Optimized control methods, equipment and storage media for greenhouse gases in wastewater treatment plants

Abstract

This application discloses a method, equipment, and storage medium for optimizing greenhouse gas control in wastewater treatment plants, relating to the field of environmental protection technology. The method includes: discretizing the wastewater treatment process in the aeration tank into multiple series-connected completely mixed reactors, and constructing a wastewater treatment process mechanism model based on an activated sludge model; optimizing the parameters of the wastewater treatment process mechanism model using spatial distribution data of target greenhouse gas concentrations, target greenhouse gas emission fluxes, and spatial distribution data of water quality to obtain a digital twin model; and based on the digital twin model, using the dissolved oxygen setpoints of each completely mixed reactor zone in the aeration tank as decision variables, employing a multi-objective evolutionary algorithm to solve the problem and generate a Pareto optimal control strategy. This addresses the technical problem of inaccurate greenhouse gas optimization control strategies in existing technologies, achieving precise quantification and collaborative optimization control of greenhouse gas emissions.

Description

Technical Field

[0001] This application relates to the field of environmental protection technology, and in particular to a method, equipment and storage medium for optimizing the control of greenhouse gases in a wastewater treatment plant. Background Technology

[0002] Wastewater treatment plants, as vital urban infrastructure, bear the core function of wastewater purification and are also key sources of greenhouse gas emissions. Precisely reducing their emissions while ensuring operational efficiency has become an urgent industry need. Currently, greenhouse gas emission monitoring methods rely on assumptions of uniform sampling or mixed sampling, failing to capture instantaneous, meter-level emission hotspots, leading to errors in total emission estimation. Furthermore, traditional activated sludge models, based on the simplified assumption of a completely mixed reactor, neglect the dynamic changes in water quality and gas concentration along the aeration tank. This means that the calibration of the resulting wastewater treatment mechanism model directly depends on the final effluent data from the aeration tank, failing to reflect the driving mechanism of emissions based on spatial heterogeneity. These limitations in current monitoring and mechanism modeling directly lead to risks in the application of digital twins for wastewater treatment plants. Greenhouse gas control strategies guided by such digital twins may even increase greenhouse gas emissions or damage effluent quality. Summary of the Invention

[0003] The main objective of this application is to provide a method, equipment, and storage medium for optimizing greenhouse gas control in wastewater treatment plants, aiming to solve the technical problem that existing technologies cannot effectively characterize the spatiotemporal heterogeneity of greenhouse gas emissions in aeration tanks, resulting in inaccurate optimization control strategies for greenhouse gases.

[0004] To achieve the above objectives, this application proposes a method for optimizing the control of greenhouse gases in wastewater treatment plants, the method comprising:

[0005] The spatial distribution data of the target greenhouse gas concentration on the surface of the aeration tank is collected by a drone carrying a first gas analyzer, and the target greenhouse gas emission flux of the aeration tank is collected by a floating static box carrying a second gas analyzer, and the spatial distribution data of the water quality of the aeration tank is determined.

[0006] The wastewater treatment process in the aeration tank is discretized into multiple fully mixed reactors connected in series, and a wastewater treatment process mechanism model based on the activated sludge model is constructed.

[0007] The parameters of the wastewater treatment process mechanism model are optimized by using the spatial distribution data of the target greenhouse gas concentration, the target greenhouse gas emission flux, and the spatial distribution data of water quality to obtain a digital twin model. The digital twin model is used to simulate the biogeochemical processes and spatiotemporal dynamic distribution of wastewater treatment in the aeration tank.

[0008] Based on the digital twin model, the dissolved oxygen setpoint of each completely mixed reactor zone in the aeration tank is used as the decision variable, and a multi-objective evolutionary algorithm is used to solve the problem to generate a Pareto optimal control strategy. The Pareto optimal control strategy includes the optimal dissolved oxygen setpoint of each of the completely mixed reactor zones.

[0009] In one embodiment, the steps of collecting spatial distribution data of target greenhouse gas concentration on the surface of the aeration tank using a drone carrying a first gas analyzer, and collecting the target greenhouse gas emission flux of the aeration tank using a floating static box carrying a second gas analyzer, include:

[0010] The flight shooting parameters of the UAV are obtained, including the flight path, flight speed and flight altitude on the water surface of the aeration tank;

[0011] According to the flight shooting parameters, the UAV carrying the first gas analyzer is controlled to fly over the aeration tank at the flight speed along the flight path to obtain spatial distribution data of methane concentration and spatial distribution data of nitrous oxide concentration on the water surface of the aeration tank.

[0012] A floating static tank equipped with a second gas analyzer was used to measure methane emission flux and nitrous oxide emission flux at multiple discrete points on the surface of the aeration tank.

[0013] The spatial distribution data of methane concentration and the spatial distribution data of nitrous oxide concentration are determined as the spatial distribution data of the target greenhouse gas concentration, and the methane emission flux and the nitrous oxide emission flux are determined as the target greenhouse gas emission flux.

[0014] In one embodiment, the step of determining the spatial distribution data of the water quality in the aeration tank includes:

[0015] Water samples were collected from multiple discrete points along the process flow direction of the aeration tank. Water quality analysis was performed on the water samples from each discrete point to obtain key water quality parameters for each discrete point. The key water quality parameters include dissolved oxygen concentration, ammonium nitrogen concentration, nitrite nitrogen concentration and nitrate nitrogen concentration.

[0016] The coordinates of each discrete point are obtained, and the key water quality parameters of each discrete point are associated with the corresponding coordinates to generate the spatial distribution data of the water quality of the aeration tank.

[0017] In one embodiment, the step of discretizing the wastewater treatment process in the aeration tank into multiple series of completely mixed reactors and constructing a wastewater treatment process mechanism model based on an activated sludge model includes:

[0018] The aeration tank is divided into multiple spatial sub-regions along the process flow direction; and each spatial sub-region is modeled as a fully mixed reactor.

[0019] Based on the activated sludge model, a set of bioreaction kinetic equations including carbon and nitrogen conversion and greenhouse gas generation pathways were established in each of the completely mixed reactors.

[0020] The effluent from the previous fully mixed reactor is used as the influent to the next fully mixed reactor, so that the discrete fully mixed reactors are connected in series in the direction of the process flow of the aeration tank, forming a wastewater treatment process mechanism model that simulates the wastewater treatment process of the aeration tank.

[0021] In one embodiment, the step of optimizing the parameters of the wastewater treatment process mechanism model using the spatial distribution data of the target greenhouse gas concentration, the target greenhouse gas emission flux, and the spatial distribution data of water quality to obtain a digital twin model includes:

[0022] Based on the spatial distribution data of the target greenhouse gas concentration, the target greenhouse gas concentration at each discrete point is determined, and based on the target greenhouse gas emission flux, the target greenhouse gas emission at each discrete point is determined.

[0023] For each discrete point, the target greenhouse gas emissions at the discrete point are correlated with the target greenhouse gas concentration at the discrete point, and the effective mass transfer coefficient at the discrete point is calculated based on the principles of fluid dynamics and mass transfer.

[0024] The Gaussian process regression algorithm is used to spatially interpolate the effective mass transfer coefficients at each discrete point to generate a continuous effective mass transfer coefficient field covering the aeration tank.

[0025] Based on the spatial distribution data of the target greenhouse gas concentration, a continuous spatial distribution map of the target greenhouse gas concentration is generated, and the continuous spatial distribution map of the target greenhouse gas concentration is multiplied point by point with the continuous effective mass transfer coefficient field to obtain a target greenhouse gas emission flux map with meter-level resolution.

[0026] Using the target greenhouse gas emission flux map and the water quality spatial distribution data, the key parameters in the wastewater treatment process mechanism model are calibrated until the error between the simulated value and the measured value output by the wastewater treatment process mechanism model is less than a preset threshold, thus obtaining the spatially validated digital twin model.

[0027] In one embodiment, the step of calibrating key parameters in the wastewater treatment process mechanism model using the target greenhouse gas emission flux map and the water quality spatial distribution data until the error between the simulated and measured values ​​output by the wastewater treatment process mechanism model is less than a preset threshold, thereby obtaining the spatially validated digital twin model, includes:

[0028] Extract the measured values ​​of target greenhouse gas emission fluxes and key water quality parameters for each of the completely mixed reactors from the target greenhouse gas emission flux map and the water quality spatial distribution data.

[0029] The target greenhouse gas emission flux map and the water quality spatial distribution data are input into the wastewater treatment process mechanism model. The wastewater treatment process mechanism model is used to process the target greenhouse gas emission flux map and the water quality spatial distribution data, and outputs the simulated values ​​of the target greenhouse gas emission flux and key water quality parameters for each of the completely mixed reactors.

[0030] The simulated values ​​of each of the fully mixed reactors are compared with the measured values ​​of the corresponding fully mixed reactors, and the average absolute percentage error of each of the fully mixed reactors is calculated.

[0031] The key parameters in the mechanistic model are adjusted according to the mean absolute percentage error until the mean absolute percentage error of each of the fully mixed reactors is less than the preset threshold, thereby obtaining the spatially validated digital twin model. The key parameters include the yield coefficient and the half-saturation constant.

[0032] In one embodiment, after obtaining the target greenhouse gas emission flux map with meter-level resolution, the method further includes:

[0033] Along the process flow direction, multiple virtual sampling points are virtually and uniformly extracted from the target greenhouse gas emission flux map with different spatial densities.

[0034] Based on each combination of virtual sampling points, an interpolation method is used to estimate the target total greenhouse gas emissions of the aeration tank.

[0035] Based on the target greenhouse gas emission flux map, calculate the true total integral flux of the target greenhouse gas corresponding to the target greenhouse gas emission flux map;

[0036] The estimated total target greenhouse gas emissions are compared with the actual total integrated flux of the target greenhouse gas, and the relative error is calculated.

[0037] Based on the relationship between the relative error and the sampling density, the minimum sampling density that makes the relative error lower than a preset error threshold is determined.

[0038] In one embodiment, the step of generating a Pareto optimal control strategy based on the digital twin model, using the dissolved oxygen setpoints of each completely mixed reactor zone in the aeration tank as decision variables, and employing a multi-objective evolutionary algorithm to solve the problem includes:

[0039] The target total greenhouse gas emissions and total aeration energy consumption are used as joint optimization objectives, and the total nitrogen concentration, ammonium nitrogen concentration and total phosphorus concentration in the effluent are all lower than the corresponding preset emission standards as constraints. A multi-objective joint optimization model is constructed, wherein the joint optimization objective is to minimize the target total greenhouse gas emissions while minimizing the total aeration energy consumption.

