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

Abstract

The present invention discloses a low-carbon intelligent treatment and resource utilization method for landfill leachate based on "end-edge-cloud" collaborative control. The method is that the terminal layer obtains real-time monitoring data of each sensor and sends it to the edge layer, and uses a machine learning algorithm at the edge layer to perform multi-objective prediction of effluent water quality indicators, system comprehensive energy consumption, and anaerobic hydrogen production rate, and then constructs an objective function to obtain the ideal value of the operating parameters of each module; according to the ideal value 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. The present invention optimizes and controls the operating parameters of the biochemical system through the "end-edge-cloud" Internet of Things perception network, realizes the intelligent management and control of the overall process of landfill leachate treatment and resource / energy utilization, and realizes the dynamic adjustment of key influencing factors in the process of landfill leachate treatment and resource utilization.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental protection and resource utilization, and specifically relates to a low-carbon intelligent treatment and resource utilization method of landfill leachate based on "end-edge-cloud" collaborative control. Background Art

[0002] Landfill leachate contains high levels of suspended solids, organic matter, nutrients, salts, and heavy metals, making it challenging to treat. Currently, the main treatment methods typically involve a combination of physicochemical pretreatment, biochemical treatment (anaerobic-aerobic), and back-end deep treatment. Physicochemical pretreatment typically utilizes coagulation / flocculation. Commonly used coagulants / flocculants, such as polyaluminum chloride (PAC), polyferric sulfate (PFS), and polyacrylamide (PAM), have limited effectiveness in leachate pretreatment. These agents require high dosages, resulting in high sludge production and a slow sedimentation process. Furthermore, the dosage ratio cannot be adjusted promptly based on changes in raw water quality, impacting the activity of microorganisms in the biochemical system and the service life of the back-end membrane treatment system.

[0003] Furthermore, the leachate contains high concentrations of organic pollutants, ammonia nitrogen, and other pollutants with poor biodegradability, resulting in high aeration energy consumption, a relatively low degree of system automation, high labor intensity, and lags in water quality monitoring and feedback control. Consequently, these issues hinder the desired treatment results in practical applications. Therefore, utilizing sensors integrated into leachate treatment equipment to collect various data on production equipment, processes, operations, and maintenance, and to perform intelligent algorithm optimization and process parameter control, is crucial for achieving intelligent operation and maintenance of landfill leachate wastewater treatment facilities and improving operational capabilities and supervisory capacity. Summary of the Invention

[0004] The purpose of the present invention is to address the shortcomings of the existing technology and provide a low-carbon intelligent treatment and resource utilization method for landfill leachate based on "end-edge-cloud" collaborative control.

[0005] 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 in cascade order;

[0006] The method comprises the following steps:

[0007] Step S1: Acquisition of real-time monitoring data from sensors at the terminal layer.

[0008] 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.

[0009] Step S2: The real-time monitoring data of each sensor is sent to the edge layer through the communication network, and a machine learning algorithm is used at the edge layer to perform multi-objective prediction on the effluent water quality indicators, the system comprehensive energy consumption, and the anaerobic hydrogen production rate of the anaerobic reactor.

[0010] Step S3: Construct an objective function based on the effluent water quality indicators, the comprehensive energy consumption of the system, and the anaerobic hydrogen production rate of the anaerobic reactor. Under the constraints, use the decision-level optimization model in the cloud server to obtain the ideal values ​​of the operating parameters of the regulating tank, flocculation pretreatment module, anaerobic reactor, anaerobic-aerobic biochemical system, and membrane bioreactor.

[0011] 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 the effluent water quality indicators, anaerobic hydrogen production rate, and system comprehensive energy consumption.

[0012] Preferably, in step S1, the operating parameters of the regulating tank include pH value, water temperature, dissolved oxygen (DO), oxidation-reduction potential (ORP), and influent water quality indicators, wherein the influent water quality indicators include influent flow rate, influent chemical oxygen demand (COD), ammonia nitrogen (NH4 + -N), total nitrogen (TN).

[0013] The operating parameters of the flocculation pretreatment module include flocculant dosage, sedimentation residence time, pH value, ORP, and water temperature.

[0014] The operating parameters of the anaerobic reactor include the hydraulic retention time of the anaerobic fermentation tank, the dosage of the external redox mediator, the pH value of the anaerobic tank, the water temperature, the ORP, the flow rate, temperature, total pressure and partial pressure of the generated biogas.

[0015] The operating parameters of the anaerobic tank and the aerobic tank in the anaerobic-aerobic biochemical system include: DO, ORP, water temperature, and pH value.

