Intelligent prediction and process optimization system and method for carbon emission of sewage treatment plant

Abstract

The invention discloses a sewage treatment plant carbon emission intelligent prediction and process optimization system and method. The system comprises a data acquisition module which is responsible for acquiring inflow water quality, process operation, equipment energy consumption and environmental data; the BP neural network module is used for establishing nonlinear mapping between key parameters and carbon emission and outputting a carbon emission predicted value and an effluent quality standard reaching probability; the process parameter intelligent optimization module takes a first-level A standard as a constraint and carbon emission minimization as a target, and searches for optimal process parameters by adopting a particle swarm algorithm and a BP network; the adaptive emission factor correction module dynamically corrects the emission factor library based on the measured data. The carbon emission accounting module is used for accounting direct, indirect and negative carbon emissions in combination with the correction factors; the visual analysis module is used for generating a carbon emission analysis report to realize full-process intelligent management; according to the invention, the BP neural network and the particle swarm optimization algorithm are fused, so that intelligent optimization of sewage treatment process parameters and accurate accounting of carbon emission are realized.

Description

Technical Field

[0001] This invention relates to the fields of wastewater treatment and carbon emission management, and more specifically to a system and method for intelligent prediction and process optimization of carbon emissions from wastewater treatment plants. Background Technology

[0002] Currently, wastewater treatment plants face a series of pressing issues in carbon emission management. First, traditional carbon emission accounting methods rely primarily on post-hoc calculations. This model makes it impossible to accurately predict carbon emission trends in advance, hindering the provision of forward-looking guidance for operational control and making it difficult for wastewater treatment plants to adjust their operational strategies promptly based on carbon emission data. Second, most existing accounting methods use fixed emission factors. However, different wastewater treatment plants exhibit significant individual differences in process type, geographical location, and operating conditions. Fixed emission factors fail to take these factors into account, leading to large deviations in the accounting results. Related literature reports errors reaching 20-30%, severely impacting the accuracy and scientific rigor of carbon emission management. Third, regarding process parameter optimization, a systematic approach is currently lacking. Operators can only adjust parameters based on their experience. While ensuring effluent quality meets standards, minimizing carbon emissions is difficult, hindering the balance between energy conservation, emission reduction, and efficient operation in wastewater treatment plants. Fourth, the presentation of carbon emission data is relatively simple and lacks systematic analysis and reporting tools. This makes it difficult for managers to quickly identify high-carbon emission links from massive amounts of data and to formulate targeted emission reduction measures, thus hindering the effective advancement of carbon emission management. Summary of the Invention

[0003] The purpose of this invention is to provide a smart prediction and process optimization system and method for carbon emissions from wastewater treatment plants. By integrating BP neural networks and particle swarm optimization algorithms, it achieves intelligent optimization of wastewater treatment process parameters and accurate calculation of carbon emissions.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A smart carbon emission prediction and process optimization system for wastewater treatment plants includes: The data acquisition module is used to collect production and operation data of the wastewater treatment plant; the production and operation data includes: influent water quality data, process operating parameters, equipment energy consumption data, and environmental parameters; The BP neural network prediction module is used to establish a nonlinear mapping relationship between influent water quality, process operating parameters and carbon emissions using a BP neural network, and output the predicted carbon emissions of the wastewater treatment plant and the probability of effluent water quality meeting standards. The intelligent optimization module for process parameters is used to introduce the particle swarm optimization algorithm. The BP neural network is used as the fitness function of the particle swarm algorithm. With the constraint that the effluent quality meets the Class A standard, and with the goal of minimizing carbon emissions, the optimal combination of process parameters is searched. The adaptive emission factor correction module is used to establish an emission factor correction model using a BP neural network and dynamically correct the emission factor of the emission factor correction model based on measured carbon emission data. The carbon emission accounting module, combined with corrected emission factors, calculates the carbon emissions of wastewater treatment plants using emission accounting methods. These methods include: emission factor method, material balance method, and measurement method. Carbon emissions include: direct carbon emissions, indirect carbon emissions, and negative carbon emissions. The visualization analysis module is used to generate carbon emission analysis reports from the calculated direct carbon emissions, indirect carbon emissions, and negative carbon emissions of wastewater treatment plants.

[0005] Furthermore, the influent water quality data includes: COD, BOD5, TN, TP, SS, and ammonia nitrogen, collected once per hour; The process operating parameters include: dissolved oxygen concentration (DO) in the aeration tank, sludge return ratio, mixed liquor sludge concentration (MLSS), carbon source dosage, and hydraulic retention time (HRT), collected once per hour. The equipment energy consumption data includes: blower power and booster pump operating status, and the data is collected in real time. The environmental parameters include water temperature and season, and are collected once a day.

[0006] Furthermore, the BP neural network prediction module includes: an input layer, a hidden layer, and an output layer; The input layer includes 14 neurons; the 14 neurons correspond to influent COD, BOD5, TN, TP, SS, ammonia nitrogen, dissolved oxygen concentration (DO) in the aeration tank, sludge return ratio, mixed liquor sludge concentration (MLSS), carbon source dosage, hydraulic retention time (HRT), water temperature, seasonal coding, and treatment load rate. The hidden layer includes: a first hidden layer and a second hidden layer; the first hidden layer includes 28 neurons and the activation function is the Sigmoid function; the second hidden layer includes 18 neurons and the activation function is the Tanh function. The output layer includes 6 neurons, which output predicted values ​​for CH4 emissions, N2O emissions, CO2 emissions, carbon emissions due to electricity consumption, carbon emissions due to chemical use, and the overall probability of effluent quality meeting standards.

[0007] Furthermore, the process of using a BP neural network as the fitness function of the particle swarm optimization algorithm includes: S1. Randomly initialize the position, velocity, and algorithm parameters of the particle swarm; S2. Evaluate the fitness of each particle's position using a BP neural network, and update the individual and global optimum of the particles; S3. Update the velocity and position of particles based on individual and global optimal information of particles, and perform boundary constraint processing; S4. Determine if the termination condition is met. If it is, output the optimal solution; otherwise, return to S2 to continue evaluating the fitness of each particle position.

[0008] Furthermore, the adaptive emission factor correction module includes an input layer, a hidden layer, and an output layer; The input layer includes 10 neurons, corresponding to the scale of the wastewater treatment plant, process type code, geographical location latitude, water temperature, influent COD concentration, MLSS, DO concentration, HRT, seasonal code, and the average monthly carbon emission measured over the past 3 months. The hidden layer comprises: a first layer of 15 neurons and a second layer of 8 neurons; The output layer includes 5 neurons, which respectively output the CH4 emission factor correction coefficient, N2O emission factor correction coefficient, CO2 emission factor correction coefficient, electricity emission factor correction coefficient, and pharmaceutical emission factor correction coefficient.