[0040] Based on the aeration control baseline strategy, multiple candidate aeration control strategies are constructed within the search range, and the candidate aeration control strategies include candidate dissolved oxygen setpoints for each of the fully mixed reactor partitions.

[0041] The candidate aeration control strategies are evaluated using the multi-objective joint optimization model to obtain evaluation results for each candidate aeration control strategy. Based on the evaluation results, a multi-objective evolutionary algorithm is used to process the results and generate the optimal solution set on the Pareto front.

[0042] Based on the joint optimization objective, a Pareto optimal control strategy is determined from the optimal solution set on the Pareto front.

[0043] Furthermore, to achieve the above objectives, this application also proposes a greenhouse gas optimization control device for wastewater treatment plants, the wastewater treatment plant greenhouse gas optimization control device comprising:

[0044] The monitoring and acquisition module is used to collect spatial distribution data of target greenhouse gas concentration on the surface of the aeration tank by using a drone carrying a first gas analyzer, to collect the target greenhouse gas emission flux of the aeration tank by using a floating static box carrying a second gas analyzer, and to determine the spatial distribution data of the water quality of the aeration tank.

[0045] The mechanism model construction module is used to discretize the wastewater treatment process in the aeration tank into multiple series of completely mixed reactors, and construct a wastewater treatment process mechanism model based on the activated sludge model.

[0046] A digital twin generation module is used to optimize the parameters of the wastewater treatment process mechanism model by using the spatial distribution data of the target greenhouse gas concentration, the target greenhouse gas emission flux, and the spatial distribution data of water quality, so as to obtain a digital twin model. The digital twin model is used to simulate the biogeochemical processes and spatiotemporal dynamic distribution of wastewater treatment in the aeration tank.

[0047] A multi-objective optimization control module is used to generate a Pareto optimal control strategy based on the digital twin model, using the dissolved oxygen setpoints of each completely mixed reactor partition in the aeration tank as decision variables, and employing a multi-objective evolutionary algorithm. The Pareto optimal control strategy includes the optimal dissolved oxygen setpoints for each of the completely mixed reactor partitions.

[0048] In addition, to achieve the above objectives, this application also proposes a greenhouse gas optimization control device for a wastewater treatment plant, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the greenhouse gas optimization control method for a wastewater treatment plant as described above.

[0049] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the greenhouse gas optimization control method for wastewater treatment plants as described above.

[0050] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the greenhouse gas optimization control method for wastewater treatment plants as described above.

[0051] One or more technical solutions proposed in this application have at least the following technical effects: collecting spatial distribution data of target greenhouse gas concentration on the surface of the aeration tank by using a drone carrying a first gas analyzer, collecting target greenhouse gas emission flux in the aeration tank by using a floating static box carrying a second gas analyzer, and determining the spatial distribution data of water quality in the aeration tank, capturing greenhouse gas data and water quality data with spatiotemporal heterogeneity, and providing a real data basis for subsequent modeling. The wastewater treatment process in the aeration tank is discretized into multiple series of completely mixed reactors, and a wastewater treatment mechanism model adapted to the aeration tank based on the activated sludge model is constructed. The parameters of the wastewater treatment mechanism model are optimized using spatial distribution data of target greenhouse gas concentrations, target greenhouse gas emission fluxes, and water quality spatial distribution data to obtain a digital twin model. This digital twin model is used to simulate the biogeochemical processes and spatiotemporal dynamics of wastewater treatment in the aeration tank. Based on the digital twin model, the dissolved oxygen setpoints of each completely mixed reactor zone in the aeration tank are used as decision variables, and a multi-objective evolutionary algorithm is employed to solve the problem, generating a Pareto optimal control strategy. The Pareto optimal control strategy includes the optimal dissolved oxygen setpoints for each completely mixed reactor zone, with the dissolved oxygen of each reactor as an independent decision variable, achieving targeted regulation while avoiding the limitations of traditional single-objective optimization. This solves the technical problem in existing technologies where the spatiotemporal heterogeneity of greenhouse gas emissions within the aeration tank cannot be effectively characterized, leading to inaccurate greenhouse gas optimization control strategies. This achieves precise quantification and collaborative optimization control of greenhouse gas emissions. This application discloses a method for optimizing greenhouse gas control in wastewater treatment plants. It collects spatial distribution data of target greenhouse gas concentrations, target greenhouse gas emission fluxes, and water quality spatial distribution data. Breaking away from the limitations of traditional single-point or end-of-pipe monitoring, it directly captures the spatiotemporal heterogeneity characteristics of different regions within the aeration tank, such as differences in emission intensity and changes in water quality gradients, providing real data support for subsequent accurate modeling. The aeration tank is discretized into multiple series-connected completely mixed reactors. A wastewater treatment process mechanism model is constructed using an activated sludge model. Through discretization design adapted to the gradient characteristics along the aeration tank, the model can simulate wastewater treatment reactions in different local areas, avoiding the shortcomings of traditional single models that cannot reflect local heterogeneity, thus achieving structural adaptation to heterogeneity. Next, the parameters of the mechanism model are optimized using the spatial distribution data of target greenhouse gas concentrations, target greenhouse gas emission fluxes, and water quality spatial distribution data. The resulting digital twin model can faithfully reproduce the spatiotemporal characteristics of biogeochemical processes such as nitrification and denitrification within the aeration tank, as well as dynamic changes in water quality. This solves the problem of existing technologies relying solely on end-of-pipe data calibration and failing to reproduce heterogeneity, providing a reliable virtual experimental field for greenhouse gas regulation.By using the dissolved oxygen setpoint of each zone as an independent decision variable, a Pareto optimal strategy is generated through a multi-objective evolutionary algorithm to achieve targeted regulation of different heterogeneous regions. This solves the problem of control inaccuracy caused by the distortion of digital twin models in existing technologies, and enables the greenhouse gas optimization strategy to be truly based on the real process inside the aeration tank, thereby achieving accurate quantification and collaborative optimization control of greenhouse gas emissions. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a schematic flowchart of an embodiment of the wastewater treatment plant greenhouse gas optimization control method of this application;

[0055] Figure 2 This is a schematic diagram of the modular structure of the greenhouse gas optimization control device for a wastewater treatment plant, as described in an embodiment of this application.

[0056] Figure 3 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the greenhouse gas optimization control method for wastewater treatment plants in the embodiments of this application. Detailed Implementation

[0057] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0058] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0059] The main solution of this application embodiment is as follows: A drone carrying a first gas analyzer collects spatial distribution data of the target greenhouse gas concentration on the surface of the aeration tank; a floating static box carrying a second gas analyzer collects the target greenhouse gas emission flux from the aeration tank; and the spatial distribution data of the water quality in the aeration tank is determined. The wastewater treatment process in the aeration tank is discretized into multiple series-connected completely mixed reactors, and a wastewater treatment process mechanism model based on an activated sludge model is constructed. The parameters of the wastewater treatment process mechanism model are optimized using the spatial distribution data of the target greenhouse gas concentration, the target greenhouse gas emission flux, and the spatial distribution data of the water quality to obtain a digital twin model. The digital twin model is used to simulate the biogeochemical processes and spatiotemporal dynamic distribution of wastewater treatment in the aeration tank. Based on the digital twin model, the dissolved oxygen setpoints of each completely mixed reactor zone in the aeration tank are used as decision variables, and a multi-objective evolutionary algorithm is employed to solve the problem, generating a Pareto optimal control strategy. The Pareto optimal control strategy includes the optimal dissolved oxygen setpoints for each completely mixed reactor zone.

[0060] In this embodiment, for ease of description, the following description will focus on the greenhouse gas optimization control system of a wastewater treatment plant.

[0061] Wastewater treatment plants are an important part of urban infrastructure and also a significant source of non-carbon dioxide greenhouse gases, particularly methane (CH4) and nitrous oxide (N2O). With increasing global concern about climate change, accurately calculating and effectively reducing greenhouse gas emissions from wastewater treatment processes has become an urgent challenge for the industry.

[0062] However, accurately quantifying greenhouse gas emissions from wastewater treatment plants faces significant obstacles, primarily due to the strong spatiotemporal heterogeneity of these emissions. Take nitrogen (N2O) as an example: N2O production involves complex microbial metabolic pathways and is influenced by dynamic factors such as influent load fluctuations and aeration control, leading to instantaneous, meter-level emission "hot spots" within treatment units (such as aeration tanks). Emissions from these hot spots may dominate the total emissions of the entire treatment plant. Similarly, CH4 emissions are not uniformly distributed. Dissolved CH4 generated in upstream anaerobic environments (such as sewage pipe networks and anaerobic selective tanks) is physically stripped during aeration in the aerobic zone, creating localized high-flux emission zones.

[0063] Existing greenhouse gas monitoring methods are ill-equipped to effectively address this high degree of heterogeneity. Internationally accepted emission factor methods (such as Tier 1 / 2) from the Intergovernmental Panel on Climate Change (IPCC) are too coarse to capture operational differences at specific plants, resulting in estimation uncertainties of up to an order of magnitude. Traditional direct measurement techniques, such as static sampling boxes, while providing accurate flux data for individual points, suffer from insufficient spatial representativeness due to sparse sampling, leading to potentially large and unpredictable biases in extrapolating total plant emissions from limited data. Emerging remote sensing technologies, while possessing the potential for wide-area coverage, still lack a scientific framework to determine the sampling density required for stable and reliable total emissions estimates. These measurement bottlenecks in existing technologies prevent wastewater treatment plant managers from accurately locating emission sources, and make it difficult for regulatory agencies to obtain reliable emission inventories.

[0064] Equally prominent as the measurement bottleneck is the application challenge of predictive models. Although complex mechanistic models exist to analyze the biogeochemical processes of greenhouse gas production, their predictive power is limited by insufficient resolution of validation data. The calibration and validation of existing mechanistic models typically rely on the mixed effluent quality indicators at the end of the treatment unit, neglecting the dynamic changes at the meter level within the unit.

[0065] The current limitations of monitoring and mechanistic modeling directly lead to risks in the application of digital twins in wastewater treatment plants. Greenhouse gas control strategies guided by such digital twins may even inadvertently increase greenhouse gas emissions or damage effluent quality.