[0016] The operating parameters of the membrane bioreactor include pH, ORP, water temperature, DO, and transmembrane pressure difference.

[0017] The water quality indicators of the effluent after the membrane bioreactor treatment include turbidity, effluent COD, NH4 + -N, TN.

[0018] Preferably, in step S2, the machine learning algorithm includes at least one of random forest (RF), extreme gradient boosting (XGBoost), long short-term memory network (LSTM), and artificial neural network (ANN).

[0019] Preferably, in step S3, the decision-level optimization model is specifically one of a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO).

[0020] Preferably, in step S3, the objective function is:

[0021]

[0022]

[0023]

[0024]

[0025] in, is the weight; Indicates that the system's comprehensive energy consumption is minimized. Indicates that the water quality meets the discharge target value. Indicates that the anaerobic hydrogen production rate of the anaerobic reactor is the highest; E(x) represents the comprehensive energy consumption of the system under certain operating parameters; α1, α2, α3 are the weights of the importance of each effluent water quality index COD, NH4⁺-N and TN to the optimization result of the target emission value; COD( ) represents the relationship between the effluent COD value and the emission standard value under certain operating parameters; NH4⁺-N( ) represents the relationship between the effluent NH4⁺-N value and the emission standard value under certain operating parameters; TN( ) represents the relationship between the effluent TN value 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.

[0026] The constraints are specifically:

[0027]

[0028] Where E0 is the energy consumption of the process equipment before the system performs “end-edge-cloud” collaborative control; The actual energy consumption of process equipment after the system performs “end-edge-cloud” collaborative control; E A is the energy consumption of the system during data collection, transmission, calculation, feedback, and control when performing “end-edge-cloud” collaborative control; C is the actual COD value of the system’s effluent, and C （t） is the standard value of effluent COD discharge; N is the actual effluent NH4⁺-N concentration, N （t） is the standard value of NH4⁺-N emission in effluent; T is the actual TN concentration in effluent, T （t） is the standard value of TN discharge for effluent; is the Sigmoid function; H2 is the actual hydrogen production rate of the system, H 2（opt） is the maximum hydrogen production rate; Tanh is the hyperbolic tangent function; S1 and S2 are parameters used to scale the data.

[0029] Preferably, in step S4, the key process operating conditions of the system include: key process operating conditions of the flocculation pretreatment module, key process operating conditions of the anaerobic-aerobic biochemical system, and key process operating conditions of the membrane bioreactor system.

[0030] 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.

[0031] The key process operating conditions of the anaerobic-aerobic biochemical system include aeration intensity, stirring speed of the mixer, and sludge return volume.

[0032] The key process operating conditions of the membrane bioreactor system include the working pressure of the water production pump and the aeration intensity.

[0033] The beneficial effects of the present invention are:

[0034] The present invention's "end-edge-cloud" collaboratively controlled low-carbon intelligent treatment and resource utilization method for landfill leachate utilizes an "end-edge-cloud" IoT sensing network to interconnect leachate treatment and resource / energy utilization process units. By optimizing and controlling the operating parameters of the biochemical system, the process achieves intelligent management and control of the entire leachate treatment and resource / energy utilization process. This process integrates technologies such as high-efficiency pretreatment using chemical coagulation / magnetic flocculation, anaerobic fermentation for hydrogen / acid production, and a membrane bioreactor based on modular ultrafiltration membranes. This process can shorten the overall system residence time, reduce the biochemical system load, and reduce both floor space and system energy consumption. It can also generate new energy hydrogen, enhance the fermentation capacity of organic matter in wastewater to produce volatile fatty acids, and provide a carbon source for efficient biological denitrification and phosphorus removal, achieving efficient removal of particulate matter, organic matter, and nutrients during leachate treatment, ultimately realizing resource utilization, energy utilization, and intelligent leachate treatment.

[0035] Under the "cloud-edge-end" intelligent control architecture, the real-time monitoring data of each sensor at the terminal layer of each link of landfill leachate treatment is first sent to the edge layer through a dedicated network or operator network, and the intelligent algorithm deployed at the edge layer is used to analyze the system's comprehensive energy consumption, effluent water quality (COD, NH4 +-N, TN), and fermentation hydrogen production rates are predicted in real time. Monitoring data is uploaded to the cloud via the edge, where it is explored in a multidimensional parameter space, the objective function value is evaluated, and the solution is gradually converged to the optimal solution. By combining methods such as gradient descent and genetic algorithms, the global optimal point of key influencing factors is precisely located. The optimal result is then returned to the edge. The edge generates corresponding operational instructions and transmits them back to the execution equipment of the key process parameters of the control system to perform specific operations, enabling dynamic adjustment of key influencing factors in the landfill leachate treatment and resource utilization process, thereby achieving the highest treatment efficiency and hydrogen production rate, while minimizing the overall comprehensive energy consumption of the system, and achieving the goal of "energy conservation and emission reduction." BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of sensor deployment at the terminal layer of the landfill leachate treatment system.

