Sewage treatment biochemical unit pollutant residual capacity calculation system and method

Abstract

The invention discloses a system and a method for calculating residual pollutant capacity of a sewage treatment biochemical unit. According to the invention, the operation parameters of the sewage treatment plant are monitored based on the sewage treatment biochemical unit pollutant residual capacity calculation system, and the obtained operation parameters of the sewage treatment plant are subjected to data processing by using the sewage biochemical data processing system; a sewage biochemical treatment unit capacity correction coefficient calculation model which is trained in advance and is preset in a sewage biochemical treatment unit capacity correction coefficient intelligent calculation system is matched to calculate a sewage biochemical treatment unit capacity correction coefficient; and determining the residual pollutant capacity of the sewage treatment biochemical unit and transmitting the residual pollutant capacity to the intelligent management and control system to generate a sewage treatment biochemical unit inlet and outlet water optimization regulation and control scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to a system and method for calculating the residual capacity of pollutants in a sewage treatment biochemical unit. Background Art

[0002] As one of my country's new national strategic industries, the sewage treatment industry is one of the key industries of my country's "Water Pollution Prevention and Control Action Plan" (i.e., the Water Ten Articles), which clearly states the need to comprehensively control water pollutants. Petrochemical enterprises, as large production capacity companies, will produce a large amount of oil-containing, sulfur-containing and other sewage during production and operation. This type of sewage has complex components, large fluctuations in water quality and quantity, and there are treatment bottlenecks such as high organic matter concentration and poor biodegradability. The microorganisms in the sewage treatment biochemical unit are extremely vulnerable to impact. If the control is not timely, it will easily lead to abnormal operating conditions in the sewage treatment plant and even excessive wastewater discharge.

[0003] Due to the late start of my country's water treatment industry, the current sewage treatment technology of sewage treatment plants is relatively backward and the management level is low, resulting in unstable operation of sewage treatment biochemical units and difficulty in achieving accurate and standard discharge of sewage pollutants.

[0004] Chinese invention patent CN 112363391 A discloses a sludge bulking suppression method based on adaptive segmented sliding mode control. Aiming at the problem that the sewage treatment process is prone to abnormal sludge bulking and difficult to transition to normal conditions, the sludge bulking suppression is achieved. The method adopts a fuzzy neural network to obtain water quality parameters of the sewage treatment operation process, predict the sludge volume index in real time to judge the operating condition of the operation process, provide a reference signal for the switching of the controller, and design a segmented sliding mode controller with an adaptive switching mechanism to regulate the dissolved oxygen concentration and the nitrate nitrogen concentration, coordinate the biochemical reaction process, improve the sludge sedimentation characteristics, and ensure that the sewage treatment process can be restored to normal conditions when sludge bulking occurs.

[0005] Chinese invention patent CN 115578015 A discloses a method, system and storage medium for monitoring the whole process of sewage treatment based on the Internet of Things, which specifically includes: obtaining the current sewage treatment process information, setting the key water quality monitoring indicators of the water quality monitoring points; obtaining the water quality monitoring results of each point through the key water quality monitoring indicators, building a sewage treatment whole process model, and using the sewage treatment whole process model for real-time monitoring and control according to the water quality monitoring results; judging and generating fault warning information of the sewage treatment process, adding a fault detection model through deep learning for real-time learning, tracing the fault of the sewage treatment process according to the water quality monitoring results and multivariate working condition data, and visualizing it through the sewage treatment whole process model. The invention uses the Internet of Things technology to remotely monitor the whole process of sewage treatment, realizes the dynamic monitoring of water quality changes and the identification of abnormal working conditions in the sewage treatment process, and significantly improves the efficiency and accuracy of water quality monitoring.

[0006] However, the above methods all emphasize deep learning methods, and do not characterize the treatment capacity of the biochemical unit in the chemical wastewater treatment process. They cannot effectively evaluate the treatment capacity of the biochemical unit and it is difficult to ensure that the wastewater treatment facilities reach a stable operation state. Therefore, it is urgent to propose a system and method for calculating the residual capacity of pollutants in the biochemical unit of wastewater treatment, and to achieve dynamic optimization of the influent water quality and flow rate of the wastewater treatment process by calculating the biochemical treatment potential of the biochemical unit in the petrochemical wastewater treatment process. Summary of the invention

[0007] In view of the problem that it is difficult to dynamically calculate the remaining capacity of pollutants in the biochemical unit of sewage treatment at the current stage, the present invention proposes a system and method for calculating the remaining capacity of pollutants in the biochemical unit of sewage treatment, which combines chemical sewage control data with an artificial intelligence algorithm, determines the remaining capacity of pollutants in the biochemical unit of sewage treatment by real-time calculation of the capacity correction coefficient of the biochemical treatment unit of sewage, timely grasps the biochemical treatment potential of the biochemical unit in the process of petrochemical sewage treatment, optimizes the control plan of the sewage treatment plant, and alarms for water inlet abnormalities, thereby realizing dynamic optimization of the water quality and flow rate of the sewage treatment plant's inlet water.

[0008] The present invention specifically adopts the following technical solutions:

[0009] A system for calculating the residual capacity of pollutants in a sewage treatment biochemical unit comprises a monitoring system, a sewage biochemical data processing system, an intelligent calculation system for a capacity correction coefficient of a sewage biochemical treatment unit and an intelligent management and control system, wherein the monitoring system, the sewage biochemical data processing system, the intelligent calculation system for a capacity correction coefficient of a sewage biochemical treatment unit and the intelligent management and control system are connected in sequence.

[0010] Preferably, the monitoring system, the sewage biochemical data processing system, the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system and the intelligent management and control system are connected via a wireless network or a wired network.

[0011] Preferably, the monitoring system is connected to various monitoring instruments of the sewage treatment plant for real-time monitoring and acquisition of the operating parameters of the sewage treatment plant, including the sewage flow and pollutant concentration entering the biochemical unit of the sewage treatment plant, the sewage flow and pollutant concentration leaving the biochemical unit of the sewage treatment plant, the sewage liquid level of the biochemical unit, the pollutant concentration and operating process parameters.

[0012] Preferably, the operating parameters of the biochemical unit include dissolved oxygen, nitrification liquid return ratio, sludge return ratio, sludge concentration, sludge age and sludge discharge.

[0013] Preferably, the pollutants include COD, ammonia nitrogen, total nitrogen, total phosphorus, petroleum, volatile phenols, and sulfides.

[0014] Preferably, the sewage biochemical data processing system is connected to a monitoring system and is used for performing data preprocessing on the sewage treatment plant operation parameters acquired by the monitoring system.

[0015] Preferably, the data preprocessing includes eliminating garbled data and converting to a standard format. By eliminating garbled data in the sewage treatment plant operating parameters and converting them into a preset standard format, the sewage treatment plant operating parameters after data processing are obtained and sent to the sewage biochemical treatment capacity correction coefficient intelligent calculation system.

