upflow reactor and treating with an oxidant, adding an inorganic coagulant and cationic polymer to the oxidized mixture to form particles, and microfiltering the mixture to remove particles.

[0023] In an aspect, a system for treating a waste source may include a management system structured to monitor and control aspects of the system, wherein the management system monitors for an indication of a cleaning need; a treatment and separation system structured to receive a process condition from the management system and execute it on the waste source; and an automatic cleaning and shut down system that is automatically activated in response to the indication. The automatic cleaning and shut down system may perform a back flushing of a filter used in the waste treatment process system or a resin stripping of an ion exchange media used in the waste treatment process system.

[0024] These and other systems, methods, objects, features, and advantages of the present disclosure will be apparent to those skilled in the art from the following detailed description of the many exemplary embodiments with reference to the figures.

[0025] All documents mentioned herein are hereby incorporated in their entirety by reference. References to items in the singular should be understood to include items in the plural, and vice versa, unless explicitly stated otherwise or clear from the text. Grammatical conjunctions are intended to express any and all disjunctive and conjunctive combinations of conjoined clauses, sentences, words, and the like, unless otherwise stated or clear from the context.

BRIEF DESCRIPTION OF THE FIGURES

[0026] The disclosure and the following detailed description of certain embodiments thereof may be understood by reference to the following figures:

[0027] Fig. 1 is a diagrammatic view that depicts a waste treatment system in accordance with the present disclosure.

[0028] Fig 2 is a diagrammatic view that depicts a management system in accordance with the present disclosure.

[0029] Fig. 3 is a diagrammatic view that depicts a treatment and separation system in accordance with the present disclosure.

[0030] Fig. 4 is a diagrammatic view that depicts a method for carotenoid extraction in accordance with the present disclosure.

[0031] Fig. 5 is a diagrammatic view that illustrates an example of a method for treating hydraulic fracturing water in accordance with the present disclosure.

[0032] Fig. 6 is a diagrammatic view that illustrates an example of a method for treating agricultural impound water in accordance with the present disclosure. [0033] Fig. 7 is a diagrammatic view that illustrates an example of a method for treating agricultural impound water and reducing biofouling in accordance with the present disclosure.

DETAILED DESCRIPTION

[0034] Fig. 1 depicts the general ecosystem of the waste treatment solution, also termed a waste treatment system herein, according to some embodiments of the present disclosure. The environment includes a waste source 102, a management system 104, a waste treatment and separation system 108, and a collection system 110. The waste source 102 may be a substance to be treated by the treatment and separation system 108 and may typically be a liquid but may be any other fluid or solid. It may be stored in a waste source reservoir or may be channeled in directly from an external system such as an industrial or agricultural processing operation.

[0035] The waste source 102 may be, but is not limited to any one fruit or vegetable processing waste, fracturing or flow back water from one or more oil wells, agricultural impound water, wastewater, sewage, post-fermentation streams, post-digestion streams, and the like. Components or substances may be extracted or removed from the waste source by the treatment and separation system, such as metals and certain non-metals (e.g. Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), Silica (S1O2)), animal processing wastes, fruit and vegetable processing components including carotenoids (e.g. Beta-carotene (b, b-Carotene, lycopene (y, y-Carotene, C40H56), lutein or xanthophylls (b, s-Carotene-3,3'-diol, C40H56O2) and zeaxanthin (b, b- Carotene-3,3'-diol, C40H56O2), uranium, selenium, arsenic, EPS, biological material, and/or water.

[0036] In embodiments, the management system 104 monitors and controls aspects of the treatment and separation system 108, waste source 102, and collection system 110. In some embodiments, the management system 104 tests properties of the waste source 102 to determine optimal process conditions in the treatment and separation system 108 such as chemicals to be added, separation systems to use, temperature, pressure, mixing times, mixing speeds, and the like. In some embodiments, the management system 104 monitors one or more properties of the waste source 102 as its fluids moves through the system to update upstream or downstream process conditions, as needed. In some embodiments, the management system 104 tests post treatment component concentrations to determine if additional treatment may be needed.

[0037] In embodiments, the treatment and separation system 108 may process the waste source by adding chemicals, performing separations, heating or cooling, and the like. In embodiments, the management system 104 may determine process conditions or may implement a pre programmed process. Treatments may include, but are not limited to, the following: addition of solvent, addition of surfactant, addition of coagulant, addition of polymer, addition of ions, addition of reducing agent, addition of oxidizing agent, addition of other chemical, addition of enzyme, pH adjustment (i.e. addition of an acid to lower pH or a base to increase pH), pressure variation, temperature variation, and/or separations.

[0038] In embodiments, the collection system 110 may collect desired extracted outputs such as clean water or carotenoids as well as other system outputs, such as waste. In embodiments, the collection system 110 collects may clean water in a clean water reservoir. In other embodiments, the collection system may discharge clean water into the environment.

[0039] Referring now to Fig. 2, the management system 104 may include, but is not limited to, the following subsystems: a monitoring, testing, and control system 202, an analytics system 204, a machine learning system 208, and an artificial intelligence system 210.

[0040] Some embodiments of the described systems and methods involve monitoring one or more parts of the systems or methods as they are used. In this manner, the described systems and methods can provide feedback information that can be used to dynamically tailor the methods to the particular characteristics of the waste source. For instance, in some embodiments in which the system 202 may determine that characteristics of the waste source 102 are changing as the method progresses, the system can dynamically change an aspect of the process to best suit the waste source’s newly discovered characteristics, such as making an addition to the process of an item or modifying a condition of the process. The monitoring, testing, and control system 202 may monitor or test various properties of the system or subsystems. Monitoring and testing can be performed by sensors and the like. For example, the monitoring, testing, and control system 202 may be coupled to the temperature sensors and heating elements to provide continuous, regulated heating of subsystems. In another example, the system may include a plurality of pH sensors.

[0041] In embodiments, a plurality of chemical inlets may be coupled to the monitoring, testing, and control system 202 to introduce acid or base to a waste processing stream or reservoir to control the pH based upon pH measurements from the pH sensors. In yet another example, a viscosity sensor may be disposed in a waste processing stream or reservoir to measure the viscosity of the waste source and post-processed waste stream. In embodiments, an increase in viscosity, which may be caused by polymerization processes, may cause the monitoring, testing, and control system 202 to add an enzyme to mitigate the increase and control fouling of the processing system components, such as downstream membranes. Other sensors may be dispersed and connected throughout the system 202 as needed, to monitor operations and make appropriate changes, such as the addition of appropriate chemical or biological additives. [0042] The monitoring, testing, and control system 202 may include, but is not limited to, the following sensors: chemical sensors, pH sensors, temperature sensors, waste solids analysis, volatile organic compound (VOC) sensors, viscosity sensors, imaging/optical sensors, electrochemical sensors, mass sensors, level sensors, and/or pressure sensors. The monitored or tested properties may include, but are not limited to: component presence, component concentration, pH, temperature, pressure, and/or flow rate.

[0043] The analytics system 204 performs analytics relating to various aspects of the waste treatment system. The analytics system 204 may analyze stream properties at any point in the system to determine which chemicals or other additives to add and in what amounts, mixing periods, mixing speeds, temperature settings, pressure settings, pH settings, separation systems, and the like, and if processing should continue or terminate·

[0044] In embodiments, the machine learning system 208 may train models, such as predictive models (e.g., various types of neural networks, regression based models, and other machine- learned models), including treatment prediction models. Training may be supervised, semi- supervised, or unsupervised. Training may be done using training data, which may be collected empirically or generated for training purposes, or both.

[0045] In embodiments, a treatment prediction model (or prediction model) may be a model that receives waste source properties and treatment data and outputs one or more predictions regarding the treatment. Examples of predictions may be component yield, component concentration, flow rates, and the like. The machine learning system 208 may train a model based on training data, feedback, or a combination thereof. In embodiments, the machine learning system 208 may receive vectors containing waste source properties (e.g., temperature, flow rate, component concentrations, or the like), treatment data (e.g., chemicals added, amount of chemicals added, time reacted, separation type, and the like), and outcomes (e.g., component yield, component concentrations, output temperature, or the like). Each vector may correspond to a respective outcome and the attributes of the respective treatment and respective waste source 102. The machine learning system 208 may take in the vectors and generates a predictive model based thereon. In embodiments, the machine learning system 208 may store the predictive models in a model datastore.

[0046] Training done based on feedback received by the system may also be referred to as “reinforcement learning.” In embodiments, the machine learning system 208 may receive a set of circumstances that led to a prediction (e.g., contaminant component concentration) and an outcome related to the treatment (e.g., post-treatment contaminant concentration), and may update the model according to the feedback. [0047] Machine learning techniques include, but are not limited to, the following: decision trees, k-nearest neighbor, linear regression, k-means clustering, deep learning neural networks, random forest, logistic regression, naive Bayes, learning vector quantization, support vector machines, linear discriminant analysis, boosting, principal component analysis, and/or a hybrid of k-means and linear regression.

[0048] The Artificial Intelligence (AI) system 210 leverages the predictive models to make predictions regarding treatment outcomes with respect to waste source properties and treatments. Examples of predictions include post-treatment component yield, post-treatment contaminant concentration, and the like. Examples of additional attributes that can be used to make predictions about a treatment include: temperature, pressure, pH, component presence, component concentration, flow rate, chemical additives, and/or separation type.

[0049] Referring to Fig. 3, an example treatment and separation system 108 according to some embodiments of the present disclosure is illustrated. In embodiments, the treatment and separation system 108 may include chemical treatment subsystems 302 for adding chemicals, adjusting pH, mixing, and the like.

