CN119430521A - A low-carbon intelligent treatment and resource utilization method of landfill leachate based on "end-edge-cloud" collaborative control - Google Patents

A low-carbon intelligent treatment and resource utilization method of landfill leachate based on "end-edge-cloud" collaborative control Download PDF

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CN119430521A
CN119430521A CN202411352146.2A CN202411352146A CN119430521A CN 119430521 A CN119430521 A CN 119430521A CN 202411352146 A CN202411352146 A CN 202411352146A CN 119430521 A CN119430521 A CN 119430521A
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黄进刚
汪海
徐晓滨
韩伟
裘姗姗
刘洁
侯平智
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Hangzhou Dianzi University
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Abstract

The invention discloses a garbage leachate low-carbon intelligent treatment and recycling method based on 'end-side-cloud' cooperative control. The method comprises the steps that a terminal layer obtains real-time monitoring data of each sensor and sends the real-time monitoring data to an edge layer, a machine learning algorithm is adopted at the edge layer to conduct multi-objective prediction on a water quality index, a system comprehensive energy consumption and an anaerobic hydrogen production rate, then an objective function is constructed, and further an ideal value of operation parameters of each module is obtained, and key process operation conditions of the system are adjusted according to the ideal value and the real-time monitoring data of the terminal layer sensor, so that optimal results of the water quality index, the anaerobic hydrogen production rate and the system comprehensive energy consumption are obtained. The invention optimizes and controls the operation parameters of the biochemical system through the 'end-side-cloud' internet-of-things sensing network, realizes the intelligent control of the whole process flow of landfill leachate treatment and resource/energy, and realizes the dynamic adjustment of key influencing factors in the landfill leachate treatment and resource process.

Description

Garbage leachate low-carbon intelligent treatment and recycling method based on 'end-side-cloud' cooperative control
Technical Field
The invention belongs to the technical field of environmental protection and resource utilization, and particularly relates to a garbage leachate low-carbon intelligent treatment and resource utilization method based on 'end-side-cloud' cooperative control.
Background
The garbage percolate has high contents of suspended solids, organic matters, nutrient salts, heavy metals and the like, and has high treatment difficulty, and the main treatment mode at present generally adopts the processes of physical and chemical pretreatment, biochemical treatment (anaerobic-aerobic) and rear-end deep treatment. Among them, the physicochemical pretreatment usually adopts a coagulation/flocculation method. The coagulation/flocculating agents such as common polyaluminium chloride (PAC), polymeric Ferric Sulfate (PFS) and Polyacrylamide (PAM) have limited pretreatment effects on percolate, large dosage of agents, large sludge yield and slow precipitation process, and the dosage proportion of the agents cannot be timely adjusted according to the change of raw water quality, so that the activity of microorganisms in a biochemical system and the service life of a rear end membrane treatment system are influenced.
In addition, the concentration of organic pollutants, ammonia nitrogen and other pollutants in the percolate is high, the biodegradability is poor, the aeration energy consumption is high, the system operation automation degree is relatively low, the labor amount is large, the problems of hysteresis and the like exist in water quality monitoring and feedback control, and the expected treatment effect cannot be achieved in practical application. Therefore, various data such as production equipment, processes, operation and maintenance are collected by utilizing the sensors integrated in the leachate treatment equipment, intelligent algorithm optimization and process parameter regulation are performed, and the method has important significance for realizing intelligent operation and maintenance of the landfill leachate sewage treatment facility and improving the operation level and the supervision capability.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art and provides a garbage leachate low-carbon intelligent treatment and recycling method based on 'end-side-cloud' cooperative control.
The garbage leachate low-carbon intelligent treatment and recycling method based on the 'end-side-cloud' cooperative control is based on a garbage leachate treatment system, wherein the garbage leachate treatment system comprises an adjusting tank, a flocculation pretreatment module, a biochemical system and a membrane bioreactor which are sequentially cascaded;
the method comprises the following steps:
and S1, acquiring real-time monitoring data of each sensor of the terminal layer.
The real-time monitoring data of each sensor comprises the operation parameters of an adjusting tank, the operation parameters of a flocculation pretreatment module, the operation parameters of an anaerobic reactor, the operation parameters of an anaerobic tank and an aerobic tank in an anaerobic-aerobic biochemical system and the operation parameters of a membrane bioreactor.
And step S2, sending the real-time monitoring data of each sensor to an edge layer through a communication network, and performing multi-objective prediction on the water quality index of the effluent, the comprehensive energy consumption of the system and the anaerobic hydrogen production rate of the anaerobic reactor by adopting a machine learning algorithm at the edge layer.
And S3, constructing an objective function according to the effluent quality index, the comprehensive energy consumption of the system and the anaerobic hydrogen production rate of the anaerobic reactor, and obtaining the ideal values of the operation parameters of the regulating tank, the flocculation pretreatment module, the anaerobic reactor, the anaerobic-aerobic biochemical system and the membrane bioreactor by utilizing a decision-making level optimization model in the cloud server under the constraint condition.
And S4, adjusting the operation conditions of the key process of the system based on the decision-making level optimization model optimization result and the real-time monitoring data of the terminal layer sensor, so as to obtain the optimal result of the effluent quality index, the anaerobic hydrogen production rate and the comprehensive energy consumption of the system.
Preferably, in step S1, the operation parameters of the regulating tank include pH, water temperature, dissolved Oxygen (DO), oxidation-reduction potential (ORP), and water quality index of the inlet water, including inlet water flow rate, chemical Oxygen Demand (COD) of the inlet water, ammonia nitrogen (NH 4 + -N), total Nitrogen (TN).
The operation parameters of the flocculation pretreatment module comprise flocculant dosage, sedimentation residence time, pH value, ORP and water temperature.
The operation parameters of the anaerobic reactor comprise anaerobic fermentation tank hydraulic retention time, external redox mediator addition amount, anaerobic tank pH value, water temperature and ORP, and the flow rate, temperature and total pressure of the generated biogas and the partial pressure of each of different biogas.