[0066] This application provides a solution that, by collecting spatial distribution data of target greenhouse gas concentrations, target greenhouse gas emission fluxes, and water quality spatial distribution data, breaks through the limitations of traditional single-point or end-point monitoring. It directly captures the spatiotemporal heterogeneity characteristics of different regions within an aeration tank, such as differences in emission intensity and changes in water quality gradients, providing real data support for subsequent accurate modeling. The aeration tank is discretized into multiple series-connected completely mixed reactors. A wastewater treatment process mechanism model is constructed by combining an activated sludge model. Through discretization design adapted to the gradient characteristics along the aeration tank, the model can simulate wastewater treatment reactions in each local area separately, avoiding the shortcomings of traditional single models that cannot reflect local heterogeneity, and achieving structural adaptation to heterogeneity. Then, the mechanism model parameters are optimized using spatial distribution data of target greenhouse gas concentrations, target greenhouse gas emission fluxes, and water quality spatial distribution data. The resulting digital twin model can faithfully reproduce the spatiotemporal characteristics of biogeochemical processes such as nitrification and denitrification within the aeration tank, as well as dynamic changes in water quality. This solves the problem of existing technologies relying solely on end-point data calibration and failing to reproduce heterogeneity, providing a reliable virtual experimental field for greenhouse gas regulation. By using the dissolved oxygen setpoint of each zone as an independent decision variable, a Pareto optimal strategy is generated through a multi-objective evolutionary algorithm to achieve targeted regulation of different heterogeneous regions. This solves the problem of control inaccuracy caused by the distortion of digital twin models in existing technologies, and enables the greenhouse gas optimization strategy to be truly based on the real process inside the aeration tank, thereby achieving accurate quantification and collaborative optimization control of greenhouse gas emissions.

[0067] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a greenhouse gas optimization control device for wastewater treatment plants capable of achieving the above functions. The following description uses a greenhouse gas optimization control system for wastewater treatment plants as an example to illustrate this embodiment and the subsequent embodiments.

[0068] Based on this, embodiments of this application provide a method for optimizing greenhouse gas control in wastewater treatment plants, referring to... Figure 1 , Figure 1 This is a schematic flowchart of the first embodiment of the greenhouse gas optimization control method for wastewater treatment plants in this application.

[0069] In this embodiment, the method for optimizing and controlling greenhouse gases in wastewater treatment plants includes steps 101-104:

[0070] Step 101: Collect spatial distribution data of target greenhouse gas concentration on the surface of the aeration tank using a drone carrying a first gas analyzer, collect target greenhouse gas emission flux in the aeration tank using a floating static box carrying a second gas analyzer, and determine the spatial distribution data of water quality in the aeration tank.

[0071] The greenhouse gas optimization control method for wastewater treatment plants proposed in this application is applied to the greenhouse gas optimization control system of wastewater treatment plants. Specifically, the greenhouse gas optimization control system for wastewater treatment plants includes a high-resolution monitoring module, a digital twin verification module, and a multi-objective optimization control module. The high-resolution monitoring module is used to collect spatial distribution data of target greenhouse gas concentrations on the surface of the aeration tank using a drone carrying a first gas analyzer, and to collect the target greenhouse gas emission flux of the aeration tank using a floating static tank carrying a second gas analyzer. Based on the spatial distribution data of target greenhouse gas concentrations and the target greenhouse gas emission flux, a target greenhouse gas emission flux map is generated; and the spatial distribution data of water quality in the aeration tank is determined. The digital twin verification module is used to discretize the wastewater treatment process in the aeration tank into multiple series-connected completely mixed reactors, constructing a wastewater treatment process mechanism model based on an activated sludge model; and to optimize the parameters of the wastewater treatment process mechanism model using the target greenhouse gas emission flux map and water quality spatial distribution data to obtain a digital twin model. The multi-objective optimization control module is used to generate a Pareto optimal control strategy based on a digital twin model, using the dissolved oxygen setpoint of each completely mixed reactor zone in the aeration tank as the decision variable and employing a multi-objective evolutionary algorithm.

[0072] In some embodiments, the high-resolution monitoring module includes at least one UAV monitoring submodule, at least one surface flux measurement submodule, a data fusion and flux field reconstruction unit, and a water quality synchronous sampling unit.

[0073] The UAV monitoring submodule is used to acquire the flight shooting parameters of the UAV, including the flight path, flight speed, and flight altitude on the surface of the aeration tank. Based on the flight shooting parameters, the UAV carrying the first gas analyzer is controlled to fly over the aeration tank along the flight path and based on the flight speed to acquire the spatial distribution data of methane concentration and nitrous oxide concentration on the surface of the aeration tank. The spatial distribution data of methane concentration and nitrous oxide concentration are then identified as the spatial distribution data of the target greenhouse gas concentration.

[0074] The surface flux measurement submodule is used to measure methane and nitrous oxide emission fluxes at multiple discrete points on the surface of the aeration tank using a floating static box equipped with a second gas analyzer; and to determine the methane and nitrous oxide emission fluxes as the target greenhouse gas emission fluxes.

[0075] The data fusion and flux field reconstruction unit is used to determine the target greenhouse gas concentration at each discrete point based on the spatial distribution data of the target greenhouse gas concentration, and to determine the target greenhouse gas emission amount at each discrete point based on the target greenhouse gas emission flux. For each discrete point, the target greenhouse gas emission amount at the discrete point is correlated with the target greenhouse gas concentration at the discrete point, and the effective mass transfer coefficient at the discrete point is calculated according to the principles of fluid dynamics and mass transfer. A Gaussian process regression algorithm is used to spatially interpolate the effective mass transfer coefficient at each discrete point to generate a continuous effective mass transfer coefficient field covering the aeration tank. A continuous target greenhouse gas concentration spatial distribution map is generated based on the spatial distribution data of the target greenhouse gas concentration, and the continuous target greenhouse gas concentration spatial distribution map is multiplied point by point with the continuous effective mass transfer coefficient field to obtain a target greenhouse gas emission flux map with meter-level resolution.

[0076] The water quality synchronous sampling unit is used to collect water samples from multiple discrete points along the process flow direction of the aeration tank, analyze the water quality of each discrete point, and obtain the key water quality parameters of each discrete point, including dissolved oxygen concentration, ammonium nitrogen concentration, nitrite nitrogen concentration, and nitrate nitrogen concentration; obtain the coordinates of each discrete point, and associate the key water quality parameters of each discrete point with the corresponding coordinates to generate the spatial distribution data of water quality in the aeration tank.

[0077] Specifically, the target greenhouse gases are the non-carbon dioxide greenhouse gases mainly emitted during wastewater treatment, which can be methane (CH4) and nitrous oxide (N2O). The aeration tank is the core unit for aerobic biological treatment in the wastewater treatment plant. In this embodiment, the aeration tank can be a four-channel plug-flow aeration tank with dimensions of 160m × 40m × 5m. The spatial distribution data of the target greenhouse gas concentration refers to the concentration data of the target greenhouse gases at different spatial locations (such as each channel or area) above the water surface of the aeration tank. The spatial distribution data of the target greenhouse gas concentration includes the spatial distribution data of methane concentration and nitrous oxide concentration. The target greenhouse gas emission flux refers to the mass of greenhouse gas released from the water surface into the atmosphere per unit time and per unit area (e.g., mg·m³). -2 ·h -1 The target greenhouse gas emission flux is a direct indicator of actual emission intensity, and includes methane emission flux and nitrous oxide emission flux. Water quality spatial distribution data refers to key water quality parameters in different regions of the aeration tank along the process flow direction. Key water quality parameters include dissolved oxygen (DO) concentration and ammonium nitrogen (NH4+) concentration. + -N) concentration, nitrite nitrogen (NO2) - -N) concentration, nitrate nitrogen (NO3) - -N) concentration, spatial distribution data of water quality can reflect the gradient change of water quality along the course.

[0078] In some embodiments, a hexacopter UAV equipped with a high-precision gas analyzer flies along a preset gridded route at a height of 2 meters above the aeration tank surface, collecting real-time CH4 and N2O concentration data and correlating them with spatial coordinates to generate spatial distribution data of methane and nitrous oxide concentrations on the aeration tank surface. Simultaneously, floating static boxes are placed at discrete points represented by each corridor in the aeration tank, and a connected portable analyzer continuously measures for 15 minutes to obtain the methane and nitrous oxide emission fluxes at each discrete point. Simultaneously, along the process flow direction, at locations corresponding to the floating box locations, automatic samplers collect water samples. Combined with in-situ measurements of dissolved oxygen, ammonium nitrogen, nitrite nitrogen, and nitrate nitrogen concentrations by online sensors, the data is organized according to spatial coordinates to form spatial distribution data of water quality. After noise reduction preprocessing, the spatial distribution data of target greenhouse gas concentrations, target greenhouse gas emission fluxes, and water quality spatial distribution data can provide a reliable data source for subsequent flux field reconstruction and model calibration.

[0079] Optionally, the steps of collecting spatial distribution data of target greenhouse gas concentrations on the surface of the aeration tank using a drone carrying a first gas analyzer, and collecting target greenhouse gas emission fluxes from the aeration tank using a floating static box carrying a second gas analyzer, include:

[0080] Acquire the drone's flight shooting parameters, including its flight path, speed, and altitude on the surface of the aeration tank.

[0081] Based on the flight shooting parameters, the drone carrying the first gas analyzer is controlled to fly over the aeration tank along the flight path based on the flight speed, and to obtain the spatial distribution data of methane concentration and nitrous oxide concentration on the water surface of the aeration tank.

[0082] A floating static tank equipped with a second gas analyzer was used to measure methane emission flux and nitrous oxide emission flux at multiple discrete points on the surface of the aeration tank.

[0083] The spatial distribution data of methane concentration and nitrous oxide concentration were determined as the spatial distribution data of target greenhouse gas concentrations, and the emission fluxes of methane and nitrous oxide were determined as the emission fluxes of target greenhouse gases.

[0084] Specifically, the flight photography parameters are key parameters used to ensure the accuracy and integrity of the spatial distribution data of the target greenhouse gas concentration, including flight path, flight speed, and flight altitude.

[0085] The flight path is a gridded route covering all areas of the aeration tank, such as a zigzag or parallel route, ensuring no monitoring blind spots. The flight speed can be controlled between 1 and 2 m / s to balance data acquisition density and efficiency, avoiding concentration measurement distortion due to excessive speed. The flight altitude can be 2 m above the water surface, reducing interference from surface airflow while ensuring the gas analyzer can accurately capture released greenhouse gases. The first gas analyzer is a high-precision gas sensor mounted on the UAV, such as a laser absorption spectrometer, which can detect the concentration of CH4 and N2O (unit: ppm) in real time to meet the rapid measurement needs during dynamic flight. The floating static box is a sealed box with an open bottom that can float on the water surface. It is used to seal off a local water surface and measure the change in gas concentration inside the box over time to calculate the emission flux. The second gas analyzer is a portable, high-precision instrument connected to the floating static tank. It can be a cavity ring-down spectrometer (CRDS). The second gas analyzer can be installed inside the floating static tank. By continuously measuring the increase in gas concentration within the tank, the second gas analyzer calculates the target greenhouse gas emission flux per unit area. Discrete sampling points are sampling points set in each corridor of the aeration tank. For example, 8 to 10 points are set in each corridor of the aeration tank, covering different operating areas such as the inlet, middle section, and outlet. This represents the differences in emissions throughout the tank, and the discrete sampling points must cover potential emission hotspots.