[0016] Preferably, the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system is connected to the sewage biochemical data processing system, and is used to calculate the sewage biochemical treatment unit capacity correction coefficient and the residual capacity of pollutants in the sewage treatment biochemical unit, including a sewage biochemical treatment unit capacity correction coefficient calculation model training module and a sewage biochemical treatment unit capacity correction coefficient calculation model.

[0017] Preferably, the intelligent management and control system is connected to the intelligent calculation system of the capacity correction coefficient of the sewage biochemical treatment unit, and is used to generate an optimized control plan for the inlet and outlet water of the sewage treatment biochemical unit according to the residual capacity of pollutants in the sewage treatment biochemical unit, the inlet and outlet water quality and the inlet and outlet water flow rate of the sewage treatment biochemical unit.

[0018] Preferably, the intelligent management and control system is provided with an early warning alarm prompt module, in which the dynamic grading alarm standard of pollutant capacity and the dynamic grading alarm standard of sewage capacity are preset. The early warning alarm prompt module is connected to a preset terminal and is used to timely push early warning alarm information and the optimization control plan of the inlet and outlet water of the sewage treatment biochemical unit according to the remaining capacity of pollutants in the sewage treatment biochemical unit.

[0019] Preferably, the terminal is configured as a mobile terminal and a central control room, and the early warning alarm module is wirelessly connected to the mobile terminal and the central control room.

[0020] Preferably, the early warning alarm module sends early warning alarm information and the inlet and outlet water optimization control plan of the sewage treatment biochemical unit to a preset mobile terminal in the form of text messages.

[0021] A method for calculating the remaining capacity of pollutants in a biochemical unit for sewage treatment, using the above-mentioned system for calculating the remaining capacity of pollutants in a biochemical unit for sewage treatment, specifically comprises the following steps:

[0022] Step 1: Use the monitoring system to collect monitoring data from various monitoring instruments in the sewage treatment plant, obtain the operating parameters of the sewage treatment plant, transmit the acquired operating parameters of the sewage treatment plant to the sewage biochemical data processing system for data preprocessing, and input them into the pre-debugged sewage biochemical treatment unit capacity correction coefficient intelligent calculation system;

[0023] Step 2, using a sewage biochemical treatment unit capacity correction coefficient calculation model in a sewage biochemical treatment unit capacity correction coefficient intelligent calculation system to calculate and obtain a sewage biochemical treatment unit capacity correction coefficient;

[0024] Step 3, based on the sewage biochemical treatment unit capacity correction coefficient, calculate the residual capacity of pollutants in the sewage biochemical treatment unit;

[0025] Step 4: The intelligent calculation system for the correction coefficient of the sewage biochemical treatment unit capacity transmits the calculated residual capacity of pollutants in the sewage biochemical treatment unit to the intelligent management and control system, and the intelligent management and control system generates an optimized control plan for the inlet and outlet water of the sewage biochemical treatment unit according to the residual capacity of pollutants in the sewage biochemical treatment unit, the inlet and outlet water quality and the inlet and outlet water flow of the sewage biochemical treatment unit;

[0026] Step 5. The intelligent management and control system generates early warning alarm information according to the preset dynamic classification alarm standard of pollutant capacity and the dynamic classification alarm standard of sewage capacity, transmits the early warning alarm information and the optimization control plan of the inlet and outlet water of the sewage treatment biochemical unit to the preset terminal, and reminds the staff to dynamically adjust the inlet and outlet water of the sewage treatment generation unit.

[0027] Preferably, during the data preprocessing process, the garbled data in the operating parameters of the sewage treatment plants are firstly eliminated, and then the operating parameters of each sewage treatment plant are normalized to convert the operating parameters of each sewage treatment plant into a preset standard format.

[0028] Preferably, the sewage treatment plant operating parameters do not include sewage treatment plant operating data during shutdown periods.

[0029] Preferably, the debugging process of the intelligent calculation system for the capacity correction coefficient of the sewage biochemical treatment unit includes a sample data set construction phase, a training phase and a testing phase;

[0030] Step 2.1, sample data set construction phase;

[0031] Acquire multiple groups of sewage treatment plant operating parameters and perform preprocessing, remove garbled data in the sewage treatment plant operating parameters, normalize the operating parameters of each sewage treatment plant and convert them into a preset standard format, determine the sludge loss and sewage biochemical treatment unit capacity correction coefficient corresponding to the operating parameters of each sewage treatment plant, and obtain multiple sewage biochemical treatment unit capacity correction coefficient intelligent calculation data samples;

[0032] Randomly assigning the intelligent calculation data samples of the sewage biochemical treatment unit capacity correction coefficient to the training sample set and the test sample set, generating a sample data set including the training sample set and the test sample set, which is used to train and verify the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system;

[0033] Step 2.2, establishing an intelligent calculation model for the capacity correction coefficient of the sewage biochemical treatment unit based on the RBF artificial neural network;

[0034] Step 2.3, using the training set to train the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit, to obtain a trained intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit;

[0035] Step 2.4, using the test set to train the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit, to obtain the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit after the test;

[0036] Step 2.5, encapsulate the tested sewage biochemical treatment unit capacity correction coefficient intelligent calculation model in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system.

[0037] Preferably, the sludge loss is the ratio between the change in concentration of all pollutants and the change in sludge concentration in the process from when the sewage enters the sewage treatment plant to when it is treated and discharged from the sewage treatment plant, and is used to characterize the sewage treatment capacity.

[0038] Preferably, the step 2.3 specifically comprises the following steps:

[0039] Step 2.3.1, set the training accuracy value;

[0040] Step 2.3.2, inputting the intelligent calculation data samples of the sewage biological treatment unit capacity correction coefficient in the training set into the sewage biological treatment unit capacity correction coefficient intelligent calculation model constructed in step 2.2, and calculating using the sewage biological treatment unit capacity correction coefficient intelligent calculation model to obtain the calculated value of the sewage biological treatment unit capacity correction coefficient;

[0041] Step 2.3.3, comparing the sewage biochemical treatment unit capacity correction coefficient calculated by the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model with the sewage biochemical treatment unit capacity correction coefficient in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation data sample, and calculating the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model. If the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is less than the preset accuracy value, proceed to step 2.3.4; otherwise, proceed to step 2.3.5;

[0042] Step 2.3.4, update the weights between the hidden layer and the output layer in the intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, obtain the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, continue to randomly select sewage biochemical treatment unit capacity correction coefficient intelligent calculation data samples in the training set and input them into the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, calculate the sewage biochemical treatment unit capacity correction coefficient using the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, and after obtaining the calculated value of the sewage biochemical treatment unit capacity correction coefficient, return to step 2.3.3;

[0043] Step 2.3.5, complete the training of the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit, and obtain the trained intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit.