The anaerobic tank and the aerobic tank in the anaerobic-aerobic biochemical system have operating parameters including DO, ORP, water temperature and pH value.
The operating parameters of the membrane bioreactor include pH, ORP, water temperature, DO, transmembrane pressure difference.
The water quality indexes of the effluent treated by the membrane bioreactor comprise turbidity, effluent COD and NH 4 + -N, TN.
Preferably, in step S2, the machine learning algorithm includes at least one of Random Forest (RF), extreme gradient boosting (XGBoost), long-term short-term memory network (LSTM), artificial Neural Network (ANN).
Preferably, in step S3, the decision-level optimization model is specifically one of a Genetic Algorithm (GA) and a particle swarm optimization algorithm (PSO).
Preferably, in step S3, the objective function is specifically:
Wherein, Is the weight; indicating that the overall energy consumption of the system is minimized, Indicating the water quality to reach the discharge target value,Represents the highest anaerobic hydrogen production rate of the anaerobic reactor, E (x) represents the comprehensive energy consumption of the system in the process under a certain operation parameter, alpha 1、α2、α3 is the weight of the water quality indexes COD, NH 4 + -N and TN of each effluent to the importance of optimizing the result of the standard emission target value, and COD is #) Represents the relation between the COD value of the effluent and the discharge standard value of the system under a certain operation parameter, and NH 4 + -N #) Represents the relation between the NH 4 + -N value of the effluent and the standard emission value of the system under a certain operation parameter, TN #) The relation between the TN value of the effluent and the standard emission value of the system under a certain operation parameter is shown; indicating the hydrogen production rate of the system in the anaerobic reactor at a certain operating parameter.
The constraint conditions are specifically as follows:
Wherein E 0 is the energy consumption of process equipment before the system performs 'end-side-cloud' cooperative control; E A is the energy consumption of the system in the process of data acquisition, transmission, calculation, feedback and control when the system performs the end-side-cloud cooperative control, C is the actual COD value of the system effluent, C （t） is the standard value of the COD emission of the effluent, N is the actual NH 4 + -N concentration of the effluent, N （t） is the standard value of the NH 4 + -N emission of the effluent, T is the actual TN concentration of the effluent, and T （t） is the standard value of the TN emission of the effluent; Is a Sigmoid function, H 2 is the actual hydrogen production rate of the system, H 2（opt） is the maximum hydrogen production rate, tanh is a hyperbolic tangent function, and S 1、S2 is a parameter for scaling data.
Preferably, in the step S4, the key process operation conditions of the system comprise key process operation conditions of a flocculation pretreatment module, key process operation conditions of an anaerobic-aerobic biochemical system and key process operation conditions of a membrane bioreactor system.
The key process operation conditions of the flocculation pretreatment module comprise the stirring speed of a stirrer and the rotating speed of a high-speed shearing machine.
The key technological operation conditions of the anaerobic-aerobic biochemical system comprise aeration intensity, stirring speed of a stirrer and sludge reflux quantity.
The key process operation conditions of the membrane bioreactor system comprise the working pressure of a water producing pump and the aeration intensity.
The beneficial effects of the invention are as follows:
The invention discloses an end-side-cloud cooperative control garbage leachate low-carbon intelligent treatment and recycling method, which is characterized in that the intercommunication and interconnection of leachate treatment and resource/energy process flow units are realized through an end-side-cloud Internet of things sensing network, and the intelligent management and control of the garbage leachate treatment and resource/energy integrated process flow are realized through optimizing and controlling the operation parameters of a biochemical system. The technology integrates the technologies of chemical coagulation/magnetic flocculation high-efficiency pretreatment, anaerobic fermentation hydrogen production/acid production, membrane bioreactor based on modularized ultrafiltration membrane and the like, can shorten the overall residence time of the system, reduce the load of a biochemical system, reduce the occupied area and the energy consumption of the system, can generate new energy hydrogen, improve the capability of producing volatile fatty acid by fermenting organic matters in wastewater, can be used for providing biological high-efficiency denitrification and dephosphorization carbon sources, realizes the high-efficiency removal of particles, organic matters and nutrient salts in the treatment process of the percolate, and realizes the reclamation, energy and intellectualization of the treatment of the percolate.
Under the 'cloud-side-end' intelligent regulation and control architecture, the real-time monitoring data of each sensor of the terminal layer of each link of garbage leachate treatment is firstly sent to the edge layer through a special network or an operator network, and the intelligent algorithm deployed at the edge layer is utilized to predict the comprehensive energy consumption, the effluent quality (COD, NH 4 + -N, TN) and the fermentation hydrogen production rate of the system in real time. The monitoring data are uploaded to the cloud end through the edge, explored in the multidimensional parameter space, evaluated for objective function values and gradually converged to the optimal solution. Combining gradient descent, genetic algorithm and other methods, the global optimal point of key influencing factors is precisely positioned. And returning the optimal result to the edge. The side end forms corresponding operation instructions to be transmitted back to the execution equipment for controlling key process parameters of the system to execute specific operations, so that dynamic adjustment of key influencing factors in the garbage leachate treatment and recycling processes is realized, the highest treatment efficiency and hydrogen production rate are realized, the overall comprehensive energy consumption of the system is the lowest, and the aim of energy conservation and emission reduction is fulfilled.
Drawings
FIG. 1 is a schematic diagram of a sensor deployment at a terminal level of a landfill leachate treatment system.
Fig. 2 is a map of monitoring points of a landfill leachate treatment system of an incineration power plant according to example 1.
FIG. 3 is a chart of monitoring points of a leachate sewage treatment station of a landfill in example 2.
Fig. 4 is a model topology of a machine learning algorithm.
Detailed Description
The invention is further illustrated below in connection with specific examples, but the scope of the invention is not limited thereto.