[0086] As an example, the drone flight parameters are set according to the geometry of the aeration tank (such as length, width, and corridor distribution), including the flight path (e.g., using a parallel grid flight path to cover the entire tank without blind spots), flight speed (1-1.5 m / s to ensure density of concentration data collection), and flight altitude (2 meters above the water surface to reduce airflow interference). Subsequently, the drone, equipped with a first gas analyzer (such as a laser absorption spectrometer), flies according to the preset parameters, recording CH4 and N2O concentrations in real time and correlating the coordinates of the collection points to generate spatial distribution data of methane and nitrous oxide concentrations with geographic coordinates covering the entire tank. Simultaneously, multiple discrete points (e.g., 8-10 per corridor, with increased density at the inlet and uneven aeration areas) are deployed along each corridor of the aeration tank. A floating static tank (0.5 m² surface area) carrying a second gas analyzer (such as a cavity ring-down spectrometer) is then used for this purpose. 2The floating static tank was placed at various discrete locations, and measurements were taken continuously for 15 minutes after sealing the water surface. The methane and nitrous oxide emission fluxes at each discrete location were calculated by the slope of the gas concentration increase over time within the tank. The spatial distribution data of methane and nitrous oxide concentrations acquired by the UAV were identified as the spatial distribution data of the target greenhouse gas concentrations, and the methane and nitrous oxide emission fluxes measured by the floating tank were identified as the target greenhouse gas emission fluxes. The combination of these two data forms a complete emission characteristic dataset, providing a reliable basis for subsequent construction of high-resolution flux maps and digital twin model calibration. By combining large-scale UAV scanning with fixed-point measurements by the floating static tank, the spatial distribution data of the target greenhouse gases (methane and nitrous oxide) concentrations and emission fluxes on the surface of the aeration tank were accurately acquired, providing high-resolution basic data for subsequent calibration and optimization control of wastewater treatment process mechanism models. By integrating two measurement technologies, UAVs and floating boxes, meter-level resolution full-coverage imaging of greenhouse gas emission fluxes in wastewater treatment units (such as aeration tanks) can be achieved, which can accurately identify and quantify emission "hot spots" and overcome the shortcomings of insufficient spatial representativeness of traditional point-based measurements.

[0087] Optionally, the steps for determining the spatial distribution data of water quality in the aeration tank include:

[0088] Water samples were collected from multiple discrete points along the process flow direction of the aeration tank. Water quality analysis was performed on the water samples from each discrete point to obtain the key water quality parameters for each discrete point. The key water quality parameters include dissolved oxygen concentration, ammonium nitrogen concentration, nitrite nitrogen concentration and nitrate nitrogen concentration.

[0089] The coordinates of each discrete point are obtained, and the key water quality parameters of each discrete point are associated with the corresponding coordinates to generate spatial distribution data of water quality in the aeration tank.

[0090] Specifically, the process flow direction refers to the flow path of wastewater within the aeration tank (from the influent end to the effluent end), and water quality parameters (such as decreasing ammonium nitrogen and increasing nitrate nitrogen) exhibit a gradient change along this direction. Discrete sampling points are water quality sampling points strategically placed along the process flow direction, covering key areas such as the influent zone, reaction midstream, and effluent zone. The number of discrete sampling points should match the number of greenhouse gas sampling points (e.g., 8-10 per corridor). Key water quality parameters are core indicators that directly affect greenhouse gas production, including dissolved oxygen (DO) concentration and ammonium nitrogen (NH4) concentration. + -N), nitrite nitrogen (NO2) - -N) concentration and nitrate nitrogen (NO3) - The concentrations of nitrite nitrogen (NH4+) and dissolved oxygen (DO) can regulate the nitrification / denitrification process. Too low a DO concentration will lead to the accumulation of nitrite nitrogen, while too high a DO concentration will inhibit denitrification. The concentration of ammonium nitrogen (NH4+)... +Nitrite (NO2) is a substrate for nitration, and its residual amount affects the potential for N2O production; nitrite concentration (NO2) - Nitrate nitrogen (NO3-N) is a key intermediate product in N2O formation, and excessively high concentrations can significantly promote emissions; nitrate nitrogen concentration (NO3-N) is also important. - Nitrate nitrogen (N-N) is a precursor to the final product of denitrification, and its distribution reflects the degree of denitrification completion. Spatial water quality data is a dataset formed by associating key water quality parameters at discrete points with their corresponding coordinates. Continuous spatial distribution maps can be generated through interpolation, visually reflecting the spatial heterogeneity of these parameters. The spatial distribution data includes spatial distribution data for dissolved oxygen, ammonium nitrogen, nitrite nitrogen, and nitrate nitrogen.

[0091] As an example, multiple discrete sampling points are deployed at key locations along the process flow direction of the aeration tank (such as the flow path of a plug-flow multi-channel system). Within a time window synchronized with greenhouse gas monitoring, water samples can be collected at approximately 0.5 meters below the water surface at each point using automated samplers or manually. Dissolved oxygen (DO) concentration is immediately measured on-site, and the water samples are then refrigerated and sent to a laboratory for analysis of ammonium nitrogen (NH4+) concentration using ion chromatography or standard spectrophotometry. + -N), nitrite nitrogen (NO2) - -N) and nitrate nitrogen (NO3) - The system collects nitrogen (N2O) concentration data and records the precise coordinates of each discrete point (sampling point). It then structurally correlates the key water quality parameters measured at each discrete point with their corresponding coordinates to generate spatial distribution data of water quality covering the entire aeration tank. This spatial distribution data accurately reflects the gradient changes in nitrogen form transformation and dissolved oxygen along the process, providing crucial experimental evidence for subsequent spatial calibration of the digital twin model and analysis of N2O generation mechanisms. By collecting water quality parameters along the aeration tank process and correlating them with spatial coordinates, a data foundation reflecting the spatiotemporal heterogeneity of water quality is constructed, providing key support for digital twin model calibration and greenhouse gas emission mechanism analysis.

[0092] Step 102: Discretize the wastewater treatment process in the aeration tank into multiple fully mixed reactors connected in series, and construct a wastewater treatment process mechanism model based on the activated sludge model.

[0093] Specifically, the wastewater treatment process in the aeration tank refers to the process by which wastewater undergoes organic matter degradation and nitrogen and phosphorus removal through the metabolic action of microorganisms in the activated sludge within the aeration tank, while simultaneously generating and releasing greenhouse gases (such as N2O and CH4). Multiple series-connected continuously stirred-tank reactors (CSTRs) represent the aeration tank as N ideal reactors connected end-to-end. Each CSTR is assumed to be completely mixed (uniform concentration), but a concentration gradient exists between adjacent CSTRs, thus approximating plug flow characteristics. The Activated Sludge Model (ASM) is a standardized mechanistic model framework proposed by the International Water Association (IWA), such as ASM1, ASM2d, and ASM3. The activated sludge model uses differential equations to describe microbial growth, substrate consumption, and product generation during organic matter degradation, nitrification, and denitrification processes. The wastewater treatment process mechanism model is a mathematical model constructed based on ASM theory and combined with a discretized structure of multiple fully mixed reactors. It can describe the changes in water quality, microbial activity and greenhouse gas production in the aeration tank. The wastewater treatment process mechanism model can simulate the spatiotemporal evolution of water quality parameters and greenhouse gas precursors.

[0094] In some embodiments, based on the actual structure of the aeration tank (e.g., total length, number of channels, and hydraulic retention time), the aeration tank is divided into N (e.g., 8) series of completely mixed reactors (CSTRs) of equal volume or segmented according to process characteristics along the water flow direction. Each CSTR is assumed to be in a completely mixed state internally, and the CSTRs are sequentially connected by water flow to approximate plug flow characteristics. Based on the activated sludge model, a mass balance equation is established for each CSTR, which can incorporate nitrification (NH4+). + →NO2 - →NO3 - ), denitrification (NO3) - The biodynamics of reactions such as →N2O→N2 were analyzed to quantify the correlation between microbial metabolism and dissolved oxygen (DO) and nitrogen forms. Reactor volume, hydraulic retention time, and biodynamic parameters (referencing ASM default values) were initialized to construct a complete wastewater treatment process mechanism model, enabling preliminary simulation of water quality changes and greenhouse gas generation potential along the aeration tank. Discretization modeling transformed the complex reaction process in the aeration tank into a quantifiable mathematical model, providing a basic framework for simulating wastewater treatment bioreactions and greenhouse gas generation, supporting the subsequent construction of a digital twin model.

[0095] Step 103: Optimize the parameters of the wastewater treatment process mechanism model by using spatial distribution data of target greenhouse gas concentration, target greenhouse gas emission flux and spatial distribution data of water quality to obtain a digital twin model. The digital twin model is used to simulate the biogeochemical processes and spatiotemporal dynamic distribution of wastewater treatment in the aeration tank.

[0096] Specifically, the digital twin model is a mechanistic model calibrated and validated with high-resolution data. It can simulate the internal state of a physical aeration tank in real-time or near real-time, mapping the physical state, biological reaction processes, and spatiotemporal dynamics of greenhouse gas emissions in the aeration tank in real time, achieving synchronization between the virtual and real environments. Biogeochemical processes refer to the material transformation processes involving microbial metabolism within the aeration tank, such as carbon and nitrogen cycles and biochemical reactions that produce greenhouse gases. Spatiotemporal dynamic distribution refers to the digital twin model's ability to output data such as "N2O at the 5th CSTR due to NO2..." - Dynamic behaviors with location and time information, such as "accumulation and instantaneous increase", rather than just the average value of the endpoints.