[0044] Preferably, the step 2.4 specifically comprises the following steps:

[0045] Step 2.4.1, inputting the intelligent calculation data samples of the sewage biochemical treatment unit capacity correction coefficient in the test set into the trained sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, calculating the sewage biochemical treatment unit capacity correction coefficient using the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, and obtaining the calculated value of the sewage biochemical treatment unit capacity correction coefficient;

[0046] Step 2.4.2, compare the sewage biochemical treatment unit capacity correction coefficient calculated by the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model with the sewage biochemical treatment unit capacity correction coefficient in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation data sample, and calculate the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model. If the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is not less than the preset accuracy value, proceed to step 2.5; otherwise, return to step 2.3 and continue to use the training set to train the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model.

[0047] Preferably, in step 2, the sewage biochemical treatment unit capacity correction coefficient calculation model is constructed based on an RBF artificial neural network, and the RBF artificial neural network is set as follows:

[0048] The RBF artificial neural network includes an input layer, an RBF layer, a hidden layer and an output layer;

[0049] The input variable of the RBF artificial neural network is set to X, input variable X=[x1, x2, ..., xm], xm is the mth input variable, the base width variable of the RBF artificial neural network is set to B, base width variable B=[b1, b2, ..., bm], bm is the mth base width variable;

[0050] The RBF artificial neural network uses a Gaussian function as a radial basis function, as shown in formula (1):

[0051]

[0052] Where X is the input variable, Cj is the center of the Gaussian function, j is the serial number of the Gaussian function center, j = 1, 2, ..., m, m is the total number of Gaussian function centers, ||X-Cj|| is the Euclidean norm, and b is the basis width parameter of the node in the RBF artificial neural network;

[0053] The calculation formula of the sewage biochemical treatment unit capacity correction coefficient in the RBF artificial neural network is:

[0054] ym(k)=wh=w1h1+w2h2+……+wmhm (2)

[0055] Wherein, w is the weight between the hidden layer and the output layer, and ym(k) is the sewage biochemical treatment unit capacity correction coefficient calculated by the sewage biochemical treatment unit capacity correction coefficient calculation model at time k.

[0056] Preferably, in step 3, the calculation formula for the residual capacity of pollutants in the sewage treatment biochemical unit is:

[0057] R n =R×α (3)

[0058] Where Rn is the pollutant residual capacity of the biochemical unit of the sewage treatment plant at time n, R is the treatment scale in the design plan of the sewage treatment plant, and α is the capacity correction coefficient of the sewage biochemical treatment unit.

[0059] The beneficial effects of the present invention are:

[0060] The present invention proposes a system for calculating the residual capacity of pollutants in a sewage treatment biochemical unit. The system can grasp the treatment status of a sewage treatment plant in real time without adding or replacing complex monitoring instruments and equipment. By combining chemical sewage control data with an artificial intelligence algorithm, the capacity correction coefficient of the sewage biochemical treatment unit is calculated in real time to determine the residual capacity of pollutants in the sewage treatment biochemical unit. This forms an abnormal water inlet warning and control technology for sewage treatment plants. With the inlet water quality and flow rate as the optimization targets, the optimized control scheme for the inlet and outlet water of the sewage treatment biochemical unit of the sewage treatment plant is updated in time, thereby realizing digital intelligent management of the daily operation of the sewage treatment plant.

[0061] The present invention proposes a method for calculating the residual capacity of pollutants in a sewage treatment biochemical unit. An intelligent calculation model for the capacity correction coefficient of the sewage biochemical treatment unit is established based on an RBF artificial neural network. By introducing characteristic parameters into the intelligent calculation model for the capacity correction coefficient of the sewage biochemical treatment unit constructed based on the artificial neural network, accurate calculation of the capacity correction coefficient of the sewage biochemical treatment unit is achieved, which greatly shortens the calculation time and accuracy of the capacity correction coefficient of the sewage biochemical treatment unit, and provides reliable data support for sewage treatment plant staff to provide accurate sewage treatment biochemical unit inlet and outlet water optimization control schemes. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is a flow chart of a method for calculating the residual capacity of pollutants in a sewage treatment biochemical unit. DETAILED DESCRIPTION

[0063] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0064] Example 1

[0065] This embodiment proposes a system for calculating the residual capacity of pollutants in a sewage treatment biochemical unit, including a monitoring system, a sewage biochemical data processing system, an intelligent calculation system for a capacity correction coefficient of a sewage biochemical treatment unit, and an intelligent management and control system. The monitoring system, the sewage biochemical data processing system, the intelligent calculation system for a capacity correction coefficient of a sewage biochemical treatment unit, and the intelligent management and control system are connected in sequence via a wireless network or a wired network.

[0066] The monitoring system is connected to various monitoring instruments of the sewage treatment plant and is used to monitor and obtain the operating parameters of the sewage treatment plant in real time, including the sewage flow and pollutant concentration entering the biochemical unit of the sewage treatment plant, the sewage flow and pollutant concentration leaving the biochemical unit of the sewage treatment plant, the sewage liquid level of the biochemical unit, the pollutant concentration and operating process parameters.

[0067] In this embodiment, the biochemical unit operation parameters include dissolved oxygen, nitrification liquid return ratio, sludge return ratio, sludge concentration, sludge age and sludge discharge. The pollutants include COD, ammonia nitrogen, total nitrogen, total phosphorus, petroleum, volatile phenols and sulfides.

[0068] The sewage biochemical data processing system is connected to the monitoring system and is used for performing data preprocessing on the sewage treatment plant operation parameters acquired by the monitoring system.

[0069] In this embodiment, the data preprocessing includes eliminating garbled data and converting to a standard format. By eliminating garbled data in the sewage treatment plant operating parameters and converting them into a preset standard format, the sewage treatment plant operating parameters after data processing are obtained and sent to the sewage biochemical treatment capacity correction coefficient intelligent calculation system.

[0070] The sewage biochemical treatment unit capacity correction coefficient intelligent calculation system is connected to the sewage biochemical data processing system, and is used to calculate the sewage biochemical treatment unit capacity correction coefficient and the residual capacity of pollutants in the sewage treatment biochemical unit, including a sewage biochemical treatment unit capacity correction coefficient calculation model training module and a sewage biochemical treatment unit capacity correction coefficient calculation model.

[0071] The intelligent management and control system is connected to the intelligent calculation system of the capacity correction coefficient of the sewage biochemical treatment unit, and is used to generate an optimized control plan for the inlet and outlet water of the sewage treatment biochemical unit according to the residual capacity of pollutants in the sewage treatment biochemical unit, the inlet and outlet water quality of the sewage treatment biochemical unit, and the inlet and outlet water flow rate.

[0072] The intelligent management and control system is provided with an early warning alarm prompt module, in which the dynamic classification alarm standard of pollutant capacity and the dynamic classification alarm standard of sewage capacity are preset. The early warning alarm prompt module is connected to a preset terminal and is used to timely push early warning alarm information and the optimization control plan of the inlet and outlet water of the sewage treatment biochemical unit according to the remaining capacity of pollutants in the sewage treatment biochemical unit.