The embodiment of the invention provides a garbage leachate low-carbon intelligent treatment and recycling method based on end-side-cloud cooperative control, which is realized based on a garbage leachate treatment system, wherein the garbage leachate treatment system comprises a regulating tank, a flocculation pretreatment module, a biochemical system and a Membrane Bioreactor (MBR) which are sequentially cascaded.
The regulating tank is responsible for receiving landfill leachate and uniformly regulating water quality.
The flocculation pretreatment module is responsible for efficiently and quickly removing particles in the inlet water, and preferably, coagulation/flocculation pretreatment or magnetic flocculation pretreatment.
The biochemical system comprises an anaerobic reactor, a single-stage or multistage anaerobic-aerobic biochemical system which are connected in series in sequence.
The anaerobic reactor is responsible for converting organic matters in the wastewater into hydrogen by utilizing the metabolism of anaerobic microorganisms, so that the degradation and stabilization of the organic matters are realized, and is preferably an anaerobic IC reactor or a USAB reactor.
The anaerobic-aerobic biochemical system comprises an anaerobic tank (A tank), an aerobic aeration tank (O tank) and a clarification tank which are sequentially cascaded, wherein part of effluent of the aerobic aeration tank (O tank) flows back to the anaerobic tank (A tank), the anaerobic tank and the aerobic aeration tank are responsible for sequentially carrying out anaerobic biochemical reaction and aerobic biochemical reaction on anaerobic fermentation products to complete preliminary denitrification, and the clarification tank is responsible for carrying out mud-water separation on effluent of the aerobic aeration tank (O tank).
The Membrane Bioreactor (MBR) is responsible for carrying out high-efficiency solid-liquid separation on anaerobic-aerobic biochemical products, so that the biochemical reaction efficiency is further enhanced.
The method for intelligently treating and recycling the landfill leachate under the cooperative control of end-side-cloud comprises the following steps:
and S1, acquiring real-time monitoring data of each sensor of the terminal layer.
The real-time monitoring data of each sensor comprises the operation parameters of an adjusting tank, the operation parameters of a flocculation pretreatment module, the operation parameters of an anaerobic reactor, the operation parameters of an anaerobic tank (A tank) and an aerobic tank (O tank) in an anaerobic-aerobic biochemical system, and the operation parameters of a Membrane Bioreactor (MBR).
The operation parameters of the regulating tank mainly comprise pH value, water temperature, dissolved Oxygen (DO), oxidation-reduction potential (ORP) and inflow water quality indexes, wherein the inflow water quality indexes comprise inflow water flow, inflow water Chemical Oxygen Demand (COD), ammonia nitrogen (NH 4 + -N) and Total Nitrogen (TN).
The operation parameters of the flocculation pretreatment module comprise flocculant dosage, sedimentation residence time, pH value, ORP, water temperature and the like.
The operation parameters of the anaerobic reactor comprise anaerobic fermentation tank hydraulic retention time, external redox mediator addition amount, anaerobic tank pH value, water temperature and ORP, and the flow rate, temperature and total pressure of the generated biogas and the partial pressure of each of different biogas, wherein the biogas comprises H 2、CH4、CO2.
The operation parameters of the anaerobic tank (A tank) and the aerobic tank (O tank) in the anaerobic-aerobic biochemical system mainly comprise DO, ORP, water temperature and pH value.
The operating parameters of the Membrane Bioreactor (MBR) include pH, ORP, water temperature, DO, transmembrane pressure difference.
The effluent quality indexes after the Membrane Bioreactor (MBR) treatment comprise turbidity, COD and NH 4 + -N, TN.
The deployment of the terminal layer sensor in step S1 is specifically shown in fig. 1 to fig. 3, and is specifically as follows:
(1) The method comprises the steps of arranging a flow sensor at the water inlet position of the regulating tank to mainly monitor the water inlet flow of the landfill leachate, and arranging a first pH sensor, a first temperature sensor, a first water temperature sensor, a first ORP sensor, a first COD measuring sensor, a first NH 4 + -N measuring sensor, a first TN measuring sensor and a first turbidity measuring sensor in the regulating tank.
(2) The water outlet position or the inside of the flocculation pretreatment module is provided with a second pH sensor and a second water temperature sensor.
(3) The gas outlet of the anaerobic reactor is provided with a gas phase component sensor and a state sensor, the gas phase component sensor is used for detecting H 2、CH4、CO2, the state sensor comprises a gas flow sensor, a gas temperature sensor and a pressure sensor, and a water outlet of the anaerobic reactor is provided with a third pH sensor, a third water temperature sensor and a third ORP sensor.
(5) A pool A and an O pool of the anaerobic-aerobic biochemical system are respectively provided with a fourth pH sensor, a fourth water temperature sensor, a fourth ORP sensor and a first DO sensor.
(6) A fifth pH sensor, a fifth water temperature sensor and a fifth ORP sensor are arranged in a clarification tank of the anaerobic-aerobic biochemical system and are responsible for online monitoring of pH, ORP and water temperature of supernatant fluid.
(7) The Membrane Bioreactor (MBR) is provided with a sixth pH sensor, a sixth water temperature sensor, a sixth ORP sensor, a second DO sensor, a transmembrane pressure difference sensor and the like, the running state in the MBR pool is monitored in real time, and a seventh pH sensor, a seventh water temperature sensor, a seventh ORP sensor, a second COD measuring sensor, a second NH 4 + -N measuring sensor, a second TN measuring sensor and a second turbidity measuring sensor are arranged at the water outlet position of the Membrane Bioreactor (MBR).
And S2, sending the real-time monitoring data of each sensor in the step S1 to an edge layer through a communication network, and performing multi-objective prediction on the water quality index of the effluent, the comprehensive energy consumption of the system and the anaerobic hydrogen production rate of the anaerobic reactor by adopting a machine learning algorithm at the edge layer.
In step S2, the machine learning algorithm includes one or more of Random Forest (RF), extreme gradient lifting (XGBoost), long-term short-term memory network (LSTM), artificial Neural Network (ANN), etc., and the model topology is shown in fig. 4.
