WO2024263648A1

WO2024263648A1 - Modular sensor gateway

Info

Publication number: WO2024263648A1
Application number: PCT/US2024/034629
Authority: WO
Inventors: David Lu; Annie LU; Libby ALBANESE; Joseph Sanchez
Original assignee: H2Ok Innovations Inc.
Priority date: 2023-06-20
Filing date: 2024-06-19
Publication date: 2024-12-26

Abstract

A modular gateway system is disclosed. The gateway system can include at least one control board including a first interface to receive first fluid measurements of a first sensor, wherein the first sensor is an impedance sensor or an optical sensor, the first fluid measurements indicating a first condition of production of a product by the production system and a second interface to receive second fluid measurements of a second sensor, the second fluid measurements indicating a second condition of the production of the product by the production system. The at least one control board can include a data processing system including one or more processors, coupled with memory, to generate, using the first fluid measurements and the second fluid measurements, output data for optimizing the production of the product and transmit the output data to a display or a controller.

Description

MODULAR SENSOR GATEWAY

CROSS-REFERENCE TO RELATED PATENT APPLICATION

[0001] This application claims the benefit of, and priority to, U.S. Provisional Patent Application No. 63/509,273 filed June 20, 2023, the entirety of which is incorporated by reference herein.

INTRODUCTION

[0002] An sensor system can include sensors that measure environmental conditions of the environment. The sensor measurements can be used to control the environment.

SUMMARY

[0003] At least one aspect of the present disclosure is directed to a gateway system of a production system. The gateway system can include at least one control board. The at least one board can include a first interface to receive first fluid measurements of a first sensor, wherein the first sensor is an impedance sensor or an optical sensor, the first fluid measurements indicating a first condition of production of a product by the production system. The at least one board can include a second interface to receive second fluid measurements of a second sensor, the second fluid measurements indicating a second condition of the production of the product by the production system. The at least one control board include a data processing system including one or more processors, coupled with memory, to generate, using the first fluid measurements and the second fluid measurements, output data for optimizing the production of the product. The data processing system to transmit the output data to a display or a controller.

[0004] At least one aspect of the present disclosure is directed to a method. The method can include receiving, by a first interface of a gateway system, first fluid measurements of a first sensor, wherein the first sensor is an impedance sensor or an optical sensor, the first fluid measurements indicating a first condition of production of a product by the production system. The method can include receiving, by a second interface of the gateway system, second fluid measurements of a second sensor, the second fluid measurements indicating a second condition of the production of the product by the production system. The method can include generating, by a data processing system including one or more processors, coupled with memory, using the first fluid measurements and the second fluid measurements, output data for optimizing the production of the product. The method can include transmitting, by the data processing system, the output data to a display or a controller.

[0005] At least one aspect of the present disclosure is directed to a computing system disposed on-premises at a production system. The computing system can include at least one control board. The control board can include a first interface to receive first fluid measurements of a first sensor, wherein the first sensor is an impedance sensor or an optical sensor, the first fluid measurements indicating a first condition of production of a product by the production system. The control board can include a second interface to receive second fluid measurements of a second sensor, the second fluid measurements indicating a second condition of the production of the product by the production system. The control board can include a data processing system including one or more processors, coupled with memory, to generate, using the first fluid measurements and the second fluid measurements, output data for optimizing the production of the product and transmit the output data to a display or a controller.

[0006] These and other aspects and implementations are discussed in detail below. The foregoing information and the following detailed description include illustrative examples of various aspects and implementations, and provide an overview or framework for understanding the nature and character of the claimed aspects and implementations. The drawings provide illustration and a further understanding of the various aspects and implementations, and are incorporated in and constitute a part of this specification. The foregoing information and the following detailed description and drawings include illustrative examples and should not be considered as limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The accompanying drawings are not intended to be drawn to scale. Like reference numbers and designations in the various drawings indicate like elements. For purposes of clarity, not every component may be labeled in every drawing. In the drawings:

[0008] FIG. 1 is an example system including a gateway system collecting measurements from sensors.

[0009] FIG. 2 is an example control board of a gateway system.

[0010] FIG. 3 is an example housing of a gateway system.

[0011] FIG. 4 is an example housing of a gateway system including a display.

[0012] FIG. 5 is an example housing of a gateway system including connectors.

[0013] FIG. 6 is a perspective view of an example gateway system.

[0014] FIG. 7 is an example housing of a gateway system including a control board.

[0015] FIG. 8 is an example method of collecting and processing data by a gateway system. [0016] FIG. 9 is an example computing architecture of a data processing system.

DETAILED DESCRIPTION

[0017] Following below are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, devices, and gateway systems. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways.

[0018] An environment, such as a manufacturing environment, factory, laboratory, warehouse, building, or other environment can have multiple sensors. The sensors can be spectral sensors, flow rate sensors, temperature sensors, cameras, pressure sensors, conductivity sensors, impedance sensors, capacitance sensors, pH sensors, chlorine sensors, dissolved oxygen (DO) sensors, chemical oxygen demand (COD) sensors, optical sensors, chemical sensors, electrical sensors, or mechanical sensors. A central system can collect data from the sensors, perform analytics, and generate operational insights, control recommendations, control decisions, or control commands for controlling the environment. However, the sensors can all communicate via different communication protocols. Furthermore, at least some of the sensors can be spectral sensors that measure spectral data of a fluid, such as water. Spectral sensors can communicate large amounts of data measurements at a high rate, e.g., 2-5 kilobytes per second, 1-10 kilobytes per second, less than 1 kilobyte per second, more than 10 kilobytes per second. A system, therefore, can have difficulty in collecting data from the various sensors of different communication protocols. The system can also have technical issues receiving and processing the large amount of data measurements received from the spectral sensors. Therefore, there can be technical challenges to receive, process, and display data received from the sensors.

[0019] The sensors can be connected directly to a cloud platform as the cloud platform can have sufficient processing resources to process the spectral data. The sensors can be Internet enabled sensors that communicate directly with the cloud, or can communicate with the cloud through an interface. The cloud platform can then process the sensor data, determine output data, and provide the output data back to the environment. Internet connected or enabled sensors can rely on stable Internet connectivity and high Internet speeds. A process or a production occurring at the environment can be halted, slowed, or encumbered if the sensor loses Internet connection with the cloud platform or Internet speeds slow. Furthermore, there can be data privacy concerns communicating the sensor data to the cloud. Also, the systems may not have immediate access to the data if the data is stored in the cloud. An entity can lose track of or control of the data at the cloud, and may not have deep insight to the processes that the cloud performs with the data. [0020] A programmable logic controller (PLC) can also connect networks of the environment. However, the PLC can lack advanced computational capabilities, visualization capabilities, or flexible programming capabilities. While the PLC can connect with some sensors, the PLC may not be able to connect with other sensors. Furthermore, the PLC may not have the processing power to receive a high volume of data at a fast rate. For example, the PLC can receive single sensor measurements periodically, e.g., receive one temperature sensor measurement (e.g., 1-2 bytes of data) from a temperature sensor every ten seconds. However, the PLC may not be able to handle high volumes of data of spectral sensors, e.g., 500 kilobytes of data per second. Furthermore, the PLC may not be able to quickly process the data, e.g., compare sensor measurements against other sensor measurements, or execute advanced control or analytics functions, e.g., neural networks or machine learning models.