[0037] Figure 2 Example 1 is a monitoring point map of a landfill leachate treatment system in an incineration power plant.

[0038] Figure 3 Example 2 is a monitoring point map of a leachate sewage treatment station at a domestic waste landfill.

[0039] Figure 4 The model topology of the machine learning algorithm. DETAILED DESCRIPTION

[0040] The present invention will be further described below with reference to specific embodiments, but the protection scope of the present invention is not limited thereto.

[0041] An embodiment of the present invention provides a low-carbon intelligent treatment and resource utilization method for landfill leachate based on "end-edge-cloud" collaborative control, which is implemented 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 (MBR) cascaded in sequence.

[0042] The regulating tank is responsible for receiving the landfill leachate and regulating the water quality uniformly.

[0043] The flocculation pretreatment module is responsible for efficiently and quickly removing particulate matter in the influent; preferably, coagulation / flocculation physicochemical pretreatment or magnetic flocculation pretreatment.

[0044] The biochemical system includes anaerobic reactors connected in cascade, and a single-stage or multi-stage anaerobic-aerobic biochemical system connected in series.

[0045] The anaerobic reactor is responsible for converting organic matter in the wastewater into hydrogen by utilizing the metabolism of anaerobic microorganisms, thereby achieving degradation and stabilization of the organic matter. It is preferably an anaerobic IC reactor or a USAB reactor.

[0046] The anaerobic-aerobic biochemical system includes an anaerobic tank (tank A), an aerobic aeration tank (tank O), and a clarifier tank connected in cascade order. A portion of the effluent from the aerobic aeration tank (tank O) is returned to the anaerobic tank (tank A). The anaerobic tank and the aerobic aeration tank are responsible for performing anaerobic and aerobic biochemical reactions on the anaerobic fermentation products in sequence to complete preliminary denitrification. The clarifier tank is responsible for separating mud and water from the effluent from the aerobic aeration tank (tank O).

[0047] The membrane bioreactor (MBR) is responsible for high-efficiency solid-liquid separation of anaerobic-aerobic biochemical products, further enhancing the efficiency of the biochemical reaction.

[0048] The "end-edge-cloud" collaboratively controlled low-carbon intelligent treatment and resource utilization method for landfill leachate includes the following steps:

[0049] Step S1: Acquisition of real-time monitoring data from sensors at the terminal layer.

[0050] 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 (A tank) and the aerobic tank (O tank) in the anaerobic-aerobic biochemical system, and the operating parameters of the membrane bioreactor (MBR).

[0051] The operating parameters of the regulating pool mainly include pH value, water temperature, dissolved oxygen (DO), oxidation-reduction potential (ORP), and influent water quality indicators, which include influent flow rate, influent chemical oxygen demand (COD), ammonia nitrogen (NH4 + -N), total nitrogen (TN).

[0052] The operating parameters of the flocculation pretreatment module include flocculant dosage, sedimentation residence time, pH value, ORP, water temperature, etc.

[0053] The operating parameters of the anaerobic reactor include the hydraulic retention time of the anaerobic fermentation tank, the dosage of the external redox mediator, the pH value, water temperature, ORP of the anaerobic tank, the flow rate, temperature, total pressure and partial pressure of different biogas produced; the biogas includes H2, CH4 and CO2.

[0054] The operating parameters of the anaerobic tank (A tank) and the aerobic tank (O tank) in the anaerobic-aerobic biochemical system mainly include: DO, ORP, water temperature, and pH value.

[0055] The operating parameters of the membrane bioreactor (MBR) include pH, ORP, water temperature, DO, and transmembrane pressure difference.

[0056] The effluent quality indicators after the membrane bioreactor (MBR) treatment include turbidity, COD, NH4 + -N, TN.

[0057] The deployment of the terminal layer sensor in step S1 is detailed in Figure 1-Figure 3 , as follows:

[0058] (1) A flow sensor is set at the water inlet of the regulating tank to mainly monitor the inlet flow of the landfill leachate; a first pH sensor, a first temperature sensor, a first water temperature sensor, a first ORP sensor, a first COD measurement sensor, a first NH4 + -N measurement sensor, first TN measurement sensor, first turbidity measurement sensor.