[0097] In some embodiments, the spatial distribution data of target greenhouse gas concentration, target greenhouse gas emission flux, and water quality spatial distribution data (including the concentrations of dissolved oxygen, ammonium nitrogen, nitrite nitrogen, and nitrate nitrogen at each discrete point) are spatially mapped to each discretized completely mixed reactor (CSTR) unit of the aeration tank. Subsequently, key model parameters sensitive to N2O generation and nitrogen conversion processes (such as the yield coefficient of N2O production by ammonia oxidizing bacteria, the denitrification half-saturation constant, etc.) can be selected to construct an optimization function with the goal of minimizing multivariate simulation error. The optimization function comprehensively considers the deviations between the measured values ​​of water quality parameters and target greenhouse gas flux at each CSTR region location and the model output values. Next, parameter optimization algorithms, such as genetic algorithms or gradient descent, can be used to iteratively adjust the model parameters until the average absolute percentage error of all calibration points is lower than a preset threshold (e.g., 15%). This results in a digital twin model that has undergone rigorous spatial verification. This digital twin model can not only accurately predict effluent quality but also realistically reproduce the spatiotemporal dynamic distribution of greenhouse gas generation and water quality evolution within the aeration tank, providing a high-fidelity simulation platform for subsequent zoned optimization control. By optimizing the mechanistic model parameters using measured data, a digital twin model that accurately reproduces the dynamic processes of the aeration tank is constructed, providing a virtual experimental platform for subsequent optimization control. This approach overturns the traditional paradigm of relying on end-of-pipe effluent data for model calibration. By utilizing spatially distributed data along the process, at multiple points, and with multiple parameters, the mechanistic model undergoes rigorous spatial verification, ensuring that the digital twin model can realistically reflect the complex biogeochemical processes within the treatment unit, providing a solid foundation for subsequent prediction and optimization.

[0098] Step 104: Based on the digital twin model, the dissolved oxygen setpoint of each completely mixed reactor zone in the aeration tank is used as the decision variable, and a multi-objective evolutionary algorithm is used to solve the problem to generate a Pareto optimal control strategy. The Pareto optimal control strategy includes the optimal dissolved oxygen setpoint of each completely mixed reactor zone.

[0099] Specifically, the dissolved oxygen setpoint is the target dissolved oxygen concentration for each zone (completely mixed reactor zone). The dissolved oxygen setpoint is a key control parameter affecting microbial metabolism (such as nitrification / denitrification) and greenhouse gas production. In actual control, the dissolved oxygen setpoint can be adjusted by regulating the blower airflow or valve opening. The multi-objective evolutionary algorithm is an intelligent optimization algorithm that simulates biological evolution, such as NSGA-II, which can simultaneously optimize multiple objectives (such as greenhouse gas emissions and aeration energy consumption) to find non-dominated solutions. The Pareto optimal control strategy is the set of optimal dissolved oxygen setpoints for multiple objectives that cannot be further optimized (i.e., improving one objective would harm another). The optimal dissolved oxygen setpoint is the specific dissolved oxygen (DO) distribution corresponding to each strategy on the Pareto front, such as CSTR1: 1.2 mg / L, CSTR2: 0.8 mg / L, etc. The optimal dissolved oxygen setpoint can be directly used to guide zone aeration control.

[0100] In some embodiments, the dissolved oxygen setpoint of each completely mixed reactor (CSTR) partition after discretization of the aeration tank can be defined as a decision variable, forming an N-dimensional optimization vector. Subsequently, a multi-objective optimization problem is constructed, using a digital twin model as the simulation kernel, with the total emissions of target greenhouse gases (such as N2O) and the total energy consumption of the aeration tank as the minimization objective functions, while setting the total nitrogen (TN) and ammonia nitrogen (NH4) in the effluent. +With dissolved oxygen (DO) concentrations not exceeding emission standards as hard constraints, a multi-objective evolutionary algorithm is then used to solve the optimization problem. In each iteration, the algorithm generates several DO setting combinations, calls a digital twin model to simulate the water quality evolution and target generation process under corresponding operating conditions, evaluates the satisfaction of target values ​​and constraints, and continuously evolves the population through selection, crossover, and mutation operations. The algorithm converges to generate a set of non-dominant Pareto optimal solutions, i.e., Pareto optimal control strategies, which include specific optimal dissolved oxygen settings for each CSTR zone. These Pareto optimal control strategies can be directly used to guide the zoned regulation of distributed aeration systems, achieving synergistic optimization of greenhouse gas emission reduction and energy consumption reduction while ensuring effluent compliance. Based on the combination of digital twin model simulation and multi-objective optimization, a dissolved oxygen control strategy adapted to the heterogeneity of aeration tank zones is generated, achieving synergistic optimization of greenhouse gas emission reduction, aeration energy saving, and water quality compliance. Based on a spatially validated digital twin model, multi-objective optimization is performed to design a zoned and differentiated aeration control strategy (Pareto optimal control strategy). The Pareto optimal control strategy can not only significantly reduce the target greenhouse gas emissions, but also save aeration energy consumption. Moreover, the entire optimization process ensures that the effluent quality meets the standards, thus guaranteeing that the core functions of the wastewater treatment plant are not affected.

[0101] Based on the greenhouse gas optimization control method for wastewater treatment plants disclosed in this application, spatial distribution data of target greenhouse gas concentration and target greenhouse gas emission flux are collected on the surface of the aeration tank, and spatial distribution data of water quality in the aeration tank are determined. This captures greenhouse gas and water quality data with spatiotemporal heterogeneity, providing a real data foundation for subsequent modeling. The wastewater treatment process in the aeration tank is discretized into multiple series of completely mixed reactors, and a wastewater treatment mechanism model adapted to the aeration tank based on the activated sludge model is constructed. The parameters of the wastewater treatment mechanism model are optimized using spatial distribution data of target greenhouse gas concentrations, target greenhouse gas emission fluxes, and water quality spatial distribution data to obtain a digital twin model. This digital twin model is used to simulate the biogeochemical processes and spatiotemporal dynamics of wastewater treatment in the aeration tank. Based on the digital twin model, the dissolved oxygen setpoints of each completely mixed reactor zone in the aeration tank are used as decision variables, and a multi-objective evolutionary algorithm is employed to solve the problem, generating a Pareto optimal control strategy. The Pareto optimal control strategy includes the optimal dissolved oxygen setpoints for each completely mixed reactor zone, with the dissolved oxygen of each reactor as an independent decision variable, achieving targeted regulation while avoiding the limitations of traditional single-objective optimization. This solves the technical problem in existing technologies where the spatiotemporal heterogeneity of greenhouse gas emissions within the aeration tank cannot be effectively characterized, leading to inaccurate greenhouse gas optimization control strategies. This achieves precise quantification and collaborative optimization control of greenhouse gas emissions. This application discloses a method for optimizing greenhouse gas control in wastewater treatment plants. It collects spatial distribution data of target greenhouse gas concentrations, target greenhouse gas emission fluxes, and water quality spatial distribution data. Breaking away from the limitations of traditional single-point or end-of-pipe monitoring, it directly captures the spatiotemporal heterogeneity characteristics of different regions within the aeration tank, such as differences in emission intensity and changes in water quality gradients, providing real data support for subsequent accurate modeling. The aeration tank is discretized into multiple series-connected completely mixed reactors. A wastewater treatment process mechanism model is constructed using an activated sludge model. Through discretization design adapted to the gradient characteristics along the aeration tank, the model can simulate wastewater treatment reactions in different local areas, avoiding the shortcomings of traditional single models that cannot reflect local heterogeneity, thus achieving structural adaptation to heterogeneity. Next, the parameters of the mechanism model are optimized using the spatial distribution data of target greenhouse gas concentrations, target greenhouse gas emission fluxes, and water quality spatial distribution data. The resulting digital twin model can faithfully reproduce the spatiotemporal characteristics of biogeochemical processes such as nitrification and denitrification within the aeration tank, as well as dynamic changes in water quality. This solves the problem of existing technologies relying solely on end-of-pipe data calibration and failing to reproduce heterogeneity, providing a reliable virtual experimental field for greenhouse gas regulation. By using the dissolved oxygen setpoint of each zone as an independent decision variable, a Pareto optimal strategy is generated through a multi-objective evolutionary algorithm to achieve targeted regulation of different heterogeneous regions. This solves the problem of control inaccuracy caused by the distortion of digital twin models in existing technologies, and enables the greenhouse gas optimization strategy to be truly based on the real process inside the aeration tank, thereby achieving accurate quantification and collaborative optimization control of greenhouse gas emissions.

[0102] In some embodiments, the wastewater treatment process in the aeration tank is discretized into multiple fully mixed reactors connected in series. The steps for constructing a wastewater treatment process mechanism model based on an activated sludge model include:

[0103] The aeration tank is divided into multiple spatial sub-regions along the process flow direction; and each spatial sub-region is modeled as a fully mixed reactor.

[0104] Based on the activated sludge model, a set of bioreaction kinetic equations including carbon and nitrogen conversion and greenhouse gas generation pathways were established in each completely mixed reactor.

[0105] The effluent from the previous fully mixed reactor is used as the influent to the next fully mixed reactor, so that the discrete fully mixed reactors are connected in series in the direction of the aeration tank process flow to form a wastewater treatment process mechanism model that simulates the wastewater treatment process of the aeration tank.

[0106] Specifically, carbon and nitrogen conversion and greenhouse gas generation pathways are key reaction mechanisms extended from the ASM model, which can include organic carbon oxidation, ammonia oxidation, nitrification, denitrification, and N2O generation pathways. The biokinetic equation set is a set of equations based on the Activated Sludge Model (ASM) describing carbon degradation, nitrogen conversion, and greenhouse gas generation, such as nitrification equations, denitrification equations, and methanogenesis equations.

[0107] As an example, based on the actual structure of the aeration tank (such as total length, number of channels, and hydraulic retention time), it is divided into multiple continuous spatial sub-regions along the wastewater flow process direction. For instance, a 160m long four-channel aeration tank is divided into eight spatial sub-regions, each representing a local area with relatively homogeneous hydraulic and biochemical conditions. Each spatial sub-region is modeled as an idealized completely mixed reactor (CSTR), assuming that its internal water quality parameters are instantaneously uniform. Within each CSTR, a set of biokinetic equations is constructed based on an activated sludge model. This not only covers conventional carbon and nitrogen conversion processes such as organic carbon degradation, ammonia oxidation, nitrification, and denitrification, but also extends the nitrous oxide generation pathway, including key mechanisms such as the production of nitrous oxide by ammonia-oxidizing bacteria (AOB) through hydroxylamine oxidation under low dissolved oxygen conditions, and the incomplete reduction by denitrifying bacteria when nitrite nitrogen accumulates or carbon sources are insufficient. The effluent concentration of the preceding CSTR is used as the influent concentration of the subsequent CSTR, and all completely mixed reactors (CSTRs) are sequentially connected to form a series structure consistent with the actual process flow. The mass balance equations of all CSTRs are integrated into the simulation platform to construct a wastewater treatment process mechanism model that can dynamically simulate the spatiotemporal evolution of dissolved oxygen, nitrogen speciation, and greenhouse gas precursors within the aeration tank. This wastewater treatment process mechanism model provides a structured, clearly defined, and spatially analytical digital foundation for subsequent spatial calibration and zonal optimization control using high-resolution measured data. Through spatial discretization and biodynamic modeling, the complex reaction process in the aeration tank is transformed into a quantifiable series reactor, providing a structured model framework for accurately simulating wastewater treatment and greenhouse gas emissions, supporting subsequent digital twin construction and optimized control.