[0021] To solve for these and other technical issues, the technical solutions described herein can include a gateway system configured to communicate with sensors via multiple different communications protocols. The gateway system can include processing, memory, and power resources for handling large volumes of data, handling complex datasets, and executing advanced control algorithms (e.g., such as neural networks, model predictive control, or other processing intensive tasks). The gateway system can include multiple different physical interfaces to connect with sensors via a variety of communication networks, buses, or ports. The gateway system can receive and collect data from the different interfaces in different protocols, for example, from sensors, controllers, actuators, distributed control system (DCS), building management systems (BMSs), PLCs, or cameras.

[0022] The gateway system can include a data processing system capable of receiving data from the sensors at a high bit rate, e.g., 500 kilobytes per second. The gateway system can include a data processing system capable of processing a complex dataset in real-time, or in near real-time, as measurements are received. Furthermore, the gateway system can include processors, memory devices, or other computing equipment that can process complex models, algorithms, or software (e.g., machine learning models, neural networks, etc.) with low latency or in real-time. The gateway system can generate control data or output data in real-time, e.g., with low latency less than ten seconds, less than one second, less than half a second, less than a millisecond. The gateway system can generate control data in real-time as sensor measurements are received. For example, the sensors can be spectral sensors communicating spectral measurements to the data processing system at a high bit rate, that the gateway system can process, filter, perform computations on, in real-time, or in near- real time. The gateway system can display processed or filtered spectral measurements in real-time, or in near real-time. The dataset can further include data of various different data types or formats, e.g., spectral measurements from spectral sensors, temperature measurements received from a PLC, flowrate measurements received from a flow sensor via a pulse, 4-20 milliamps (mA), industrial network protocol information such as MODBUS, EthemetIP, Profinet, CCLINK, lOLink, BACNET, or HART protocol information, facility data (e.g., data of part of, or an entire facility). The gateway system can be capable of handling a complex dataset with data formatted in a variety of formats or according to a variety of different communication protocols.

[0023] The gateway system can process the data to generate control commands, control decisions, control suggestions, control recommendations, insights, or other data. The gateway system can convert a complex dataset into a logic command that a PLC or other controller can consume. The gateway system can generate the logic command in a format for the PLC to receive and operate on. The gateway system can interface with multiple layers or networks of an environment, e.g., legacy networks, modem networks, new networks, etc. Furthermore, the gateway system can connect and communicate with at least one backhaul network. For example, the gateway system can interface with cloud platforms, server systems, or other platforms that can process data collected by the gateway system. The data received by the gateway system can flow to a remote system outside the environment where the gateway system is disposed or located via at least one of the backhaul networks.

[0024] Furthermore, the gateway system can be disposed on-premises within an environment. The gateway system can include a housing and user interface that can be disposed in a wet environment, sandy environment, dusty environment, gas fdled environment, environment with vibrations. The housing can include a control board of the gateway system and a user interface. The user interface can be positioned behind a transparent material of the housing. The transparent material can allow a user to view decisions or outputs of the gateway system within the environment itself. Because the housing can be watertight, airtight, or gastight, or because the housing can limit water, dust, dirt, gas, or another substance from entering the housing, the gateway system can be disposed on-premises, even when the gateway system is located in an adverse environment. Because the gateway system can locally process and display data to a user within an environment, significant amounts of delay time can be reduced compared to sending the sensor measurements to a remote visualization platform, and then returning visualization results to the gateway system.

[0025] Referring to FIG. 1, among others, an example system 100 including a gateway system 105 collecting measurements from sensors 110 is shown. The system 100 can be implemented with a single sensor 110, or multiple sensors 110. The gateway system 105 can include at least one control board 125. The control board 125 can be or include a printed circuit board (PCB), a circuit board, or other apparatus that includes electrical components, electrical traces, or electrical connections. The control board 125 can be one or a combination or collection of different PCBs or circuit boards. The control board 125 can include at least one connector or interface 135. The interface 135 can be coupled with sensors 110. The interface 135 can be coupled with any type of data source (e.g., database, controller, or server). The interfaces 135 can couple with various networks or connections, e.g., universal serial bus (USB) connections, Ethernet connections, RS-485 connections, universal synchronous asynchronous receiver transmitter (USART) connections. The interfaces 135 can communicate according to one or many different communication protocols. The interfaces 135 can receive data from the data sources or the sensors 110 via any protocol, e.g., receive first data via a first protocol and second data of a second protocol. The interfaces 135 can receive process data, process status, operational data, data from a control system, etc. The interfaces 135 can electrically couple at least one data processing system 130 of the gateway system 105 with the sensors 110. For example, the gateway system 105 can include a first interface 135 to couple with a first sensor 110 via a first protocol. The gateway system 105 can include a second interface 135 to couple with a second sensor 110. The first measurements can be first spectral measurements of a fluid measured by a first sensor 110, and the second measurements can be second spectral measurements of a fluid measured by a second sensor 110. In some implementations, the sensors 110 connect directly with the Internet and communicate with a cloud platform or server system.

[0026] The gateway can communicate with cloud systems, server systems, remote systems, etc. via a network, such as a backhaul network. The gateway system 105 can transmit sensor measurements via cellular networks, the Internet, a local area network, a wide area network, or any other network to the cloud system. Furthermore, the gateway system 105 can act as a local data system or database, and store the sensor measurements of the sensors 110 locally within the environment.

[0027] The gateway system 105 can be modular. For example, the gateway system 105 can be updated with new hardware or other electrical components. The gateway system 105 can receive software updates or other software modules to operate the new hardware. For example, the gateway system 105 can be expanded to operate additional protocols or communicate via different communication networks. Furthermore, the gateway system 105 can receive software updates to perform different analysis or control algorithms or to handle new datatypes. Furthermore, if different input-output (I/O) modules are coupled with the gateway system 105, the data processing system 130 can be updated with a new piece of software to run the new module.

[0028] The gateway system 105 can receive sensor measurements from sensors 110 via one or multiple different connections, networks, protocols, or communication schemes. The sensors 110 can be spectral sensors, cameras, flow rate sensors, flow indicators, temperature sensors, pressure sensors, conductivity meters, optical sensors, impedance sensors, capacitance sensors, chemical sensors, mechanical sensors, controllers, or data generation devices. Optical sensors 110 can provide optical data, e.g., measures of reflectance, absorbance, intensity, or power of various wavelengths of light. Impedance sensors 110 can provide measures of electrical impedance via a sensing component of a material on an electrical signal generated by a signal generating component of the impedance sensor 110. Spectral sensors 110 can be a type of optical sensor 110. Spectral sensors 110 can include both optical spectral sensors or impedance based spectral sensors. For example, the spectral sensors 110 can provide the gateway system 105 with spectral data, such as impedance spectroscopy data or optical spectroscopy data. The sensors 110 can be spectral sensors or non-spectral sensors. Nonspectral sensors 110 can include conductivity sensors, flow rate sensors, turbidity sensors, temperature sensors, pH sensors, pressure sensors, connectivity sensors, viscosity sensors, a genetic sequencing apparatus, etc.