[0050] Chemical treatment in the chemical treatment system 302 may include adding organic solvent. Organic solvents that may be added include, but are not limited to, the following:

ethanol, methanol, n-propanol, i-propanol, n-butanol, i-butanol, s-butanol, n-amyl alcohol, i-amyl alcohol, cyclohexanol, n-octanol, ethanediol, and/or 1, 2-propanediol. Chemical treatment in the chemical treatment system 302 may include adding surfactant. Surfactants may be non-ionic. Surfactants that may be added include, but are not limited to, the following: linear alkyl alkoxylate, linear alkylbenzenesulfonates, lignin sulfonates, fatty alcohol ethoxylates, and alkylphenolethoxylates. Chemical treatment may include pH adjustment by adding acid to lower the pH. Acids that may be added include, but are not limited to, the following: H2SO4, HC1, and/or HNO3. Chemical treatment may include pH adjustment by adding base to increase the pH. Bases that may be added include, but are not limited to, the following: NaOH, KOH, Ca(OH)₂, Mg(OH)2, CaO, and/or MgO. Chemical treatment may include adding coagulant. Typically, inorganic coagulants are used. Coagulants that may be added include, but are not limited to, the following: ferric sulfate, ferric chloride, ferric chloro sulfate, ferrous sulfate, ferrous chloride, ferrous iron compounds, ferric iron compounds, aluminum sulfate, aluminum chlorohydrate, aluminum chloride, aluminum hydro xychloride, aluminum chlorohydroxide, aluminum chlorohydrol, polyaluminum chloride, polyaluminum sulfate, sodium aluminate, aluminum and iron polymers, silicates, dithiocarbamate, and/or dithiocarbonic acid. Chemical treatment may include adding polymer. Typically, cationic polymers are used. Polymers that may be added include, but are not limited to, the following: Epi-dma, DADMAC, polyacrylamide, and/or acrylamide. Chemical treatment may include adding other chemical additives such as ferric ions, ferrous ions calcium oxide, hydroxide, enzymes, and the like. Chemical treatment may include adding reducing agent. Reducing agents that may be added include, but are not limited to, the following: iron filings and/or steel wool. Chemical treatment may include adding oxidizing agent. Oxidizing agents that may be added include, but are not limited to, the following: ozone (O3), hypochlorite (NaOCl), persulfates ((NfUkSxOx, ICSxOx or NaxSxOx), peroxide (H2O2), permanganate (KMn0₄), Fenton's reagent (peroxide catalyzed by Fe²⁺ ions), Fe²⁺ catalyzed persulfate, and/or ferrates.

[0051] In embodiments, the treatment and separation system 108 may include separation systems 304 for separating solids from liquids, liquid fractions from liquids, and the like. Separation systems 304 may include, but are not limited to, the following: microfiltration, inclined plate separator, clarifier/sand filter, carbon column, drum, settling, activated bentonite clay, belt press, centrifuge, filter press, formed solids on membrane (see United States Serial No. 10/706,168), filter paper, reverse osmosis, deionization, mixed bed deionizers, electro-separation, fractional distillation, thermal distillation, distillation, equalization, American Petroleum Institute oily water separator (oil/water separation), separation funnel, and/or fine mesh strainer.

[0052] Where separation subsystems 304 include microfiltering, microfiltration materials may include, but are not limited to, the following: polypropylene felt with PTFE coating,

polypropylene membrane bonded to a polypropylene or polyethylene felt backing, polysulfone, polyethylene, and/or polytetrafluoroethylene. In embodiments where separation systems 304 include microfiltration membranes, an automatic backflushing system may automatically detect and backflush solids.

[0053] In embodiments, the treatment and separation system 108 includes an anaerobic digester for processing solids and producing methane. In some embodiments, the anaerobic digester is connected to an aerobic pre-treatment chamber. In embodiments, the treatment and separation system 108 includes an automatic cleaning and shut down system.

[0054] Referring to Fig. 4, an example carotenoid extraction method according to some embodiments of the present disclosure is illustrated. Carotenoids to be extracted may include, but are not limited to, the following: beta-carotene, lycopene, lutein or xanthophylls, or zeaxanthin. Carotenoids may be found in plant-based or other sources, including, but not limited to, the following: leafy greens (e.g. kale, spinach, cress, parsley, beet greens, carrots, red peppers), flowers, fruits (e.g. berries, tomatoes, peaches), roots, fungi, and/or bacteria.

[0055] Waste sources containing carotenoids may include, but are not limited to, the following: solids obtained from the waste treatment of the suspended solids present in wastewater from fruit or vegetable processing plants, pumice or rough cut grinding of the exterior of the fruit or vegetable, fines and/or slices of the fruit or vegetable present as a waste or as a disclaimed product, and/or solids present from floor sleeping or general maintenance of the fruit or vegetable processing facility.

[0056] In some embodiments, processing waste sources 102 containing carotenoids may need pre-treatment by the following methods: mincing (e.g. cut, chopped, blended), macerating with distilled water (e.g. water to dry pumice ratio of 3:1, water to vines ratio of 2:1, water to leafy greens ratio of 1500 g: 1 kg), and/or caustic peel process (e.g. remove skin using high pH solution of water and sodium hydroxide, send peel material to high shear pump, lower pH of peel with citric acid).

[0057] With reference now to step 402, a carotenoid-containing waste source is provided. At step 404, the process moves to admixing the waste source, a first organic solvent and a surfactant to form a slurry. Admixing may decrease the surface tension in the tissue cell structure of components in the waste source, thereby enhancing penetration of the surfactant into the tissue cell structure so that the carotenoids and the surfactant may form a combination. Generally, the desired period of time for mixing in step 404 ranges from 1 to 12 hours, but about 2 hours is preferable. Generally, the desired speed for mixing ranges from 20 to 100 revolutions per minute (“rpm”), but about 60 rpm is preferable. The first organic solvent is preferably an alcohol, and may be selected from the group consisting of ethanol, methanol, n-propanol, i-propanol, n- butanol, i-butanol, s-butanol, n-amyl alcohol, i-amyl alcohol, cyclohexanol, n-octanol, ethanediol, and 1, 2-propanediol. Approximately 50-500 milliliters of first organic solvent may be admixed for each kilogram of the waste source 102 (Fig. 1). The surfactant may be a linear alkyl surfactant. Approximately 0.1-10 milliliters of surfactant may be admixed for each kilogram of waste source, but preferably, approximately 2 grams of surfactant (e.g. Tween 60, Tween 80, SLS [sodium laureth sulfate], SDS [sodium dodecyl sulfate]) may be admixed for each kilogram of source.

[0058] Step 408 of Fig. 4 includes treating the slurry with a second organic solvent which solubilizes the combination. The second organic solvent may be a polar organic solvent, such as carbon disulfide, THF [tetrahydrofuran], hexane or heptane. It will be appreciated in light of the disclosure that use of carbon disulfide to solubilize the combination is advantageous because carbon disulfide will permit higher concentrations of carotenoids per unit of volume.

Approximately at least 200 grams of second organic solvent may be used to treat each approximately 200-250 grams of slurry. Generally, the desired period of time for mixing in this step may range from 5 to 60 minutes. In some embodiments the desired period of time for mixing may be approximately 20 minutes. Generally, the desired speed for mixing may range from 20 to 100 rpm, but about 60 rpm may be preferable. [0059] With respect to step 410, Fig. 4 shows separating the treated slurry into a liquid fraction and a solid fraction. The liquid fraction may be separated from the treated slurry by a mechanical separation system such as a fine mesh strainer, a press, and the like. A plurality of mechanical mechanisms may be used sequentially to separate virtually all of the liquid fraction from the solid fraction. Thereafter, the solid fraction may be disposed of.

[0060] Step 412 of Fig. 4 illustrates separating a first portion from the liquid fraction. The first portion comprises a solution of the second organic solvent and the combination, i.e. carotenoid and surfactant. The first portion may be separated by various separation methods or apparatus, examples in which the first portion may be separated from the liquid fraction by a separation funnel after the liquid fraction is allowed to stabilize. Distinct layers may form in the liquid fraction. The lowest level of the liquid fraction in the funnel may contain the first portion. The first portion may be rich in color because of the presence of the carotenoid. The first portion may be removed from the separation funnel in a conventionally accepted manner. The final first portion of the process of Fig. 4 in accordance with embodiments of the present disclosure is a composition of matter composed of a solution including a combination composed of a surfactant and the carotenoids and an organic solvent. The first portion may be a solid fraction and may be processed in an anaerobic digester to produce methane. In embodiments, the solid fraction may be processed in an aerobic pre-treatment chamber prior to its being processed in an anaerobic digester.

[0061] Some embodiments of the present disclosure involve collecting carotenoid crystals rather than a carotenoid solution. Collecting the carotenoids may include the steps of: concentrating the carotenoids present in the first portion to a desired level; treating the concentrated first portion with a mixture to precipitate the carotenoids in crystalline form; and separating the crystalline carotenoids from the treated first portion. Some embodiments of the present invention include a step of alternately washing the crystalline carotenoids with ethanol and distilled water for a desired number of cycles. In many embodiments, cool ethanol is used. Subsequently, in some embodiments, the crystalline carotenoids may be allowed to dry. The washed and dried crystalline carotenoids may be collected and stored in a closed vessel at a cool temperature. Conventional preservation steps, e.g. nitrogen blanket or any other suitable protective measure, may be taken at reduced temperatures. All aspects of this process may be controlled by the management system 104 as described herein.

[0062] Referring now to Fig. 5, a method for treating hydraulic fracturing water according to some embodiments of the present disclosure begins at step 502 by providing contaminated water as a waste source from an oil well. While this contaminated water can come from any suitable source, in some embodiments, such water comprises flow back water and/or tracked well water that exits in the well.

[0063] With reference now to step 504, Fig. 5 shows the method may include ensuring that the pH of the waste source is in a suitable range that allows a particulate to form in the waste source when one or more suitable coagulants and polymers are added (as discussed below with respect to step 508). The waste source's pH may be maintained and/or adjusted to any suitable range that allows the flocculent to form. Indeed, in some embodiments, the contaminated water's pH may be adjusted so that it is in the range from about 4.5 to about 8.1. Where the waste source's pH is adjusted to a suitable range, it can be adjusted in any suitable manner, including without limitation, through the addition of one or more bases and/or acids.

[0064] With reference now to step 508, Fig. 5 shows that the method can include forming a flocculant in the waste source. In this manner, the method enables the size of contaminants and solid flocculant in the waste source to be increased so that the contaminants and flocculant can be easily removed from the contaminated water, thereby leaving treated water that is clearer and cleaner than the original contaminated water. The flocculant may be formed in any suitable manner, including without limitation, through the addition of one or more inorganic coagulants and one or more polymers. The coagulant may include any suitable inorganic coagulant that forms a flocculant with contamination and particulates (e.g., sand, metals, proppant, dirt, ions, etc.) in the contaminated water when the coagulant and water are mixed with the polymer (discussed below) at a suitable pH. The polymer may include any suitable polymer that forms a flocculant with contaminants and particulates (e.g., sand, metals, proppant, dirt, ions, etc.) in the contaminated water when the polymer and contaminated water are mixed with the coagulant at a suitable pH.