[0108] In some embodiments, the steps of optimizing the parameters of a wastewater treatment process mechanism model using spatial distribution data of target greenhouse gas concentrations, target greenhouse gas emission fluxes, and spatial distribution data of water quality to obtain a digital twin model include:

[0109] Based on the spatial distribution data of target greenhouse gas concentrations, the target greenhouse gas concentrations at each discrete point are determined, and based on the target greenhouse gas emission fluxes, the target greenhouse gas emissions at each discrete point are determined.

[0110] For each discrete point, the target greenhouse gas emissions at the discrete point are correlated with the target greenhouse gas concentration at the discrete point, and the effective mass transfer coefficient at the discrete point is calculated based on the principles of fluid dynamics and mass transfer.

[0111] The Gaussian process regression algorithm is used to spatially interpolate the effective mass transfer coefficients at each discrete point to generate a continuous effective mass transfer coefficient field covering the aeration tank.

[0112] A continuous target greenhouse gas concentration spatial distribution map is generated based on the target greenhouse gas concentration spatial distribution data. The continuous target greenhouse gas concentration spatial distribution map is then multiplied point by point with the continuous effective mass transfer coefficient field to obtain a target greenhouse gas emission flux map with meter-level resolution.

[0113] By using target greenhouse gas emission flux maps and water quality spatial distribution data, the key parameters in the wastewater treatment process mechanism model are calibrated until the error between the simulated value and the measured value output by the wastewater treatment process mechanism model is less than a preset threshold, thus obtaining a spatially validated digital twin model.

[0114] Specifically, the effective mass transfer coefficient is a parameter characterizing the efficiency of gas transfer from the liquid phase to the gas phase, and it is affected by factors such as wind speed, turbulence, and bubble disturbance. The continuous effective mass transfer coefficient field is a distribution map covering the entire aeration tank, obtained through Gaussian process regression interpolation, reflecting local differences in water-gas exchange capacity. The continuous target greenhouse gas concentration spatial distribution map is a continuous concentration field generated after spatial smoothing or interpolation of the raw concentration data collected by the UAV. The target greenhouse gas emission flux map is a high-resolution full-tank emission distribution map obtained by multiplying the continuous target greenhouse gas concentration spatial distribution map point-by-point with the continuous effective mass transfer coefficient field, which can compensate for the sparseness of the static tank data points.

[0115] As an example, based on the spatial distribution data of target greenhouse gas concentrations, the methane and nitrous oxide concentrations (target greenhouse gas concentrations) at each discrete point are extracted. Simultaneously, based on the measured target greenhouse gas emission flux from a floating static tank, the emission rate per unit area at each discrete point is determined. For each discrete point, the methane and nitrous oxide concentrations (target greenhouse gas concentrations) are converted to dissolved concentrations in water, and the effective mass transfer coefficient at that discrete point is calculated by inversion based on the fundamental principles of fluid dynamics and mass transfer (i.e., emission flux = effective mass transfer coefficient × dissolved concentration). Using the Gaussian Process Regression (GPR) algorithm, with the effective mass transfer coefficients at each discrete point as input samples, spatial interpolation is performed to generate a continuous effective mass transfer coefficient field covering the entire aeration tank. Simultaneously, spatial smoothing and interpolation processing can be performed on the spatial distribution data of target greenhouse gas concentrations to generate a continuous spatial distribution map of target greenhouse gas concentrations. This continuous spatial distribution map is then multiplied point-by-point on a spatial grid with the continuous effective mass transfer coefficient field to obtain a target greenhouse gas emission flux map with meter-level resolution, significantly improving the spatial integrity and accuracy of the emission data. The target greenhouse gas emission flux map and simultaneously acquired water quality spatial distribution data are used as calibration benchmarks, mapped to each discretized fully mixed reactor. Key kinetic parameters (such as yield coefficients and half-saturation constants) affecting carbon and nitrogen conversion and N2O generation in the wastewater treatment process mechanism model are iteratively optimized. By minimizing the comprehensive error between the model simulation values ​​and measured values ​​at all spatial locations until the error is less than a preset threshold (e.g., 15%), a spatially rigorously validated digital twin model is obtained. This digital twin model can simulate the biogeochemical processes and spatiotemporal dynamic distribution of greenhouse gas emissions within the aeration tank with high fidelity, providing a reliable decision-making basis for subsequent zonal optimization control. By integrating gas concentration, emission flux, and water quality data, and constructing a greenhouse gas emission map with meter-level resolution through mass transfer modeling and spatial interpolation, and performing fine spatial dimension calibration on the mechanistic model, the generated digital twin model can not only predict the effluent water quality, but also accurately reproduce the spatiotemporal dynamics of greenhouse gas generation and release inside the aeration tank. This provides a reliable virtual platform for optimized control and solves the problem that traditional models cannot reflect spatial heterogeneity.

[0116] In some embodiments, the steps of calibrating key parameters in a wastewater treatment process mechanism model using target greenhouse gas emission flux maps and water quality spatial distribution data until the error between the simulated and measured values ​​output by the wastewater treatment process mechanism model is less than a preset threshold, and obtaining a spatially validated digital twin model, include:

[0117] The measured values ​​of target greenhouse gas emission fluxes and key water quality parameters for each completely mixed reactor were extracted from the target greenhouse gas emission flux profile and water quality spatial distribution data.

[0118] The target greenhouse gas emission flux map and water quality spatial distribution data are input into the wastewater treatment process mechanism model. The wastewater treatment process mechanism model is used to process the target greenhouse gas emission flux map and water quality spatial distribution data, and output the simulated values ​​of the target greenhouse gas emission flux and key water quality parameters for each completely mixed reactor.

[0119] The simulated values ​​of each fully mixed reactor are compared with the measured values ​​of the corresponding fully mixed reactors, and the average absolute percentage error of each fully mixed reactor is calculated.

[0120] The key parameters in the mechanism model are adjusted based on the mean absolute percentage error until the mean absolute percentage error of each fully mixed reactor is less than the preset threshold, resulting in a spatially validated digital twin model. The key parameters include the yield coefficient and the half-saturation constant.

[0121] Specifically, the preset threshold is the upper limit of acceptable error for engineering, which can be 17.5%, used to determine whether the model passes spatial validation. Mean absolute percentage error (MASE) is an indicator used to measure the deviation between simulated and measured values. The mathematical expression for calculating the MASE of a completely mixed reactor is shown below:

[0122]

[0123] in, The mean absolute percentage error of the i-th completely mixed reactor. This refers to the total number of simulation parameters, which include key water quality parameters and target greenhouse gas emission fluxes. Key water quality parameters can include DO and NH4. + -N, NO2 - -N, NO3 - -N, the target greenhouse gas emission flux can be methane emission flux and nitrous oxide emission flux. The output of the i-th completely mixed reactor is the first Simulated values ​​of each simulation parameter. The output of the i-th completely mixed reactor is the first Measured values ​​of simulated parameters.

[0124] As an example, the aeration tank is discretized into multiple series-connected completely mixed reactors. Measured values ​​corresponding to the location of each completely mixed reactor are extracted from high-resolution target greenhouse gas emission flux maps and simultaneously acquired spatial distribution data of water quality. These values ​​include measured values ​​of nitrous oxide and methane emission fluxes, as well as measured values ​​of key water quality parameters such as dissolved oxygen, ammonium nitrogen, nitrite nitrogen, and nitrate nitrogen. The influent water quality, hydraulic load, and key parameters of the current model can be used as inputs to drive the wastewater treatment process mechanism model, simulating the target greenhouse gas emission fluxes and key water quality parameters output by each completely mixed reactor. The simulated values ​​of each completely mixed reactor are compared item by item with their corresponding measured values ​​to calculate the mean absolute percentage error (MASE). The MASE comprehensively reflects the simulation deviation between water quality and gas emissions. If the mean absolute percentage error (MAE) of any given reactor exceeds a preset threshold (e.g., 17.5%), key kinetic parameters that significantly contribute to the error can be identified, such as the yield coefficient and half-saturation constant affecting the N2O generation pathway. A parameter optimization algorithm is then used to iteratively adjust these key parameters. This simulation-comparison-parameter tuning process is repeated until the MAE of all completely mixed reactors is less than the preset threshold, indicating that the model closely matches the measured data in all spatial regions. The optimized parameter set is then frozen, forming a digital twin model rigorously validated in the spatial dimension. This digital twin model can not only accurately predict effluent quality but also realistically reproduce the local dynamic processes of greenhouse gas generation and release within the aeration tank. It provides a high-fidelity, reliable simulation basis for subsequent zoned dissolved oxygen optimization control based on multi-objective evolutionary algorithms, thereby enabling wastewater treatment plants to synergistically reduce greenhouse gas emissions and energy consumption while ensuring effluent compliance. By comparing simulated values ​​with measured data in different regions, the key parameters of the mechanism model are iteratively optimized, and a spatially validated digital twin model is constructed. This ensures that the digital twin model can accurately reproduce the greenhouse gas emissions and water quality dynamics of each region, providing a highly reliable virtual tool for optimized control and solving the problem that traditional models are difficult to adapt to spatial heterogeneity.

[0125] Furthermore, correlation analysis can be performed using spatially validated digital twin models and measured data. For example, the analysis results show that N2O emission flux is correlated with local nitrite nitrogen (NO2) emissions. - The concentrations showed a very strong positive correlation, with a Pearson correlation coefficient of 0.92, which can indicate key regulatory targets for subsequent optimization control.

[0126] In some embodiments, after obtaining the target greenhouse gas emission flux map with meter-level resolution, the method further includes:

[0127] Along the process flow direction, multiple virtual sampling points are virtually and uniformly extracted from the target greenhouse gas emission flux map with different spatial densities.

[0128] Based on each combination of virtual sampling points, an interpolation method is used to estimate the target total greenhouse gas emissions of the aeration tank.

[0129] Based on the target greenhouse gas emission flux map, calculate the true total integral flux of the target greenhouse gas corresponding to the target greenhouse gas emission flux map;

[0130] The relative error is calculated by comparing the estimated total target greenhouse gas emissions with the actual total integrated flux of the target greenhouse gases.