[0009] Furthermore, in the carbon emission accounting module: Direct carbon emissions accounting includes: CH4 emissions, N2O emissions, and CO2 emissions; The CH4 emission calculation formula is as follows: ; Where COD removal capacity = (influent COD - effluent COD) × treated water volume / 1000, The CH4 emission factor is corrected using the emission factor correction module. The global warming potential of CH4; The formula for calculating N2O emissions is: ; Wherein, TN removal capacity = (influent TN - effluent TN) × treated water volume / 1000, The corrected N2O emission factor is used. The potential for global warming from N2O; CO2 emissions are calculated using the material balance method, with the following formula: ; in This refers to the amount of organic matter added as raw material, i.e., the influent. The carbon content of the raw materials. This refers to the product output, specifically the amount of water produced. For the carbon content of the product, This refers to the amount of waste, specifically sludge, produced. The carbon content of the waste is 44 / 12, which is the molar mass ratio of carbon to CO2.

[0010] Furthermore, the system also includes: a data layer, an application layer, and a presentation layer; The data layer uses a MySQL database to store data; The application layer deploys a BP neural network, a particle swarm optimization algorithm, and a report generation module. The presentation layer uses HTML5 and JavaScript to implement a responsive web interface, supporting connection with SCADA systems via OPC protocol or with online monitoring instruments via Modbus TCP protocol.

[0011] This invention also provides a method for intelligent prediction and process optimization of carbon emissions in wastewater treatment plants, comprising the following steps: S1. Collect production and operation data of the wastewater treatment plant; the production and operation data includes: influent water quality data, process operating parameters, equipment energy consumption data and environmental parameters; S2. Use a BP neural network to establish a nonlinear mapping relationship between influent water quality, process operating parameters and carbon emissions, and output the predicted carbon emissions of the wastewater treatment plant and the probability of effluent water quality meeting standards. S3. Introduce the particle swarm optimization algorithm, using the BP neural network as the fitness function of the particle swarm algorithm, with the effluent quality meeting the Class A standard as a constraint and the goal of minimizing carbon emissions, to search for the optimal combination of process parameters. S4. Establish an emission factor correction model using a BP neural network, and dynamically correct the emission factor of the emission factor correction model based on measured carbon emission data. S5. Based on the corrected emission factors, calculate the carbon emissions of the wastewater treatment plant using emission accounting methods. These methods include: emission factor method, material balance method, and measurement method. Carbon emissions include: direct carbon emissions, indirect carbon emissions, and negative carbon emissions. S6. Generate a carbon emission analysis report based on the calculated direct carbon emissions, indirect carbon emissions, and negative carbon emissions from the wastewater treatment plant.

[0012] Furthermore, the BP neural network training adopts the Levenberg-Marquardt algorithm, with an initial learning rate of 0.01 that is adaptively adjusted, a maximum training epoch of 1000 epochs, and a target mean square error of less than 0.001.

[0013] Furthermore, in S3, the fitness function of the particle swarm optimization algorithm is specifically: if the probability P of the effluent water quality meeting the standard is ≥ 0.95, the effluent is considered to meet the standard, and the fitness function is set accordingly. The iteration terminates when the global optimal fitness remains unchanged for 20 consecutive generations, and the optimal combination of process parameters is output.

[0014] According to specific embodiments provided by the present invention, the present invention has the following technical effects compared to the prior art: This invention provides comprehensive data support to the system through a data acquisition module; a BP neural network prediction module accurately predicts carbon emissions and the probability of effluent water quality compliance by establishing a nonlinear mapping, laying the foundation for optimized decision-making; a process parameter intelligent optimization module innovatively combines particle swarm optimization and BP neural networks, using water quality compliance as a constraint and minimizing carbon emissions as the objective, intelligently optimizing process parameter combinations to achieve the dual goals of carbon reduction and compliance; an adaptive emission factor correction module dynamically updates the emission factor library, significantly improving calculation accuracy; a carbon emission accounting module integrates multiple methods to ensure the comprehensiveness and scientific nature of the calculation results; and finally, a visualization analysis module transforms complex data into intuitive reports, providing managers with a scientific basis for decision-making. This entire technology forms a closed-loop management system of "data-driven - intelligent prediction - dynamic optimization - accurate accounting - visual decision-making," effectively improving the refined management level and energy-saving and carbon-reduction capabilities of wastewater treatment plants, and has significant practical value for promoting the green and sustainable development of the industry. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] The present invention provides a smart prediction and process optimization system and method for carbon emissions in wastewater treatment plants, with reference to the accompanying drawings. Figure 1 This is the overall architecture diagram of the intelligent carbon emission prediction and process optimization system for wastewater treatment plants in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the BP neural network prediction module in Embodiment 1 of the present invention; Figure 3 This is a flowchart of the particle swarm optimization algorithm in Embodiment 1 of the present invention; Figure 4 This is a flowchart of the emission factor correction process in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of the functional areas of the carbon emission analysis report interface in Embodiment 1 of the present invention; Figure 6 This is a flowchart of the intelligent prediction and process optimization method for carbon emissions from wastewater treatment plants in Embodiment 2 of the present invention. Detailed Implementation

[0017] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0018] To better understand the purpose, structure, and function of this invention, the invention will be described in further detail below with reference to the accompanying drawings.

[0019] like Figure 1 As shown, this invention provides an intelligent prediction and process optimization system for carbon emissions from wastewater treatment plants, characterized by comprising: The data acquisition module is used to collect production and operation data of the wastewater treatment plant; the production and operation data includes: influent water quality data, process operating parameters, equipment energy consumption data, and environmental parameters; In this embodiment, the data acquisition module collects data from the SCADA system, online monitoring instruments, and energy consumption metering devices of the wastewater treatment plant. Data types include: Influent water quality data, including COD, BOD5, TN, TP, SS, and ammonia nitrogen, are collected once per hour.

[0020] Process parameters, including dissolved oxygen concentration (DO) in the aeration tank, sludge return ratio, mixed liquor sludge concentration (MLSS), carbon source dosage, and hydraulic retention time (HRT), are collected once per hour.

[0021] Equipment energy consumption data, including blower power and booster pump operating status, are collected in real time.

[0022] Environmental parameters, including water temperature and season, are collected once daily.

[0023] The data is stored in the database after preprocessing, which includes removing outliers and imputing missing values. Outliers are identified and removed using the 3σ principle. Missing values ​​are imputed using linear interpolation or mean imputation.