[0065] Once the flocculant has been formed, pursuant to step 510 in Fig. 5, at least a portion of the flocculant is removed from the contaminated water to leave a cleaner and clearer treated water. The flocculant may be separated from the contaminated water in any suitable manner, including without limitation, through settling, microfiltration, fractional distillation techniques, thermal distillation techniques, and/or another suitable method. In some embodiments, the described method also includes an equalization step in which treated water (e.g., water from which at least a portion of the particulate has been removed) is placed in an equalization tank or storage tank. In such embodiments, placing the treated water in an equalization or storage tank may enable a system implementing the method to maintain appropriate process flows and to accommodate temporary shutdown of the system during back flushing of the filter and resin stripping of any ion exchange media. In still another example, certain embodiments of the described systems and methods allow contaminated water from oil wells to be cleaned such that the treated water can then be further cleaned through a reverse osmosis procedure, a deionization procedure, a fractional distillation procedure, and/or any other suitable separation process. As a result, the treated water may easily be reused and recycled in fracturing fluid, potable water, and a variety of other uses. All aspects of this process may be controlled by the management system 104 as described herein.

[0066] Fig. 6 illustrates an example of a method for treating agricultural impound water according to some embodiments of the present disclosure. Impound water may be treated by adding ferric ions, reducing the mixture using an upflow reactor with iron filings or steel wool, adding an oxidant and an inorganic coagulant, and microfiltration. A reverse osmosis system with recycle may further be used. The treatment may result in the capture of arsenic, selenium, uranium, and the like.

[0067] With respect to step 602, the methods include providing agricultural impound water as a waste source. In embodiments, the method includes a pretreatment step 604 wherein

contaminated water is oxidized, pH adjusted, treated with a coagulant, and treated with a polymer. The pretreatment to the contaminated water increases the physical size of contaminants and particulates in the contaminated water and to form a flocculent comprising bulk solids and fine particles. In step 608, bulk solids are removed using any separation technique. Examples of separation techniques include, but are not limited to, settling, filter press, centrifuge, belt press, and combinations thereof.

[0068] In one example, process of generating ferrous ions, ferrous ions may be generated or regenerated in one or more columns that contain iron filings or steel wool. The generation of the ferrous ion (Fe²⁺) is accomplished through the in situ reduction of the ferric ion (Fe³⁺) in columns called upflow reactors. These upflow reactors contain the iron filings or steel wool and contact the ferric iron to produce sufficient ferrous compounds to provide the advanced oxidation required for the ion and organic species. The ferric ions are reduced to form ferrous ion, while the iron filings or steel wool oxidize to form additional ferrous ion. In some non-limiting embodiments, a ratio of ferrous ions (Fe²⁺) to contaminant metal ions in the contaminated water may be between about 2.4:1 to 1 :1. In some non- limiting embodiments, a ratio of ferrous ions (Fe²⁺) to oxidizable organics in the contaminated water may be between about 2.4:1 to 1:1. In some embodiments, an optimum pH for these reactions to proceed is slightly acidic. Non- limiting examples of such oxidation processes include, but are not limited to: persulfate, ozone, or hydrogen peroxide treatment.

[0069] In embodiments, the contaminated water may first be treated with the ferrous ions followed by one or more oxidation reagents, such as those identified previously herein. In addition to assisting in the removal of undesired metals or ions, the advanced oxidation step works well for the organic species destruction, which may serve to reduce or eliminate any organic seleno-species. In embodiments, this may be a strict order of additional steps, the metal (ferrous in this case) first, with a mix time of about 10 to 30 minutes, and then the addition of the oxidant.

[0070] With respect to step 610 of Fig. 6, a liquid portion containing fine particles is applied to a low pressure deadhead micro filtration unit to remove the fine particles from the contaminated water resulting in a microfilter effluent. In embodiments, the operating pressures may range from 5 to 15 psi. In embodiments, the GFD [gallons per square foot of membrane] may range from 750 GFD to 1,100 GFD, high flow at low pressure across the membranes. In embodiments, the average particle sizing may be 75-80 microns. In embodiments, solids may be automatically backwashed off the membrane of the microfiltration unit.

[0071] Following the pretreatment in step 604, in some cases, the microfilter effluent is canal grade quality water and can be sent to the collections reservoir or discharged into the environment. However, in other cases certain trace contaminants may remain at unacceptably high levels to be considered canal grade quality water, so, the microfilter effluent may be directly fed to one or more reverse osmosis (R/O) units or other separation systems. The R/O reject water may be oxidized and then recycled back to the front of the system to be retreated. All aspects of this process may be controlled by the management system 104 as described herein.

[0072] Referring now to Fig. 7, an example of a method for treating agricultural impound water and reducing biofouling according to some embodiments of the present disclosure is illustrated. In particular, the disclosure includes processes and systems for reducing bio fouling of microfiltration membranes that are biofouled with a biomolecule-based exopolymeric substance (EPS) contained in agricultural impound water.

[0073] With respect to step 702, the methods include reducing biofouling of microfiltration membranes by an EPS by providing agricultural impound water comprising an EPS as a waste source.

[0074] In step 704, the waste source may be reacted with calcium oxide or calcium hydroxide to generate a calcium-treated EPS mixture. In some cases, the calcium oxide or calcium hydroxide may be reacted at a final concentration of between about 100 mg/L to 225 mg/L. In other cases, the inorganic coagulant may comprise aluminum chlorohydrate and may be reacted at a final concentration of about 25 mg/L to 75 mg/L.

[0075] In step 708, the EPS may be encapsulated into filterable, non-tacky particles by reacting the calcium-treated EPS mixture with an aluminum-based inorganic coagulant and a cationic polymer. In some instances, the inorganic coagulant and polymer may be reacted with the calcium-treated EPS mixture at a ratio of about 2.5:1 to 10:1. [0076] Step 710 in some embodiments includes removing a first portion of the encapsulated EPS as bulk solid, and subsequently, in step 712, removing a second portion of the encapsulated EPS by low pressure microfiltration through a microfiltration membrane. The microfiltration membrane may have a pore size of between about 0.7 to 12 microns. In other embodiments, the microfiltering may further include microfiltering with a micro filter membrane at a back pressure of less than about 15 psi and at a flow rate of at least 650 gallons per square foot of micro filter membrane per day and periodically backwashing the micro filter membrane to remove collected filterable non-tacky particles. All aspects of this process may be controlled by the management system 104 as described herein.

[0077] In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing micro filtration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for automatic cleaning and shut-down.

[0078] In embodiments, provided herein is a method for extracting lycopene from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a method for extracting lycopene from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting lycopene from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting lycopene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting lycopene from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for automatic cleaning and shut-down.

[0079] In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing the carotenoid- containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting Beta- carotene from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting Beta- carotene from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting Beta-carotene from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for automatic cleaning and shut-down.

[0080] In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing micro filtration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a

microfiltration system with an automatic backflushing system. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by

microfiltration· In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a reverse osmosis system for treating micro filtration effluent. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having an analytics system. In embodiments, provided herein is a method for extracting zeaxanthin and lutein from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, ethanol and surfactant, admixing carbon disulfide with the mixture, separating a liquid fraction from a solid fraction, and separating a first portion from the liquid fraction having a system for automatic cleaning and shut-down.

[0081] In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a monitoring, testing, and control system. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having an analytics system. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a monitoring, testing, and control system. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having an analytics system. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid- containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid- containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a monitoring, testing, and control system. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid- containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having an analytics system. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a monitoring, testing, and control system. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid- containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having an analytics system. In embodiments, provided herein is a method for collecting carotenoids in crystalline form from a carotenoid-containing source having the steps of concentrating carotenoids to a desired level, treating the concentrated portion with a mixture of ethanol and citric acid, and separating crystals from the liquid portion having a system for automatic cleaning and shut-down.

[0082] In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a monitoring, testing, and control system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having an analytics system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a monitoring, testing, and control system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having an analytics system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a system for automatic cleaning and shut-down. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing micro filtration. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing

microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a monitoring, testing, and control system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid- containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having an analytics system. In

embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a micro filtration system with an automatic backflushing system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a system for automatic cleaning and shut-down. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a monitoring, testing, and control system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having an analytics system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having an alcohol and also having a non-ionic surfactant that forms a combination with the carotenoids and where the combination is solubilized in the polar organic solvent having a system for automatic cleaning and shut-down.

[0083] In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid- containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a monitoring, testing, and control system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having an analytics system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid- containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a monitoring, testing, and control system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having an analytics system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a system for automatic cleaning and shut-down. In

embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a monitoring, testing, and control system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having an analytics system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a reverse osmosis system for treating micro filtration effluent. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a system for automatic cleaning and shut-down. In

embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid- containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a monitoring, testing, and control system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having an analytics system. In embodiments, provided herein is a composition resulting from a process of extracting carotenoids from a carotenoid-containing source having a solution including a combination of a surfactant and the carotenoids solubilized in a polar organic solvent having a system for automatic cleaning and shut-down.

[0084] In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having an analytics system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having an analytics system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing micro filtration. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having an analytics system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In

embodiments, provided herein is a method for extracting carotenoids from a carotenoid- containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a monitoring, testing, and control system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid- containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having an analytics system. In embodiments, provided herein is a method for extracting carotenoids from a carotenoid-containing source having the steps of admixing the carotenoid-containing source, organic solvent and surfactant, admixing a second organic solvent with the mixture, separating a liquid fraction from a solid fraction, separating a first portion from the liquid fraction, and feeding the solid fraction to an anaerobic digester to produce methane having a system for automatic cleaning and shut-down.

[0085] In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having an analytics system. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having an analytics system. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having an analytics system. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having an analytics system. In embodiments, provided herein is a method for treating fruit or vegetable processing wastewater and forming solid particles of specific and controllable sizes and weights having the steps of admixing a coagulant to form particles, admixing a cationic polymer to neutralize the coagulated solid particles into solid particles of specific and controllable size and weight, and separating the solid particles having a system for automatic cleaning and shut-down.

[0086] In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having an analytics system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having a monitoring, testing, and control system having a monitoring, testing, and control system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having an analytics system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having a monitoring, testing, and control system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing micro filtration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having a monitoring, testing, and control system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having a monitoring, testing, and control system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having a monitoring, testing, and control system having a monitoring, testing, and control system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having an analytics system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having a monitoring, testing, and control system having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having a monitoring, testing, and control system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having an analytics system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a monitoring, testing, and control system having a system for automatic cleaning and shut-down.

[0087] In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having an analytics system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a monitoring, testing, and control system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having an analytics system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing micro filtration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing micro filtration. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing micro filtration. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a monitoring, testing, and control system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having an analytics system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a reverse osmosis system for treating

microfiltration effluent. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a monitoring, testing, and control system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having an analytics system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having a machine learning system and artificial intelligence system for determining optimal process conditions having a system for automatic cleaning and shut-down.