And step S3, constructing an objective function according to the effluent quality index, the anaerobic hydrogen production rate and the comprehensive energy consumption of the system, and obtaining ideal operating parameter values of the regulating tank, the flocculation pretreatment module, the anaerobic reactor, the anaerobic-aerobic biochemical system and the MBR system by utilizing a decision-level optimization model in the cloud server under the constraint condition.
In step S3, the decision-level optimization model is specifically one of Genetic Algorithm (GA) and particle swarm optimization algorithm (PSO).
In step S3, the objective function is that the effluent quality index meets the emission standard requirement, the comprehensive energy consumption of the system is minimum, and the anaerobic hydrogen production rate is maximum. The objective function is expressed as follows:
Wherein, The weight of each target reflects the minimization of the comprehensive energy consumption of the system) Target value of up-to-standard water quality discharge) And the hydrogen production rate is the highest) Priority of the two.
Constraint conditions:=1
The system synthesizes an energy consumption minimization expression:
Wherein E (x) represents the comprehensive energy consumption of the process of the system under a certain operation parameter; the method represents the minimization of the comprehensive energy consumption in the running process of the system, and can be realized by adjusting the running frequency, the starting and stopping time and the like of the equipment.
Constraint that E (x) is less than or equal to E 0;E(x)=E0'+EA
Wherein E 0 is the energy consumption of the process equipment before the system performs the ' end-side-cloud ' cooperative control, E 0 ' is the actual energy consumption of the process equipment after the system performs the ' end-side-cloud ' cooperative control, E A is the energy consumption of each unit module in the ' end-side-cloud ' cooperative control system in the processes of data acquisition, transmission, calculation, feedback and control;
the effluent quality reaches the standard and discharges the target value The optimization expression:
In the formula, The method is used for optimizing target values of all parameters of the water quality of the effluent, and target optimized water quality indexes are COD, NH 4 + -N and TN, wherein alpha 1、α2、α3 is the weight of the importance of all water quality indexes on the target value optimized result of standard emission. COD (chemical oxygen demand)) Represents the relation between the COD value of the effluent and the discharge standard value of the system under a certain operation parameter, and NH 4 + -N #) Represents the relation between the NH 4 + -N value of the effluent and the standard emission value of the system under a certain operation parameter, TN #) And the relation between the TN value of the effluent and the standard emission value of the system under a certain operation parameter is shown.
Water quality constraint conditions of effluent:
wherein C is the actual COD value of the system effluent, C （t） is the COD discharge standard value of the effluent (C （t） is more than or equal to 0 and less than or equal to 500 mg/L), N is the actual NH 4 + -N concentration of the effluent, N （t） is the NH 4 + -N discharge standard value of the effluent (N （t） is more than or equal to 0 and less than or equal to 25 mg/L), T is the actual TN concentration of the effluent, and T （t） is the TN discharge standard value of the effluent (T （t） is more than or equal to 0 and less than or equal to 70 mg/L); And S 1 is a parameter for scaling data, so that the scales among different variables are comparable.
The anaerobic hydrogen production rate is maximizedThe expression:
In the formula, Indicating that the anaerobic hydrogen production rate is maximized; indicating the hydrogen production rate of the system in the anaerobic reactor at a certain operating parameter.
Constraint conditions:
H 2 is the actual hydrogen production rate of the system, H 2（opt） is the maximum hydrogen production rate, tanh is the hyperbolic tangent function for controlling the hydrogen production rate to be close to the maximum value, and S 2 is the parameter for scaling the data.
And S4, adjusting the key process operation conditions of the system based on decision-making level optimization model optimization results (namely, the ideal values of the operation parameters of the regulating tank, the flocculation pretreatment module, the anaerobic reactor, the anaerobic-aerobic biochemical system and the MBR system) and terminal layer sensor real-time monitoring data, so as to obtain the optimal results of the effluent quality index, the anaerobic hydrogen production rate and the comprehensive energy consumption of the system.
The key process operation conditions of the system comprise:
1. Critical process operating conditions of flocculation pretreatment module
The working frequency and the stirring speed of the stirrer are that the rotating speed of the stirrer is adjusted by changing the working frequency (20 HZ-50 HZ) of a frequency converter of the stirrer, and the rotating speed range of the stirrer is 20-600 revolutions per minute.
And when the flocculation unit selects the magnetic flocculation technology, the rotating speed of the high-speed shearing machine is adjusted by changing the working frequency (30 HZ-50 HZ) of the frequency converter, and the rotating speed range of the high-speed shearing machine is 2000-2000 revolutions per minute.
2. Key technological operation condition of anaerobic-aerobic biochemical system
Aeration intensity, namely in an aerobic tank (O tank), according to the real-time monitoring, prediction and optimization results of DO, the working frequency (25 HZ-50 HZ) and the start-stop time of a frequency converter are adjusted, and the air quantity and the aeration time of aeration equipment are adjusted.
And the stirring speed is that the rotation speed of the stirrer is regulated in the anoxic pond (pond A) by changing the working frequency (20 HZ-45 HZ) of the frequency converter of the stirrer, and the rotation speed range of the stirrer is 40-1000 rpm.
And the sludge reflux quantity is regulated by regulating the motor rotating speed (30 HZ-50 HZ) of the reflux pump, so that the sludge reflux quantity and the internal reflux quantity are regulated, and the proper sludge concentration and denitrification efficiency in the system are maintained.
3. Critical process operating conditions for Membrane Bioreactor (MBR) systems
And the working pressure of the water producing pump is regulated according to the real-time monitoring, predicting and optimizing results of the transmembrane pressure difference, and the membrane flux and the water yield of the MBR system are ensured to be stable by regular backwashing or chemical cleaning.
Aeration intensity, namely adjusting working frequency (35 HZ-50 HZ) and start-stop time of a frequency converter according to real-time monitoring, prediction and optimization results of DO in an MBR tank, and adjusting air quantity and aeration time of aeration equipment.