[0024] The BP neural network prediction module is used to establish a nonlinear mapping relationship between influent water quality, process operating parameters and carbon emissions using a BP neural network, and output the predicted carbon emissions of the wastewater treatment plant and the probability of effluent water quality meeting standards. In this embodiment, the BP neural network prediction module adopts a three-layer structure, such as... Figure 2 As shown, it includes an input layer, a hidden layer, and an output layer.

[0025] The input layer contains 14 neurons, each corresponding to one of the 14 input parameters. Neurons x1 to x6 correspond to the influent water quality parameters, including influent COD, influent BOD5, influent TN, influent TP, influent SS, and influent ammonia nitrogen, with units of mg / L. Neurons x7 to x... 11 Each neuron corresponds to a process parameter, including dissolved oxygen concentration (DO) in the aeration tank, sludge return ratio, mixed liquor sludge concentration (MLSS), carbon source dosage, and hydraulic retention time (HRT), in units of mg / L, %, mg / L, kg / d, and h, respectively. 12 To x 14 The neurons correspond to environmental parameters, including water temperature, seasonal coding, and processing load rate. The seasonal coding for spring, summer, autumn, and winter are 1, 2, 3, and 4, respectively, and the processing load rate is in units of _____.

[0026] The hidden layers consist of a first hidden layer and a second hidden layer. The first hidden layer contains 28 neurons, and the activation function is the sigmoid function, as shown in the formula: The second hidden layer contains 18 neurons, and the activation function used is the Tanh function, as shown in the formula: .

[0027] The output layer contains six neurons, which output predicted values ​​for CH4 emissions, N2O emissions, CO2 emissions, carbon emissions from electricity consumption, carbon emissions from chemical use, and the overall probability of effluent quality meeting standards. The first five neurons output values ​​in kg CO2 / d, representing daily emissions in CO2 equivalents. The sixth neuron uses a sigmoid activation function, with an output value ranging from 0 to 1, representing the probability of effluent quality meeting standards.

[0028] The backpropagation (BP) neural network was trained using the Levenberg-Marquardt algorithm. The training data came from historical operational data of a wastewater treatment plant, spanning at least one year and covering all four seasons, with a sample size of no fewer than 300 groups. Before training, the data was normalized using the Min-Max standardization method, scaling the data to the [0, 1] interval, as shown in the formula. .

[0029] The training parameters were set as follows: the initial learning rate was 0.01, using an adaptive adjustment strategy; the maximum number of training epochs was 1000; the target error was a mean squared error (MSE) less than 0.001; the data was divided into a training set (70%), a validation set (20%), and a test set (10%). To prevent overfitting, an Early Stopping mechanism was used, stopping training when the validation set error did not decrease for 10 consecutive epochs; L2 regularization was also used, with a regularization coefficient set to 0.001.

[0030] Taking a wastewater treatment plant with a capacity of 100,000 tons / day using the A² / O process as an example, operational data from January to December 2023 was collected, totaling 365 samples. Table 1 lists some examples of training data.

[0031] Table 1

[0032] The complete training dataset in Table 1 contains 365 data sets; only a portion of the samples are shown here. The units for influent water quality parameters and process parameters in the table are consistent with those described above; the units for CH4 emissions, N2O emissions, CO2 emissions, power generation emissions, and chemical emissions are all kgCO2 / d; the probability of compliance is a value between 0 and 1.

[0033] After training, the performance of the BP neural network was evaluated using a test set. The test results showed that the mean squared error (MSE) of the training set was 0.0008, the MSE of the validation set was 0.0012, and the MSE of the test set was 0.0010, all meeting the target requirements. The prediction accuracy was 92%, meaning that 92% of the samples had a deviation of less than 10% between the measured and predicted values.

[0034] In practical applications, by inputting the predicted influent water quality for the next 7 days and the current process parameters into a trained BP neural network, a carbon emission prediction curve for the next 7 days can be obtained. The predicted influent water quality can be obtained based on the ARIMA time series model. For example, when predicting carbon emissions for day 2, the input parameters are: influent COD 310 mg / L, influent BOD5 175 mg / L, influent TN 44 mg / L, influent TP 4.3 mg / L, influent SS 180 mg / L, influent ammonia nitrogen 35 mg / L, DO 2.6 mg / L, reflux ratio 80%, MLSS 3500 mg / L, carbon source 50 kg / d, HRT 8.0 h, water temperature 10℃, season 4, load rate 74%. The BP network output parameters are: CH4 440 kg CO2 / d, N2O 34 kg CO2 / d, CO2 200 kg CO2 / d, electricity 1100 kg CO2 / d, reagents 80 kg CO2 / d, and compliance probability 0.98. Considering the global warming potential (GWP) of N2O is 298, the total carbon emissions are: 440 + 34 × 298 + 200 + 1100 + 80 = 11952 kg CO2 / d.

[0035] The intelligent optimization module for process parameters is used to introduce the particle swarm optimization algorithm. The BP neural network is used as the fitness function of the particle swarm algorithm. With the constraint that the effluent quality meets the Class A standard, and with the goal of minimizing carbon emissions, the optimal combination of process parameters is searched. In this embodiment, the intelligent optimization module for process parameters uses a combination of particle swarm optimization algorithm and backpropagation neural network to optimize process parameters, such as... Figure 3As shown, the optimization problem is modeled as follows: The objective function is to minimize the total carbon emissions, i.e. The constraints are that the effluent quality meets the Class A standard of GB 18918-2002, specifically including: effluent COD ≤ 50 mg / L, effluent BOD5 ≤ 10 mg / L, effluent TN ≤ 15 mg / L, effluent TP ≤ 0.5 mg / L, and effluent ammonia nitrogen ≤ 5 mg / L. The decision variables include four adjustable process parameters: dissolved oxygen concentration in the aeration tank, sludge return ratio, carbon source dosage, and hydraulic retention time, with values ​​ranging from 1.5-4.0 mg / L, 50-200%, 0-100 kg / d, and 6-12 h, respectively.

[0036] The steps of the particle swarm optimization algorithm are as follows: Step S1: Initialize the particle swarm. Set the number of particles N to 40, and the position x of each particle... i The velocity v of each particle is randomly initialized within the range of values ​​for the decision variables. i Initialize randomly within the interval [-1, 1]. Set the maximum inertia weight w. max The minimum value is 0.9. min The learning factor is set to 0.4, and both learning factors c1 and c2 are set to 2.0. The maximum number of iterations T is... max It is 100.