[0029] Spectral sensors 110 can generate or take a data measurement, such as a spectral measurement, of a fluid in the line 120, and provide the data measurement to the gateway 105. The data measurement can be at least one signal, data, dataset, at least one data frame, or at least one data packet. The data measurement can indicate a level of reflectance, absorbance, or scattering for wavelengths across the spectrum at a particular resolution. For example, the resolution could be every nanometer (nm), every half nm, every picometer (pm). The sensor 110 can transmit or communicate the data measurement 120 to the gateway 105 via at least one network, cable, or communication medium.

[0030] The spectral sensor 110 can be a broadband spectral sensor. The sensor 110 can be an optical, spectroscopy, or spectral sensor that measures a range of wavelengths. The sensor 110 can include a light source that produces light which a sensor of the spectral sensor 110 can measure after the material interacts with the light. The light source can be a monochromatic light source. The light source can be a polychromatic light source that emits many wavelengths of light to obtain a spectrum of wavelengths. The sensor 110 can measure how a material interacts with light (e.g., reflects light, absorbs light, scatters light, etc.) across a spectrum of wavelengths). The spectrum of wavelengths can include visible, ultraviolet (UV), and infrared (IR) wavelengths. The wavelengths can be 395 nm to 955 nm. The wavelengths can be less than 395 nm. The wavelengths can be greater than 955 nm. The wavelengths can be 200 nm to 1000 nm. The wavelengths can be 1000 nm to 3000 nm or greater. The sensor 110 can include a light source, such as one or multiple light emitting diodes (LEDs) or lightbulbs of varying types such as tungsten, incandescent, xenon, flash bulbs, halogen that generate the light that is reflected, absorbed, or scattered by the liquid of the line 120 and measured by the sensor 110. The gateway 105 can combine measurements of multiple different sensors to infer or determine a measurement, e.g., combine measurements of a first and second sensor to infer a condition, such as viscosity.

[0031] Furthermore, the gateway system 105 can receive data from at least one controller 145 (such as a PLC, or an actuator controller) or other system. In some implementations, the gateway system 105 can be or include a PLC or PLC software. The data can be control data, actuator settings, historical control commands, actuator statuses, valve positions. The system can include at least one device, apparatus, or pipe 120. For example, the device 120 can be a pipe, conduit, tank, container, basin, or other fluid holding or supporting device. The sensors 110 can be disposed on or in a pipe 120 of an industrial manufacturing system (e.g., a food production system). For example, the sensors 110 can be disposed on input lines or output lines of a piece of equipment or a system. The pipes 120 can carry fluids or gasses or powders or gels or combinations thereof, e.g., water, cleaning solutions, chemicals to or from the system, food products, ingredients. The pipes can carry a food product, a chemical, an oil, a cleaning product, a hygiene product, etc. The sensors 110 can measure conditions of the production of a product (e.g., a food, a drink, a dessert) of a production system or a production process. For example, the sensors 110 can measure the fluids of the lines 120. The fluid measurements can be measurements indicating conditions of production, e.g., the fluids can be the product or can be materials or components of the product. For example, the sensors 110 can measure a concentration of an ingredient, concentration of a product, emulsification levels, temperature levels of the ingredients or product, flow rate of fluids through lines, product changeover status, equipment cleaning status, etc.

[0032] The gateway system 105 can couple with the sensors 110 or a controller 145 via a network switch 115, such as an ethemet switch. The gateway system 105 can connect directly to the sensors 110 or the controller 145 without needing a network switch 115. The gateway system 105 can be powered via power over Ethemet, e.g., PoE or PoE+. The gateway system 105 can be powered based on the switch 115 , or the gateway system 105 can power the switch 115. Furthermore, the switch 115 or the gateway system 105 can power the sensors 110 or the controller 145 via PoE or PoE+.

[0033] The data processing system 130 can process sensor measurements received from the sensors 110, e.g., first measurements received from a first sensor 110 and second measurements received from a second sensor 110. The measurements can be received in the same or different formats or protocols. The sensor measurements can be received at a high bit rate. For example, the data processing system 130 can receive the measurements at at least 1 kilobyte per second, 2-5 kilobytes per second, 1-10 kilobytes per second, less than 1 kilobyte per second, more than 10 kilobytes per second, 500 kilobytes per second or more. The data processing system 130 can receive the measurements continuously, or over a period of time (e.g., a minute, an hour, a day, a week, a year, as long as the sensor 110 is measuring values). The data processing system 130 can process the sensor measurements in real-time, e.g., as the measurements are received.

[0034] The data processing system 130 can generate output data, which can include a control command, a control decision, an insight, a recommendation. The data processing system 130 can execute or perform one or multiple control algorithms, models, machine learning models to generate the output data. The gateway system 105 can provide, communicate, or transmit, via an interface 135, the output data to a display 140 or controller 145. The data processing system 130 can generate the output data in a format for a controller 145, e.g., a PLC. The data processing system 130 can transmit or communicate the output data to the controller 145 via the interface 135. The data processing system 130 can generate the output data in a format different than, or the same as, the format that the sensor measurements are received in. The control commands can cause the controller 145 to open a valve, close a valve, start a process, stop a process, adjust parameters of a process.

[0035] For example, the machine learning model can be trained by a machine learning technique to combine multiple different types of data together to determine output data (e.g., a control or operating decision for a production system). The data received by the interfaces 135 can be different types of data, which can be provided to the data processing system 130 to be input into the model. For example, a first interface 135 can receive optical data from an optical sensor 110 or impedance data from an impedance sensor 110, while a second interface 135 can receive flow rate data from a flow sensor 110, turbidity data from a turbidity sensor 110, conductivity data from a conductivity sensor 110, or temperature data from a temperature sensor 110. For example, the data processing system 130 can execute the model using multiple different input data sources, e.g., a combination of at least two of optical data, impedance data, spectral data, turbidity data, flow rate data, temperature data, conductivity data, etc. For example, the model can execute with an input of one sensor 110 (e.g., optical data or impedance data) and input of another senor 110 (e.g., temperature, pressure, conductivity, turbidity, pH, etc.).

[0036] The data processing system 130 can execute the model to combine the various types of data, and make correlations between the various data inputs to drive a process optimization. For example, the data processing system 130 can use the output of the model to schedule cleaning, determine when cleaning is concluded and stop cleaning, increase the product yield of a production process, perform quality assurance or quality control decisions to make a substance pure or cause a product to have a desirable characteristic, etc. For example, a first interface 135 can electrically connect or couple with a first sensor 110 (e.g., an optical or impedance based sensor) to receive optical data or impedance data (e.g., optical spectral information or impedance spectral information) and a second interface 135 can electrically connect or couple with a second sensor 110 (e.g., a temperature sensor, a flow rate sensor, a turbidity sensor, etc. The data received by the first interface 135 and the second interface 135 can be used by an algorithm, process, model, or machine learning technique to generate output data for improving the production of the system 100.