[0073] In this embodiment, the terminal is set as a mobile terminal and a central control room, the early warning alarm module is wirelessly connected to the mobile terminal and the central control room, and the early warning alarm module sends early warning alarm information and the optimization control plan for the inlet and outlet water of the sewage treatment biochemical unit to the preset mobile terminal in the form of text messages.

[0074] Example 2

[0075] This embodiment proposes a method for calculating the remaining capacity of pollutants in a sewage treatment biochemical unit, using the sewage treatment biochemical unit pollutant remaining capacity calculation system as described in Example 1, such as Figure 1 As shown, the specific steps include:

[0076] Step 1: Use the monitoring system to collect monitoring data from various monitoring instruments in the sewage treatment plant, obtain the operating parameters of the sewage treatment plant, transmit the acquired operating parameters of the sewage treatment plant to the sewage biochemical data processing system for data preprocessing, and input them into the pre-debugged sewage biochemical treatment unit capacity correction coefficient intelligent calculation system.

[0077] In this embodiment, during the data preprocessing process, the garbled data in the sewage treatment plant operating parameters are first eliminated, and then the operating parameters of each sewage treatment plant are normalized to convert the operating parameters of each sewage treatment plant into a preset standard format.

[0078] Step 2, using the sewage biochemical treatment unit capacity correction coefficient calculation model in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system to calculate and obtain the sewage biochemical treatment unit capacity correction coefficient.

[0079] Among them, the debugging process of the intelligent calculation system of the capacity correction coefficient of the sewage biochemical treatment unit includes the sample data set construction stage, the training stage and the testing stage.

[0080] Step 2.1, sample data set construction phase;

[0081] Acquire multiple groups of sewage treatment plant operating parameters and perform preprocessing. After eliminating the garbled data in the sewage treatment plant operating parameters, normalize the operating parameters of each sewage treatment plant and convert them into a preset standard format. Determine the sludge loss and sewage biochemical treatment unit capacity correction coefficient corresponding to the operating parameters of each sewage treatment plant, and obtain multiple sewage biochemical treatment unit capacity correction coefficient intelligent calculation data samples.

[0082] The data samples of intelligent calculation of capacity correction coefficient of biological sewage treatment unit are randomly assigned to training sample sets and test sample sets to generate a sample data set including training sample sets and test sample sets, which are used to train and verify the intelligent calculation model of capacity correction coefficient of biological sewage treatment unit in the intelligent calculation system of capacity correction coefficient of biological sewage treatment unit.

[0083] Step 2.2, establish an intelligent calculation model for the capacity correction coefficient of the sewage biochemical treatment unit based on the RBF artificial neural network.

[0084] Step 2.3, using the training set to train the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit, to obtain the trained intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit, specifically includes the following steps:

[0085] Step 2.3.1, set the training accuracy value;

[0086] Step 2.3.2, inputting the intelligent calculation data samples of the sewage biological treatment unit capacity correction coefficient in the training set into the sewage biological treatment unit capacity correction coefficient intelligent calculation model constructed in step 2.2, and calculating using the sewage biological treatment unit capacity correction coefficient intelligent calculation model to obtain the calculated value of the sewage biological treatment unit capacity correction coefficient;

[0087] Step 2.3.3, compare the sewage biochemical treatment unit capacity correction coefficient calculated by the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model with the sewage biochemical treatment unit capacity correction coefficient in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation data sample, calculate the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, if the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is less than the preset accuracy value, then proceed to step 2.3.4; otherwise, proceed to step 2.3.5;

[0088] Step 2.3.4, update the weights between the hidden layer and the output layer in the intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, obtain the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, continue to randomly select sewage biochemical treatment unit capacity correction coefficient intelligent calculation data samples in the training set and input them into the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, calculate the sewage biochemical treatment unit capacity correction coefficient using the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, and after obtaining the calculated value of the sewage biochemical treatment unit capacity correction coefficient, return to step 2.3.3;

[0089] Step 2.3.5, complete the training of the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit, and obtain the trained intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit.

[0090] Step 2.4, using the test set to train the intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, to obtain the tested intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, specifically includes the following steps:

[0091] Step 2.4.1, inputting the intelligent calculation data samples of the sewage biochemical treatment unit capacity correction coefficient in the test set into the trained sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, calculating the sewage biochemical treatment unit capacity correction coefficient using the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, and obtaining the calculated value of the sewage biochemical treatment unit capacity correction coefficient;

[0092] Step 2.4.2, compare the sewage biochemical treatment unit capacity correction coefficient calculated by the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model with the sewage biochemical treatment unit capacity correction coefficient in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation data sample, and calculate the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model. If the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is not less than the preset accuracy value, proceed to step 2.5; otherwise, return to step 2.3 and continue to use the training set to train the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model.

[0093] Step 2.5, encapsulate the tested sewage biochemical treatment unit capacity correction coefficient intelligent calculation model in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system.

[0094] Step 3: Calculate the residual capacity of pollutants in the sewage treatment biochemical unit based on the sewage treatment unit capacity correction coefficient.

[0095] In this embodiment, the calculation formula for the residual capacity of pollutants in the sewage treatment biochemical unit is:

[0096] R n =R× α (3)

[0097] Where Rn is the pollutant residual capacity of the biochemical unit of the sewage treatment plant at time n, R is the treatment scale in the design plan of the sewage treatment plant, and α is the capacity correction coefficient of the sewage biochemical treatment unit.

[0098] Step 4: The intelligent calculation system for the correction coefficient of the sewage biochemical treatment unit capacity transmits the calculated residual capacity of pollutants in the sewage biochemical treatment unit to the intelligent management and control system, and the intelligent management and control system generates an optimized control plan for the inlet and outlet water of the sewage biochemical treatment unit according to the residual capacity of pollutants in the sewage biochemical treatment unit, the inlet and outlet water quality and the inlet and outlet water flow of the sewage biochemical treatment unit;

[0099] Step 5. The intelligent management and control system generates early warning alarm information according to the preset dynamic classification alarm standard of pollutant capacity and the dynamic classification alarm standard of sewage capacity, transmits the early warning alarm information and the optimization control plan of the inlet and outlet water of the sewage treatment biochemical unit to the preset terminal, and reminds the staff to dynamically adjust the inlet and outlet water of the sewage treatment generation unit.

[0100] It can be seen that the present invention determines the residual capacity of pollutants in the sewage treatment biochemical unit by calculating the capacity correction coefficient of the sewage biochemical treatment unit, timely grasps the biochemical treatment potential of the biochemical unit in the petrochemical sewage treatment process, optimizes the control plan of the sewage treatment plant, and provides technical support for realizing dynamic optimization of the influent water quality and flow of the sewage treatment plant.