[0037] Step S2, Fitness Assessment. For each particle i, the particle position (i.e., the combination of process parameters) and the current influent water quality are input into the BP neural network prediction module to obtain the output, which includes the total carbon emissions E. total And the probability P that the effluent water quality meets the standards. If P ≥ 0.95, the effluent is considered to meet the standards, and the fitness function is... In other words, the lower the carbon emissions, the higher the fitness. If P < 0.95, the effluent is considered substandard, and a penalty is imposed. (Fitness function) If fitness(i) is greater than the individual optimal fitness of the particle, then update the individual optimal position. For the current position x i Update the individual's optimal fitness _fitness(i) represents the current fitness (fitness(i)). If fitness(i) is greater than the global optimal fitness... If _fitness is updated, the global optimal position gbest is updated to the current position xi, and the global optimal fitness is updated. _fitness represents the current fitness (i).

[0038] Step S3: Update particle velocity and position. Calculate the current inertia weight. , where t is the current iteration number, and the inertia weight decreases linearly with the iteration number. For each particle i, the velocity update formula is used. Update speed, where r1 and r2 are random numbers in the interval [0, 1]. Update according to the position update formula. Update the position. Perform boundary processing on the position; if x... i If (t+1) exceeds the range of the decision variable, then it is set as the boundary value.

[0039] Step S4, Termination condition determination. If the current iteration number t ≥ T max If the global optimal fitness (gbest_fitness) shows no improvement after 20 consecutive generations, then the global optimal position (gbest) is output as the optimal process parameter, and the algorithm terminates. Otherwise, let t = t + 1 and return to step S2.

[0040] Taking a wastewater treatment plant on a certain day as an example, the influent conditions were COD 320 mg / L, BOD5 180 mg / L, TN 45 mg / L, and TP 4.5 mg / L. Before optimization, the process parameters were DO 2.8 mg / L, reflux ratio 100%, carbon source 50 kg / d, and HRT 8 h. A BP neural network predicted carbon emissions of 4500 kg CO2 / d, and predicted effluent COD 35 mg / L and TN 12 mg / L, meeting the compliance requirements. After particle swarm optimization, the optimized process parameters were DO 2.2 mg / L, reflux ratio 85%, carbon source 42 kg / d, and HRT 8.5 h. The BP neural network predicted carbon emissions of 3600 kg CO2 / d, a 20% reduction compared to before optimization, and predicted effluent COD 38 mg / L and TN 13 mg / L, still meeting the compliance requirements.

[0041] The optimization effects are analyzed as follows: DO decreased by 0.6 mg / L, reducing aeration energy consumption by approximately 15%. The reflux ratio decreased by 15%, reducing reflux pump energy consumption by approximately 12%. Carbon source dosage decreased by 16%, reducing chemical carbon emissions by approximately 16%. The HRT was slightly increased by 0.5 h, improving biochemical reaction efficiency and ensuring effluent meets standards. Total carbon emissions decreased by 20%, while ensuring effluent quality meets GB18918-2002 Class A standard.

[0042] The adaptive emission factor correction module is used to establish an emission factor correction model using a BP neural network and dynamically correct the emission factor of the emission factor correction model based on measured carbon emission data. In this specific embodiment, the adaptive emission factor correction module establishes an emission factor correction model based on a BP neural network, such as... Figure 4 As shown, the emission factor is dynamically corrected based on the measured carbon emission data of the wastewater treatment plant.

[0043] The emission factors recommended by national standards are used as initial values, including: CH4 emission factor 0.30 kg CH4 / kg COD removal, N2O emission factor 0.005 kg N2O / kg TN removal, power emission factor 0.5810 t CO2 / MWh, and chemical emission factors such as sodium acetate 2.0 t CO2 / t and PAM 1.5 t CO2 / t. The CO2 emission factor is obtained through carbon balance calculations.

[0044] The backpropagation neural network design for emission factor correction is as follows. The input layer contains 10 neurons, corresponding to the wastewater treatment plant scale, process type code, geographical location latitude, current water temperature, influent COD concentration, mixed liquor sludge concentration, aeration tank DO concentration, hydraulic retention time, current season code, and the average monthly carbon emission value measured over the past three months. In the process type code, A² / O is 1, SBR is 2, oxidation ditch is 3, and MBR is 4, etc. The hidden layers consist of a first layer with 15 neurons and a second layer with 8 neurons. The output layer contains 5 neurons, outputting the CH4 emission factor correction coefficient, N2O emission factor correction coefficient, CO2 emission factor correction coefficient, electricity emission factor correction coefficient, and chemical emission factor correction coefficient, respectively.

[0045] The emission factor correction process includes the following stages: The initial operation phase will last from 0 to 3 months. The system will use national standard emission factors for carbon emission accounting. Simultaneously, real-world data acquisition will be initiated, obtaining measured carbon emission data through online monitoring of CH4 and N2O concentrations.

[0046] The initial calibration phase takes place after 3 months. The plant's characteristic parameters and 3 months of measured carbon emission data are input into the emission factor calibration BP neural network. The network outputs a calibration coefficient, for example, a CH4 calibration coefficient of 1.15. The emission factor is then updated; the calibrated CH4 emission factor is 0.30 × 1.15 = 0.345 kg / kg.

[0047] The continuous operation phase will last for a period of 3 months thereafter. The calibration process will be repeated quarterly. If the deviation between measured and predicted carbon emissions exceeds 10%, a manual review of data quality will be triggered, and the calibration network will be retrained if necessary.

[0048] Taking a wastewater treatment plant as an example, this plant uses the A² / O process and has a treatment capacity of 100,000 tons / day, located in North China. Initially, the national standard emission factor was used for calculation, with a theoretical daily carbon emission of 4500 kg CO₂ / d. After three months of operation, the measured average daily carbon emission was 3900 kg CO₂ / d, a deviation of (4500-3900) / 3900 = 15.4%, indicating that the theoretical value was too high. After emission factor correction using a BP network, the CH₄ correction coefficient was obtained at 0.85, indicating that the actual CH₄ emission was lower than the national standard, possibly because higher DO levels inhibited the anaerobic reaction. The N₂O correction coefficient was 1.05, slightly higher than the national standard. The electricity emission factor correction coefficient was 0.98, indicating a high proportion of clean energy in the local power grid. The chemical emission factor correction coefficient was 1.00, with no significant deviation. After applying the correction factor, the calculated daily carbon emission value is 3950 kg CO2 / d, with a deviation of (3950-3900) / 3900=1.3%, which significantly improves the accuracy.

[0049] The carbon emission accounting module, combined with corrected emission factors, calculates the carbon emissions of wastewater treatment plants using emission accounting methods. These methods include: emission factor method, material balance method, and measurement method. Carbon emissions include: direct carbon emissions, indirect carbon emissions, and negative carbon emissions. In this specific embodiment, the carbon emission accounting module uses a combination of emission factor method, material balance method and actual measurement method to perform carbon emission accounting.