[0088] In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having a monitoring, testing, and control system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having an analytics system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the

microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having a monitoring, testing, and control system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having an analytics system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having an analytics system having a system for automatic cleaning and shut down. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having an analytics system having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having a monitoring, testing, and control system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid-containing source having an analytics system having an analytics system. In embodiments, provided herein is a system for extracting carotenoids from a carotenoid- containing source having an analytics system having a system for automatic cleaning and shut down.

[0089] In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a monitoring, testing, and control system. In

embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having an analytics system. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having an analytics system. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a micro filtration system with an automatic backflushing system. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by

microfiltration· In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having an analytics system. In embodiments, provided herein is a method for treating hydraulic fracturing water having the steps of adjusting the pH of the water, admixing the water with inorganic coagulant and polymer, and separating the solid fraction from the liquid fraction having a system for automatic cleaning and shut-down.

[0090] In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by

microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a monitoring, testing, and control system having a system for automatic cleaning and shut-down.

[0091] In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing micro filtration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing micro filtration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a machine learning system and artificial intelligence system for determining optimal process conditions having a system for automatic cleaning and shut-down.

[0092] In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having a system for treating removed solids for use as a softening salt in industrial applications. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing micro filtration. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing micro filtration. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having an analytics system having a system for automatic cleaning and shut-down.

[0093] In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications.

In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing micro filtration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for treating removed solids for use as a softening salt in industrial applications having a system for automatic cleaning and shut-down.

[0094] In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut down having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having a micro filtration system with an automatic backflushing system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by micro filtration. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having an analytics system. In embodiments, provided herein is a system for treating hydraulic fracturing water having a system for automatic cleaning and shut-down having a system for automatic cleaning and shut-down.

[0095] In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing micro filtration. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having an analytics system. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by micro filtration. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a reverse osmosis system for treating micro filtration effluent. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having an analytics system. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a system for automatic cleaning and shut-down.

[0096] In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing micro filtration. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing micro filtration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing

microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having an analytics system. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing micro filtration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing micro filtration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing micro filtration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing microfiltration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having an analytics system. In embodiments, provided herein is a method for treating impound water having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, performing micro filtration, treating the microfiltration effluent using a reverse osmosis system, and oxidizing reject water from the reverse osmosis system, and recycling the oxidized reject water by combining it with input impound water having a system for automatic cleaning and shut-down.

[0097] In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having an analytics system. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a reverse osmosis system for treating micro filtration effluent. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having an analytics system. In embodiments, provided herein is a method for treating impound water by removing uranium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a system for automatic cleaning and shut-down. [0098] In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration. In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having an analytics system. In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having an analytics system. In embodiments, provided herein is a method for treating impound water by removing selenium having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a system for automatic cleaning and shut-down.

[0099] In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration· In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having an analytics system. In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a reverse osmosis system for treating micro filtration effluent. In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having an analytics system. In embodiments, provided herein is a method for treating impound water by removing arsenic having the steps of admixing ferric ions, reducing the mixture with an upflow reactor having iron filings or steel wool, admixing an oxidizing reagent and an inorganic coagulant, and performing microfiltration having a system for automatic cleaning and shut-down.

[0100] In embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system having an analytics system. In embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by

microfiltration· In embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system having a waste source, a management system, a treatment and separation system, and a collection system. In

embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system having an analytics system. In embodiments, provided herein is a system for treating impound water having a monitoring, testing, and control system having a system for automatic cleaning and shut-down.

[0101] In embodiments, provided herein is a system for treating impound water having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating impound water having a machine learning system and artificial intelligence system for determining optimal process conditions having an analytics system. In embodiments, provided herein is a system for treating impound water having a machine learning system and artificial intelligence system for determining optimal process conditions having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a system for treating impound water having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a system for treating impound water having a machine learning system and artificial intelligence system for determining optimal process conditions having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a system for treating impound water having a machine learning system and artificial intelligence system for determining optimal process conditions having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a system for treating impound water having a machine learning system and artificial intelligence system for determining optimal process conditions having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating impound water having a machine learning system and artificial intelligence system for determining optimal process conditions having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a system for treating impound water having a machine learning system and artificial intelligence system for determining optimal process conditions having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating impound water having a machine learning system and artificial intelligence system for determining optimal process conditions having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating impound water having a machine learning system and artificial intelligence system for determining optimal process conditions having an analytics system. In embodiments, provided herein is a system for treating impound water having a machine learning system and artificial intelligence system for determining optimal process conditions having a system for automatic cleaning and shut-down.

[0102] In embodiments, provided herein is a system for treating impound water having an analytics system. In embodiments, provided herein is a system for treating impound water having an analytics system having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a system for treating impound water having an analytics system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by

microfiltration· In embodiments, provided herein is a system for treating impound water having an analytics system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration· In embodiments, provided herein is a system for treating impound water having an analytics system having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a system for treating impound water having an analytics system having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating impound water having an analytics system having a waste source, a management system, a treatment and separation system, and a collection system. In

embodiments, provided herein is a system for treating impound water having an analytics system having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating impound water having an analytics system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating impound water having an analytics system having an analytics system. In embodiments, provided herein is a system for treating impound water having an analytics system having a system for automatic cleaning and shut-down.

[0103] In embodiments, provided herein is a system for treating impound water having a microfiltration system with an automatic backflushing system. In embodiments, provided herein is a system for treating impound water having a microfiltration system with an automatic backflushing system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a system for treating impound water having a microfiltration system with an automatic backflushing system having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a system for treating impound water having a microfiltration system with an automatic backflushing system having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a system for treating impound water having a microfiltration system with an automatic backflushing system having a system for automatic cleaning and shut down. In embodiments, provided herein is a system for treating impound water having a microfiltration system with an automatic backflushing system having a waste source, a management system, a treatment and separation system, and a collection system. In

embodiments, provided herein is a system for treating impound water having a microfiltration system with an automatic backflushing system having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating impound water having a microfiltration system with an automatic backflushing system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating impound water having a microfiltration system with an automatic backflushing system having an analytics system. In embodiments, provided herein is a system for treating impound water having a microfiltration system with an automatic backflushing system having a system for automatic cleaning and shut-down.

[0104] In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by micro filtration having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating impound water and reducing

microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane bio fouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having an analytics system. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane bio fouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with an inorganic coagulant and a cationic polymer, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having a system for automatic cleaning and shut-down.

[0105] In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having a system for automatic cleaning and shut-down. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane bio fouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having a waste source, a management system, a treatment and separation system, and a collection system. In

embodiments, provided herein is a method for treating impound water and reducing

microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having a monitoring, testing, and control system. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having an analytics system. In embodiments, provided herein is a method for treating impound water and reducing microfiltration membrane biofouling having the steps of admixing impound water with calcium oxide or calcium hydroxide, admixing the mixture with aluminum chlorohydrate and epi-dma, removing a first portion of a contaminant as a bulk solid, and removing a second portion of the contaminant by microfiltration having a system for automatic cleaning and shut-down.

[0106] In embodiments, provided herein is a system for treating impound water having a reverse osmosis system for treating microfiltration effluent. In embodiments, provided herein is a system for treating impound water having a reverse osmosis system for treating microfiltration effluent having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating impound water having a reverse osmosis system for treating microfiltration effluent having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a system for treating impound water having a reverse osmosis system for treating microfiltration effluent having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating impound water having a reverse osmosis system for treating microfiltration effluent having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating impound water having a reverse osmosis system for treating micro filtration effluent having an analytics system. In embodiments, provided herein is a system for treating impound water having a reverse osmosis system for treating microfiltration effluent having a system for automatic cleaning and shut-down.

[0107] In embodiments, provided herein is a system for treating impound water having a system for automatic cleaning and shut-down. In embodiments, provided herein is a system for treating impound water having a system for automatic cleaning and shut-down having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a system for treating impound water having a system for automatic cleaning and shut-down having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating impound water having a system for automatic cleaning and shut-down having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating impound water having a system for automatic cleaning and shut-down having an analytics system. In embodiments, provided herein is a system for treating impound water having a system for automatic cleaning and shut-down having a system for automatic cleaning and shut down.

[0108] In embodiments, provided herein is a system for treating a waste source having a waste source, a management system, a treatment and separation system, and a collection system. In embodiments, provided herein is a system for treating a waste source having a waste source, a management system, a treatment and separation system, and a collection system having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating a waste source having a waste source, a management system, a treatment and separation system, and a collection system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating a waste source having a waste source, a management system, a treatment and separation system, and a collection system having an analytics system. In embodiments, provided herein is a system for treating a waste source having a waste source, a management system, a treatment and separation system, and a collection system having a system for automatic cleaning and shut down.

[0109] In embodiments, provided herein is a system for treating a waste source having a monitoring, testing, and control system. In embodiments, provided herein is a system for treating a waste source having a monitoring, testing, and control system having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating a waste source having a monitoring, testing, and control system having an analytics system. In embodiments, provided herein is a system for treating a waste source having a monitoring, testing, and control system having a system for automatic cleaning and shut-down.

[0110] In embodiments, provided herein is a system for treating a waste source having a machine learning system and artificial intelligence system for determining optimal process conditions. In embodiments, provided herein is a system for treating a waste source having a machine learning system and artificial intelligence system for determining optimal process conditions having an analytics system. In embodiments, provided herein is a system for treating a waste source having a machine learning system and artificial intelligence system for determining optimal process conditions having a system for automatic cleaning and shut-down.

[0111] In embodiments, provided herein is a system for treating a waste source having an analytics system. In embodiments, provided herein is a system for treating a waste source having an analytics system having a system for automatic cleaning and shut-down.

[0112] In embodiments, provided herein is a system for treating a waste source having a system for automatic cleaning and shut-down.

[0113] While only a few embodiments of the present disclosure have been shown and described, it will be obvious to those skilled in the art that many changes and modifications may be made thereunto without departing from the spirit and scope of the present disclosure as described in the following claims. All patent applications and patents, both foreign and domestic, and all other publications referenced herein are incorporated herein in their entireties to the full extent permitted by law.