[0131] Based on the relationship between relative error and sampling density, the minimum sampling density that makes the relative error lower than the preset error threshold is determined.

[0132] As an example, multiple sets of sampling points are virtually and uniformly extracted on the target greenhouse gas emission flux map at different spatial intervals (e.g., every 5 m, 10 m, 15 m, 20 m, etc.) along the process flow direction of the aeration tank (e.g., multi-corridor water flow path). Each set of sampling points represents an actual monitoring point layout scheme. For each set of virtual sampling points, a spatial interpolation method (e.g., inverse distance weighting or Kriging interpolation) can be used to reconstruct the emission flux field of the entire tank. By integrating the reconstructed field by area, the corresponding estimated value of the total target greenhouse gas emissions is estimated. Simultaneously, based on the target greenhouse gas emission flux map, a full-area numerical integration is directly performed to calculate a high-precision true total integrated flux of the target greenhouse gas as the baseline. The estimated value of the total target greenhouse gas emissions at each sampling density is compared with the true total integrated flux of the target greenhouse gas, the relative error is calculated, and the trend curve of the relative error changing with the sampling density is analyzed to determine the minimum sampling density that can keep the relative error below a preset error threshold (e.g., 5% or 10%). By virtually sampling, interpolating, estimating, and analyzing errors on high-resolution target greenhouse gas emission flux maps, the relationship between sampling density and the accuracy of total emission estimation is quantified, thus providing a theoretical basis for actual monitoring site selection. Through the aforementioned numerical secondary sampling method, the impact of different sampling densities on the total emission estimation error can be quantitatively assessed based on high-resolution maps, providing a direct basis for developing scientific, economical, and reliable monitoring schemes, and avoiding resource waste and data distortion caused by insufficient or excessive sampling.

[0133] In some embodiments, the steps of generating a Pareto optimal control strategy based on a digital twin model, using the dissolved oxygen setpoints of each completely mixed reactor zone in the aeration tank as decision variables, and employing a multi-objective evolutionary algorithm to solve the problem include:

[0134] The target total greenhouse gas emissions and total aeration energy consumption are used as joint optimization objectives, and the total nitrogen concentration, ammonium nitrogen concentration and total phosphorus concentration in the effluent are all lower than the corresponding preset emission standards as constraints. A multi-objective joint optimization model is constructed, in which the joint optimization objective is to minimize the target total greenhouse gas emissions while minimizing the total aeration energy consumption.

[0135] Based on the aeration control baseline strategy, multiple candidate aeration control strategies are constructed within the search range. The candidate aeration control strategies include candidate dissolved oxygen setpoints for each completely mixed reactor zone.

[0136] The candidate aeration control strategies are evaluated using a multi-objective joint optimization model to obtain the evaluation results of each candidate aeration control strategy. Based on the evaluation results, a multi-objective evolutionary algorithm is used to process the results and generate the optimal solution set on the Pareto front.

[0137] Based on the joint optimization objective, the Pareto optimal control strategy is determined from the set of optimal solutions on the Pareto front.

[0138] Specifically, the target total greenhouse gas emissions are the total emissions of nitrous oxide (N2O).

[0139] As an example, a multi-objective joint optimization model is constructed, taking the total greenhouse gas (N2O) emissions and the total aeration energy consumption as two optimization objectives that need to be minimized simultaneously, and taking the total nitrogen (TN) and ammonium nitrogen (NH4) in the effluent as the optimization objectives. + The concentrations of NH4+ and total phosphorus (TP) are both below national or local emission standards, such as TN ≤ 15 mg / L and NH4+ ≤ 15 mg / L. +-N ≤ 5 mg / L and TP ≤ 0.5 mg / L are used as hard constraints. Using the current operating aeration control baseline strategy (e.g., uniform DO = 2.0 mg / L for the entire tank) as a reference, multiple candidate aeration control strategies are randomly generated within a reasonable dissolved oxygen setting range (e.g., 0.5–3.0 mg / L) for each completely mixed reactor zone. Each initial candidate aeration control strategy consists of candidate dissolved oxygen setting values ​​corresponding to each completely mixed reactor. Each candidate aeration control strategy is input into a spatially validated digital twin model for dynamic or steady-state simulation. The digital twin model automatically calculates the target total greenhouse gas (N2O) emissions, total aeration energy consumption, and effluent quality under that strategy, generating an evaluation result that includes the target values ​​and constraint satisfaction status. Building upon this foundation, multi-objective evolutionary algorithms (such as NSGA-II) can be employed to iteratively optimize the candidate strategy population across multiple generations. Through operations such as non-dominated sorting, crowding calculation, selection, crossover, and mutation, better DO setting combinations are continuously screened and evolved, ultimately converging to generate an optimal solution set located on the Pareto front. No strategy in the optimal solution set on the Pareto front can improve one objective without worsening another. Finally, based on actual operational needs (such as carbon trading costs, electricity price fluctuations, or emission reduction priorities), the scheme that best meets the operational objectives can be selected from the optimal solution set on the Pareto front, determining it as the final Pareto optimal control strategy. This strategy includes the specific optimal dissolved oxygen setting values ​​for each CSTR partition. The Pareto optimal control strategy can be directly deployed to the distributed intelligent aeration control system for execution, achieving synergistic minimization of greenhouse gas emissions and energy consumption while ensuring effluent quality meets standards, significantly improving the green and low-carbon operation level of the wastewater treatment plant. For example, the Pareto optimal control strategy can simultaneously reduce N2O emissions by approximately 12% and save aeration energy consumption by approximately 9%. The optimized DO profile exhibits spatial heterogeneity. In areas with the highest nitrification rates, the DO setpoint is significantly increased to promote complete ammonia oxidation and inhibit the accumulation of nitrite, a major precursor to N2O. Conversely, in areas with lower oxygen demand, aeration is appropriately reduced to avoid energy waste. This precise regulation significantly suppresses the N2O production pathway driven by ammonia-oxidizing bacteria (AOB). Although N2O production via heterotrophic denitrification increases slightly, overall net N2O emissions across the entire treatment plant are reduced. Using a high-fidelity digital twin model as a simulation engine, combined with a multi-objective optimization algorithm, a set of unmanageable Pareto optimal control strategies is generated to synergistically minimize greenhouse gas emissions and aeration energy consumption while meeting the hard constraints of effluent quality. This addresses the performance imbalance caused by single-objective optimization in traditional control methods, providing a scientifically controllable operating scheme for wastewater treatment plants.

[0140] This invention provides a complete technical framework from high-resolution measurement to model-driven optimization, providing operable and auditable technical support for wastewater treatment plants to carry out higher-level (Tier 2 / 3) greenhouse gas inventory compilation, reporting and verification (MRV), which helps to promote the development of the entire industry towards a more refined and low-carbon direction.

[0141] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the greenhouse gas optimization control method for wastewater treatment plants in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0142] This application also provides a greenhouse gas optimization and control device for wastewater treatment plants; please refer to [reference needed]. Figure 2 The greenhouse gas optimization and control device for wastewater treatment plants includes:

[0143] The monitoring and acquisition module 201 is used to collect spatial distribution data of target greenhouse gas concentration on the surface of the aeration tank by a drone carrying a first gas analyzer, collect target greenhouse gas emission flux in the aeration tank by a floating static box carrying a second gas analyzer, and determine the spatial distribution data of water quality in the aeration tank.

[0144] Mechanism model construction module 202 is used to discretize the wastewater treatment process in the aeration tank into multiple series of completely mixed reactors, and construct a wastewater treatment process mechanism model based on the activated sludge model.

[0145] The digital twin generation module 203 is used to optimize the parameters of the wastewater treatment process mechanism model by using the spatial distribution data of the target greenhouse gas concentration, the target greenhouse gas emission flux and the spatial distribution data of water quality, so as to obtain a digital twin model. The digital twin model is used to simulate the biogeochemical processes and spatiotemporal dynamic distribution of wastewater treatment in the aeration tank.

[0146] The multi-objective optimization control module 204 is used to generate a Pareto optimal control strategy based on a digital twin model, using the dissolved oxygen setpoints of each completely mixed reactor zone in the aeration tank as decision variables and employing a multi-objective evolutionary algorithm. The Pareto optimal control strategy includes the optimal dissolved oxygen setpoints of each completely mixed reactor zone.

[0147] The wastewater treatment plant greenhouse gas optimization control device provided in this application, employing the wastewater treatment plant greenhouse gas optimization control method in the above embodiments, can solve the technical problem that existing technologies cannot effectively characterize the spatiotemporal heterogeneity of greenhouse gas emissions within the aeration tank, leading to inaccurate greenhouse gas optimization control strategies. Compared with the prior art, the beneficial effects of the wastewater treatment plant greenhouse gas optimization control device provided in this application are the same as those of the wastewater treatment plant greenhouse gas optimization control method provided in the above embodiments, and other technical features in the wastewater treatment plant greenhouse gas optimization control device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0148] This application provides a greenhouse gas optimization control device for a wastewater treatment plant. The wastewater treatment plant greenhouse gas optimization control device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the wastewater treatment plant greenhouse gas optimization control method in Embodiment 1 above.

[0149] The following is for reference. Figure 3 The diagram illustrates a structural schematic suitable for implementing a greenhouse gas optimization control device for a wastewater treatment plant according to embodiments of this application. The greenhouse gas optimization control device for a wastewater treatment plant in these embodiments may include, but is not limited to, mobile terminals such as laptops and tablets (Portable Application Description, PADs), and fixed terminals such as digital TVs and desktop computers. Figure 3 The greenhouse gas optimization and control equipment for wastewater treatment plants shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments in this application.

[0150] like Figure 3As shown, the greenhouse gas optimization control equipment for a wastewater treatment plant may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the wastewater treatment plant greenhouse gas optimization control equipment. The processing unit 1001, the ROM 1002, and the RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the wastewater treatment plant greenhouse gas optimization control equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the wastewater treatment plant greenhouse gas optimization control equipment with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0151] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0152] The wastewater treatment plant greenhouse gas optimization control equipment provided in this application, employing the wastewater treatment plant greenhouse gas optimization control method described in the above embodiments, can solve the technical problem that existing technologies cannot effectively characterize the spatiotemporal heterogeneity of greenhouse gas emissions within the aeration tank, leading to inaccurate greenhouse gas optimization control strategies. Compared with the prior art, the beneficial effects of the wastewater treatment plant greenhouse gas optimization control equipment provided in this application are the same as those of the wastewater treatment plant greenhouse gas optimization control method provided in the above embodiments, and other technical features of this wastewater treatment plant greenhouse gas optimization control equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0153] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0154] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0155] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the greenhouse gas optimization control method for wastewater treatment plants in the above embodiments.