[0101] Example 3

[0102] In this embodiment, a method for calculating the remaining capacity of pollutants in a sewage treatment biochemical unit is proposed, using the sewage treatment biochemical unit pollutant remaining capacity calculation system as described in Example 1. The method for calculating the remaining capacity of pollutants in a sewage treatment biochemical unit specifically includes the following steps:

[0103] Step 1: Use the monitoring system to collect monitoring data from various monitoring instruments in the sewage treatment plant, obtain the operating parameters of the sewage treatment plant, transmit the acquired operating parameters of the sewage treatment plant to the sewage biochemical data processing system for data preprocessing, and input them into the pre-debugged sewage biochemical treatment unit capacity correction coefficient intelligent calculation system.

[0104] In this embodiment, during the data preprocessing process, the garbled data in the sewage treatment plant operating parameters are first eliminated, and then the operating parameters of each sewage treatment plant are normalized to convert the operating parameters of each sewage treatment plant into a preset standard format.

[0105] Step 2, using the sewage biochemical treatment unit capacity correction coefficient calculation model in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system to calculate and obtain the sewage biochemical treatment unit capacity correction coefficient.

[0106] Among them, the debugging process of the intelligent calculation system of the capacity correction coefficient of the sewage biochemical treatment unit includes the sample data set construction stage, the training stage and the testing stage.

[0107] Step 2.1, sample data set construction phase;

[0108] Acquire multiple groups of sewage treatment plant operating parameters and perform preprocessing. After eliminating the garbled data in the sewage treatment plant operating parameters, normalize the operating parameters of each sewage treatment plant and convert them into a preset standard format. Determine the sludge loss and sewage biochemical treatment unit capacity correction coefficient corresponding to the operating parameters of each sewage treatment plant, and obtain multiple sewage biochemical treatment unit capacity correction coefficient intelligent calculation data samples.

[0109] The data samples of intelligent calculation of capacity correction coefficient of biological sewage treatment unit are randomly assigned to training sample sets and test sample sets to generate a sample data set including training sample sets and test sample sets, which are used to train and verify the intelligent calculation model of capacity correction coefficient of biological sewage treatment unit in the intelligent calculation system of capacity correction coefficient of biological sewage treatment unit.

[0110] Step 2.2, establish an intelligent calculation model for the capacity correction coefficient of the sewage biochemical treatment unit based on the RBF artificial neural network.

[0111] Step 2.3, using the training set to train the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit, to obtain the trained intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit, specifically includes the following steps:

[0112] Step 2.3.1, set the training accuracy value;

[0113] Step 2.3.2, inputting the intelligent calculation data samples of the sewage biological treatment unit capacity correction coefficient in the training set into the sewage biological treatment unit capacity correction coefficient intelligent calculation model constructed in step 2.2, and calculating using the sewage biological treatment unit capacity correction coefficient intelligent calculation model to obtain the calculated value of the sewage biological treatment unit capacity correction coefficient;

[0114] Step 2.3.3, compare the sewage biochemical treatment unit capacity correction coefficient calculated by the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model with the sewage biochemical treatment unit capacity correction coefficient in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation data sample, calculate the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, if the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is less than the preset accuracy value, then proceed to step 2.3.4; otherwise, proceed to step 2.3.5;

[0115] Step 2.3.4, update the weights between the hidden layer and the output layer in the intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, obtain the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, continue to randomly select sewage biochemical treatment unit capacity correction coefficient intelligent calculation data samples in the training set and input them into the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, calculate the sewage biochemical treatment unit capacity correction coefficient using the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, and after obtaining the calculated value of the sewage biochemical treatment unit capacity correction coefficient, return to step 2.3.3;

[0116] Step 2.3.5, complete the training of the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit, and obtain the trained intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit.

[0117] Step 2.4, using the test set to train the intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, to obtain the tested intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, specifically includes the following steps:

[0118] Step 2.4.1, inputting the intelligent calculation data samples of the sewage biochemical treatment unit capacity correction coefficient in the test set into the trained sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, calculating the sewage biochemical treatment unit capacity correction coefficient using the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, and obtaining the calculated value of the sewage biochemical treatment unit capacity correction coefficient;

[0119] Step 2.4.2, compare the sewage biochemical treatment unit capacity correction coefficient calculated by the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model with the sewage biochemical treatment unit capacity correction coefficient in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation data sample, and calculate the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model. If the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is not less than the preset accuracy value, proceed to step 2.5; otherwise, return to step 2.3 and continue to use the training set to train the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model.

[0120] Step 2.5, encapsulate the tested sewage biochemical treatment unit capacity correction coefficient intelligent calculation model in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system.

[0121] In this embodiment, the calculation model of the capacity correction coefficient of the sewage biochemical treatment unit is constructed based on the RBF artificial neural network, and the RBF artificial neural network is set as follows:

[0122] The RBF artificial neural network includes an input layer, an RBF layer, a hidden layer and an output layer;

[0123] The input variable of the RBF artificial neural network is set to X, input variable X=[x1, x2, ..., xm], xm is the mth input variable, the base width variable of the RBF artificial neural network is set to B, base width variable B=[b1, b2, ..., bm], bm is the mth base width variable;

[0124] The RBF artificial neural network uses a Gaussian function as a radial basis function, as shown in formula (1):

[0125]

[0126] Where X is the input variable, Cj is the center of the Gaussian function, j is the serial number of the Gaussian function center, j = 1, 2, ..., m, m is the total number of Gaussian function centers, ||X-Cj|| is the Euclidean norm, and b is the basis width parameter of the node in the RBF artificial neural network;

[0127] The calculation formula of the sewage biochemical treatment unit capacity correction coefficient in the RBF artificial neural network is:

[0128] ym(k)=wh=w1h1+w2h2+……+wmhm (2)

[0129] Wherein, w is the weight between the hidden layer and the output layer, and ym(k) is the sewage biochemical treatment unit capacity correction coefficient calculated by the sewage biochemical treatment unit capacity correction coefficient calculation model at time k.

[0130] Step 3: Calculate the residual capacity of pollutants in the sewage treatment biochemical unit based on the sewage treatment unit capacity correction coefficient.

[0131] In this embodiment, the calculation formula for the residual capacity of pollutants in the sewage treatment biochemical unit is:

[0132] Rn=R×α (3)

[0133] Where Rn is the pollutant residual capacity of the biochemical unit of the sewage treatment plant at time n, R is the treatment scale in the design plan of the sewage treatment plant, and α is the capacity correction coefficient of the sewage biochemical treatment unit.

[0134] Step 4: The intelligent calculation system for the correction coefficient of the sewage biochemical treatment unit capacity transmits the calculated residual capacity of pollutants in the sewage biochemical treatment unit to the intelligent management and control system, and the intelligent management and control system generates an optimized control plan for the inlet and outlet water of the sewage biochemical treatment unit according to the residual capacity of pollutants in the sewage biochemical treatment unit, the inlet and outlet water quality and the inlet and outlet water flow of the sewage biochemical treatment unit;

[0135] Step 5. The intelligent management and control system generates early warning alarm information according to the preset dynamic classification alarm standard of pollutant capacity and the dynamic classification alarm standard of sewage capacity, transmits the early warning alarm information and the optimization control plan of the inlet and outlet water of the sewage treatment biochemical unit to the preset terminal, and reminds the staff to dynamically adjust the inlet and outlet water of the sewage treatment generation unit.