Example 1
A sewage treatment station of a garbage incineration power plant adopts a treatment process of regulating tank-chemical coagulation-anaerobic fermentation-anaerobic (A) tank-O tank-MBR-nano tube discharge. By adopting the cloud-side-end cooperative control method, all links of sewage treatment in the whole factory are intelligently regulated and controlled. The terminal layer sensor of each processing link is shown in fig. 2:
(1) The water inlet No.1 is provided with a flow sensor, and the water inlet flow is monitored in real time.
(2) Setting pH, ORP, water temperature, turbidity and other online sensors, simultaneously automatically collecting water sample, and pumping to online monitoring equipment for online monitoring of COD, ammonia nitrogen (NH 4 + -N) and Total Nitrogen (TN).
(3) After chemical coagulation, an online sensor such as pH sensor, water temperature sensor and the like is arranged above the clarification tank.
(4) The anaerobic IC reactors No. 4 and No. 5 are provided with gas phase component sensors such as H 2/CH4/CO2 and the like at the top and the empty position, and state sensors such as temperature/flow rate/pressure and the like, which are used for monitoring the partial pressure of the generated biogas components in real time, and are provided with running state sensors such as pH, ORP, water temperature and the like at the water phase.
(5) Sensors pH, DO, ORP, water temperature and the like are arranged in the pool A and the pool O in the anaerobic-aerobic biochemical system (A/O system) No. 6, and the running state of the biochemical system is monitored in real time.
(6) Pumping the water discharged from the A/O biochemical tank to a small-sized clarification tank outside the system for mud-water separation, and setting pH, water temperature, ORP and other sensors in the supernatant.
(7) Sensors pH, ORP, DO, water temperature, water level, transmembrane pressure difference and the like are arranged in the MBR tank, and the running state in the MBR tank is monitored in real time.
(8) The MBR effluent 9# is that sensors such as pH, ORP, water temperature and the like are arranged in an MBR effluent pool, and meanwhile, water is pumped out to on-line monitoring equipment for on-line monitoring of COD, NH 4 + -N, TN and turbidity.
Secondly, training and optimizing algorithms such as Random Forest (RF), extreme gradient lifting (XGBoost) and the like are carried out on the built robust data set, the comprehensive energy consumption, the effluent quality (COD, NH 4 + -N, TN) and the fermentation hydrogen production rate of the system are predicted at the edge layer, the characteristic importance analysis is carried out, and key influencing factors influencing the effluent quality of percolate of an incineration plant, the anaerobic hydrogen production rate and the comprehensive energy consumption of the system are determined.
The optimized XGBoost algorithm is taken as a kernel, a particle swarm optimization algorithm (PSO) is combined, a decision-level optimization model (XGBoost-PSO model) of multiple objective functions such as effluent quality, anaerobic hydrogen production rate, comprehensive energy consumption of the system and the like is established on a cloud platform layer, and reverse engineering design under the overall optimal condition (minimum comprehensive energy consumption, standard water quality emission and higher hydrogen production rate) of the system is carried out.
The overall energy consumption of the system is weighted omega 1 =0.32 on the overall optimization result of the objective function.
The weight omega 2 =0.41 of the effluent quality (COD and NH 4 + -N, TN) to the overall optimization result of the objective function, wherein the weight alpha 1=0.55,NH4 + -N of the importance of the COD to the optimization result of the target value of the standard emission to the importance of the optimization result of the target value of the standard emission to the weight alpha 2 =0.28, and the weight alpha 3 =0.17 of the importance of the TN to the optimization result of the target value of the standard emission to the target value of the standard emission.
The weight ω 3 =0.27 of the anaerobic hydrogen production rate to the overall optimization result of the objective function.
Under the multi-objective optimization condition, based on real-time monitoring, prediction and optimization data of a terminal layer, the stirring speed of a stirring motor of a chemical flocculation pretreatment module, the air quantity and the aeration time of an aeration fan of an aerobic tank (O tank) of an anaerobic-aerobic biochemical system, the rotating speed of a stirring machine of an anoxic tank (O tank), the flow of a sludge reflux pump, the air quantity and the aeration time of an aeration device of an MBR system, the working pressure of a water production pump and the like are regulated and controlled, so that the on-line self-adaptive adjustment of key parameters is realized.
The treatment efficiency and the recycling level of the leachate of the garbage incineration plant based on the cloud-side-end cooperative control method are shown in the attached table 1.
Attached Table 1. Leachate treatment efficacy and recycling level of refuse incineration plants under cooperative control of cloud-side-end
Example 2
A leachate sewage treatment station of a household refuse landfill adopts a treatment process of regulating tank-magnetic coagulation-UASB-two-stage anaerobic/aerobic (A/O) -MBR-nanofiltration-nanotube discharge. By adopting the cloud-side-end cooperative control method, all links of sewage treatment in the whole factory are intelligently regulated and controlled. The terminal layer sensor of each processing link is shown in fig. 3:
(1) The water inlet No.1 is provided with a flow sensor, and the water inlet flow is monitored in real time.
(2) Setting pH, ORP, water temperature, turbidity and other online sensors, simultaneously automatically collecting water sample, and pumping to online monitoring equipment for online monitoring of COD, ammonia nitrogen (NH 4 + -N) and Total Nitrogen (TN).
(3) After magnetic coagulation, an online sensor such as pH sensor, water temperature sensor and the like is arranged above the clarification tank.
(4) The anaerobic UASB reactors No. 4 and No. 5 are provided with gas phase component sensors such as H 2/CH4/CO2 and the like at the top and the empty position, and state sensors such as temperature/flow rate/pressure and the like, which are used for monitoring the partial pressure of the generated biogas components in real time, and are provided with running state sensors such as pH, ORP, water temperature and the like at the water phase.