[0037] The data processing system 130 can execute using collected sensor data of the sensors 110 to perform a process optimization. For example, the data processing system 130 can generate output data that updates or changes the operation of the system 100 to produce a product (e.g., changing cooking times, changing cooling times, changing temperature setpoints, changing flow rates, etc.), controlling cleaning or product changeover, controlling a fermentation process, controlling microbial growth in the apparatus 120, controlling the system to prevent fouling or biofilm, measuring and detecting leaks, filtration issues, or contamination, etc. The data processing system 130 can determine optimal control decisions that cause a concentration of a chemical, an acid, a surfactant, or a disinfectant to be at or near a level for cleaning or production. For example, the data processing system 130 can control a level of iron phosphate or NaOH concentration inside a wash solution, control a contaminant, control caustic concentration (e.g., sodium hydrostic concentration). For example, the output data can control fermentation (e.g., stages or brewing), the production of soy sauce, MSG production, fermentation, the production of plastics, or various other industry production operations. The data processing system 130 can optimize a parts washing process (e.g., CNC milled parts or automotive parts) using the data collected from the sensors 110. The gateway 105 can use the data of the sensors 110 to determine how well digested or cooked a material is, e.g., how well soybeans are digested, roasted, or cooked. The gateway 105 can use the data of the sensors 110 to determine how well blended a mixture is. The gateway 105 can use the data of the sensors 110 to determine or identify a chemical, e.g., determine if the chemical is an active version, a non-active version, and what the activity level of a material or solution is. The gateway 105 can use the data of the sensors 110 to determine how much iron phosphate is in a coating or spray material. The gateway 105 can use the data of the sensors 110 to determine how well heat treated a material is, e.g., look at an active ingredient such as sugar and determined how well caramelized the sugar is.

[0038] The gateway system 105 can include at least one display 140 (e.g., liquid crystal display, light emitting diode display, touch screen). The data processing system 105 can cause the display 140 to display control insights, control commands, measurements of the sensors 110, graphics, analytics. Furthermore, the data processing system 130 can transmit or send output data to smartphones, computers, tablets, or other devices via text, email, application notifications.

[0039] The sensors 110 can be disposed in a variety of locations in a system or apparatus, e.g., in a variety of tanks or lines. For example, the sensors 110 can be disposed on or in input lines 120 (e.g., input water lines) of a system and output lines or drain lines 120 (e.g., output water lines) of the system. The sensors 110 can be disposed within or at least partially submerged in a material, e.g., a fluid. The sensors 110 can provide spectral data, temperature data, flow data, connectivity data, etc. The gateway system 105 can receive sensor data, control data, process data, operational data, status data, PLC data, or facility data. The gateway system 105 can receive data from sensors, the controller 145, and/or from a factory operations database, which can provide data of a particular process, a piece of equipment, or a whole facility. The data processing system 130 can collect the data via the interface 135 over one or multiple different protocols, and generate control decisions for the system. For example, a first interface 135 can couple with a first sensor 110 and receive measurements or data in a first protocol, while a second interface 135 can couple with a second sensor 110 and receive measurements or data in a second different protocol. The data processing system 130 can determine that a wash is completed, a wash has ended, determine the status of the wash, determine what fluid (chemical or water) is used for the wash, determine whether the system should wash with a sanitizer.

[0040] The data processing system 130 can compare measurements on a first position or area 120 with measurements on a second position or area 120 (e.g., the input, in the middle, or at the output). The data processing system 130 can deploy any number of sensors at any number of positions, and compare measurements of the various sensors against each other. The data processing system 130 can perform the comparison, e.g., in real-time, to generate a command to begin, stop, or control an operating cycle or control cycle.

[0041] The data processing system 130 can collect sensor measurements from the sensors 110 to determine whether sensor measurements indicate a product produced by the system is in spec, out of spec, going out of spec, going back into spec. The data processing system 130 can generate control commands to bring out of specification products back into specification or to ensure equipment and systems are efficiently cleaned. One spectral sensor 110 can be located upstream while a second spectral sensor 110 can be located downstream (e.g., a first spectral sensor 110 in an input line 120 to a system and a second spectral sensor 110 in an output line 120 of the system). The system can be a pump, a mixing apparatus, a boiler, a fermentation tank, a heating apparatus, a cooling apparatus, a filling machine, a washing machine, etc. The data processing system 130 can compare or contrast the spectral data of the first and second sensors 110 against each other to determine whether the system is clean, dirty, or needs cleaning. For example, during a cleaning cycle, differences between the spectral measurements of the first sensor 110 on the input line to the system being cleaned and the second sensor 110 on the output line of the system being cleaned can indicate how clean the system is. For example, if water or a cleaning agent is flowing from the input line, through the system, to the output line, the closer the spectral finger prints of the first and second sensors 110 are, the better clean the system is. The data processing system 130 can compare the measurements of the spectral sensors 110 against one another to determine whether a cleaning cycle is complete, and generate a command to stop the cleaning cycle once complete. Instead of using a timer based system to determine when a cleaning cycle is complete (e.g., waiting for a length of time to expire for a cleaning cycle), the system can determine, using the comparison, that cleaning is complete and stop the cleaning cycle early, thus cleaning between production cycles (product changeover) can occur faster and less energy can be consumed when cleaning.

[0042] Responsive to determining that a cleaning cycle is complete, the data processing system 130 can send a command to the controller 145 to cause the controller 145 to end the cleaning cycle. The data processing system 130 can further determine whether an individual step or phase of a cleaning cycle is complete, and can send a command to the controller 145 to cause the controller 145 to end the step or phase of the cleaning cycle and move or advance to the next step or phase of the cleaning cycle. For example, a cleaning cycle can first rinse equipment 120 with water, then clean the equipment 120 with a chemical, and then rinse the equipment 120 again. The commands can be commands to begin or end a cleaning step or phase of a longer cleaning cycle, or a command to begin or end an entire cleaning phase. The control commands can be control commands to perform cleaning cycles at specific times or for specific durations of time to bring the product back to a specification. For example, the data processing system 130 can track changes in a delta or difference between the spectral finger prints or spectral profiles of the input and output lines, and determine based on a rate of change in the delta a time in the future when the system needs to be cleaned. For example, the delta or difference can be a comparison or difference of the intensity or amplitude of at each frequency. The delta can be another spectrum of intensities or amplitudes for various frequencies or frequency ranges. In some implementations, the delta can be input to a machine learning model or other algorithm to detect whether a cleaning cycle is complete, or determine a future time when cleaning should be scheduled. The data processing system 130 can forecast or predict when and how long to perform a cleaning cycle. The data processing system 130 can generate a schedule to forecast of cleaning for a system. The data processing system 130 can send a command to the controller 145 to schedule cleaning at a future time, which can cause the controller 145 to initiate a cleaning cycle once the future time is reached. The data processing system 130 can send a command to the controller 145 to schedule cleaning after a future action occurs, such as at the end of a production cycle. Furthermore, the data processing system 130 can schedule when to initiate a product changeover, when to initiate producing a product, when to stop producing a produce, when to transition from producing a first product to a second product, etc.

[0043] The gateway system 105 can be connected with a clean water tank. The data processing system 130 can build a baseline of measurements of the sensors 110 for the water tank. The data processing system 130 can trend measurements of the water tank over time to identify that the water tank is becoming more dirty, less dirty. The data processing system 130 can use the baseline to optimize cleaning times, cleaning start times, cleaning stop times. If the cleaning process is a manual cleaning process, and not automatically controlled by a controller 145, the data processing system 130 can cause the display 140 to display notifications or instructions to clean the system or tank. The data processing system 130 can cause the display 140 to display different types of data to implement manual processes, e.g., a product changeover, to stop production, to clean pipes, that the system is down for cleaning, etc. The display 140 can further display spectral measurements of spectral sensors 110. The gateway system 105 can be disposed or located within an environment. The gateway system 105 can include at least one speaker. The gateway system 105 can include at least one indicator light. The gateway system 105 can operate the speaker or illuminate the light to inform an operator to start a process, stop a process, initiate a cleaning cycle, or any other manual control operation.