[0136] It can be seen that the present invention determines the residual capacity of pollutants in the sewage treatment biochemical unit by calculating the capacity correction coefficient of the sewage biochemical treatment unit, timely grasps the biochemical treatment potential of the biochemical unit in the petrochemical sewage treatment process, optimizes the control plan of the sewage treatment plant, and provides technical support for realizing dynamic optimization of the influent water quality and flow of the sewage treatment plant.

[0137] Example 4

[0138] This embodiment takes a sewage treatment plant of a certain refinery enterprise as an example. In this embodiment, the sewage treatment plant adopts AO treatment process, with a designed treatment scale of 400t / h. It is mainly designed to treat high-concentration sewage, including steam stripping purified water, alkali residue wastewater, flue gas desulfurization salt water, etc. The designed inlet and outlet water quality of the sewage treatment plant is shown in Table 1.

[0139] Table 1 Summary of designed inlet and outlet water quality of sewage treatment plants

[0140]

[0141] The system for calculating the remaining capacity of pollutants in the biochemical unit for sewage treatment described in Example 1 and the method for calculating the remaining capacity of pollutants in the biochemical unit for sewage treatment described in Example 3 are used for processing. The monitoring data of each monitoring instrument in the sewage treatment plant are collected by the monitoring system to obtain the operating parameters of the sewage treatment plant. The obtained operating parameters of the sewage treatment plant are transmitted to the sewage biochemical data processing system for data preprocessing, and are input into the pre-debugged sewage biochemical treatment unit capacity correction coefficient intelligent calculation system.

[0142] In this embodiment, the operation data of the sewage treatment plant within four months from January 1, 2022 to April 31, 2022 are selected as data samples, among which 69 groups of data samples from January 1, 2022 to March 10, 2022 (all data are daily average data) are imported into the sewage biochemical treatment unit capacity correction coefficient calculation model training module for training until the calculation accuracy of the sewage biochemical treatment unit capacity correction coefficient calculation model meets the expected requirements, and a total of 20 groups of data from March 11, 2022 to March 20, 2022 and April 1 to April 10, 2022 are selected as experimental data and imported into the sewage biochemical treatment unit capacity correction coefficient calculation model to further verify the calculation accuracy of the sewage biochemical treatment unit capacity correction coefficient calculation model.

[0143] The input variables are divided into two groups, among which Group A data input variables include: sewage pollutant concentration (COD, ammonia nitrogen, total nitrogen, total phosphorus, petroleum, volatile phenol, sulfide), process parameters of each device and link in the sewage treatment plant (mainly including dissolved oxygen, nitrification liquid return ratio, sludge return ratio, sludge age, sludge discharge and other process parameters), characteristic parameter sludge loss, a total of 12 input variables, to construct a 14-input 1-output RBF artificial neural network model.

[0144] The data input variables in Group B include: sewage pollutant concentrations (COD, ammonia nitrogen, total nitrogen, total phosphorus, petroleum, volatile phenols, sulfides), process parameters of each device and link in the sewage treatment plant (mainly including dissolved oxygen, nitrification liquid return ratio, sludge return ratio, sludge age, sludge discharge and other process parameters), totaling 11 input variables, and a 13-input and 1-output intelligent calculation model for the capacity correction coefficient of the sewage biochemical treatment unit based on the RBF artificial neural network was constructed.

[0145] The comparison between the calculation results of the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit in this embodiment and the actual treatment results is shown in Table 2.

[0146] Table 2 Comparison of calculation results of intelligent calculation model for capacity correction coefficient of sewage biochemical treatment unit

[0147]

[0148]

[0149] It can be seen from Table 2 that the system and method for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment proposed in the present invention are suitable for A / O treatment process. The calculation accuracy of the group A data model using the characteristic parameter sludge loss is higher than that of the group B data model not using the characteristic parameter, and the calculation result is more stable.

[0150] Therefore, the dynamic calculation method and system for the residual capacity of pollutants in the sewage treatment biochemical unit using the characteristic parameter sludge loss proposed in the present invention can better complete the calculation task of the residual capacity of the sewage treatment biochemical unit, realize abnormal early warning and regulation of the sewage treatment plant's water inlet, and provide a basis for guiding the sewage treatment plant staff to regulate the inlet and outlet water.

[0151] Example 5

[0152] This embodiment takes a sewage treatment plant of a certain refinery enterprise as an example. In this embodiment, the sewage treatment plant adopts A2 / O treatment process, with a designed treatment scale of 1000t / h. It is mainly designed to treat wastewater with high concentrations of pollutants such as N and P. The designed inlet and outlet water quality of the sewage treatment plant is shown in Table 3.

[0153] Table 3 Summary of designed inlet and outlet water quality of sewage treatment plants

[0154]

[0155] The system for calculating the remaining capacity of pollutants in the biochemical unit for sewage treatment described in Example 1 and the method for calculating the remaining capacity of pollutants in the biochemical unit for sewage treatment described in Example 3 are used for processing. The monitoring data of each monitoring instrument in the sewage treatment plant are collected by the monitoring system to obtain the operating parameters of the sewage treatment plant. The obtained operating parameters of the sewage treatment plant are transmitted to the sewage biochemical data processing system for data preprocessing, and are input into the pre-debugged sewage biochemical treatment unit capacity correction coefficient intelligent calculation system.

[0156] In this embodiment, the sewage treatment plant operation data within four months from April 1, 2022 to July 31, 2022 is used as a data sample, among which 71 groups of data samples from April 1, 2022 to June 10, 2022 (all data are daily average data) are imported into the sewage biochemical treatment unit capacity correction coefficient calculation model training module for training until the calculation accuracy of the sewage biochemical treatment unit capacity correction coefficient calculation model meets the expected requirements, and a total of 20 groups of data from June 11, 2022 to June 20, 2022 and from July 11 to July 20, 2022 are selected as experimental data and imported into the sewage biochemical treatment unit capacity correction coefficient calculation model to further verify the calculation accuracy of the sewage biochemical treatment unit capacity correction coefficient calculation model.

[0157] The input variables are divided into two groups, among which Group A data input variables include: sewage pollutant concentration (COD, ammonia nitrogen, total nitrogen, petroleum, total phosphorus, sulfide, volatile phenol), process parameters of each device and link in the sewage treatment plant (mainly including dissolved oxygen, nitrification liquid return ratio, sludge return ratio, sludge age, sludge discharge and other process parameters), characteristic parameter sludge loss, a total of 12 input variables, to construct a 12-input 1-output RBF artificial neural network model.