[0059] (2) A second pH sensor and a second water temperature sensor are provided at the water outlet of the flocculation pretreatment module or inside the module.

[0060] (3) A gas phase component sensor and a state sensor are set at the gas outlet of the anaerobic reactor. The gas phase component sensor is used to detect H2, CH4, and CO2; the state sensor includes a gas flow sensor, a gas temperature sensor, and a pressure sensor; a third pH sensor, a third water temperature sensor, and a third ORP sensor are set at the water outlet of the anaerobic reactor.

[0061] (5) The fourth pH sensor, the fourth water temperature sensor, the fourth ORP sensor, and the first DO sensor are installed in the A pool and the O pool of the anaerobic-aerobic biochemical system.

[0062] (6) The fifth pH sensor, the fifth water temperature sensor, and the fifth ORP sensor are installed in the clarifier of the anaerobic-aerobic biochemical system to monitor the pH, ORP, and water temperature of the supernatant on-line.

[0063] (7) The membrane bioreactor (MBR) is equipped with a sixth pH sensor, a sixth water temperature sensor, a sixth ORP sensor, a second DO sensor, a transmembrane pressure difference and other sensors to monitor the operating status of the MBR pool in real time; the outlet of the membrane bioreactor (MBR) is equipped with a seventh pH sensor, a seventh water temperature sensor, a seventh ORP sensor, a second COD measurement sensor, a second NH4 + -N measurement sensor, second TN measurement sensor, second turbidity measurement sensor.

[0064] Step S2: The real-time monitoring data of each sensor described in S1 is sent to the edge layer through the communication network, and a machine learning algorithm is used at the edge layer to perform multi-objective prediction on the effluent water quality indicators, the system comprehensive energy consumption, and the anaerobic hydrogen production rate of the anaerobic reactor.

[0065] In step S2, the machine learning algorithm includes one or more of random forest (RF), extreme gradient boosting (XGBoost), long short-term memory network (LSTM), artificial neural network (ANN), etc.; the model topology is as follows Figure 4 shown.

[0066] Step S3: Construct an objective function based on the effluent water quality index, anaerobic hydrogen production rate, and system comprehensive energy consumption. Under the constraints, use the decision-level optimization model in the cloud server to obtain the ideal operating parameters of the regulating tank, flocculation pretreatment module, anaerobic reactor, anaerobic-aerobic biochemical system, and MBR system.

[0067] In step S3, the decision-level optimization model is specifically one of a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO).

[0068] In step S3, the objective function is that the effluent water quality index meets the discharge standard requirements, the system comprehensive energy consumption is minimized, and the anaerobic hydrogen production rate is maximized. The objective function is expressed as follows:

[0069]

[0070] in, is the weight of each goal, reflecting the minimization of the system's comprehensive energy consumption ( ), water quality discharge target value ( ) and the highest hydrogen production rate ( ) between the priorities.

[0071] Constraints: =1

[0072] The system comprehensive energy consumption minimization expression is:

[0073]

[0074] Where E(x) represents the comprehensive energy consumption of the system under certain operating parameters; It means minimizing the comprehensive energy consumption during system operation, which can be achieved by adjusting the operating frequency, start and stop time of the equipment.

[0075] Constraints: E(x)≤E0; E(x)=E0'+E A

[0076] Where, E0 is the energy consumption of the process equipment before the system performs “end-edge-cloud” collaborative control; E0' is the actual energy consumption of the process equipment after the system performs “end-edge-cloud” collaborative control; E AThe energy consumption of each unit module in the "end-edge-cloud" collaborative control system during data acquisition, transmission, calculation, feedback, and control;

[0077] The effluent water quality meets the discharge target value Optimize expression:

[0078]

[0079] Where, It is used to optimize the target values ​​of various parameters of effluent water quality. The target optimized water quality indicators are COD, NH4⁺-N and TN. Among them, α1, α2, and α3 are the weights of the importance of each water quality indicator to the optimization result of the target value of the discharge standard. ) represents the relationship between the effluent COD value and the emission standard value under certain operating parameters; NH4⁺-N( ) represents the relationship between the effluent NH4⁺-N value and the emission standard value under certain operating parameters; TN( ) represents the relationship between the outlet TN value of the system under certain operating parameters and the emission standard value.