[0044] The data processing system 130 can also apply a similar techniques for non-cleaning based changeovers such as material to material change, pushing a material out with another one in a pipe, tank, or equipment 120. For example, the techniques that the data processing system 130 can apply to determine whether cleaning is complete, the data processing system 130 can use to determine whether a changeover from one product to another product is complete. For example, this can be transitioning from one material with a specific flavor, fragrance, ingredient, or compound to another one that does not have this, has another, or has a different quantity of the flavor, fragrance, ingredient, or compound.

[0045] The data processing system 130 can include at least one processor coupled with memory. The data processing system 130 can include a general purpose processor, an application specific integrated circuit, a graphics processing unit, or any other processor or processing circuitry. The data processing system 130 can include a level of computing resources to execute machine learning models. The processor of the data processing system 130 can include a level of computing power and the memory of the data processing system 130 can include a level of memory sufficient to run a machine learning model, such as a supervised, unsupervised, or reinforcement learning machine learning model or algorithm. The data processing system 130 can run a neural network that is trained to output control decisions or insights based on the measurements for the sensors 110. The output of the neural network can be displayed on the display 140. The processor can have a clock speed of 60- 70 mega hertz (MHz), 55-75 MHz, less than 55 MHz, more than 75 MHz, or any other clock speed. The memory can be 1-2 megabytes (MB), the memory can be 0.5-2.5 MB, the memory can be less than 0.5 MB, the memory can be greater than 2.5 MB, or any other memory size.

[0046] The cloud system can deploy the model to the gateway 105 via at least one network. The models can be containerized models, algorithms, software applications, or software modules. The containers can be deployed to run on the gateway 105. The containers can include all software dependencies to run on a system, such as DOCKER containers, KUBERNETES containers, or any other type of standard software unit packing, such as a packaging including all of its own dependencies. Furthermore, the models can be or be configured to run on virtual machines.

[0047] The gateway 105 can execute direct models that directly output an identified material, concentration, or parameter. The gateway 105 can further implement an indirect model system. The gateway 105 can implement an indirect model that indirectly infers or maps a measurement to a value or complex value. For example, in an indirect model system, a product can be identified via at least one model, a concentration of the product can be identified via at least one model, and a complex parameter can be mapped to a level for the identified product at the identified concentration. The mapping can map between the complex parameter and concentration of levels of the product based on at least one known measurement of the complex parameter for a particular concentration level of the product. The gateway 105 can use the indirect model system to determine complex parameters through indirect mapping such as BOD, COD, phosphorus, nitrogen, ammonia, sugars, sugar concentrations, fats, fat concentrations, oils, greases, active ingredients, passive ingredients, proteins, enzymes, antibodies, active pharmaceutical ingredients (APIs), identification of enzymes, concentration of an enzyme, enzyme reaction rate or reaction status, antibodies, acids.

[0048] The gateway 105 can determine aluminum concentration for washing aluminum products (e.g., aluminum cans). Aluminum concentration can be detected with the direct model based on visible wavelengths. The gateway 105 can determine particle size, particle density, particle amount, enzyme reaction, fermentation, or reaction optimization with the direct model. Furthermore, once a reaction is complete, a measured spectral fingerprint (or other sensor measurement) can change, indicating that the reaction is complete. For example, once concrete or another material has hardened or reached a level of hardening, the spectral fingerprint can change to an identifiable level. The gateway 105 can use a direct model to detect a fingerprint indicating that a reaction or hardening has completed or a rate of a reaction or a rate of hardening. The gateway 105 can use a direct model to determine the level of cooking or extraction in a process.

[0049] The gateway 105 can determine whether a product, such as a pharmaceutical, is an original or counterfeit product. For example, a pharmaceutical may have a defined spectral fingerprint, and a deviation from the fingerprint can indicate that the product is counterfeit. The gateway 105 can use a direct model to determine whether a product, such as a pharmaceutical, or ingredient is an original product or a counterfeit. The gateway 105 can use the direct model to determine two or three phases, e.g., how much of a material is in a gas phase, a liquid phase, or a particle phase. For example, different spectral fingerprints can be identified by the direct model that identifies the different phases. The gateway 105 can use the direct model to determine whether centrifuging, separating two or more materials, is completed or determine a status or rate of centrifuging. Furthermore, the gateway 105 can use the direct model to determine how homogenous a fluid or product is, e.g., a level of homogeneity. Furthermore, the gateway 105 can use the direct model to determine particle size or viscosity.

[0050] The gateway 105 can use the direct model to determine whether a cleaning cycle has completed. The gateway 105 can determine, based on the direct model, a level of soiling, e.g., concentration of aluminum in another material such as an oil or a detection of contamination. The gateway 105 can use the direct model to determine a turbidity or haze level. The gateway 105 can use the direct model to determine biomass or cell count. For example, different cell counts may absorb light differently, and therefore, biomass or cell count can be detected through the spectral measurements. The gateway 105 can use the direct model to determine yeast concentration. The gateway 105 can use the direct model to determine protein concentration. The gateway 105 can use the direct model to determine diatomaceous earth or kieselguhr concentration. Furthermore, the gateway 105 can use the direct model to determine dehydration, water contents, ripening, decay of cells, diseases, allergens.

[0051] The gateway 105 can use the direct model to do quality assurance or quality control. For example, the gateway 105 can use the direct model to detect whether a produced product has quality issues. The direct model can indicate an amount of an active ingredient to add to a product to correct any quality control issues. The gateway 105 can send control commands to a controller 145 to alert a user or actively control the amount of ingredient added to the product to correct the quality control issue. The gateway 105 can use the direct model to determine a color, haze, protein level, enzyme, antibody concentration, active yeast, cell count, sugar concentration, protein, or cleaning optimization for a pharmaceutical.

[0052] The computations, processed, algorithms, or models that the gateway system 105 executes or performs can be performed by one, or on multiple gateway systems 105. For example, multiple gateway systems 105 can be networked together, at, near, or off-premises of a production system. The multiple gateways 105 can work together to each perform some computations or processes of a larger computation or process. Similarly, the gateway system 105 can be a computing unit or an edge computing device, and multiple computing units or devices can be distributed at, near, or separate from a production environment to collaboratively perform the various computations, processes, or algorithms discussed herein.