[0158] The data input variables of Group B include: sewage pollutant concentrations (COD, ammonia nitrogen, total nitrogen, petroleum, total phosphorus, sulfide, volatile phenols), process parameters of each device and link in the sewage treatment plant (mainly including dissolved oxygen, nitrification liquid return ratio, sludge return ratio, sludge age, sludge discharge and other process parameters), a total of 11 input variables, and an 11-input and 1-output intelligent calculation model for the capacity correction coefficient of the sewage biochemical treatment unit based on the RBF artificial neural network is constructed.

[0159] The comparison between the calculation results of the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit in this embodiment and the actual treatment results is shown in Table 4.

[0160] Table 4 Comparison of calculation results of intelligent calculation model for capacity correction coefficient of sewage biochemical treatment unit

[0161]

[0162]

[0163] It can be seen from Table 4 that the system and method for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment proposed in the present invention are suitable for the A2 / O treatment process. The calculation accuracy of the group A data model using the characteristic parameter sludge loss is higher than that of the group B data model not using the characteristic parameter, and the calculation result is more stable.

[0164] Therefore, the dynamic calculation method and system for the residual capacity of pollutants in the sewage treatment biochemical unit using the characteristic parameter sludge loss proposed in the present invention can better complete the calculation task of the residual capacity of the sewage treatment biochemical unit, realize abnormal early warning and regulation of the sewage treatment plant's water inlet, and provide a basis for guiding the sewage treatment plant staff to regulate the inlet and outlet water.

[0165] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms used herein are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0166] In the present invention, the directions or positional relationships indicated by terms such as "upper", "lower", "bottom", "top", etc. are based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention. They do not specifically refer to any part or element in the present invention and cannot be understood as limitations on the present invention.

[0167] In the present invention, terms such as "connected" and "connection" should be understood in a broad sense, indicating that the connection can be fixed, integral or detachable; it can be directly connected or indirectly connected through an intermediate medium. Relevant scientific research or technical personnel in this field can determine the specific meaning of the above terms in the present invention according to specific circumstances, and they should not be understood as limiting the present invention.

[0168] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A system for calculating the residual capacity of pollutants in a sewage treatment biochemical unit, characterized in that: It includes a monitoring system, a sewage biochemical data processing system, an intelligent calculation system for the capacity correction coefficient of the sewage biochemical treatment unit and an intelligent management and control system, and the monitoring system, the sewage biochemical data processing system, the intelligent calculation system for the capacity correction coefficient of the sewage biochemical treatment unit and the intelligent management and control system are connected in sequence.

2. The system for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 1, characterized in that: The monitoring system, the sewage biochemical data processing system, the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system and the intelligent management and control system are connected via a wireless network or a wired network.

3. The system for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 1, characterized in that: The monitoring system is connected to various monitoring instruments of the sewage treatment plant and is used to monitor and obtain the operating parameters of the sewage treatment plant in real time, including the sewage flow and pollutant concentration entering the biochemical unit of the sewage treatment plant, the sewage flow and pollutant concentration leaving the biochemical unit of the sewage treatment plant, the sewage liquid level of the biochemical unit, the pollutant concentration and operating process parameters.

4. The system for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 3 is characterized in that: The operating parameters of the biochemical unit include dissolved oxygen, nitrification liquid return ratio, sludge return ratio, sludge concentration, sludge age and sludge discharge amount.

5. The system for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 3 is characterized in that: The pollutants include COD, ammonia nitrogen, total nitrogen, total phosphorus, petroleum, volatile phenols, and sulfides.

6. The system for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 1, characterized in that: The sewage biochemical data processing system is connected to the monitoring system and is used for performing data preprocessing on the sewage treatment plant operation parameters acquired by the monitoring system.

7. The system for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 6, characterized in that: The data preprocessing includes removing garbled data and converting to a standard format. By removing the garbled data in the sewage treatment plant operating parameters and converting them into a preset standard format, the sewage treatment plant operating parameters after data processing are obtained and sent to the sewage biochemical treatment capacity correction coefficient intelligent calculation system.

8. The system for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 1, characterized in that: The sewage biochemical treatment unit capacity correction coefficient intelligent calculation system is connected to the sewage biochemical data processing system, and is used to calculate the sewage biochemical treatment unit capacity correction coefficient and the residual capacity of pollutants in the sewage treatment biochemical unit, including a sewage biochemical treatment unit capacity correction coefficient calculation model training module and a sewage biochemical treatment unit capacity correction coefficient calculation model.

9. The system for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 1, characterized in that: The intelligent management and control system is connected to the intelligent calculation system of the capacity correction coefficient of the sewage biochemical treatment unit, and is used to generate an optimized control plan for the inlet and outlet water of the sewage treatment biochemical unit according to the residual capacity of pollutants in the sewage treatment biochemical unit, the inlet and outlet water quality of the sewage treatment biochemical unit, and the inlet and outlet water flow rate.

10. The system for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 9, characterized in that: The intelligent management and control system is provided with an early warning alarm prompt module, in which the dynamic classification alarm standard of pollutant capacity and the dynamic classification alarm standard of sewage capacity are preset. The early warning alarm prompt module is connected to a preset terminal and is used to timely push early warning alarm information and the optimization control plan of the inlet and outlet water of the sewage treatment biochemical unit according to the remaining capacity of pollutants in the sewage treatment biochemical unit.

11. The system for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 10, characterized in that: The terminal is configured as a mobile terminal and a central control room, and the early warning alarm module is wirelessly connected to the mobile terminal and the central control room.

12. The system for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 11, characterized in that: The early warning alarm module sends early warning alarm information and the inlet and outlet water optimization control plan of the sewage treatment biochemical unit to a preset mobile terminal in the form of text messages.

13. A method for calculating the residual capacity of pollutants in a sewage treatment biochemical unit, characterized in that: The system for calculating the residual capacity of pollutants in a biochemical unit for sewage treatment according to any one of claims 1 to 12 specifically comprises the following steps: Step 1: Use the monitoring system to collect monitoring data from various monitoring instruments in the sewage treatment plant, obtain the operating parameters of the sewage treatment plant, transmit the acquired operating parameters of the sewage treatment plant to the sewage biochemical data processing system for data preprocessing, and input them into the pre-debugged sewage biochemical treatment unit capacity correction coefficient intelligent calculation system; Step 2, using a sewage biochemical treatment unit capacity correction coefficient calculation model in a sewage biochemical treatment unit capacity correction coefficient intelligent calculation system to calculate and obtain a sewage biochemical treatment unit capacity correction coefficient; Step 3, based on the sewage biochemical treatment unit capacity correction coefficient, calculate the residual capacity of pollutants in the sewage biochemical treatment unit; Step 4: The intelligent calculation system for the correction coefficient of the sewage biochemical treatment unit capacity transmits the calculated residual capacity of pollutants in the sewage biochemical treatment unit to the intelligent management and control system, and the intelligent management and control system generates an optimized control plan for the inlet and outlet water of the sewage biochemical treatment unit according to the residual capacity of pollutants in the sewage biochemical treatment unit, the inlet and outlet water quality and the inlet and outlet water flow of the sewage biochemical treatment unit; Step 5. The intelligent management and control system generates early warning alarm information according to the preset dynamic classification alarm standard of pollutant capacity and the dynamic classification alarm standard of sewage capacity, transmits the early warning alarm information and the optimization control plan of the inlet and outlet water of the sewage treatment biochemical unit to the preset terminal, and reminds the staff to dynamically adjust the inlet and outlet water of the sewage treatment generation unit.