[0080] Constraints on effluent quality:

[0081]

[0082]

[0083]

[0084] Where, C is the actual COD value of the system effluent, C （t） The COD discharge standard value of the effluent (0≤C （t） ≤ 500 mg / L); N is the actual NH4⁺-N concentration in the effluent, N （t） The standard value of NH4⁺-N discharge in effluent (0≤N （t） ≤ 25 mg / L); T is the actual TN concentration of the effluent, T （t） TN is the discharge standard value of the effluent (0≤T （t） ≤ 70 mg / L); is the Sigmoid function, which is used to optimize the water quality target value so that each water quality index tends to the target value; S1 is the parameter used to scale the data so that the scales of different variables are comparable.

[0085] The anaerobic hydrogen production rate is maximized expression:

[0086]

[0087] Where, Indicates that the anaerobic hydrogen production rate is maximized; It indicates the hydrogen production rate in the anaerobic reactor under certain operating parameters of the system.

[0088] Constraints:

[0089]

[0090] H2 is the actual hydrogen production rate of the system, H 2（opt） is the maximum hydrogen production rate; Tanh is the hyperbolic tangent function, which is used to control the hydrogen production rate to make it close to the maximum value; S2 is the parameter used to scale the data.

[0091] Step S4: Based on the optimization results of the decision-level optimization model (i.e., the ideal values ​​of the operating parameters of the equalization tank, flocculation pretreatment module, anaerobic reactor, anaerobic-aerobic biochemical system, and MBR system) 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 the effluent water quality indicators, anaerobic hydrogen production rate, and comprehensive energy consumption of the system.

[0092] The key process operating conditions of the system include:

[0093] 1. Key process operating conditions of the flocculation pretreatment module

[0094] Working frequency and stirring speed of the mixer: The mixer speed is adjusted by changing the working frequency of the mixer inverter (20HZ~50HZ). The mixer speed range is 20~600 rpm.

[0095] High-speed shearing machine: When the flocculation unit selects magnetic flocculation technology, the speed of the high-speed shearing machine is adjusted by changing the operating frequency of the inverter (30HZ~50HZ). The speed range of the high-speed shearing machine is 2000~2000 rpm.

[0096] 2. Key process operating conditions of anaerobic-aerobic biochemical system

[0097] Aeration intensity: In the aerobic tank (O tank), adjust the inverter operating frequency (25HZ~50HZ) and start and stop time according to the real-time monitoring, prediction and optimization results of DO, and adjust the air volume and aeration time of the aeration equipment.

[0098] Stirring speed: In the anoxic tank (tank A), the stirrer speed is adjusted by changing the operating frequency of the stirrer inverter (20HZ~45HZ). The stirrer speed range is 40~1000 rpm.

[0099] Sludge return volume: By adjusting the motor speed of the reflux pump (30HZ~50HZ), the sludge return volume and internal reflux volume are adjusted to maintain appropriate sludge concentration and denitrification efficiency in the system.

[0100] 3. Key process operating conditions of membrane bioreactor (MBR) systems

[0101] Water production pump working pressure: Based on the real-time monitoring, prediction and optimization results of the transmembrane pressure difference, the working pressure of the water production pump is adjusted, and regular backwashing or chemical cleaning is performed to ensure the stability of the MBR system membrane flux and water production.

[0102] Aeration intensity: In the MBR pool, according to the real-time monitoring, prediction and optimization results of DO, adjust the inverter operating frequency (35HZ~50HZ) and start and stop time, and adjust the air volume and aeration time of the aeration equipment.

[0103] Example 1

[0104] The wastewater treatment station of a certain waste incineration power plant adopts a treatment process of regulating tank - chemical coagulation - anaerobic fermentation - anaerobic (A) tank - O tank - MBR - piped discharge. The "cloud-edge-end" collaborative control method of the present invention is used to intelligently control all aspects of the wastewater treatment in the plant. The terminal layer sensors of each treatment link are as follows Figure 2 As shown:

[0105] (1) Water inlet 1#: Set up a flow sensor to monitor the water inlet flow in real time.

[0106] (2) Regulating tank 2#: Set up online sensors such as pH, ORP, water temperature, and turbidity, and automatically collect water samples and pump them to online monitoring equipment for COD, ammonia nitrogen (NH4 + -N), and total nitrogen (TN).

[0107] (3) After chemical coagulation 3#: Set up online sensors such as pH and water temperature above the clarifier.

[0108] (4) Anaerobic IC reactors 4# and 5#: H2 / CH4 / CO2 and other gas phase component sensors, as well as temperature / flow / pressure and other status sensors are set at the headspace position to monitor the partial pressure of the generated biogas components in real time; pH, ORP, water temperature and other operating status sensors are set in the water phase.