(5) Sensors pH, DO, ORP, water temperature and the like are arranged in the A pool and the O pool of the first stage and the second stage of the two-stage A/O system No. 6, and the running state of the biochemical system is monitored in real time.
(6) Pumping the water discharged from the A/O biochemical tank to a small-sized clarification tank outside the system for mud-water separation, and setting pH, water temperature, ORP and other sensors in the supernatant.
(7) Sensors pH, DO, ORP, water temperature, water level, membrane filtration pressure and the like are arranged in the MBR tank, and the running state in the MBR tank is monitored in real time.
(8) The MBR effluent 9# is that sensors such as pH, water temperature, turbidity and the like are arranged in an MBR effluent pool, and meanwhile, water is pumped out to on-line monitoring equipment for on-line monitoring of COD and NH 4 + -N, TN.
Secondly, training and optimizing algorithms such as an Artificial Neural Network (ANN), a long-short-term memory network (LSTM) and the like are performed on the built robust data set, comprehensive energy consumption, effluent quality (COD, NH 4 + -N, TN) and fermentation hydrogen production rate of the system are predicted at an edge layer, feature importance analysis is performed, and key influence factors influencing comprehensive energy consumption, filtrate effluent quality and anaerobic hydrogen production rate of a landfill seepage system are determined.
An optimized LSTM algorithm is taken as a kernel, a Genetic Algorithm (GA) is combined, a decision-level optimization model (XGBoost-GA model) of multiple objective functions such as effluent quality, anaerobic hydrogen production rate, system comprehensive energy consumption and the like is established on a cloud platform layer, and reverse engineering design under the overall optimal condition (minimum comprehensive energy consumption, standard water quality emission and higher hydrogen production rate) of the system is carried out.
The overall energy consumption of the system is weighted omega 1 =0.25 on the overall optimization result of the objective function.
The weight omega 2 =0.45 of the effluent quality (COD and NH 4 + -N, TN) on the overall optimization result of the objective function, wherein the weight alpha 1=0.46,NH4 + -N of the importance of the COD on the optimization result of the target value of the standard emission is equal to 0.30 of the weight alpha 2 =0.24 of the importance of the optimization result of the target value of the standard emission, and the weight alpha 3 =0.24 of the TN on the importance of the optimization result of the target value of the standard emission.
The weight ω 3 =0.30 of the anaerobic hydrogen production rate to the overall optimization result of the objective function.
Under the multi-objective optimization condition, based on real-time monitoring, prediction and optimization data of a terminal layer, the stirring speed of a stirring motor of a magnetic flocculation pretreatment module, the rotating speed of a high-speed shearing machine, the air quantity and the aeration time of an aeration fan of an aerobic tank (O tank) of an anaerobic-aerobic biochemical system, the rotating speed of the stirring machine of an anoxic tank (O tank), the flow of a sludge reflux pump, the air quantity and the aeration time of an aeration device of an MBR system, the working pressure of a water production pump and the like are regulated and controlled, so that the on-line self-adaptive adjustment of key parameters is realized.
Table 2 shows the leachate treatment efficiency and recycling level of landfill under cooperative control of cloud-side-end
The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above embodiments, and falls within the scope of the present invention as long as the present invention meets the requirements.

Claims (10)

1.一种基于“端-边-云”协同控制的垃圾渗滤液低碳智能处理与资源化方法，基于垃圾渗滤液处理系统；所述垃圾渗滤液处理系统包括依次级联的调节池、絮凝预处理模块、生化系统、膜生物反应器；1. A low-carbon intelligent treatment and resource utilization method for landfill leachate based on "end-edge-cloud" collaborative control, based on a landfill leachate treatment system; the landfill leachate treatment system includes a regulating tank, a flocculation pretreatment module, a biochemical system, and a membrane bioreactor that are cascaded in sequence; 所述调节池负责接收垃圾渗滤液并进行水质均匀调节；The regulating tank is responsible for receiving the garbage leachate and regulating the water quality uniformly; 所述絮凝预处理模块负责对进水中的颗粒物进行高效快速去除；优选混凝/絮凝物化预处理，或磁絮凝预处理；The flocculation pretreatment module is responsible for the efficient and rapid removal of particulate matter in the influent water; preferably, coagulation/flocculation physicochemical pretreatment or magnetic flocculation pretreatment; 所述生化系统包括依次级联的厌氧反应器、单级或多级串联的厌氧-好氧生化系统；The biochemical system includes anaerobic reactors cascaded in sequence, and single-stage or multi-stage anaerobic-aerobic biochemical systems in series; 所述厌氧-好氧生化系统包括依次级联的厌氧池、好氧曝气池、澄清池，好氧曝气池的一部分出水回流至厌氧池；厌氧池、好氧曝气池负责对厌氧发酵产物依次进行厌氧生化反应和好氧生化反应，完成初步脱氮和有机物的去除；澄清池负责好氧曝气池的出水进行泥水分离；The anaerobic-aerobic biochemical system comprises an anaerobic tank, an aerobic aeration tank and a clarifier tank which are cascaded in sequence. A portion of the effluent from the aerobic aeration tank flows back to the anaerobic tank. The anaerobic tank and the aerobic aeration tank are responsible for carrying out anaerobic biochemical reactions and aerobic biochemical reactions on the anaerobic fermentation products in sequence to complete the preliminary denitrification and removal of organic matter. The clarifier tank is responsible for carrying out mud-water separation on the effluent from the aerobic aeration tank. 