[0050] Direct carbon emissions accounting includes: CH4 emissions, N2O emissions, and CO2 emissions.

[0051] The formula for calculating CH4 emissions is: ; Where COD removal capacity = (influent COD - effluent COD) × treated water volume / 1000, The CH4 emission factor is corrected using the emission factor correction module. The global warming potential of CH4 is set to 25.

[0052] The formula for calculating N2O emissions is: ; Where TN removal capacity = (influent TN - effluent TN) × treated water volume / 1000, The corrected N2O emission factor is used. The global warming potential of N2O is set at 298.

[0053] CO2 emissions are calculated using the material balance method, and the specific formula is as follows: ; in This refers to the amount of organic matter added as raw material, i.e., the influent. The carbon content of the raw materials. This refers to the product output, specifically the amount of water produced. For the carbon content of the product, This refers to the amount of waste, specifically sludge, produced. The carbon content of the waste is 44 / 12, which is the molar mass ratio of carbon to CO2.

[0054] Indirect carbon emission accounting includes energy consumption emissions and material consumption emissions; The formula for calculating energy consumption emissions is as follows: The main equipment includes blowers, inlet water lift pumps, sludge return pumps, etc., among which blowers usually account for 60-70%.

[0055] The formula for calculating material consumption emissions is as follows: The main agents include carbon sources such as sodium acetate and methanol, flocculants such as PAM and PAC, and disinfectants such as sodium hypochlorite and chlorine dioxide.

[0056] Negative carbon emission accounting includes carbon emission reductions achieved through wastewater thermal energy utilization, sludge anaerobic digestion biogas utilization, and tailwater recycling.

[0057] The formula for calculating carbon emission reduction from wastewater heat energy utilization is: Eheat emission reduction = Qheat × Theat × EFheat, where Qheat is the wastewater heat energy recovery amount (kWh), Theat is the heat energy utilization time (h), and EFheat is the carbon emission factor for heat energy substitution (tCO2 / kWh). The formula for calculating carbon emission reduction from sludge anaerobic digestion and biogas utilization is: Ebiogas emission reduction = Qbiogas × EFbiogas, where Qbiogas is the biogas production (m3), and EFbiogas is the carbon emission factor for biogas substitution (tCO2 / m3). The formula for calculating carbon emission reduction from effluent reuse is: Ereclaimed water emission reduction = Vreclaimed water × EFreclaimed water, where Vreclaimed water is the reclaimed water usage (m3), and EFreclaimed water is the carbon emission factor for reclaimed water replacing tap water (tCO2 / m3).

[0058] The formula for calculating total carbon emissions is Etotal = ECH4 + EEN2O + ECO2 + Eelectricity + Epharmaceuticals - Eheat emission reduction - Ebiogas emission reduction - Ereclaimed water emission reduction.

[0059] Taking the carbon emission calculation of a wastewater treatment plant with a capacity of 100,000 tons / day on a certain day as an example. Direct emissions include 450 kg CO2 from CH4, 35 × 298 = 10,430 kg CO2 from N2O, and 200 kg CO2 from CO2, for a total of 11,080 kg CO2.

[0060] Indirect emissions include 800 kWh × 0.581 × 24h = 11160 kg CO2 from blower electricity, 3840 kg CO2 from other equipment electricity, and 800 kg CO2 from reagent consumption, totaling 15800 kg CO2. Negative carbon emissions include -200 kg CO2 from wastewater heat energy utilization and -300 kg CO2 from biogas utilization, totaling -500 kg CO2. Total carbon emissions are 11080 + 15800 - 500 = 26380 kg CO2 / day.

[0061] The visualization analysis module calculates the direct carbon emissions, indirect carbon emissions, and negative carbon emissions of the wastewater treatment plant and generates a carbon emission analysis report.

[0062] Specifically, in this embodiment, the visualization analysis module uses an automated report generation method to integrate carbon emission accounting results, predictive analysis, and optimization effects into a standardized carbon emission analysis report.

[0063] like Figure 5 As shown, the carbon emission analysis report interface includes the following functional areas: The top navigation bar area (51) is used to display basic information such as report title, wastewater treatment plant name, report generation date, and report cycle; The key indicators summary area (52) displays the core indicators in the form of numerical cards, including total carbon emissions, daily average carbon emissions, carbon emission intensity, and change rate compared to the previous period. Carbon emission analysis area (53), which includes: carbon emission composition analysis sub-area (531), configured to display pie charts or ring charts to show the proportion of direct emissions, indirect emissions and negative carbon emissions, and provide legends to illustrate each type of emission; The Emissions Details Table (532) sub-area lists detailed data on various types of carbon emissions in tabular form, including the amount and percentage of CH4 emissions, N2O emissions, and CO2 emissions. The carbon emission trend analysis sub-area (533) is configured to display a line chart, with the horizontal axis representing the time dimension (selectable from the last 30 days or the last 12 months) and the vertical axis representing carbon emissions. The solid line shows the historical carbon emission change trend, and the dashed line shows the predicted carbon emission trend, with the highest, lowest and average values ​​marked. The process and energy consumption analysis area (54) includes: the process parameter analysis sub-area (541), which is configured to display a bar chart. The horizontal axis represents the process parameter category (DO concentration, sludge return ratio, MLSS, carbon source dosage, HRT), and the vertical axis represents the carbon emission contribution rate, showing the degree of influence of each parameter on carbon emissions. The equipment energy consumption analysis sub-area (542) is configured to display a bar chart, with the horizontal axis representing the equipment name (blower, inlet lift pump, sludge return pump, other equipment) and the vertical axis representing carbon emissions. The bars are sorted from high to low, highlighting high-emission equipment. The optimization effect comparison sub-region (543) uses a parallel bar chart to compare and show the changes in key indicators such as total carbon emissions, energy consumption, and pharmaceutical consumption before and after optimization. The effluent water quality data table sub-area (544) lists the measured values, standard values, compliance status, and compliance rate of indicators such as COD, BOD5, TN, TP, ammonia nitrogen, and SS in tabular form.

[0064] The specific attribute parameters of each chart are set through a configuration file, including chart type, data source, horizontal and vertical axis labels, legend position, and display method. In the interface, dashed boxes represent configurable chart display areas, and solid boxes represent data display areas.

[0065] The report generation technology is developed using Python, employing the ReportLab library to generate PDF reports and the Matplotlib library to generate statistical charts, and supports Chinese font display.

[0066] The report is structured as follows: The report uses A4 paper size and includes a cover, report overview, carbon emission composition analysis, carbon emission trend analysis, process parameter analysis, equipment energy consumption analysis, effluent water quality analysis, optimization effect analysis, and appendices.