[0053] Referring to FIG. 2, among others, the control board 125 of the gateway system 105 is shown. The gateway system 105 can include a power connector 210. The power connector 210 can receive power from a power grid, battery, or other energy source. The power connector 210 can receive power via PoE or PoE+. The connector 210 can be an ethemet jack, a plug, a screw terminal, a barrel jack, etc. The power connector 210 can receive 24 volt input, 20-30 volt input, less than 20 volt input, more than 30 volt input. The control board 125 can include a connector 215 to couple with an un-interruptible power supply (UPS), inter-integrated circuit (I2C) connections, jumper cables. The control board 125 can include a connector 225 to couple with digital serial interface (DSI), I2C. The control board 125 can couple with the display 140 via the connector 225 and receive user and provide display data. The graphics processing and analytics for display on the display 140 can be executed in the data processing system 130, without requiring any off-site processing. [0054] The control board 125 can include a power output 230. The power output 230 can provide a 5 volt power output, a 4-6 volt power output, a 3-7 volt power output, less than 2 volt output, more than 7 volt output. The power output 230 can power the display 140 or the switch 115. The control board 125 can include at least one module 245, e.g., an accelerometer, a temperature sensor, a humidity sensor, a current sensor. The control board 125 can include a module 255 including RS485 and universal asynchronous receiver transmitter (UART). The control board 125 can include a real-time clock module 260 including a watchdog timer. The control board 125 can include memory 265, such as electrically erasable programmable read-only memory (EEPROM). The control board 125 can include ethemet connections 280 and 270 for couple with the switch 115 or the sensors 110. The control board 125 can include universal serial bus (USB) connections 275. The control board 125 can include a slot 240 for a memory card, e.g., a secure digital (SD) card. The memory card can store an operating system for the data processing system 130.

[0055] The control board 125 can include at least one I/O slot 250. In FIG. 2, the gateway system 105 is shown to include two slots 250, but can include any number of slots. At least one I/O card can couple with the slot 250, e.g., can be inserted into the slot 250 and make at least one electrical connection with the data processing system 130 via the slot 250. The cards coupled with the slots 250 can be interfaces to connect with sensors 110. For example, if the gateway system 105 does not have an interface for a sensor 110 of a particular protocol, a technician or user can install a card for coupling the gateway system 105 with the sensor 110 via the I/O slot 250. The cards can be stackable in the slot 250. The cards can be processing units or data storage units. For example, in order to expand the capabilities of the gateway system 105, a neural network engine card, an additional processing unit, an additional storage unit, a new communication interface, etc. can be coupled with the data processing system 130 via the slot 250.

[0056] For example, a first card can couple with the data processing system 130 via the slot 250. A second card can be coupled, stacked, or at least partially inserted into the first card, and couple with the data processing system 130 via the first card and the slot 250. One, two, three, four, five, or any number of cards can be stacked in the slot 250 to expand the capabilities of the gateway system 105.

[0057] Via the interfaces of FIG. 2, the gateway system 105 can bridge different computing or hardware systems. For example, the gateway system 105 can couple with sensors 110 of a variety of protocols, e.g., via USB, RS485, Ethemet, etc. The data processing system 130 can communicate with the via Modbus RTU, Modbus TCP, CC-Link, BACnet, EtherNet/Intemet protocol (IP), Profibus, OPC Unified Architecture (OPC UA), CCLINK, EtherCAT, Profmet, or any other protocol. The data processing system 130 can receive data of a first sensor 110 via one protocol, and receive data of a second sensor 110 via another different protocol. Furthermore, the gateway system 105 can send control commands to various controllers or PLCs 145 based on processing performed on the collected measurements of the sensors 110. The gateway system 105 can transmit commands in a variety of protocols, the same or different than the protocols which the gateway system 105 communicates with the sensors 110. The commands can cause a controller 145 to open a valve, close a valve, control a motor, etc. The gateway system 105 can be designed with flexibility, having duplicate and multiple ports or expansion capabilities, e.g., Ethernet ports 270, I/O slots 250, USB connection 275, etc. The gateway system 105 can communicate, via the interfaces, with a variety of networks of a variety of different network types to bring non-uniform data together within a single device or hub located on-premises. In this regard, the gateway system 105 can be configured to communicate with at least one first network of a first network type, and at least one second network of a second network type. For example, a first interface 135 can couple the gateway system 105 with a first network of a first network type and a second interface 135 can couple the gateway system 105 via a second network of a second type different than the first type. Each interface 135 can send or receive data to sensors 110, controllers 145, databases, or other data sources via the respective network.

[0058] Referring to FIG. 3-7, among others, an example housing 300 of the gateway system 105 is shown. The housing 300 can include lateral walls or sides 500. The sides 500 can form a box, cube, or rectangular solid. The housing 300 can include a first lateral side 500, a second lateral side 500, a third lateral side 500, a fourth lateral side 500, a bottom side 500, and a top side 320. The lateral sides 500 and top side 320 can form a watertight seal, an airtight seal, a dust tight seal. The housing 300 can prevent dust, dirt, water, snow, chemicals, gas, from entering an interior cavity or opening of the housing 300. The housing 300 can be disposed within an environment, outdoors, in a harsh environment, in a wet environment, in a washdown environment, in a dusty environment, in a dirty environment, in a muddy environment. The housing 300 can be fixed, via at least one bracket 505, to a wall, a piece of equipment, a ceiling, or a floor of an environment. The housing 300 can be disposed immediately adjacent a system or piece of equipment that the gateway system 105 is coupled with. The housing 300 can be located a distance from the equipment or system.

[0059] The housing 300 can include screws, bolts, or connectors 310 that couple the top side 320 with the lateral sides 500. The top side 320 can move on a hinge 315, that coupled the top side 320 with a lateral side 500 of the housing 300. A latch 330 can couple and secure the top side with a lateral side 500 of the housing 300. The display 140, the control board 125, and the data processing system 130 can be located within, or disposed within, the housing 300. The top side 320 can include an opening. A clear panel 305 can be disposed across the opening of the side 320. The clear panel 305 can touch the display 140, such that a user can interact with the display 140 via touch inputs. A user can view information displayed by the display 140 through the clear panel 305. The panel 305 can be a plastic material, a glass material, or any other transparent material. [0060] The gateway 105 can include at least one connector 510. The connector 510 can be an opening or component that provides an entryway for a cable, wire, or electrical conductor to be inserted into the housing 300 can couple with the control board 125. The gateway 105 can electrically couple with a power source, at least one sensor 110, at least one controller 145, a network, etc. via the connectors 510. The connectors 510 can provide a watertight connection. For example, the connectors 510 can stop or limit water, fluid, dust, dirt, sand, or another material from entering the housing 300.

[0061] Referring to FIG. 8, among others, an example method 800 of collecting and processing data by a gateway system is shown. The gateway system 105, the data processing system 130, the interface 135, and the display 140 can perform at least a portion of the method 800. The method 800 can include an ACT 805 of connecting with sensors. The method 800 can include an ACT 810 of receiving measurements. The method 800 can include an ACT 815 of processing measurements. The method 800 can include an ACT 820 of outputting data.

[0062] The method 800 can include an ACT 805 of connecting, by at least on interface 135 of the gateway system 105, with sensors 110. The interface 135 can connect, via at least one cable, wire, connector with the sensors 110. The interface 135 can make electrical connections to communicate with the sensor 110, e.g., receive measurements from the sensors 110. The interface 135 can be a USB interface, an RS485 interface, an Ethernet interface. The interface 135 can send or receive signals at 4-20mA, or any other current level. A first interface 135 can couple with a first sensor 110 via a first communication protocol. A second interface 135 can couple with a second sensor 110 via a second communication protocol. The interfaces 135 can couple with spectral sensors, flow sensors, temperature sensors, pressure sensors.

[0063] The method 800 can include an ACT 810 of receiving, by the interface 135, measurements by the sensor 110. The interface 135 can receive data packets, measurements, signals, or values from the sensors 110 indicating measurements of the sensors 110. A first interface 135 can receive first measurements of a first sensor 110 via a first communication protocol. A second interface 135 can receive second measurements of a second sensor 110 via a second communication protocol. The measurements can be spectral measurements of a fluid, a flow rate of a fluid, a temperature of a fluid, or any other measurement.