所述膜生物反应器负责厌氧-好氧生化产物进行高效固液分离，进一步加强生化反应效率；The membrane bioreactor is responsible for efficient solid-liquid separation of anaerobic-aerobic biochemical products, further enhancing the efficiency of biochemical reactions; 其特征在于，所述方法包括以下步骤：Characterized in that the method comprises the following steps: 步骤S1：终端层各传感器实时监测数据的获取；Step S1: Acquisition of real-time monitoring data of each sensor at the terminal layer; 所述各传感器实时监测数据包括调节池的运行参数，絮凝预处理模块的运行参数，厌氧反应器的运行参数，厌氧-好氧生化系统中厌氧池和好氧池运行参数，膜生物反应器的运行参数；The real-time monitoring data of each sensor include the operating parameters of the regulating tank, the operating parameters of the flocculation pretreatment module, the operating parameters of the anaerobic reactor, the operating parameters of the anaerobic tank and the aerobic tank in the anaerobic-aerobic biochemical system, and the operating parameters of the membrane bioreactor; 步骤S2：将所述各传感器实时监测数据通过通信网络发送到边缘层，在边缘层采用机器学习算法对出水水质指标，以及系统综合能耗、厌氧反应器的厌氧产氢速率进行多目标预测；Step S2: sending the real-time monitoring data of each sensor to the edge layer through the communication network, and using a machine learning algorithm at the edge layer to perform multi-objective prediction on the effluent water quality index, the system comprehensive energy consumption, and the anaerobic hydrogen production rate of the anaerobic reactor; 步骤S3：根据出水水质指标、系统综合能耗、厌氧反应器的厌氧产氢速率构建目标函数，在约束条件下，利用云服务器中的决策级优化模型，得到调节池、絮凝预处理模块、厌氧反应器、厌氧-好氧生化系统、膜生物反应器的运行参数理想值；Step S3: construct an objective function based on the effluent water quality index, the system comprehensive energy consumption, and the anaerobic hydrogen production rate of the anaerobic reactor. Under the constraint conditions, the decision-level optimization model in the cloud server is used to obtain the ideal values of the operating parameters of the regulating tank, the flocculation pretreatment module, the anaerobic reactor, the anaerobic-aerobic biochemical system, and the membrane bioreactor; 步骤S4：基于决策级优化模型优化结果和终端层传感器实时监测数据，对系统关键工艺运行工况进行调整，从而获取出水水质指标、厌氧产氢速率、系统综合能耗的最优结果。Step S4: Based on the optimization results of the decision-level optimization model and the real-time monitoring data of the terminal layer sensors, the key process operating conditions of the system are adjusted to obtain the optimal results of effluent water quality indicators, anaerobic hydrogen production rate, and system comprehensive energy consumption. 2.根据权利要求1所述方法，其特征在于步骤S1中，所述调节池的运行参数包括pH值、水温、溶解氧、ORP，以及进水水质指标，所述进水水质指标包括进水流量、进水COD、氨氮、总氮；2. The method according to claim 1, characterized in that in step S1, the operating parameters of the regulating tank include pH value, water temperature, dissolved oxygen, ORP, and influent water quality indicators, and the influent water quality indicators include influent flow rate, influent COD, ammonia nitrogen, and total nitrogen; 所述絮凝预处理模块的运行参数包括絮凝剂用量、沉淀停留时间、pH值、ORP、水温；The operating parameters of the flocculation pretreatment module include flocculant dosage, sedimentation residence time, pH value, ORP, and water temperature; 所述厌氧反应器的运行参数包括厌氧发酵罐水力停留时间、外加氧化还原介体投加量、厌氧池pH值、水温、ORP，产生的生物气的流量、温度、总压力、不同生物气各自的分压；The operating parameters of the anaerobic reactor include the hydraulic retention time of the anaerobic fermentation tank, the dosage of the added redox mediator, the pH value of the anaerobic tank, the water temperature, the ORP, the flow rate, temperature, total pressure of the generated biogas, and the partial pressures of different biogases; 所述厌氧-好氧生化系统中厌氧池和好氧池运行参数包括：溶解氧、ORP、水温、pH值；The operating parameters of the anaerobic tank and the aerobic tank in the anaerobic-aerobic biochemical system include: dissolved oxygen, ORP, water temperature, and pH value; 所述膜生物反应器的运行参数包括pH、ORP、水温、溶解氧、跨膜压差；The operating parameters of the membrane bioreactor include pH, ORP, water temperature, dissolved oxygen, and transmembrane pressure difference; 所述膜生物反应器处理后的出水水质指标包括浊度、出水COD、氨氮、总氮。The water quality indicators of the effluent after the membrane bioreactor treatment include turbidity, effluent COD, ammonia nitrogen and total nitrogen. 3.根据权利要求1所述方法，其特征在于步骤S2中，所述机器学习算法包括随机森林、极端梯度提升、长短期记忆网络、人工神经网络中至少一种。3. The method according to claim 1 is characterized in that in step S2, the machine learning algorithm includes at least one of random forest, extreme gradient boosting, long short-term memory network, and artificial neural network. 4.根据权利要求1所述方法，其特征在于步骤S3中，所述决策级优化模型具体是遗传算法、粒子群优化算法中的一种。4. The method according to claim 1 is characterized in that in step S3, the decision-level optimization model is specifically one of a genetic algorithm and a particle swarm optimization algorithm. 5.根据权利要求1所述方法，其特征在于步骤S3中，所述目标函数具体是：5. The method according to claim 1, characterized in that in step S3, the objective function is specifically: 其中，为权重；表示系统综合能耗最小化，表示水质达标排放目标值，表示厌氧反应器的厌氧产氢速率最大化；E(x)表示系统在某运行参数下的过程综合能耗；α1、α2、α3是各出水水质指标COD、氨氮和总氮对达标排放目标值优化结果重要性的权重；COD()表示系统在某运行参数下的出水COD值与排放标准值间的关系；NH4 +-N()表示系统在某运行参数下的出水氨氮值与排放标准值间的关系；TN()表示系统在某运行参数下的出水总氮值与排放标准值间的关系；表示系统在某运行参数下厌氧反应器中的产氢速率。