[0067] The report cover includes the report title "Carbon Emission Analysis Report of Wastewater Treatment Plant", the name of the wastewater treatment plant, the report generation date, and the reporting period.

[0068] The report overview includes a report description and a summary table of key indicators. The report description briefly explains the report's purpose, data sources, and calculation methods. The summary table of key indicators presents core data in tabular form, including total carbon emissions, daily average carbon emissions, carbon emission intensity, change rate compared to the previous period, and effluent quality compliance rate.

[0069] The carbon emission composition analysis includes detailed tables of direct carbon emissions, indirect carbon emissions, negative carbon emissions, and a pie chart of carbon emission composition. The detailed table of direct carbon emissions lists the emissions and percentages of CH4, N2O, and CO2. The detailed table of indirect carbon emissions lists the carbon emissions from equipment such as blowers, booster pumps, and return pumps, categorized by equipment type. The detailed table of negative carbon emissions lists the carbon emission reductions from wastewater thermal energy utilization, sludge anaerobic digestion biogas utilization, and effluent recycling. The pie chart of carbon emission composition shows the proportional relationship between direct, indirect, and negative carbon emissions.

[0070] Carbon emission trend analysis includes historical carbon emission line charts and carbon emission prediction line charts. The historical carbon emission line chart uses time (30 days or 12 months) as the horizontal axis and carbon emissions as the vertical axis (kgCO2 / d). The curve shows the historical trend of carbon emissions, with the highest, lowest, and average values ​​marked. The carbon emission prediction line chart uses time (7 days) as the horizontal axis and carbon emissions as the vertical axis. The solid line represents the carbon emission trend predicted by the BP neural network, while the dashed line represents the upper and lower bounds of the prediction confidence interval (±10%). The reference line represents the historical average and warning threshold. If the predicted value exceeds the warning threshold, it is marked in red with accompanying text.

[0071] The process parameter analysis includes a table of main process parameters and a bar chart showing the relationship between process parameters and carbon emissions. The table lists the current value, recommended range, and normal status of DO concentration in the aeration tank, sludge return ratio, MLSS, carbon source dosage, and HRT (Heat Reduction Time). The bar chart shows the horizontal axis for process parameter categories and the vertical axis for carbon emission contribution rate (%). The bars indicate the degree of influence of each parameter on carbon emissions.

[0072] The equipment energy consumption analysis includes a main equipment energy consumption table and a bar chart of equipment carbon emission contributions. The main equipment energy consumption table lists the power, operating time, electricity consumption, carbon emissions, and percentage of emissions for blowers, influent lift pumps, sludge return pumps, and other equipment. The equipment carbon emission contribution bar chart shows the equipment name on the horizontal axis and carbon emissions on the vertical axis, in kg CO2eq. The bars are sorted from highest to lowest, highlighting high-emission equipment.

[0073] The effluent water quality analysis includes an effluent water quality data table. This table lists the measured values, standard values, compliance status, and compliance rate for COD, BOD5, TN, TP, ammonia nitrogen, and SS. The measured values ​​are the average values ​​for the reporting period, the standard values ​​are the limits for Class A of GB 18918-2002, compliance status is indicated as either compliant or exceeding the standard, and the compliance rate is the percentage of days meeting the standard during the reporting period.

[0074] The optimization effect analysis is only included when optimization has been performed, and includes a before-and-after comparison table and a bar chart comparing the optimization effects. The before-and-after comparison table lists the total carbon emissions, main process parameters, effluent quality, energy consumption, and chemical consumption before and after optimization, as well as the magnitude of change. The bar chart comparing the optimization effects uses parallel bars, with blue representing before optimization and green representing after optimization, visually demonstrating the carbon emission reduction effect.

[0075] The appendix includes an emission factor table, calculation formulas, and a description of data sources. The emission factor table lists all emission factors used in this report and their sources. The calculation formulas list the key carbon emission calculation formulas. The data source descriptions list the data collection locations, collection frequency, and data quality.

[0076] The report generation process includes the following steps: Step 1, Data Preparation: Extract data for the reporting period from the database. Step 2, Data Processing: Statistical analysis, calculating averages, maximums, minimums, etc. Step 3, Chart Generation: Generating charts using the Matplotlib library and saving them as temporary image files. Step 4, PDF Document Assembly: Constructing a PDF using the ReportLab library, adding text, tables, and images page by page. Step 5, Report Output: Saving the PDF file and providing a download link.

[0077] Report generation can be triggered in three ways: scheduled automatic generation, manual on-demand generation, and event-triggered generation. Scheduled automatic generation includes daily, weekly, monthly, and quarterly generation. Manual on-demand generation occurs when the user clicks the "Generate Report" button. Event-triggered generation includes carbon emission warnings, reports after optimization implementation, and reports after emission factor correction.

[0078] The system also includes a system integration module that integrates the above modules to achieve coordination of data flow and control flow, forming a closed-loop management of "prediction-optimization-verification".

[0079] The system architecture adopts a three-tier B / S architecture. The data layer uses a MySQL database to store historical and real-time data. The application layer is developed using Java or Python, deploying the BP network, PSO algorithm, and report generation module. The presentation layer uses a web front-end, employing HTML5 and JavaScript technologies, with a responsive design, supporting both PC and mobile devices.

[0080] Deployment options include private cloud and public cloud deployments. Private cloud deployment, where servers are located on the wastewater treatment plant's intranet, is recommended. Public cloud deployment involves uploading data to the cloud and is suitable for multi-plant network management.

[0081] The data interface supports OPC protocol for automatic data acquisition with SCADA system, Modbus TCP protocol for connection with online monitoring instruments, and manual data import from Excel.

[0082] The daily operation process includes the following steps: Step 1: The system automatically collects data daily. Step 2: It calls the BP network to generate a 7-day carbon emission forecast. Step 3: If the forecast exceeds the threshold, the PSO-BP optimization algorithm is automatically run. Step 4: Optimization suggestions are pushed to the operator's mobile app or PC. Step 5: The operator adjusts the process parameters based on the suggestions. Step 6: Actual carbon emission data is fed back to the system for continuous optimization.

[0083] The usage process includes the following steps during regular maintenance: Step 1, perform emission factor correction quarterly. Step 2, retrain the BP network every six months, adding new data. Step 3, conduct a system performance evaluation annually, adjusting algorithm parameters as necessary.