[0156] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0157] The aforementioned computer-readable storage medium may be included in the greenhouse gas optimization and control equipment of the wastewater treatment plant; or it may exist independently and not be assembled into the greenhouse gas optimization and control equipment of the wastewater treatment plant.

[0158] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the greenhouse gas optimization control equipment of the wastewater treatment plant, cause the wastewater treatment plant greenhouse gas optimization control equipment to: collect spatial distribution data of target greenhouse gas concentration on the surface of the aeration tank using a drone carrying a first gas analyzer; collect the target greenhouse gas emission flux of the aeration tank using a floating static box carrying a second gas analyzer; and determine the spatial distribution data of water quality in the aeration tank; discretize the wastewater treatment process in the aeration tank into multiple series-connected completely mixed reactors, and construct a wastewater treatment process based on an activated sludge model. A water treatment process mechanism model was developed. The parameters of the wastewater treatment process mechanism model were optimized using spatial distribution data of target greenhouse gas concentrations, target greenhouse gas emission fluxes, and spatial distribution data of water quality to obtain a digital twin model. This digital twin model was used to simulate the biogeochemical processes and spatiotemporal dynamics of wastewater treatment in the aeration tank. Based on the digital twin model, the dissolved oxygen setpoints of each completely mixed reactor zone in the aeration tank were used as decision variables. A multi-objective evolutionary algorithm was employed to solve the problem and generate a Pareto optimal control strategy. The Pareto optimal control strategy included the optimal dissolved oxygen setpoints for each completely mixed reactor zone.

[0159] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0160] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0161] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0162] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described greenhouse gas optimization control method for wastewater treatment plants. This solves the technical problem that existing technologies cannot effectively characterize the spatiotemporal heterogeneity of greenhouse gas emissions within aeration tanks, leading to inaccurate greenhouse gas optimization control strategies. Compared to existing technologies, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the greenhouse gas optimization control method for wastewater treatment plants provided in the above embodiments, and will not be elaborated upon here.

[0163] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for optimizing greenhouse gas control in a wastewater treatment plant.

[0164] The computer program product provided in this application can solve the technical problem that the prior art cannot effectively characterize the spatiotemporal heterogeneity of greenhouse gas emissions in aeration tanks, thus leading to inaccurate optimal control strategies for greenhouse gases. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the greenhouse gas optimal control method for wastewater treatment plants provided in the above embodiments, and will not be repeated here.

[0165] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for optimizing greenhouse gas control in a wastewater treatment plant, characterized in that, The optimized control method for greenhouse gases in wastewater treatment plants includes: The spatial distribution data of the target greenhouse gas concentration on the surface of the aeration tank is collected by a drone carrying a first gas analyzer, and the target greenhouse gas emission flux of the aeration tank is collected by a floating static box carrying a second gas analyzer, and the spatial distribution data of the water quality of the aeration tank is determined. The aeration tank is divided into multiple spatial sub-regions along the process flow direction; and each spatial sub-region is modeled as a fully mixed reactor. Based on the activated sludge model, a set of bioreaction kinetic equations including carbon and nitrogen conversion and greenhouse gas generation pathways were established in each of the completely mixed reactors. The effluent from the previous fully mixed reactor is used as the influent to the next fully mixed reactor, so that the discrete fully mixed reactors are connected in series in the direction of the process flow of the aeration tank to form a wastewater treatment process mechanism model that simulates the wastewater treatment process of the aeration tank. Based on the spatial distribution data of the target greenhouse gas concentration, the target greenhouse gas concentration at each discrete point is determined, and based on the target greenhouse gas emission flux, the target greenhouse gas emission at each discrete point is determined. For each discrete point, the target greenhouse gas emissions at the discrete point are correlated with the target greenhouse gas concentration at the discrete point, and the effective mass transfer coefficient at the discrete point is calculated based on the principles of fluid dynamics and mass transfer. A Gaussian process regression algorithm is used to spatially interpolate the effective mass transfer coefficients at each discrete point to generate a continuous effective mass transfer coefficient field covering the aeration tank. Based on the spatial distribution data of the target greenhouse gas concentration, a continuous spatial distribution map of the target greenhouse gas concentration is generated, and the continuous spatial distribution map of the target greenhouse gas concentration is multiplied point by point with the continuous effective mass transfer coefficient field to obtain a target greenhouse gas emission flux map with meter-level resolution. Using the target greenhouse gas emission flux map and the water quality spatial distribution data, the key parameters in the wastewater treatment process mechanism model are calibrated until the error between the simulated value and the measured value output by the wastewater treatment process mechanism model is less than a preset threshold, thus obtaining a spatially validated digital twin model. The digital twin model is used to simulate the biogeochemical processes and spatiotemporal dynamic distribution of wastewater treatment in the aeration tank. Based on the digital twin model, the dissolved oxygen setpoint of each completely mixed reactor zone in the aeration tank is used as the decision variable, and a multi-objective evolutionary algorithm is used to solve the problem to generate a Pareto optimal control strategy. The Pareto optimal control strategy includes the optimal dissolved oxygen setpoint of each of the completely mixed reactor zones.

2. The method for optimizing greenhouse gas control in wastewater treatment plants as described in claim 1, characterized in that, The steps of collecting spatial distribution data of target greenhouse gas concentration on the surface of the aeration tank using a drone carrying a first gas analyzer, and collecting the target greenhouse gas emission flux of the aeration tank using a floating static box carrying a second gas analyzer, include: The flight shooting parameters of the UAV are obtained, including the flight path, flight speed and flight altitude on the water surface of the aeration tank; According to the flight shooting parameters, the UAV carrying the first gas analyzer is controlled to fly over the aeration tank at the flight speed along the flight path to obtain spatial distribution data of methane concentration and spatial distribution data of nitrous oxide concentration on the water surface of the aeration tank. A floating static tank equipped with a second gas analyzer was used to measure methane emission flux and nitrous oxide emission flux at multiple discrete points on the surface of the aeration tank. The spatial distribution data of methane concentration and the spatial distribution data of nitrous oxide concentration are determined as the spatial distribution data of the target greenhouse gas concentration, and the methane emission flux and the nitrous oxide emission flux are determined as the target greenhouse gas emission flux.

3. The method for optimizing greenhouse gas control in wastewater treatment plants as described in claim 2, characterized in that, The step of determining the spatial distribution data of the water quality in the aeration tank includes: Water samples were collected from multiple discrete points along the process flow direction of the aeration tank. Water quality analysis was performed on the water samples from each discrete point to obtain key water quality parameters for each discrete point. The key water quality parameters include dissolved oxygen concentration, ammonium nitrogen concentration, nitrite nitrogen concentration and nitrate nitrogen concentration. The coordinates of each discrete point are obtained, and the key water quality parameters of each discrete point are associated with the corresponding coordinates to generate the spatial distribution data of the water quality of the aeration tank.

4. The method for optimizing greenhouse gas control in wastewater treatment plants as described in claim 1, characterized in that, The step of calibrating key parameters in the wastewater treatment process mechanism model using the target greenhouse gas emission flux map and the water quality spatial distribution data until the error between the simulated and measured values ​​output by the wastewater treatment process mechanism model is less than a preset threshold, thereby obtaining the spatially validated digital twin model, includes: Extract the measured values ​​of target greenhouse gas emission fluxes and key water quality parameters for each of the completely mixed reactors from the target greenhouse gas emission flux map and the water quality spatial distribution data. The target greenhouse gas emission flux map and the water quality spatial distribution data are input into the wastewater treatment process mechanism model. The wastewater treatment process mechanism model is used to process the target greenhouse gas emission flux map and the water quality spatial distribution data, and outputs the simulated values ​​of the target greenhouse gas emission flux and key water quality parameters for each of the completely mixed reactors. The simulated values ​​of each of the fully mixed reactors are compared with the measured values ​​of the corresponding fully mixed reactors, and the average absolute percentage error of each of the fully mixed reactors is calculated. The key parameters in the mechanistic model are adjusted according to the mean absolute percentage error until the mean absolute percentage error of each of the fully mixed reactors is less than the preset threshold, thereby obtaining the spatially validated digital twin model. The key parameters include the yield coefficient and the half-saturation constant.

5. The method for optimizing greenhouse gas control in wastewater treatment plants as described in claim 1, characterized in that, Following the step of obtaining the target greenhouse gas emission flux map with meter-level resolution, the method further includes: Along the process flow direction, multiple virtual sampling points are virtually and uniformly extracted from the target greenhouse gas emission flux map with different spatial densities. Based on each combination of virtual sampling points, an interpolation method is used to estimate the target total greenhouse gas emissions of the aeration tank. Based on the target greenhouse gas emission flux map, calculate the true total integral flux of the target greenhouse gas corresponding to the target greenhouse gas emission flux map; The relative error is calculated by comparing the estimated total target greenhouse gas emissions with the actual total integrated flux of the target greenhouse gas. Based on the relationship between the relative error and the sampling density, the minimum sampling density that makes the relative error lower than a preset error threshold is determined.

6. The method for optimizing greenhouse gas control in wastewater treatment plants as described in claim 1, characterized in that, The step of generating a Pareto optimal control strategy based on the digital twin model, using the dissolved oxygen setpoints of each completely mixed reactor zone in the aeration tank as decision variables, and employing a multi-objective evolutionary algorithm to solve the problem includes: The target total greenhouse gas emissions and total aeration energy consumption are used as joint optimization objectives, and the total nitrogen concentration, ammonium nitrogen concentration and total phosphorus concentration in the effluent are all lower than the corresponding preset emission standards as constraints. A multi-objective joint optimization model is constructed, wherein the joint optimization objective is to minimize the target total greenhouse gas emissions while minimizing the total aeration energy consumption. Based on the aeration control baseline strategy, multiple candidate aeration control strategies are constructed within the search range, and the candidate aeration control strategies include candidate dissolved oxygen setpoints for each of the fully mixed reactor partitions. The candidate aeration control strategies are evaluated using the multi-objective joint optimization model to obtain evaluation results for each candidate aeration control strategy. Based on the evaluation results, a multi-objective evolutionary algorithm is used to process the results and generate the optimal solution set on the Pareto front. Based on the joint optimization objective, a Pareto optimal control strategy is determined from the optimal solution set on the Pareto front.

7. A greenhouse gas optimization and control device for a wastewater treatment plant, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the wastewater treatment plant greenhouse gas optimization control method as described in any one of claims 1 to 6.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the greenhouse gas optimization control method for wastewater treatment plants as described in any one of claims 1 to 6.

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