in, is the weight; Indicates that the overall energy consumption of the system is minimized. Indicates the target value of water quality discharge. represents the maximization of the anaerobic hydrogen production rate of the anaerobic reactor; E(x) represents the comprehensive energy consumption of the system under certain operating parameters; α 1 , α 2 , α 3 are the weights of the importance of each effluent water quality index COD, ammonia nitrogen and total nitrogen to the optimization result of the target emission value; COD( ) represents the relationship between the effluent COD value and the discharge standard value under certain operating parameters; NH 4 + -N( ) represents the relationship between the effluent ammonia nitrogen value and the discharge standard value under certain operating parameters; TN( ) represents the relationship between the total nitrogen value of the effluent and the discharge standard value under certain operating parameters of the system; It indicates the hydrogen production rate in the anaerobic reactor under certain operating parameters of the system. 6.根据权利要求5所述方法，其特征在于步骤S3中，所述约束条件具体是：6. The method according to claim 5, characterized in that in step S3, the constraint condition is specifically: 式中，E0为系统在进行“端-边-云”协同控制之前的工艺设备能耗；为系统进行“端-边-云”协同控制之后的工艺设备实际能耗；EA为系统在进行“端-边-云”协同控制时数据采集、传输、计算、反馈、控制过程中的能耗；C为系统出水实际COD值，C（t）为出水COD排放标准值；N为出水实际氨氮浓度，N（t）为出水氨氮排放标准值；T为出水实际总氮浓度，T（t）为出水总氮排放标准值；为Sigmoid函数；H2为系统实际产氢速率，H2（opt）为最大产氢速率；Tanh为双曲正切函数；S1、S2为用于缩放数据的参数。In the formula, E 0 is the energy consumption of process equipment before the system performs “end-edge-cloud” collaborative control; is the actual energy consumption of the process equipment after the system performs "end-edge-cloud" collaborative control; EA is the energy consumption of the system during data acquisition, transmission, calculation, feedback, and control when performing "end-edge-cloud" collaborative control; C is the actual COD value of the system effluent, and C (t) is the effluent COD emission standard value; N is the actual effluent ammonia nitrogen concentration, and N (t) is the effluent ammonia nitrogen emission standard value; T is the actual effluent total nitrogen concentration, and T (t) is the effluent total nitrogen emission standard value; is the Sigmoid function; H 2 is the actual hydrogen production rate of the system, H 2 (opt) is the maximum hydrogen production rate; Tanh is the hyperbolic tangent function; S 1 and S 2 are parameters used to scale the data. 7.根据权利要求1所述方法，其特征在于步骤S4中，所述系统关键工艺运行工况包括：絮凝预处理模块的关键工艺运行工况、厌氧-好氧生化系统的关键工艺运行工况、膜生物反应器系统的关键工艺运行工况。7. The method according to claim 1 is characterized in that in step S4, the key process operating conditions of the system include: the key process operating conditions of the flocculation pretreatment module, the key process operating conditions of the anaerobic-aerobic biochemical system, and the key process operating conditions of the membrane bioreactor system. 8.根据权利要求7所述方法，其特征在于步骤S4中，所述絮凝预处理模块的关键工艺运行工况包括搅拌机的搅拌速度、高速剪切机的转速。8. The method according to claim 7 is characterized in that in step S4, the key process operating conditions of the flocculation pretreatment module include the stirring speed of the stirrer and the rotation speed of the high-speed shearing machine. 9.根据权利要求7所述方法，其特征在于步骤S4中，所述厌氧-好氧生化系统的关键工艺运行工况包括曝气强度、搅拌机的搅拌速度、污泥回流量。9. The method according to claim 7, characterized in that in step S4, the key process operating conditions of the anaerobic-aerobic biochemical system include aeration intensity, stirring speed of the mixer, and sludge return volume. 10.根据权利要求7所述方法，其特征在于步骤S4中，所述膜生物反应器系统的关键工艺运行工况包括产水泵工作压力、曝气强度。10. The method according to claim 7, characterized in that in step S4, the key process operating conditions of the membrane bioreactor system include the working pressure of the water production pump and the aeration intensity.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119841453A (en) * 2025-03-21 2025-04-18 广州漓源环保技术有限公司 Staged regulation and control type anaerobic ammoniation coupling resource recovery processing method

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN209065650U (en) * 2018-06-20 2019-07-05 启迪桑德环境资源股份有限公司 A kind of system of advanced treatment of landfill leachate
CN111635071A (en) * 2020-05-29 2020-09-08 厦门牧云数据技术有限公司 Leachate treatment intelligent industrial control method based on multivariate data method
CN114735900A (en) * 2022-05-06 2022-07-12 湖南省煜城环保科技有限公司 Treatment process and treatment system for landfill leachate
US20230259075A1 (en) * 2019-06-10 2023-08-17 Beijing University Of Technology Dynamic multi-objective particle swarm optimization-based optimal control method for wastewater treatment process
CN118333417A (en) * 2024-04-10 2024-07-12 江苏达泽节能环保科技有限公司 Intelligent management system for transfer station percolate treatment equipment based on cloud edge cooperation
CN118645181A (en) * 2024-08-13 2024-09-13 山东中研环保设备有限公司 A sewage treatment process optimization method and system based on artificial intelligence

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN209065650U (en) * 2018-06-20 2019-07-05 启迪桑德环境资源股份有限公司 A kind of system of advanced treatment of landfill leachate
US20230259075A1 (en) * 2019-06-10 2023-08-17 Beijing University Of Technology Dynamic multi-objective particle swarm optimization-based optimal control method for wastewater treatment process
CN111635071A (en) * 2020-05-29 2020-09-08 厦门牧云数据技术有限公司 Leachate treatment intelligent industrial control method based on multivariate data method
CN114735900A (en) * 2022-05-06 2022-07-12 湖南省煜城环保科技有限公司 Treatment process and treatment system for landfill leachate
CN118333417A (en) * 2024-04-10 2024-07-12 江苏达泽节能环保科技有限公司 Intelligent management system for transfer station percolate treatment equipment based on cloud edge cooperation
CN118645181A (en) * 2024-08-13 2024-09-13 山东中研环保设备有限公司 A sewage treatment process optimization method and system based on artificial intelligence

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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