[0114] The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor. The present disclosure may be implemented as a method on the machine, as a system or apparatus as part of or in relation to the machine, or as a computer program product embodied in a computer readable medium executing on one or more of the machines. In embodiments, the processor may be part of a server, cloud server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions, and the like. The processor may be or may include a signal processor, digital processor, embedded processor, microprocessor, or any variant such as a co-processor (math co processor, graphic co-processor, communication co-processor, and the like) and the like that may directly or indirectly facilitate execution of program code or program instructions stored thereon. In addition, the processor may enable execution of multiple programs, threads, and codes. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application. By way of implementation, methods, program codes, program instructions, and the like described herein may be implemented in one or more thread. The thread may spawn other threads that may have assigned priorities associated with them; the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor, or any machine utilizing one, may include non-transitory memory that stores methods, codes, instructions, and programs as described herein and elsewhere. The processor may access a non-transitory storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions, or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD- ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and the like.

[0115] A processor may include one or more cores that may enhance speed and performance of a multiprocessor. In embodiments, the process may be a dual core processor, quad core processors, other chip-level multiprocessor and the like that combine two or more independent cores (called a die).

[0116] The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software on a server, client, firewall, gateway, hub, router, or other such computer and/or networking hardware. The software program may be associated with a server that may include a file server, print server, domain server, internet server, intranet server, cloud server, and other variants such as secondary server, host server, distributed server, and the like. The server may include one or more of memories, processors, computer readable transitory and/or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.

[0117] The server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, social networks, and the like. Additionally, this coupling and/or connection may facilitate remote execution of program across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without deviating from the scope of the disclosure. In addition, any of the devices attached to the server through an interface may include at least one storage medium capable of storing methods, programs, code, and/or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.

[0118] The software program may be associated with a client that may include a file client, print client, domain client, internet client, intranet client, and other variants such as secondary client, host client, distributed client, and the like. The client may include one or more of memories, processors, computer readable transitory and/or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the client. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.

[0119] The client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and/or connection may facilitate remote execution of a program across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more location without deviating from the scope of the disclosure. In addition, any of the devices attached to the client through an interface may include at least one storage medium capable of storing methods, programs, applications, code, and/or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs.

[0120] In embodiments, one or more of the controllers, circuits, systems, data collectors, storage systems, network elements, or the like as described throughout this disclosure may be embodied in or on an integrated circuit, such as an analog, digital, or mixed signal circuit, such as a microprocessor, a programmable logic controller, an application-specific integrated circuit, a field programmable gate array, or other circuit, such as embodied on one or more chips disposed on one or more circuit boards, such as to provide in hardware (with potentially accelerated speed, energy performance, input-output performance, or the like) one or more of the functions described herein. This may include setting up circuits with up to billions of logic gates, flip-flops, multiplexers, and other circuits in a small space, facilitating high speed processing, low power dissipation, and reduced manufacturing cost compared with board-level integration. In embodiments, a digital IC, typically a microprocessor, digital signal processor, microcontroller, or the like may use Boolean algebra to process digital signals to embody complex logic, such as involved in the circuits, controllers, and other systems described herein. In embodiments, a data collector, an expert system, a storage system, or the like may be embodied as a digital integrated circuit (“IC”), such as a logic IC, memory chip, interface IC (e.g., a level shifter, a serializer, a deserializer, and the like), a power management IC and/or a programmable device; an analog integrated circuit, such as a linear IC, RF IC, or the like, or a mixed signal IC, such as a data acquisition IC (including A/D converters, D/A converter, digital potentiometers) and/or a clock/timing IC.

[0121] The methods and systems described herein may be deployed in part or in whole through network infrastructures. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices and other active and passive devices, modules and/or components as known in the art. The computing and/or non-computing device(s) associated with the network

infrastructure may include, apart from other components, a storage medium such as flash memory, buffer, stack, RAM, ROM, and the like. The processes, methods, program codes, instructions described herein and elsewhere may be executed by one or more of the network infrastructural elements. The methods and systems described herein may be configured for use with any kind of private, community, or hybrid cloud computing network or cloud computing environment, including those which involve features of software as a service (“SaaS”), platform as a service (“PaaS”), and/or infrastructure as a service (“IaaS”).

[0122] The methods, program codes, and instructions described herein and elsewhere may be implemented on a cellular network having multiple cells. The cellular network may either be frequency division multiple access (“FDMA”) network or code division multiple access (“CDMA”) network. The cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, and the like. The cell network may be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.

[0123] The methods, program codes, and instructions described herein and elsewhere may be implemented on or through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music players and the like. These devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices. The computing devices associated with mobile devices may be enabled to execute program codes, methods, and instructions stored thereon. Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices.

The mobile devices may communicate with base stations interfaced with servers and configured to execute program codes. The mobile devices may communicate on a peer-to-peer network, mesh network, or other communications network. The program code may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store program codes and instructions executed by the computing devices associated with the base station.

[0124] The computer software, program codes, and/or instructions may be stored and/or accessed on machine readable transitory and/or non-transitory media that may include: computer components, devices, and recording media that retain digital data used for computing for some interval of time; semiconductor storage known as random access memory (“RAM”); mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drams, cards and other types; processor registers, cache memory, volatile memory, non-volatile memory; optical storage such as CD, DVD; removable media such as flash memory (e.g., USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, zip drives, removable mass storage, off-line, and the like; other computer memory such as dynamic memory, static memory, read/write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, and the like.

[0125] The methods and systems described herein may transform physical and/or or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and/or intangible items from one state to another.

[0126] The elements described and depicted herein, including in flow charts and block diagrams throughout the Figures, imply logical boundaries between the elements. However, according to software or hardware engineering practices, the depicted elements and the functions thereof may be implemented on machines through computer executable transitory and/or non-transitory media having a processor capable of executing program instructions stored thereon as a monolithic software structure, as standalone software modules, or as modules that employ external routines, code, services, and so forth, or any combination of these, and all such implementations may be within the scope of the present disclosure. Examples of such machines may include, but may not be limited to, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipment, servers, routers, and the like. Furthermore, the elements depicted in the flow chart and block diagrams or any other logical component may be implemented on a machine capable of executing program instructions. Thus, while the foregoing drawings and descriptions set forth functional aspects of the disclosed systems, no particular arrangement of software for implementing these functional aspects should be inferred from these descriptions unless explicitly stated or otherwise clear from the context. Similarly, it will be appreciated that the various steps identified and described above may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein. All such variations and modifications are intended to fall within the scope of this disclosure. As such, the depiction and/or description of an order for various steps should not be understood to require a particular order of execution for those steps, unless required by a particular application, or explicitly stated or otherwise clear from the context. [0127] The methods and/or processes described above, and steps associated therewith, may be realized in hardware, software or any combination of hardware and software suitable for a particular application. The hardware may include a general-purpose computer and/or dedicated computing device or specific computing device or particular aspect or component of a specific computing device. The processes may be realized in one or more microprocessors,

microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and/or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine-readable medium.

[0128] The computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low- level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software, or any other machine capable of executing program instructions.

[0129] Thus, in one aspect, methods described above and combinations thereof may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof. In another aspect, the methods may be embodied in systems that perform the steps thereof, and may be distributed across devices in a number of ways, or all of the functionality may be integrated into a dedicated, standalone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and/or software described above. All such permutations and combinations are intended to fall within the scope of the present disclosure.

[0130] While the disclosure has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereon will become readily apparent to those skilled in the art. Accordingly, the spirit and scope of the present disclosure is not to be limited by the foregoing examples, but is to be understood in the broadest sense allowable by law.

[0131] The use of the terms“a” and“an” and“the” and similar referents in the context of describing the disclosure (especially in the context of the following claims) is to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms“comprising,”“having,”“including,” and“containing” are to be construed as open-ended terms (i.e., meaning“including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g.,“such as”) provided herein, is intended merely to better illuminate the disclosure, and does not pose a limitation on the scope of the disclosure unless otherwise claimed. No language in the specification should be construed as indicating any non- claimed element as essential to the practice of the disclosure.

[0132] While the foregoing written description enables one skilled in the art to make and use what is considered presently to be the best mode thereof, those skilled in the art will understand and appreciate the existence of variations, combinations, and equivalents of specific

embodiments, methods, and examples herein. The disclosure should therefore not be limited by the above described embodiments, methods, and examples, but by all embodiments and methods within the scope and spirit of the disclosure.

[0133] Any element in a claim that does not explicitly state“means for” performing a specified function, or“step for” performing a specified function, is not to be interpreted as a“means” or “step” clause as specified in 35 U.S.C. § 112(f). In particular, any use of“step of’ in the claims is not intended to invoke the provision of 35 U.S.C. § 112(f).

[0134] Persons skilled in the art may appreciate that numerous design configurations may be possible to enjoy the functional benefits of the inventive systems. Thus, given the wide variety of configurations and arrangements of embodiments of the present invention, the scope of the invention is reflected by the breadth of the claims below rather than narrowed by the

embodiments described above.

Claims

1. A monitoring, testing, and control system for a carotenoid extraction process, the system comprising: an input system feeding into a carotenoid extraction process system; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the carotenoid extraction process system; a data analysis circuit structured to analyze the plurality of detection values to determine a state of the carotenoid extraction process; and an analysis response circuit structured to control an aspect of the carotenoid extraction process system in response to the state.

2. The system of claim 1, wherein the analysis response circuit is structured to control a mixing speed.

3. The system of claim 1, wherein the plurality of sensors is selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor.

4. The system of claim 1, wherein the carotenoid extraction process comprises admixing a carotenoid-containing source with a first organic solvent and a surfactant to form a slurry.

5. The system of claim 4, wherein the carotenoid extraction process further comprises:

treating the slurry with a second organic solvent to form a carotenoid-surfactant combination; separating the treated slurry into a liquid fraction and a solid fraction; and separating a first portion, which is a solution of the combination and the second organic solvent, from the liquid fraction.

6. The system of claim 1, wherein the plurality of detection values is at least one of an amount or a presence of a carotenoid.

7. The system of claim 6, wherein the carotenoid is selected from the group consisting of a Beta- carotene (b, b-Carotene, a lycopene (y, y-Carotene, C40H56), a lutein or a xanthophylls (b, e- Carotene-3,3'-diol, C40H56O2), or a zeaxanthin (b, b-Carotene-3,3'-diol, C40H56O2).

8. The system of claim 5, wherein the carotenoid extraction process further comprises:

processing the solid fraction in an anaerobic digester to produce methane.

9. The system of claim 1, wherein the plurality of detection values is at least one of an amount or a presence of a biological material.

10. The system of claim 1, wherein the plurality of detection values is at least one of an amount or a presence of water.

11. The system of claim 1, wherein the plurality of detection values is a component presence.

12. The system of claim 1, wherein the plurality of detection values is a component

concentration.