[0109] (5) Anaerobic-aerobic biochemical system (A / O system) #6: pH, DO, ORP, water temperature and other sensors are installed in both the A pool and the O pool to monitor the operating status of the biochemical system in real time.

[0110] (6) A / O system outlet water #7: Pump the outlet water of the A / O biochemical pool to a small clarification tank outside the system for mud and water separation, and set pH, water temperature, ORP and other sensors in the supernatant.

[0111] (7) 8# in MBR pool: Sensors such as pH, ORP, DO, water temperature, water level, and transmembrane pressure difference are installed in the MBR pool to monitor the operating status of the MBR pool in real time.

[0112] (8) MBR outlet water 9#: Set up pH, ORP, water temperature and other sensors in the MBR outlet water pool, and pump out water to the online monitoring equipment for COD, NH4 + -Online monitoring of N, TN and turbidity.

[0113] Subsequently, the historical data of various sensors at the terminal layer were preprocessed and converted into a robust data set through feature engineering. Secondly, the training and optimization of algorithms such as Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were carried out on the robust data set. At the edge layer, the system comprehensive energy consumption and effluent water quality (COD, NH4 + -N, TN), and the fermentation hydrogen production rate were predicted, and characteristic importance analysis was performed to determine the key influencing factors affecting the effluent water quality of the incineration plant leachate, the anaerobic hydrogen production rate, and the comprehensive energy consumption of the system.

[0114] With the optimized XGBoost algorithm as the core and combined with the particle swarm optimization algorithm (PSO), a decision-level optimization model (XGBoost-PSO model) with multiple objective functions such as effluent water quality, anaerobic hydrogen production rate, and system comprehensive energy consumption was established at the cloud platform layer. Reverse engineering design was carried out under the overall optimal conditions of the system (lowest comprehensive energy consumption, water quality meeting emission standards, and high hydrogen production rate).

[0115] The weight of the system comprehensive energy consumption on the overall optimization result of the objective function is ω1=0.32.

[0116] Outlet water quality (COD, NH4 + -N, TN) on the overall optimization result of the objective function. The weight ω2 of COD on the optimization result of the target value of emission compliance is 0.41; among them, the weight α1 of COD on the optimization result of the target value of emission compliance is 0.55, the weight α2 of NH4⁺-N on the optimization result of the target value of emission compliance is 0.28, and the weight α3 of TN on the optimization result of the target value of emission compliance is 0.17.

[0117] The weight of anaerobic hydrogen production rate on the overall optimization result of the objective function is ω3=0.27.

[0118] Under the above multi-objective optimization conditions, based on real-time monitoring, prediction and optimization data at the terminal layer, the stirring speed of the stirring motor of the chemical flocculation pretreatment module, the air volume and aeration time of the aeration fan of the aerobic tank (O tank) of the anaerobic-aerobic biochemical system, the speed of the mixer of the anoxic tank (O tank), the flow rate of the sludge return pump, as well as the air volume and aeration time of the aeration equipment of the MBR system and the working pressure of the water production pump are regulated to achieve online adaptive adjustment of key parameters.

[0119] The leachate treatment efficiency and resource utilization level of the waste incineration plant based on the above-mentioned "cloud-edge-end" collaborative control method are shown in Appendix 1.

[0120] Appendix 1. Treatment efficiency and resource utilization of leachate from waste incineration plants under “cloud-edge-end” collaborative control

[0121]

[0122] Example 2

[0123] The leachate sewage treatment station of a domestic waste landfill plant adopts a treatment process of regulating tank - magnetic coagulation - UASB - two-stage anaerobic / aerobic (A / O) - MBR - nanofiltration - nanotube discharge. The "cloud-edge-end" collaborative control method of the present invention is used to intelligently control all aspects of the sewage treatment in the plant. The terminal layer sensors of each treatment link are as follows Figure 3 As shown:

[0124] (1) Water inlet 1#: Set up a flow sensor to monitor the water inlet flow in real time.

[0125] (2) Regulating tank 2#: Set up online sensors such as pH, ORP, water temperature, and turbidity, and automatically collect water samples and pump them to online monitoring equipment for COD, ammonia nitrogen (NH4 + -N), and total nitrogen (TN).

[0126] (3) After magnetic coagulation 3#: Set up online sensors such as pH and water temperature above the clarification tank.

[0127] (4) Anaerobic UASB reactors 4# and 5#: H2 / CH4 / CO2 and other gas phase component sensors, as well as temperature / flow / pressure and other status sensors are set in the headspace position to monitor the partial pressure of the generated biogas components in real time; pH, ORP, water temperature and other operating status sensors are set in the water phase.