[0084] Example 2 This embodiment provides an implementation method for the intelligent carbon emission prediction and process optimization system for wastewater treatment plants in Embodiment 1, such as... Figure 6 As shown, it includes the following steps: S1. Data Acquisition. Collect influent water quality data, process operating parameters, equipment energy consumption data, and environmental parameter data from the wastewater treatment plant. Influent water quality data includes COD, BOD5, TN, TP, SS, and ammonia nitrogen. Process operating parameters include dissolved oxygen concentration in the aeration tank, sludge return ratio, mixed liquor sludge concentration, carbon source dosage, and hydraulic retention time. Equipment energy consumption data includes blower power and booster pump operating status. Environmental parameter data includes water temperature and season.

[0085] S2. Backpropagation (BP) Neural Network Training. Historical operational data was used as training samples. After data normalization, a BP neural network containing an input layer, two hidden layers, and an output layer was trained to establish a mapping relationship between influent water quality, process parameters, and carbon emissions. The BP neural network training adopted the Levenberg-Marquardt algorithm, with an initial learning rate of 0.01 that was adaptively adjusted. The maximum number of training epochs was 1000, with a target mean squared error of less than 0.001. 20% of the data was used as the validation set and 10% as the test set.

[0086] S3. Carbon Emission Prediction. Input the current or predicted influent water quality parameters and process parameters into the trained BP neural network, and output the predicted carbon emission values ​​for the next 7 days and the probability of effluent water quality meeting standards.

[0087] S4. Process Parameter Optimization. A particle swarm optimization algorithm is employed, with the constraint that the effluent quality meets the GB18918-2002 standard and the objective of minimizing carbon emissions. A backpropagation (BP) neural network is used to evaluate the effects of different combinations of process parameters and search for the optimal process parameters. The inertia weight w of the particle swarm optimization algorithm is linearly decreased from 0.9 to 0.4, the learning factors c1 and c2 are both set to 2.0, the number of particles is 30-50, and the number of iterations is 50-100.

[0088] S5. Emission Factor Correction. Collect measured carbon emission data from the plant, input it into the emission factor correction BP neural network, calculate the emission factor correction coefficient, and update the emission factor database. The emission factor correction cycle is once per quarter. When the deviation between measured carbon emissions and predicted carbon emissions exceeds 10%, an early warning is triggered, and the correction network is retrained after manual review of data quality.

[0089] S6. Carbon Emission Accounting. Using the emission factor method, material balance method, and measured method, combined with corrected emission factors, direct carbon emissions, indirect carbon emissions, and negative carbon emissions are calculated.

[0090] S7. Visual Analysis and Early Warning. Generates a carbon emission analysis report, including carbon emission data statistics, trend analysis, and forecast results. The early warning threshold is set at 1.2 times the historical average carbon emissions. When the predicted carbon emissions exceed the threshold for three consecutive days, the system recommends a process parameter adjustment plan.

[0091] S8. Closed-loop feedback. The actual carbon emission data after adjusting the process parameters is fed back to the BP neural network, and the network is retrained periodically to achieve continuous optimization.

[0092] This embodiment is specifically as follows: Taking a wastewater treatment plant with a capacity of 100,000 tons / day using the A² / O process as an example, the plant emitted approximately 16,500 tons of CO2 in 2023, with a carbon emission intensity of 0.45 kg CO2 / m³. The system of this invention was deployed in January 2024, collecting 365 sets of operational data from the entire year of 2023 to train the BP network.

[0093] The BP network training results show that the MSE of the training set is 0.0008, the MSE of the validation set is 0.0012, and the MSE of the test set is 0.0010, all meeting the target requirements. The prediction accuracy is 92%, meaning that 92% of the samples have a deviation of less than 10% between the measured and predicted values.

[0094] The effects of process parameter optimization are as follows. Before optimization, on a certain day in January 2024, the DO was 2.8 mg / L, the reflux ratio was 100%, the carbon source was 50 kg / d, the HRT was 8 h, the daily carbon emission was 4500 kg CO2, and the effluent quality was COD 35 mg / L and TN 12 mg / L, meeting the compliance requirements. After optimization, based on PSO-BP recommendations, the DO was 2.2 mg / L, the reflux ratio was 85%, the carbon source was 42 kg / d, and the HRT was 8.5 h, the daily carbon emission was 3600 kg CO2, a reduction of 20%, and the effluent quality was COD 38 mg / L and TN 13 mg / L, still meeting the compliance requirements.

[0095] The emission factor correction results are as follows. The initial calculation used the national standard emission factor, with a theoretical daily carbon emission of 4500 kg CO2 / d. The actual measured average over three months was 3900 kg CO2 / d, with a deviation of 15.4%. After correction, the CH4 correction factor was 0.85, and the N2O correction factor was 1.05. The calculated value after correction was 3950 kg CO2 / d, with a deviation of 1.3%, demonstrating a significant improvement in accuracy.

[0096] The system operated for six months from January to June 2024, with total carbon emissions of 7,800 tons of CO2-eq, a decrease of 18% compared to the same period in 2023. The carbon emission intensity was 0.37 kg CO2-eq / m³, a reduction of 18%. The effluent quality compliance rate was 100%. Energy consumption decreased by approximately 12%, and chemical consumption decreased by approximately 15%. In terms of economic benefits, electricity costs were saved by approximately 1.2 million yuan per year, and chemical costs were saved by approximately 500,000 yuan per year (calculated at 0.6 yuan / kWh), totaling approximately 1.7 million yuan per year.

[0097] The above description of the disclosed embodiments enables those skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart prediction and process optimization system for carbon emissions from wastewater treatment plants, characterized in that, include: The data acquisition module is used to collect production and operation data from the wastewater treatment plant. The production operation data includes: influent water quality data, process operating parameters, equipment energy consumption data, and environmental parameters; The BP neural network prediction module is used to establish a nonlinear mapping relationship between influent water quality, process operating parameters and carbon emissions using a BP neural network, and output the predicted carbon emissions of the wastewater treatment plant and the probability of effluent water quality meeting standards. The intelligent optimization module for process parameters is used to introduce the particle swarm optimization algorithm. The BP neural network is used as the fitness function of the particle swarm algorithm. With the constraint that the effluent quality meets the Class A standard, and with the goal of minimizing carbon emissions, the optimal combination of process parameters is searched. The adaptive emission factor correction module is used to establish an emission factor correction model using a BP neural network and dynamically correct the emission factor of the emission factor correction model based on measured carbon emission data. The carbon emission accounting module, combined with corrected emission factors, calculates the carbon emissions of wastewater treatment plants using emission accounting methods. These methods include: emission factor method, material balance method, and measurement method. Carbon emissions include: direct carbon emissions, indirect carbon emissions, and negative carbon emissions. The visualization analysis module is used to generate carbon emission analysis reports from the calculated direct carbon emissions, indirect carbon emissions, and negative carbon emissions of wastewater treatment plants.