13. The system of claim 1, wherein the plurality of detection values is a pH.

14. The system of claim 1, wherein the plurality of detection values is a temperature.

15. The system of claim 1, wherein the plurality of detection values is a pressure.

16. The system of claim 1, wherein the plurality of detection values is a flow rate.

17. The system of claim 1, wherein the analysis response circuit is structured to control an item.

18. The system of claim 17, wherein the item is at least one of a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, or an enzyme.

19. The system of claim 1, wherein the analysis response circuit is structured to control a separation process.

20. The system of claim 1 , wherein the analysis response circuit is structured to control a temperature setting.

21. The system of claim 1, wherein the analysis response circuit is structured to control a pressure setting.

22. The system of claim 1 , wherein the analysis response circuit is structured to control a pH setting.

23. The system of claim 1, wherein the analysis response circuit is structured to control a mixing period.

24. The system of claim 1 , wherein the analysis response circuit is structured to control a filtration aspect.

25. The system of claim 24, wherein the aspect is a filter pore size.

26. The system of claim 24, wherein the aspect is a filter diameter.

27. The system of claim 1, wherein the input system feeds a waste stream into the carotenoid extraction process system.

28. The system of claim 27, wherein the waste stream comprises solids obtained from a waste treatment of the solids suspended in a wastewater from a fruit processing plant or a vegetable processing plant.

29. The system of claim 27, wherein the waste stream comprises a pumice or a rough cut grinding of an exterior of a fruit or a vegetable.

30. The system of claim 27, wherein the waste stream comprises at least one of a fine or a slice of a fruit or a vegetable present as a waste or as a disclaimed product.

31. The system of claim 27, wherein the waste stream comprises a solid present from floor sleeping or a general maintenance of a fruit processing facility or vegetable processing facility

32. The system of claim 1, further comprising a pre-processing facility that processes an input to the input system.

33. The system of claim 32, wherein the pre-processing facility minces the input.

34. The system of claim 32, wherein the pre-processing facility macerates the input.

35. The system of claim 32, wherein the pre-processing facility employs a caustic peel process.

36. The system of claim 8, wherein the solid fraction is processed in an aerobic pre- treatment chamber prior to its being processed in the anaerobic digester.

37. A method for extracting carotenoids from a carotenoid-containing source, comprising:

admixing the carotenoid-containing source with a first organic solvent and a surfactant to form a slurry; treating the slurry with a second organic solvent to form a carotenoid-surfactant combination; separating the treated slurry into a liquid fraction and a solid fraction;

separating a first portion, which is a solution of the combination and the second organic solvent, from the liquid fraction; and processing the solid fraction in an anaerobic digester to produce methane, wherein the solid fraction is processed in an aerobic pre-treatment chamber prior to its being processed in the anaerobic digester.

38. A system for extracting carotenoids from a carotenoid-containing source having a machine learning system or artificial intelligence system for predicting a carotenoid extraction process outcome or state, comprising: a data acquisition circuit structured to collect a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of a carotenoid extraction process system; and a machine learning data analysis circuit structured to receive the plurality of detection values and learn received detection value patterns predictive of at least one of an outcome and a state of a carotenoid extraction process, wherein the system is structured to determine if the plurality of detection values match a learned received detection value pattern.

39. The system of claim 38, wherein the machine learning data analysis circuit is structured to learn received output detection value patterns by being seeded with a model, wherein the model is at least one of a prediction model, a physical model, an operational model, a system model, a neural network, a regression-based model, or a machine- learned model.

40. The system of claim 39, wherein the machine learning data analysis circuit improves a prediction of the outcome or the state over time as the model used by the machine learning data analysis circuit is refined or trained by feedback or training data.

41. The system of claim 40, wherein training of the model is supervised, semi-supervised, or unsupervised.

42. The system of claim 41, wherein the feedback is a set of circumstances that led to the prediction and an outcome related to a treatment.

43. The system of claim 42, wherein the feedback is a contaminant component concentration and the outcome related to the treatment is a post-treatment contaminant concentration.

44. The system of claim 42, wherein the prediction model is a treatment prediction model and receives properties of the carotenoid-containing source and a treatment, and outputs one or more predictions regarding the treatment.

45. The system of claim 44, wherein the prediction is at least one of a component yield, a component concentration, an output temperature, an output viscosity, and a flow rate.

46. The system of claim 44, wherein the properties of the carotenoid-containing source comprise at least one of a temperature, a flow rate, and a component concentration.

47. The system of claim 44, wherein the treatment is addition of at least one of a reagent, a chemical, a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, and an enzyme.

48. The system of claim 44, wherein the treatment is a time reacted, a separation type, a pressure setting, a pH setting, a temperature setting, a mixing period, and a mixing speed.

49. The system of claim 44, wherein the prediction model has vectors corresponding to an outcome, one or more attributes of a treatment that resulted in the outcome and one or more attributes of the carotenoid-containing source.

50. The system of claim 49, wherein the machine learning system generates the prediction model based on the vectors.

51. The system of claim 50, wherein the machine learning system stores the prediction model in a model datastore.

52. The system of claim 38, wherein the machine learning data analysis circuit is structured to learn received detection value patterns based on a technique selected from the group consisting of decision trees, k-nearest neighbor, linear regression, k-means clustering, deep learning neural networks, random forest, logistic regression, naive Bayes, learning vector quantization, support vector machines, linear discriminant analysis, boosting, principal component analysis, and a hybrid of k-means and linear regression.

53. The system of claim 38, wherein the plurality of sensors is selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor.

54. The system of claim 38, wherein the carotenoid extraction process comprises:

admixing the carotenoid-containing source with a first organic solvent and a surfactant to form a slurry.

55. The system of claim 38, wherein the plurality of detection values is at least one of an amount or a presence of a carotenoid.

56. The system of claim 54, wherein the carotenoid extraction process further comprises: treating the slurry with a second organic solvent to form a carotenoid-surfactant combination.

57. The system of claim 38, wherein the plurality of detection values is at least one of an amount or a presence of a biological material.

58. The system of claim 38, wherein the plurality of detection values is at least one of an amount or a presence of water.

59. The system of claim 38, wherein the plurality of detection values is a component presence.

60. The system of claim 38, wherein the plurality of detection values is a component concentration.

61. The system of claim 38, wherein the plurality of detection values is a pH.

62. The system of claim 38, wherein the plurality of detection values is a temperature.

63. The system of claim 38, wherein the plurality of detection values is a pressure.

64. The system of claim 38, wherein the plurality of detection values is a flow rate.

65. The system of claim 56, wherein the carotenoid extraction process further comprises:

separating the treated slurry into a liquid fraction and a solid fraction.

66. The system of claim 55, wherein the carotenoid is selected from the group consisting of a Beta-carotene (b, b-Carotene, a lycopene (y, y-Carotene, C40H56), a lutein or a xanthophylls (b, e - C aro tene- 3 , 3 ' - d i o 1 , C40H56O2), or a zeaxanthin (b, b-Carotene-3,3'-diol, C40H56O2).

67. The system of claim 38, wherein the carotenoid extraction process comprises:

separating a first portion, which is a solution of the carotenoid-surfactant combination and the second organic solvent, from the liquid fraction.

68. A monitoring, testing, and control system for a hydraulic fracturing water treatment process, the system comprising :an input system feeding into a hydraulic fracturing water treatment process system; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the hydraulic fracturing water treatment process system;

a data analysis circuit structured to analyze the plurality of detection values to determine a state of the hydraulic fracturing water treatment process; and an analysis response circuit structured to control an aspect of the hydraulic fracturing water treatment process system in response to the determined state.

69. The system of claim 68, wherein the plurality of sensors is selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor.

70. The system of claim 68, wherein the plurality of detection values is at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non-metal.

71. The system of claim 70, wherein the metal, metal oxide, metalloid, or non-metal is selected from the group consisting of Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), and Silica (Si0₂).

72. The system of claim 68, wherein the plurality of detection values is at least one of an amount or a presence of a biological material.

73. The system of claim 68, wherein the plurality of detection values is at least one of an amount or a presence of water.

74. The system of claim 68, wherein the plurality of detection values is a component presence.

75. The system of claim 68, wherein the plurality of detection values is a component

concentration.

76. The system of claim 68, wherein the plurality of detection values is a pH.

77. The system of claim 68, wherein the plurality of detection values is a temperature.

78. The system of claim 68, wherein the plurality of detection values is a pressure.

79. The system of claim 68, wherein the plurality of detection values is a flow rate.

80. The system of claim 68, wherein the analysis response circuit is structured to control an item.

81. The system of claim 80, wherein the item is at least one of a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, or an enzyme.

82. The system of claim 68, wherein the analysis response circuit is structured to control a separation process.

83. The system of claim 68, wherein the analysis response circuit is structured to control a temperature setting.

84. The system of claim 68, wherein the analysis response circuit is structured to control a pressure setting.

85. The system of claim 68, wherein the analysis response circuit is structured to control a pH setting.

86. The system of claim 68, wherein the analysis response circuit is structured to control a mixing period.

87. The system of claim 68, wherein the analysis response circuit is structured to control a mixing speed.

88. The system of claim 68, wherein the analysis response circuit is structured to control a filtration aspect.

89. The system of claim 88, wherein the aspect is a filter pore size.

90. The system of claim 88, wherein the aspect is a filter diameter.

91. The system of claim 68, further comprising a pre-processing facility that processes an input to the input system.

92. The system of claim 68, wherein the hydraulic fracturing water treatment process comprises adjusting a pH, adding an inorganic coagulant and a polymer to contaminated water to form a plurality of particles, and removing the plurality of particles.

93. A system for treating hydraulic fracturing water having a machine learning system or artificial intelligence system for predicting an outcome or a state of a treatment process, comprising: a data acquisition circuit structured to collect a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the system; and a machine learning data analysis circuit structured to receive the detection values and learn received detection value patterns predictive of at least one of an outcome and a state of the treatment process, wherein the system is structured to determine if the detection values match a learned received detection value pattern.

94. The system of claim 93, wherein the machine learning data analysis circuit is structured to learn received output detection value patterns by being seeded with a model, wherein the model is at least one of a prediction model, a physical model, an operational model, a system model, a neural network, a regression-based model, or a machine- learned model.

95. The system of claim 94, wherein the machine learning data analysis circuit improves a prediction of the outcome or the state over time as the model used by the machine learning data analysis circuit is refined or trained by feedback or training data.

96. The system of claim 95, wherein training of the model is supervised, semi-supervised, or unsupervised.

97. The system of claim 95, wherein the feedback is a set of circumstances that led to the prediction and an outcome related to a treatment.