[0064] The method 800 can include, at ACT 815 of processing, by the data processing system 130, the measurements of the sensors 110. The data processing system 130 can execute control algorithms, machine learning models, filters, or other software or instructions to process the measurements of the sensors 110. The data processing system 130 can generate control decisions, control commands, commands to open a valve, commands to close a value, commands to increase a temperature of a heating apparatus, commands to decrease a temperature of a heating apparatus, etc. The data processing system 130 can generate user interface data, visualizations, graphic interfaces based on the sensor measurements.

[0065] The method 800 can include, at ACT 820, outputting, by the data processing system 130, data. For example, the output data can be the result of the processing of ACT 815. The data processing system 130 can transmit the output data to the display 140. The display 140 can generate graphics, images, charts, numbers, text based on the output data. The display 140 can display information to a user. The data processing system 130 can transmit control decision, control commands, messages, packets to a controller 145 to cause the control to operate actuators (e.g., valves, pumps, heaters, cooling systems, mixers, cleaning apparatus, etc.). For example, the controller 145, based on the control commands, stop cleaning cycles, open valves, close valves, start a process, stop a process, start equipment or machinery, stop equipment or machinery, start a pump, stop a pump.

[0066] Referring to FIG. 9, among others, an example block diagram of the data processing system 130 is shown. The data processing system 130 can include or be used to implement a data processing system or its components. The architecture described in FIG. 9 can be used to implement the data processing system 130. The data processing system 130 can include at least one bus 925 or other communication component for communicating information and at least one processor 930 or processing circuit coupled to the bus 925 for processing information. The data processing system 130 can include one or more processors 930 or processing circuits coupled to the bus 925 for processing information. The data processing system 130 can include at least one main memory 910, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus 925 for storing information, and instructions to be executed by the processor 930. The main memory 910 can be used for storing information during execution of instructions by the processor 930. The data processing system 130 can further include at least one read only memory (ROM) 915 or other static storage device coupled to the bus 925 for storing static information and instructions for the processor 930. A storage device 920, such as a solid state device, magnetic disk or optical disk, can be coupled to the bus 925 to persistently store information and instructions.

[0067] The data processing system 130 can be coupled via the bus 925 to a display 140, such as a liquid crystal display, or active matrix display. The display 140 can display information to a user. An input device 140, such as a keyboard or voice interface can be coupled to the bus 925 for communicating information and commands to the processor 930. The input device 140 can include a touch screen of the display 140. The input device 140 can include a cursor control, such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor 930 and for controlling cursor movement on the display 140. [0068] The processes, systems and methods described herein can be implemented by the data processing system 130 in response to the processor 930 executing an arrangement of instructions contained in main memory 910. Such instructions can be read into main memory 910 from another computer-readable medium, such as the storage device 920. Execution of the arrangement of instructions contained in main memory 910 causes the data processing system 130 to perform the illustrative processes described herein. One or more processors in a multi-processing arrangement can be employed to execute the instructions contained in main memory 910. Hard-wired circuitry can be used in place of or in combination with software instructions together with the systems and methods described herein. Systems and methods described herein are not limited to any specific combination of hardware circuitry and software.

[0069] Although an example computing system has been described in FIG. 9, the subject matter including the operations described in this specification can be implemented in other types of digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them.

[0070] Some of the description herein emphasizes the structural independence of the aspects of the system components or groupings of operations and responsibilities of these system components. Other groupings that execute similar overall operations are within the scope of the present application. Modules can be implemented in hardware or as computer instructions on a non-transient computer readable storage medium, and modules can be distributed across various hardware or computer based components.

[0071] The systems described above can provide multiple ones of any or each of those components and these components can be provided on either a standalone system or on multiple instantiation in a distributed system. In addition, the systems and methods described above can be provided as one or more computer-readable programs or executable instructions embodied on or in one or more articles of manufacture. The article of manufacture can be cloud storage, a hard disk, a CD-ROM, a flash memory card, a PROM, a RAM, a ROM, or a magnetic tape. In general, the computer-readable programs can be implemented in any programming language, such as LISP, PERL, C, C++, C#, PROLOG, or in any byte code language such as JAVA. The software programs or executable instructions can be stored on or in one or more articles of manufacture as object code.

[0072] Example and non-limiting module implementation elements include sensors providing any value determined herein, sensors providing any value that is a precursor to a value determined herein, datalink or network hardware including communication chips, oscillating crystals, communication links, cables, twisted pair wiring, coaxial wiring, shielded wiring, transmitters, receivers, or transceivers, logic circuits, hard-wired logic circuits, reconfigurable logic circuits in a particular non-transient state configured according to the module specification, any actuator including at least an electrical, hydraulic, or pneumatic actuator, a solenoid, an op-amp, analog control elements (springs, filters, integrators, adders, dividers, gain elements), or digital control elements.

[0073] The subject matter and the operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. The subject matter described in this specification can be implemented as one or more computer programs, e.g., one or more circuits of computer program instructions, encoded on one or more computer storage media for execution by, or to control the operation of, data processing apparatuses. Alternatively or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer storage medium can be, or be included in, a computer- readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. While a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially generated propagated signal. The computer storage medium can also be, or be included in, one or more separate components or media (e.g., multiple CDs, disks, or other storage devices include cloud storage). The operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources.

[0074] The terms “computing device”, “component” or “data processing apparatus” or the like encompass various apparatuses, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations of the foregoing. The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures.

[0075] A computer program (also known as a program, software, software application, app, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program can correspond to a fde in a fde system. A computer program can be stored in a portion of a fde that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single fde dedicated to the program in question, or in multiple coordinated fdes (e.g., fdes that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

[0076] The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatuses can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). Devices suitable for storing computer program instructions and data can include non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0077] The subject matter described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described in this specification, or a combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).

[0078] While operations are depicted in the drawings in a particular order, such operations are not required to be performed in the particular order shown or in sequential order, and all illustrated operations are not required to be performed. Actions described herein can be performed in a different order.

[0079] Having now described some illustrative implementations, it is apparent that the foregoing is illustrative and not limiting, having been presented by way of example. In particular, although many of the examples presented herein involve specific combinations of method acts or system elements, those acts and those elements may be combined in other ways to accomplish the same objectives. Acts, elements and features discussed in connection with one implementation are not intended to be excluded from a similar role in other implementations or implementations.

[0080] The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including” “comprising” “having” “containing” “involving” “characterized by” “characterized in that” and variations thereof herein, is meant to encompass the items listed thereafter, equivalents thereof, and additional items, as well as alternate implementations consisting of the items listed thereafter exclusively. In one implementation, the systems and methods described herein consist of one, each combination of more than one, or all of the described elements, acts, or components.

[0081] Any references to implementations or elements or acts of the systems and methods herein referred to in the singular may also embrace implementations including a plurality of these elements, and any references in plural to any implementation or element or act herein may also embrace implementations including only a single element. References in the singular or plural form are not intended to limit the presently disclosed systems or methods, their components, acts, or elements to single or plural configurations. References to any act or element being based on any information, act or element may include implementations where the act or element is based at least in part on any information, act, or element.