14. The method for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 13, characterized in that: During the data preprocessing process, the garbled data in the operating parameters of the sewage treatment plants are firstly eliminated, and then the operating parameters of each sewage treatment plant are normalized, and the operating parameters of each sewage treatment plant are converted into a preset standard format.

15. The method for calculating the residual capacity of pollutants in the biochemical unit of sewage treatment according to claim 14, characterized in that: The sewage treatment plant operating parameters do not include the sewage treatment plant operating data during the shutdown period.

16. The method for calculating the residual capacity of pollutants in a sewage treatment biochemical unit according to claim 13, characterized in that: The debugging process of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system includes a sample data set construction phase, a training phase and a testing phase; Step 2.1, sample data set construction phase; Acquire multiple groups of sewage treatment plant operating parameters and perform preprocessing, remove garbled data in the sewage treatment plant operating parameters, normalize the operating parameters of each sewage treatment plant and convert them into a preset standard format, determine the sludge loss and sewage biochemical treatment unit capacity correction coefficient corresponding to the operating parameters of each sewage treatment plant, and obtain multiple sewage biochemical treatment unit capacity correction coefficient intelligent calculation data samples; Randomly assigning the intelligent calculation data samples of the sewage biochemical treatment unit capacity correction coefficient to the training sample set and the test sample set, generating a sample data set including the training sample set and the test sample set, which is used to train and verify the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system; Step 2.2, establishing an intelligent calculation model for the capacity correction coefficient of the sewage biochemical treatment unit based on the RBF artificial neural network; Step 2.3, using the training set to train the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit, to obtain a trained intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit; Step 2.4, using the test set to train the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit, to obtain the intelligent calculation model of the capacity correction coefficient of the sewage biochemical treatment unit after the test; Step 2.5, encapsulating the tested sewage biochemical treatment unit capacity correction coefficient intelligent calculation model in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system.

17. The method for calculating the residual capacity of pollutants in a sewage treatment biochemical unit according to claim 16, characterized in that: The sludge loss is the ratio between the change in concentration of all pollutants and the change in sludge concentration from the time the sewage enters the sewage treatment plant to the time it is treated and discharged from the sewage treatment plant, and is used to characterize the sewage treatment capacity.

18. The method for calculating the residual capacity of pollutants in a sewage treatment biochemical unit according to claim 16, characterized in that: The step 2.3 specifically includes the following steps: Step 2.3.1, set the training accuracy value; Step 2.3.2, inputting the intelligent calculation data samples of the sewage biological treatment unit capacity correction coefficient in the training set into the sewage biological treatment unit capacity correction coefficient intelligent calculation model constructed in step 2.2, and calculating using the sewage biological treatment unit capacity correction coefficient intelligent calculation model to obtain the calculated value of the sewage biological treatment unit capacity correction coefficient; Step 2.3.3, compare the sewage biochemical treatment unit capacity correction coefficient calculated by the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model with the sewage biochemical treatment unit capacity correction coefficient in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation data sample, calculate the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, if the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is less than the preset accuracy value, then proceed to step 2.3.4; otherwise, proceed to step 2.3.5; Step 2.3.4, update the weights between the hidden layer and the output layer in the intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, obtain the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, continue to randomly select sewage biochemical treatment unit capacity correction coefficient intelligent calculation data samples in the training set and input them into the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, calculate the sewage biochemical treatment unit capacity correction coefficient using the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, and after obtaining the calculated value of the sewage biochemical treatment unit capacity correction coefficient, return to step 2.3.3; Step 2.3.5, completing the training of the intelligent calculation model for the capacity correction coefficient of the sewage biochemical treatment unit, and obtaining the trained intelligent calculation model for the capacity correction coefficient of the sewage biochemical treatment unit.

19. The method for calculating the residual capacity of pollutants in a sewage treatment biochemical unit according to claim 16, characterized in that: The step 2.4 specifically includes the following steps: Step 2.4.1, inputting the intelligent calculation data samples of the sewage biochemical treatment unit capacity correction coefficient in the test set into the trained sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, calculating the sewage biochemical treatment unit capacity correction coefficient using the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, and obtaining the calculated value of the sewage biochemical treatment unit capacity correction coefficient; Step 2.4.2, compare the sewage biochemical treatment unit capacity correction coefficient calculated by the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model with the sewage biochemical treatment unit capacity correction coefficient in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation data sample, and calculate the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model. If the accuracy value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is not less than the preset accuracy value, proceed to step 2.5; otherwise, return to step 2.3 and continue to use the training set to train the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model.

20. The method for calculating the residual capacity of pollutants in a sewage treatment biochemical unit according to claim 16, characterized in that: In step 2, the calculation model of the sewage biochemical treatment unit capacity correction coefficient is constructed based on the RBF artificial neural network, and the RBF artificial neural network is set as follows: The RBF artificial neural network includes an input layer, an RBF layer, a hidden layer and an output layer; The input variable of the RBF artificial neural network is set to X, input variable X=[x1, x2, ..., xm], xm is the mth input variable, the base width variable of the RBF artificial neural network is set to B, base width variable B=[b1, b2, ..., bm], bm is the mth base width variable; The RBF artificial neural network uses a Gaussian function as a radial basis function, as shown in formula (1): Where X is the input variable, Cj is the center of the Gaussian function, j is the serial number of the Gaussian function center, j = 1, 2, ..., m, m is the total number of Gaussian function centers, ||X-Cj|| is the Euclidean norm, and b is the basis width parameter of the node in the RBF artificial neural network; The calculation formula of the sewage biochemical treatment unit capacity correction coefficient in the RBF artificial neural network is: ym(k)=wh=w1h1+w2h2+……+wmhm (2) Wherein, w is the weight between the hidden layer and the output layer, and ym(k) is the sewage biochemical treatment unit capacity correction coefficient calculated by the sewage biochemical treatment unit capacity correction coefficient calculation model at time k.

21. The method for calculating the residual capacity of pollutants in a sewage treatment biochemical unit according to claim 13, characterized in that: In step 3, the calculation formula for the residual capacity of pollutants in the sewage treatment biochemical unit is: Rn=R×α (3) Where Rn is the pollutant residual capacity of the biochemical unit of the sewage treatment plant at time n, R is the treatment scale in the design plan of the sewage treatment plant, and α is the capacity correction coefficient of the sewage biochemical treatment unit.

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