98. The system of claim 97, wherein the feedback is a contaminant component concentration and the outcome related to the treatment is a post-treatment contaminant concentration.

99. The system of claim 94, wherein the prediction model is a treatment prediction model and receives properties of a carotenoid-containing source and a treatment, and outputs one or more predictions regarding the treatment.

100. The system of claim 99, wherein the prediction is at least one of a component yield, a component concentration, an output temperature, an output viscosity, and a flow rate.

101. The system of claim 99, wherein the properties of the carotenoid-containing source comprise at least one of a temperature, a flow rate, and a component concentration.

102. The system of claim 99, wherein the treatment is addition of at least one of a reagent, a chemical, a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, and an enzyme.

103. The system of claim 99, wherein the treatment is a time reacted, a separation type, a pressure setting, a pH setting, a temperature setting, a mixing period, and a mixing speed.

104. The system of claim 94, wherein the model has vectors corresponding to an outcome, one or more attributes of a treatment that resulted in the outcome and one or more attributes of a source of the hydraulic fracturing water.

105. The system of claim 104, wherein the machine learning system generates the prediction model based on the vectors.

106. The system of claim 94, wherein the machine learning system stores the prediction model in a model datastore.

107. The system of claim 93, wherein the machine learning data analysis circuit is structured to learn received detection value patterns based on a technique selected from the group consisting of decision trees, k-nearest neighbor, linear regression, k-means clustering, deep learning neural networks, random forest, logistic regression, naive Bayes, learning vector quantization, support vector machines, linear discriminant analysis, boosting, principal component analysis, and a hybrid of k-means and linear regression.

108. The system of claim 93, wherein the plurality of sensors is selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor.

109. The system of claim 93, wherein the plurality of detection values is at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non-metal.

110. The system of claim 109, wherein the metal, metal oxide, metalloid, or non-metal is selected from the group consisting of Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), and Silica (Si0₂).

111. The system of claim 93 , wherein the plurality of detection values is at least one of an amount or a presence of a biological material.

112. The system of claim 93, wherein the plurality of detection values is at least one of an amount or a presence of water.

113. The system of claim 93, wherein the plurality of detection values is a component presence.

114. The system of claim 93, wherein the plurality of detection values is a component concentration.

115. The system of claim 93, wherein the plurality of detection values is a pH.

116. The system of claim 93, wherein the plurality of detection values is a temperature.

117. The system of claim 93, wherein the plurality of detection values is a pressure.

118. The system of claim 93, wherein the plurality of detection values is a flow rate.

119. The system of claim 93, wherein the treatment process for hydraulic fracturing water comprises adjusting a pH, adding an inorganic coagulant and a polymer to contaminated water to form a plurality of particles, and removing the plurality of particles.

120. A monitoring, testing, and control system for a hydraulic fracturing water treatment process, the system comprising: an input system feeding into a hydraulic fracturing water treatment process system;_a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the hydraulic fracturing water treatment process system;

a data analysis circuit structured to analyze the plurality of detection values to determine a state of the hydraulic fracturing water treatment process; and an automatic cleaning and shut down system that is automatically activated in response to the determined state.

121. The system of claim 120, wherein the automatic cleaning and shut down system performs a back flushing of a filter used in hydraulic fracturing water treatment process system.

122. The system of claim 120, wherein the automatic cleaning and shut down system performs a resin stripping of an ion exchange media used in hydraulic fracturing water treatment process system.

123. The system of claim 120, wherein the hydraulic fracturing water treatment process comprises adjusting a pH, adding an inorganic coagulant and a polymer to contaminated water to form a plurality of particles, and removing the plurality of particles.

124. A monitoring, testing, and control system for an impound water treatment process, the system comprising: an input system feeding into an impound water treatment process system; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the impound water treatment process system; a data analysis circuit structured to analyze the plurality of detection values to determine a state of the impound water treatment process; and an analysis response circuit structured to control an aspect of the impound water treatment process system in response to the determined state.

125. The system of claim 124, wherein the plurality of sensors is selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor.

126. The system of claim 124, wherein the plurality of detection values is at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non-metal.

127. The system of claim 124, wherein the plurality of detection values is at least one of an amount or a presence of an exopolymeric substance.

128. The system of claim 124, wherein the plurality of detection values is at least one of an amount or a presence of a biological material.

129. The system of claim 124, wherein the plurality of detection values is at least one of an amount or a presence of water.

130. The system of claim 124, wherein the plurality of detection values is a component presence.

131. The system of claim 124, wherein the plurality of detection values is a component concentration.

132. The system of claim 124, wherein the plurality of detection values is a pH.

133. The system of claim 124, wherein the plurality of detection values is a temperature.

134. The system of claim 124, wherein the plurality of detection values is a pressure.

135. The system of claim 124, wherein the plurality of detection values is a flow rate.

136. The system of claim 124, wherein the analysis response circuit is structured to control an item.

137. The system of claim 136, wherein the item is at least one of a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, or an enzyme.

138. The system of claim 124, wherein the analysis response circuit is structured to control a separation process.

139. The system of claim 124, wherein the analysis response circuit is structured to control a temperature setting.

140. The system of claim 124, wherein the analysis response circuit is structured to control a pressure setting.

141. The system of claim 124, wherein the analysis response circuit is structured to control a pH setting.

142. The system of claim 124, wherein the analysis response circuit is structured to control a mixing period.

143. The system of claim 124, wherein the analysis response circuit is structured to control a mixing speed.

144. The system of claim 124, wherein the analysis response circuit is structured to control a filtration aspect.

145. The system of claim 144, wherein the aspect is a filter pore size.

146. The system of claim 144, wherein the aspect is a filter diameter.

147. The system of claim 124, further comprising a pre-processing facility that processes an input to the input system.

148. The system of claim 124, wherein the impound water treatment process comprises treating impound water with metal ion and organic species by treating the impound water with ferric iron ions to form a ferrous-treated mixture, flowing the ferrous-treated mixture through an upflow reactor filled with iron filings or steel wool, removing the ferrous-treated mixture from the upflow reactor and treating with an oxidant to form an oxidized mixture, adding an inorganic coagulant and cationic polymer to the oxidized mixture to form particles, and microfiltering the oxidized mixture to remove particles.

149. A system for treating impound water having a machine learning system or artificial intelligence system for predicting an outcome or a state of a treatment process, comprising:

a data acquisition circuit structured to collect a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the system; and

a machine learning data analysis circuit structured to receive the plurality of detection values and learn received detection value patterns predictive of at least one of an outcome and a state of the treatment process, wherein the system is structured to determine if the plurality of detection values match a learned received detection value pattern.

150. The system of claim 149, wherein the machine learning data analysis circuit is structured to learn received output detection value patterns by being seeded with a model, wherein the model is at least one of a prediction model, a physical model, an operational model, a system model, a neural network, a regression-based model, or a machine- learned model.

151. The system of claim 150, wherein the machine learning data analysis circuit improves a prediction of the outcome or the state over time as the model used by the machine learning data analysis circuit is refined or trained by feedback or training data.

152. The system of claim 151, wherein training of the model is supervised, semi-supervised, or unsupervised.

153. The system of claim 151, wherein the feedback is a set of circumstances that led to the prediction and an outcome related to a treatment.

154. The system of claim 153, wherein the feedback is a contaminant component concentration and the outcome related to the treatment is a post-treatment contaminant concentration.

155. The system of claim 150, wherein the prediction model is a treatment prediction model and receives properties of a carotenoid-containing source and a treatment, and outputs one or more predictions regarding the treatment.

156. The system of claim 155, wherein the one or more predictions are at least one of a component yield, a component concentration, an output temperature, an output viscosity, and a flow rate.

157. The system of claim 155, wherein the properties of the carotenoid-containing source comprise at least one of a temperature, a flow rate, and a component concentration.

158. The system of claim 155, wherein the treatment is addition of at least one of a reagent, a chemical, a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, and an enzyme.

159. The system of claim 155, wherein the treatment is a time reacted, a separation type, a pressure setting, a pH setting, a temperature setting, a mixing period, and a mixing speed.

160. The system of claim 155, wherein the model has vectors corresponding to an outcome, one or more attributes of a treatment that resulted in the outcome and one or more attributes of an impound water source.

161. The system of claim 160, wherein the machine learning system generates the prediction model based on the vectors.

162. The system of claim 160, wherein the machine learning system stores the prediction model in a model datastore.

163. The system of claim 155, wherein the machine learning data analysis circuit is structured to learn received detection value patterns based on a technique selected from the group consisting of decision trees, k-nearest neighbor, linear regression, k-means clustering, deep learning neural networks, random forest, logistic regression, naive Bayes, learning vector quantization, support vector machines, linear discriminant analysis, boosting, principal component analysis, and a hybrid of k-means and linear regression.

164. The system of claim 155, wherein the plurality of sensors is selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor.

165. The system of claim 155, wherein the plurality of detection values is at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non-metal.

166. The system of claim 155, wherein the plurality of detection values is at least one of an amount or a presence of an exopolymeric substance.

167. The system of claim 155, wherein the plurality of detection values is at least one of an amount or a presence of a biological material.

168. The system of claim 155, wherein the plurality of detection values is at least one of an amount or a presence of water, a component presence, a component concentration, a pH, a temperature, a pressure, or a flow rate.

169. The system of claim 155, wherein the treatment process comprises treating impound water with metal ion and organic species by treating the impound water with ferric iron ions to form a ferrous-treated mixture, flowing the ferrous-treated mixture through an upflow reactor filled with iron filings or steel wool, removing the ferrous-treated mixture from the upflow reactor and treating with an oxidant to form an oxidized mixture, adding an inorganic coagulant and cationic polymer to the oxidized mixture to form particles, and microfiltering the oxidized mixture to remove particles.

170. A monitoring, testing, and control system for an impound water treatment process, the system comprising: an input system feeding into an impound water treatment process system; a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the impound water treatment process system; a data analysis circuit structured to analyze the plurality of detection values to determine a state of the impound water treatment process; and an automatic cleaning and shut down system that is automatically activated in response to the determined state.

171. The system of claim 170, wherein the automatic cleaning and shut down system performs a back flushing of a filter used in the impound water treatment process system.

172. The system of claim 170, wherein the automatic cleaning and shut down system performs a resin stripping of an ion exchange media used in the impound water treatment process system.

173. A system for treating a waste source, comprising: a waste source; a management system structured to monitor and control aspects of the system; a treatment and separation system structured to receive a process condition from the management system and execute it on the waste source; and a collection system.

174. The system of claim 173, wherein the management system tests properties of the waste source to determine the process condition to use in the treatment and separation system.