[0128] (5) Two-stage A / O system 6#: pH, DO, ORP, water temperature and other sensors are installed in the A pool and O pool of the first and second levels to monitor the operating status of the biochemical system in real time.

[0129] (6) A / O system outlet water #7: Pump the outlet water of the A / O biochemical pool to a small clarification tank outside the system for mud and water separation, and set pH, water temperature, ORP and other sensors in the supernatant.

[0130] (7) 8# in MBR pool: Sensors such as pH, DO, ORP, water temperature, water level, and membrane filtration pressure are installed in the MBR pool to monitor the operating status of the MBR pool in real time.

[0131] (8) MBR outlet water 9#: Set up pH, water temperature, turbidity and other sensors in the MBR outlet water pool, and pump out the water to the online monitoring equipment for COD, NH4 + -Online monitoring of N and TN.

[0132] Subsequently, the historical data of various sensors at the terminal layer were preprocessed and converted into a robust data set through feature engineering. Secondly, the artificial neural network (ANN) and long short-term memory network (LSTM) algorithms were trained and optimized on the robust data set. At the edge layer, the system comprehensive energy consumption and effluent water quality (COD, NH4 + -N, TN), fermentation hydrogen production rate was predicted, and characteristic importance analysis was performed to determine the key influencing factors affecting the comprehensive energy consumption of the landfill infiltration system, filtrate effluent quality, and anaerobic hydrogen production rate.

[0133] With the optimized LSTM algorithm as the core and combined with the genetic algorithm (GA), a decision-level optimization model (XGBoost-GA model) with multiple objective functions such as effluent water quality, anaerobic hydrogen production rate, and system comprehensive energy consumption was established at the cloud platform layer. Reverse engineering design was carried out under the overall optimal conditions of the system (lowest comprehensive energy consumption, water quality meeting emission standards, and high hydrogen production rate).

[0134] The weight of the system comprehensive energy consumption on the overall optimization result of the objective function is ω1=0.25.

[0135] Outlet water quality (COD, NH4 + -N, TN) on the overall optimization result of the objective function. The weight ω2 of COD on the optimization result of the target value of emission compliance is 0.45; among them, the weight α1 of COD on the optimization result of the target value of emission compliance is 0.46, the weight α2 of NH4⁺-N on the optimization result of the target value of emission compliance is 0.30, and the weight α3 of TN on the optimization result of the target value of emission compliance is 0.24.

[0136] The weight of anaerobic hydrogen production rate on the overall optimization result of the objective function is ω3=0.30.

[0137] Under the above-mentioned multi-objective optimization conditions, based on real-time monitoring, prediction and optimization data at the terminal layer, the stirring speed of the stirring motor of the magnetic flocculation pretreatment module, the speed of the high-speed shear machine, the air volume and aeration time of the aeration fan of the aerobic tank (O tank) of the anaerobic-aerobic biochemical system, the speed of the mixer of the anoxic tank (O tank), the flow rate of the sludge return pump, as well as the air volume and aeration time of the aeration equipment of the MBR system and the working pressure of the water production pump are regulated to achieve online adaptive adjustment of key parameters.

[0138] Appendix 2: Landfill leachate treatment efficiency and resource utilization level under “cloud-edge-end” collaborative control

[0139]

[0140] The above embodiments are not limitations of the present invention, and the present invention is not limited to the above embodiments. As long as the requirements of the present invention are met, they belong to the protection scope of the present invention.