[0082] Any implementation disclosed herein may be combined with any other implementation or embodiment, and references to “an implementation,” “some implementations,” “one implementation” or the like are not necessarily mutually exclusive and are intended to indicate that a particular feature, structure, or characteristic described in connection with the implementation may be included in at least one implementation or embodiment. Such terms as used herein are not necessarily all referring to the same implementation. Any implementation may be combined with any other implementation, inclusively or exclusively, in any manner consistent with the aspects and implementations disclosed herein.

[0083] References to “or” may be construed as inclusive so that any terms described using “or” may indicate any of a single, more than one, and all of the described terms. References to at least one of a conjunctive list of terms may be construed as an inclusive OR to indicate any of a single, more than one, and all of the described terms. For example, a reference to “at least one of ‘A’ and ‘B’” can include only ‘A’, only ‘B’, as well as both ‘A’ and ‘B’. Such references used in conjunction with “comprising” or other open terminology can include additional items. [0084] Where technical features in the drawings, detailed description or any claim are followed by reference signs, the reference signs have been included to increase the intelligibility of the drawings, detailed description, and claims. Accordingly, neither the reference signs nor their absence have any limiting effect on the scope of any claim elements. [0085] Modifications of described elements and acts such as variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations can occur without materially departing from the teachings and advantages of the subject matter disclosed herein. For example, elements shown as integrally formed can be constructed of multiple parts or elements, the position of elements can be reversed or otherwise varied, and the nature or number of discrete elements or positions can be altered or varied. Other substitutions, modifications, changes and omissions can also be made in the design, operating conditions and arrangement of the disclosed elements and operations without departing from the scope of the present disclosure.

Claims

CLAIMS What is claimed is:

1. A gateway system of a production system, comprising: at least one control board, comprising: a first interface to receive first fluid measurements of a first sensor, wherein the first sensor is an impedance sensor or an optical sensor, the first fluid measurements indicating a first condition of production of a product by the production system; a second interface to receive second fluid measurements of a second sensor, the second fluid measurements indicating a second condition of the production of the product by the production system; and a data processing system comprising one or more processors, coupled with memory, to: generate, using the first fluid measurements and the second fluid measurements, output data for optimizing the production of the product; and transmit the output data to a display or a controller.

2. The gateway system of claim 1, comprising: the data processing system to: execute a model trained by machine learning to generate a control command based on: optical data of the optical sensor or impedance data of the impedance sensor; and the second fluid measurements of the second sensor; and transmit the control command to the controller to control the production system.

3. The gateway system of claim 1, comprising: the at least one control board, comprising: the first interface to receive the first fluid measurements from the first sensor in a first protocol; and the second interface to receive the second fluid measurements from the second sensor in a second protocol.

4. The gateway system of claim 1, comprising: the at least one control board, comprising: the first interface to receive optical data from the optical sensor or impedance data from the impedance sensor; and the second interface to receive flow rate data, turbidity data, conductivity data, pH, or temperature data from the second sensor.

5. The gateway system of claim 1, comprising: a housing comprising a plurality of lateral sides forming a watertight seal; and the display, wherein the display and the at least one control board are disposed within the housing.

6. The gateway system of claim 1, comprising: the data processing system to: receive input data comprising the first fluid measurements from the first sensor at a rate of at least one kilobyte per second over a time period; and process the input data as the input data is received to generate the output data.

7. The gateway system of claim 1, wherein: the one or more processors or the memory provide a level of computing resources to execute a model trained by machine learning to generate the output data.

8. The gateway system of claim 1, comprising: the data processing system to: generate the output data in real-time as the first fluid measurements and the second fluid measurements are received.

9. The gateway system of claim 1, comprising: the at least one control board comprising a connector; a first input-output card inserted into the connector, the first input-output card coupled with the data processing system through the connector; and a second input-output card stacked on the first input-output card, the second input-output card coupled with the data processing system through the first input-output card and the connector.

10. The gateway system of claim 1, comprising the at least one control board, comprising: the first interface to receive first spectral measurements of the first sensor; and the second interface to receive second spectral measurements of the second sensor.

11. The gateway system of claim 1, comprising: the display; and the data processing system to: receive spectral measurements from the first sensor and the second sensor; and generate the output data in a protocol for the controller based on the spectral measurements, the protocol different than a sensor protocol of the first sensor or the second sensor.

12. The gateway system of claim 1, comprising: a third interface to receive measurements or control data from a programmable logic controller in a protocol, the protocol of the controller different than a sensor protocol of the first sensor or the second sensor.

13. The gateway system of claim 1, comprising: the data processing system to: receive first spectral measurements from the first sensor disposed in an input line of a system; receive second spectral measurements from the second sensor disposed in an output line of the system; and determine that a cleaning cycle of the system is complete based on the first spectral measurements and the second spectral measurements.

14. The gateway system of claim 1, comprising: the data processing system to: receive first spectral measurements from the first sensor disposed in an input line of a system; receive second spectral measurements from the second sensor disposed in an output line of the system; determine that a cleaning cycle of the system is complete based on the first spectral measurements and the second spectral measurements; and transmit a command to end the cleaning cycle responsive to a determination that the cleaning cycle is complete.

15. The gateway system of claim 1, comprising: the data processing system to: receive first spectral measurements from the first sensor disposed in an input line of a system; receive second spectral measurements from the second sensor disposed in an output line of the system; predict a future point in time for the system to perform a cleaning cycle based on the first spectral measurements and the second spectral measurements; and transmit a command to the controller to perform the cleaning cycle at the future point in time.

16. The gateway system of claim 1, comprising: the at least one control board comprising; the first interface to connect with a first network of a first type; and the second interface to connect with a second network of a second type different that the first type.

17. A method, comprising: receiving, by a first interface of a gateway system, first fluid measurements of a first sensor, wherein the first sensor is an impedance sensor or an optical sensor, the first fluid measurements indicating a first condition of production of a product by a production system; receiving, by a second interface of the gateway system, second fluid measurements of a second sensor, the second fluid measurements indicating a second condition of the production of the product by the production system; generating, by a data processing system comprising one or more processors, coupled with memory, using the first fluid measurements and the second fluid measurements, output data for optimizing the production of the product; and transmitting, by the data processing system, the output data to a display or a controller.

18. The method of claim 17, comprising: executing, by the data processing system, a model trained by machine learning to generate a control command based on: optical data of the optical sensor or impedance data of the impedance sensor; and the second fluid measurements of the second sensor; and transmitting, by the data processing system, the control command to the controller to control the production system.

19. A computing system disposed on-premises at a production system, comprising: at least one control board, comprising: a first interface to receive first fluid measurements of a first sensor, wherein the first sensor is an impedance sensor or an optical sensor, the first fluid measurements indicating a first condition of production of a product by the production system; a second interface to receive second fluid measurements of a second sensor, the second fluid measurements indicating a second condition of the production of the product by the production system; and a data processing system comprising one or more processors, coupled with memory, to: generate, using the first fluid measurements and the second fluid measurements, output data for optimizing the production of the product; and transmit the output data to a display or a controller.

20. The computing system of claim 19, comprising: the data processing system to: execute a model trained by machine learning to generate a control command based on: optical data of the optical sensor or impedance data of the impedance sensor; and the second fluid measurements of the second sensor; and transmit the control command to the controller to control the production system.