175. The system of claim 173, wherein the process condition is an addition of at least one of a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, or an enzyme.

176. The system of claim 173, wherein the process condition relates to at least one of a separation process, a temperature setting, a pressure setting, a pH setting, a mixing period, a mixing speed, or a filtration aspect.

177. The system of claim 173, wherein the management system monitors waste source properties as the waste source moves through the system to update an upstream or a downstream process condition.

178. The system of claim 173, wherein the management system tests a post-treatment component concentration to determine if additional treatment is needed.

179. The system of claim 173, wherein the management system comprises a monitoring, testing, and control system, an analytics system, a machine learning system, and an artificial intelligence system.

180. The system of claim 173, wherein the collection system collects extracted outputs and waste outputs.

181. The system of claim 180, wherein the collection system collects clean water in a clean water reservoir.

182. The system of claim 180, wherein the collection system discharges clean water into an environment.

183. A monitoring, testing, and control system for a waste treatment process, the system comprising: an input system feeding into a waste treatment process system;

a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of the waste treatment process system; a data analysis circuit structured to analyze the plurality of detection values to determine a state of the waste treatment process; and an analysis response circuit structured to control an aspect of the waste treatment process system in response to the state.

184. The system of claim 183, wherein the plurality of sensors is selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor.

185. The system of claim 183, wherein the plurality of detection values is at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non-metal.

186. The system of claim 185, wherein the metal, metal oxide, metalloid, or non-metal is selected from the group consisting of Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), and Silica (Si0₂).

187. The system of claim 183, wherein the plurality of detection values is at least one of an amount or a presence of a carotenoid.

188. The system of claim 187, wherein the carotenoid is selected from the group consisting of a Beta-carotene (b, b-Carotene, a lycopene (y, y-Carotene, C40H56), a lutein or a xanthophylls (b, e - C aro tene- 3 , 3 ' - d i o 1 , C40H56O2), or a zeaxanthin (b, b-Carotene-3,3'-diol, C40H56O2)

189. The system of claim 183, wherein the plurality of detection values is at least one of an amount or a presence of an exopolymeric substance.

190. The system of claim 183, wherein the plurality of detection values is at least one of an amount or a presence of a biological material.

191. The system of claim 183, plurality of detection values is at least one of an amount or a presence of water, a component presence, a component concentration, a pH, a temperature, a pressure, or a flow rate.

192. The system of claim 183, wherein the analysis response circuit is structured to control an item.

193. The system of claim 192, wherein the item is at least one of a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, or an enzyme.

194. The system of claim 183, wherein the analysis response circuit is structured to control at least one of a separation process, a temperature setting, a pressure setting, a pH setting, a mixing period, a mixing speed, a filtration aspect, a filter pore size, or a filter diameter.

195. The system of claim 183, wherein the input system feeds a waste stream into the waste treatment process system.

196. The system of claim 195, wherein the waste stream comprises solids obtained from a waste treatment of the solids suspended in a wastewater from a fruit processing plant or a vegetable processing plant.

197. The system of claim 195, wherein the waste stream comprises a pumice or a rough cut grinding of an exterior of a fruit or a vegetable.

198. The system of claim 195, wherein the waste stream comprises at least one of a fine or a slice of a fruit or a vegetable present as a waste or as a disclaimed product.

199. The system of claim 195, wherein the waste stream comprises a solid present from floor sleeping or a general maintenance of a fruit processing facility or vegetable processing facility

200. The system of claim 183, further comprising a pre-processing facility that processes an input to the input system.

201. The system of claim 200, wherein the pre-processing facility minces the input.

202. The system of claim 200, wherein the pre-processing facility macerates the input.

203. The system of claim 200, wherein the pre-processing facility employs a caustic peel process.

204. The system of claim 183, wherein the waste treatment process comprises admixing a waste source with a first organic solvent and a surfactant to form a slurry; treating the slurry with a second organic solvent to form a waste component-surfactant combination; separating the treated slurry into a liquid fraction and a solid fraction; and separating a first portion, which is a solution of the waste component-surfactant combination and the second organic solvent, from the liquid fraction.

205. The system of claim 183, wherein the waste treatment process comprises adjusting a pH, adding an inorganic coagulant and a polymer to waste to form a plurality of particles, and removing the plurality of particles.

206. The system of claim 183, wherein the waste treatment process comprises treating a waste source with metal ion and organic species by treating the waste source with ferric iron ions to form a ferrous-treated mixture, flowing the ferrous-treated mixture through an upflow reactor filled with iron filings or steel wool, removing the ferrous-treated mixture from the upflow reactor and treating with an oxidant to form an oxidized mixture, adding an inorganic coagulant and cationic polymer to the oxidized mixture to form particles, and microfiltering the oxidized mixture to remove particles.

207. A system for treating a waste source having a machine learning system or artificial intelligence system for predicting a carotenoid extraction process outcome or state, comprising: a data acquisition circuit structured to collect a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of sensors, each of the plurality of sensors operatively coupled to at least one of a plurality of components of a waste treatment process system; and a machine learning data analysis circuit structured to receive the plurality of detection values and learn received detection value patterns predictive of at least one of an outcome and a state of a carotenoid extraction process, wherein the system is structured to determine if the plurality of detection values match a learned received detection value pattern.

208. The system of claim 207, wherein the machine learning data analysis circuit is structured to learn received output detection value patterns by being seeded with a model, wherein the model is at least one of a prediction model, a physical model, an operational model, a system model, a neural network, a regression-based model, or a machine- learned model.

209. The system of claim 208, wherein the machine learning data analysis circuit improves a prediction of the outcome or the state over time as the model used by the machine learning data analysis circuit is refined or trained by feedback or training data.

210. The system of claim 209, wherein training of the model is supervised, semi-supervised, or unsupervised.

211. The system of claim 209, wherein the feedback is a set of circumstances that led to the prediction and an outcome related to a treatment.

212. The system of claim 211, wherein the feedback is a contaminant component concentration and the outcome related to the treatment is a post-treatment contaminant concentration.

213. The system of claim 208, wherein the prediction model is a treatment prediction model and receives waste source properties and a treatment, and outputs one or more predictions regarding the treatment.

214. The system of claim 213, wherein the one or more predictions are at least one of a component yield, a component concentration, an output temperature, an output viscosity, and a flow rate.

215. The system of claim 213, wherein the waste source properties comprise at least one of a temperature, a flow rate, and a component concentration.

216. The system of claim 213, wherein the treatment is addition of at least one of a reagent, a chemical, a solvent, a surfactant, a coagulant, a polymer, an ion, a reducing agent, an oxidizing agent, an acid, a base, a chemical, and an enzyme.

217. The system of claim 213, wherein the treatment is a time reacted, a separation type, a pressure setting, a pH setting, a temperature setting, a mixing period, and a mixing speed.

218. The system of claim 213, wherein the model has vectors corresponding to an outcome, one or more attributes of a treatment that resulted in the outcome and one or more attributes of the waste source.

219. The system of claim 218, wherein the machine learning system generates the prediction model based on the vectors.

220. The system of claim 218, wherein the machine learning system stores the prediction model in a model datastore.

221. The system of claim 207, wherein the machine learning data analysis circuit is structured to learn received detection value patterns based on a technique selected from the group consisting of decision trees, k-nearest neighbor, linear regression, k-means clustering, deep learning neural networks, random forest, logistic regression, naive Bayes, learning vector quantization, support vector machines, linear discriminant analysis, boosting, principal component analysis, and a hybrid of k-means and linear regression.

222. The system of claim 207, wherein the plurality of sensors is selected from the group consisting of a chemical sensor, a pH sensor, a biological sensor, a temperature sensor, and a waste solids analysis sensor.

223. The system of claim 207, wherein the plurality of detection values is at least one of an amount or a presence of a metal, a metal oxide, a metalloid, or a non-metal.

224. The system of claim 223, wherein the metal, metal oxide, metalloid, or non-metal is selected from the group consisting of Silver (Ag), Gold (Au), Barium (Ba), Cadmium (Cd), Chromium (Cr), Copper (Cu), Mercury (Hg), Nickel (Ni), Lead (Pb), Zinc (Zn), Fluoride (F ), and Silica (Si0₂).

225. The system of claim 207, wherein the plurality of detection values is at least one of an amount or a presence of a carotenoid.

226. The system of claim 225, wherein the carotenoid is selected from the group consisting of a Beta-carotene (b, b-Carotene, a lycopene (y, y-Carotene, C40H56), a lutein or a xanthophylls (b, e - C aro tene- 3 , 3 ' - d i o 1 , C40H56O2), or a zeaxanthin (b, b-Carotene-3,3'-diol, C40H56O2)

227. The system of claim 207, wherein the plurality of detection values is at least one of an amount or a presence of an exopolymeric substance.

228. The system of claim 207, wherein the plurality of detection values is at least one of an amount or a presence of a biological material.

229. The system of claim 207, wherein the plurality of detection values is at least one of an amount or a presence of water, a component presence, a component concentration, a pH, a temperature, a pressure, or a flow rate.

230. The system of claim 207, wherein a waste treatment process comprises admixing a waste source with a first organic solvent and a surfactant to form a slurry; treating the slurry with a second organic solvent to form a waste component-surfactant combination; and separating the treated slurry into a liquid fraction and a solid fraction; separating a first portion, which is a solution of the waste component- surfactant combination and the second organic solvent, from the liquid fraction.

231. The system of claim 207, wherein ta waste treatment process comprises adjusting a pH, adding an inorganic coagulant and a polymer to waste to form particles, and removing the particles.

232. The system of claim 207, wherein a waste treatment process comprises treating a waste source with metal ion and organic species by treating the waste source with ferric iron ions to form a ferrous-treated mixture, flowing the ferrous-treated mixture through an upflow reactor filled with iron filings or steel wool, removing the ferrous-treated mixture from the upflow reactor and treating with an oxidant to form an oxidized mixture, adding an inorganic coagulant and cationic polymer to the oxidized mixture to form particles, and microfiltering the oxidized mixture to remove particles.

233. A system for treating a waste source, comprising: a management system structured to monitor and control aspects of the system, wherein the management system monitors for an indication of a cleaning need; a treatment and separation system structured to receive a process condition from the management system and execute it on the waste source; and

an automatic cleaning and shut down system that is automatically activated in response to the indication.

234. The system of claim 233, wherein the automatic cleaning and shut down system performs a back flushing of a filter used in the system.

235. The system of claim 233, wherein the automatic cleaning and shut down system performs a resin stripping of an ion exchange media used in the system.