Claims

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 cascaded regulating tank, a flocculation pretreatment module, a biochemical system, and a membrane bioreactor; The regulating tank is responsible for receiving the landfill leachate and regulating the water quality uniformly; The flocculation pretreatment module is responsible for the efficient and rapid removal of particulate matter in the influent; The biochemical system includes anaerobic reactors connected in cascade, and single-stage or multi-stage anaerobic-aerobic biochemical systems; The anaerobic-aerobic biochemical system includes an anaerobic tank, an aerobic aeration tank, and a clarifier tank connected in cascade. A portion of the effluent from the aerobic aeration tank is returned to the anaerobic tank. The anaerobic tank and the aerobic aeration tank are responsible for carrying out anaerobic and aerobic biochemical reactions on the anaerobic fermentation products in sequence, completing preliminary denitrification and removal of organic matter. The clarifier tank is responsible for separating mud and water from the effluent from the aerobic aeration tank. The membrane bioreactor is responsible for the efficient solid-liquid separation of anaerobic and aerobic biochemical products, further enhancing the efficiency of the biochemical reaction; It is characterized in that The method comprises the following steps: Step S1: Acquisition of real-time monitoring data from sensors at the terminal layer; 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 aeration tank in the anaerobic-aerobic biochemical system, and the operating parameters of the membrane bioreactor; Step S2: The real-time monitoring data of each sensor is sent to the edge layer via the communication network, and a machine learning algorithm is used at the edge layer to perform multi-objective prediction on the effluent water quality indicators, the system comprehensive energy consumption, and the anaerobic hydrogen production rate of the anaerobic reactor; 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 constraints, use the decision-level optimization model in the cloud server to obtain the ideal operating parameters of the regulating tank, flocculation pretreatment module, anaerobic reactor, anaerobic-aerobic biochemical system, and membrane bioreactor; 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 the effluent water quality indicators, anaerobic hydrogen production rate, and system comprehensive energy consumption; 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; The operating parameters of the flocculation pretreatment module include flocculant dosage, sedimentation residence time, pH value, ORP, and water temperature; The operating parameters of the anaerobic reactor include the hydraulic retention time of the anaerobic reactor, the dosage of the external redox mediator, the pH value of the anaerobic reactor, the water temperature, the ORP, the flow rate, temperature, total pressure and partial pressure of the generated biogas; The operating parameters of the anaerobic tank and the aerobic aeration tank in the anaerobic-aerobic biochemical system include: dissolved oxygen, ORP, water temperature, and pH value; The operating parameters of the membrane bioreactor include pH, ORP, water temperature, dissolved oxygen, and transmembrane pressure difference; The effluent water quality indicators after the membrane bioreactor treatment include turbidity, effluent COD, ammonia nitrogen, and total nitrogen; In step S3, the objective function is specifically: f(x)=ω1·f1(x)+ω2·f2(x)+ω3·f3(x) f1(x)=minE(x) f2(x)=min(α1·COD(x)+α2·NH4 + -N(x)+α3·TN(x)) Among them, ω1, ω2, and ω3 are weights; f1(x) represents the minimization of the system's comprehensive energy consumption, f2(x) represents the water quality discharge target value, and f3(x) represents the maximization of the anaerobic hydrogen production rate of the anaerobic reactor; E(x) represents the process comprehensive energy consumption of the system under certain operating parameters; α1, α2, and α3 are the weights of the importance of each effluent water quality index COD, ammonia nitrogen, and total nitrogen to the optimization result of the discharge target value; COD(x) represents the relationship between the effluent COD value and the discharge standard value under certain operating parameters; NH4 + -N(x) represents the relationship between the effluent ammonia nitrogen value and the emission standard value under certain operating parameters of the system; TN(x) represents the relationship between the effluent total nitrogen value and the emission 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; The constraints are specifically: Where, E0 is the energy consumption of the process equipment before the system implements the "end-edge-cloud" collaborative control; E0' is the actual energy consumption of the process equipment after the system implements the "end-edge-cloud" collaborative control; E A is the energy consumption of the system during data collection, transmission, calculation, feedback, and control when performing "end-edge-cloud" collaborative control; C is the actual COD value of the system's effluent, and C (t) is the standard value of effluent COD discharge; N is the actual ammonia nitrogen concentration in effluent, N (t) is the standard value of ammonia nitrogen discharge in effluent; T is the actual total nitrogen concentration in effluent, T (t) is the standard value of total nitrogen discharge in effluent; σ is the Sigmoid function; is the actual hydrogen production rate of the system, H 2(opt) is the maximum hydrogen production rate; Tanh is the hyperbolic tangent function; S1 and S2 are parameters used to scale the data; In step S4, the key process operating conditions of the system include: key process operating conditions of the flocculation pretreatment module, key process operating conditions of the anaerobic-aerobic biochemical system, and key process operating conditions of the membrane bioreactor system; the key process operating conditions of the flocculation pretreatment module include the stirring speed of the mixer and the speed of the high-speed shear machine; the key process operating conditions of the anaerobic-aerobic biochemical system include aeration intensity, stirring speed of the mixer, and sludge return volume; the key process operating conditions of the membrane bioreactor system include the working pressure of the water production pump and the aeration intensity.

2. The method according to claim 1, 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.

3. The method according to claim 1, characterized in that In step S3, the decision-level optimization model is specifically one of a genetic algorithm and a particle swarm optimization algorithm.

4. The method according to claim 1, characterized in that The flocculation pretreatment module is a coagulation / flocculation physicochemical pretreatment or a magnetic flocculation pretreatment.

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