2. The intelligent carbon emission prediction and process optimization system for wastewater treatment plants according to claim 1, characterized in that, The influent water quality data includes: COD, BOD5, TN, TP, SS, and ammonia nitrogen, and the data is collected once per hour. The process operating parameters include: dissolved oxygen concentration (DO) in the aeration tank, sludge return ratio, mixed liquor sludge concentration (MLSS), carbon source dosage, and hydraulic retention time (HRT), collected once per hour. The equipment energy consumption data includes: blower power and booster pump operating status, and the data is collected in real time. The environmental parameters include water temperature and season, and are collected once a day.

3. The intelligent carbon emission prediction and process optimization system for wastewater treatment plants according to claim 1, characterized in that, The BP neural network prediction module includes: an input layer, a hidden layer, and an output layer; The input layer includes 14 neurons; the 14 neurons correspond to influent COD, BOD5, TN, TP, SS, ammonia nitrogen, dissolved oxygen concentration (DO) in the aeration tank, sludge return ratio, mixed liquor sludge concentration (MLSS), carbon source dosage, hydraulic retention time (HRT), water temperature, seasonal coding, and treatment load rate. The hidden layer includes: a first hidden layer and a second hidden layer; the first hidden layer includes 28 neurons and the activation function is the Sigmoid function; the second hidden layer includes 18 neurons and the activation function is the Tanh function. The output layer includes 6 neurons, which output predicted values ​​for CH4 emissions, N2O emissions, CO2 emissions, carbon emissions due to electricity consumption, carbon emissions due to chemical use, and the overall probability of effluent quality meeting standards.

4. The intelligent carbon emission prediction and process optimization system for wastewater treatment plants according to claim 1, characterized in that, The process of using a BP neural network as the fitness function of a particle swarm optimization algorithm includes: S1. Randomly initialize the position, velocity, and algorithm parameters of the particle swarm; S2. Evaluate the fitness of each particle's position using a BP neural network, and update the individual and global optimum of the particles; S3. Update the velocity and position of particles based on individual and global optimal information of particles, and perform boundary constraint processing; S4. Determine if the termination condition is met. If it is, output the optimal solution; otherwise, return to S2 to continue evaluating the fitness of each particle position.

5. The intelligent carbon emission prediction and process optimization system for wastewater treatment plants according to claim 1, characterized in that, The adaptive emission factor correction module includes an input layer, a hidden layer, and an output layer; The input layer includes 10 neurons, corresponding to the scale of the wastewater treatment plant, process type code, geographical location latitude, water temperature, influent COD concentration, MLSS, DO concentration, HRT, seasonal code, and the average monthly carbon emission measured over the past 3 months. The hidden layer comprises: a first layer of 15 neurons and a second layer of 8 neurons; The output layer includes 5 neurons, which respectively output the CH4 emission factor correction coefficient, N2O emission factor correction coefficient, CO2 emission factor correction coefficient, electricity emission factor correction coefficient, and pharmaceutical emission factor correction coefficient.

6. The intelligent carbon emission prediction and process optimization system for wastewater treatment plants according to claim 1, characterized in that, In the carbon emission accounting module: Direct carbon emissions accounting includes: CH4 emissions, N2O emissions, and CO2 emissions; The CH4 emission calculation formula is as follows: ; Where COD removal capacity = (influent COD - effluent COD) × treated water volume / 1000, The CH4 emission factor is corrected using the emission factor correction module. The global warming potential of CH4; The formula for calculating N2O emissions is: ; Wherein, TN removal capacity = (influent TN - effluent TN) × treated water volume / 1000, The corrected N2O emission factor is used. The global warming potential of N2O; CO2 emissions are calculated using the material balance method, with the following formula: ； in This refers to the amount of organic matter added as raw material, i.e., the influent. The carbon content of the raw materials. This refers to the product output, specifically the amount of water produced. For the carbon content of the product, This refers to the amount of waste, specifically sludge, produced. The carbon content of the waste is 44 / 12, which is the molar mass ratio of carbon to CO2.

7. The intelligent carbon emission prediction and process optimization system for wastewater treatment plants according to claim 1, characterized in that, The system also includes: a data layer, an application layer, and a presentation layer; The data layer uses a MySQL database to store data; The application layer deploys a BP neural network, a particle swarm optimization algorithm, and a report generation module. The presentation layer uses HTML5 and JavaScript to implement a responsive web interface, supporting connection with SCADA systems via OPC protocol or with online monitoring instruments via Modbus TCP protocol.

8. A method for intelligent prediction and process optimization of carbon emissions from wastewater treatment plants, applied to the intelligent prediction and process optimization system for carbon emissions from wastewater treatment plants as described in any one of claims 1-7, characterized in that, Including the following steps: S1. Collect production and operation data of the wastewater treatment plant; The production operation data includes: influent water quality data, process operating parameters, equipment energy consumption data, and environmental parameters; S2. Use a BP neural network to establish a nonlinear mapping relationship between influent water quality, process operating parameters and carbon emissions, and output the predicted carbon emissions of the wastewater treatment plant and the probability of effluent water quality meeting standards. S3. Introduce the particle swarm optimization algorithm, using the BP neural network as the fitness function of the particle swarm algorithm, with the effluent quality meeting the Class A standard as a constraint and the goal of minimizing carbon emissions, to search for the optimal combination of process parameters. S4. Establish an emission factor correction model using a BP neural network, and dynamically correct the emission factor of the emission factor correction model based on measured carbon emission data. S5. Based on the corrected emission factors, calculate the carbon emissions of the wastewater treatment plant using emission accounting methods. These methods include: emission factor method, material balance method, and measurement method. Carbon emissions include: direct carbon emissions, indirect carbon emissions, and negative carbon emissions. S6. Generate a carbon emission analysis report based on the calculated direct carbon emissions, indirect carbon emissions, and negative carbon emissions from the wastewater treatment plant.

9. The intelligent prediction and process optimization method for carbon emissions from wastewater treatment plants according to claim 8, characterized in that, The BP neural network training uses the Levenberg-Marquardt algorithm, with an initial learning rate of 0.01 that is adaptively adjusted. The maximum number of training epochs is 1000, and the target mean square error is less than 0.

001.

10. The intelligent prediction and process optimization method for carbon emissions from wastewater treatment plants according to claim 8, characterized in that, In S3, the fitness function of the particle swarm optimization algorithm is specifically as follows: if the probability of effluent water quality meeting the standard P ≥ 0.95, the effluent is considered to meet the standard, and the fitness function is set accordingly. The iteration terminates when the global optimal fitness remains unchanged for 20 consecutive generations, and the optimal combination of process parameters is output.

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