A system and method for calculating the residual capacity of pollutants in a biochemical unit of sewage treatment

Abstract

The application discloses a sewage treatment biochemical unit pollutant residual capacity calculation system and method. The sewage treatment biochemical unit pollutant residual capacity calculation system is used for monitoring operation parameters of a sewage treatment plant, sewage biochemical data processing system is used for processing the obtained operation parameters of the sewage treatment plant, a sewage biochemical treatment unit capacity correction coefficient calculation model pre-trained and pre-stored in a sewage biochemical treatment unit capacity correction coefficient intelligent calculation system is used for calculating the sewage biochemical treatment unit capacity correction coefficient, the sewage treatment biochemical unit pollutant residual capacity is determined, and the sewage treatment biochemical unit pollutant residual capacity is transmitted to an intelligent management and control system to generate a sewage treatment biochemical unit influent and effluent optimization regulation and control scheme.

Description

TECHNICAL FIELD

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

[0002] The sewage treatment industry is one of the new strategic industries in China, and is one of the key industries in the Water Pollution Prevention and Control Action Plan (Water Ten Articles) of China. The plan clearly states that water pollutants need to be comprehensively controlled. Petrochemical enterprises, as major producers, generate a large amount of oily and sulfur-containing sewage during production and operation. This type of sewage has complex composition, large fluctuations in water quality and quantity, high organic matter concentration, poor biodegradability, and other processing bottlenecks. Microorganisms in the biochemical unit of sewage treatment are easily impacted, and timely control can cause abnormal operating conditions in the sewage treatment plant, and even lead to wastewater discharge exceeding standards.

[0003] Due to the late start of the water treatment industry in China, the sewage treatment technology and management level of sewage treatment plants are relatively backward at the present stage, leading to unstable operation of the biochemical unit of sewage treatment and difficulty in achieving precise and standard discharge of sewage pollutants.

[0004] Chinese patent CN 112363391 A discloses a sludge bulking suppression method based on adaptive segmented sliding mode control. The method is aimed at the problem of sludge bulking abnormal operating conditions in the sewage treatment process, which is difficult to transition to normal conditions. The method realizes the suppression of sludge bulking. The method uses a fuzzy neural network to obtain water quality parameters in the sewage treatment process to predict the sludge volume index and determine the operating conditions of the process, providing a reference signal for the switching of the controller. A segmented sliding mode controller with an adaptive switching mechanism is designed to regulate the dissolved oxygen concentration and nitrate nitrogen concentration, coordinate the biochemical reaction process, and improve the sludge settling characteristics, ensuring that the sewage treatment process can return to normal conditions when sludge bulking occurs.

[0005] The Chinese invention patent CN 115578015 A discloses a sewage treatment whole process monitoring method and system based on Internet of Things and a storage medium, which specifically comprises: obtaining current sewage treatment process information, setting key water quality monitoring indicators of water quality monitoring points; obtaining water quality monitoring results of each point through the key water quality monitoring indicators, constructing a sewage treatment whole process model, and performing real-time monitoring and control according to the water quality monitoring results by using the sewage treatment whole process model; judging fault early warning information of the sewage treatment process, adding a fault detection model for real-time learning through deep learning, performing fault tracing of the sewage treatment process according to the water quality monitoring results and multivariate working condition data, and performing visual display through the sewage treatment whole process model. The invention realizes remote monitoring of the whole sewage treatment process through the Internet of Things technology, realizes dynamic monitoring of water quality changes and 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 emphasize the method of deep learning, and do not characterize the biochemical unit treatment capacity of the chemical sewage treatment process, which cannot effectively evaluate the disposal capacity of the biochemical unit, and it is difficult to ensure that the sewage treatment facility reaches a stable operation state. Therefore, it is urgent to propose a sewage treatment biochemical unit pollutant residual capacity calculation system and method, which calculates the biochemical disposal potential of the biochemical unit of the petrochemical sewage treatment process, realizes dynamic optimization of the influent water quality and flow of the sewage treatment process. SUMMARY

[0007] In view of the problem that the dynamic calculation of the pollutant residual capacity of the sewage treatment biochemical unit is difficult at present, the present application proposes a sewage treatment biochemical unit pollutant residual capacity calculation system and method, which combines chemical sewage control data with artificial intelligence algorithm, determines the pollutant residual capacity of the sewage treatment biochemical unit by real-time calculation of the sewage biochemical treatment unit capacity correction coefficient, timely grasps the biochemical disposal potential of the biochemical unit in the petrochemical sewage treatment process, optimizes the regulation and control scheme of the sewage treatment plant and alarms the abnormal influent, and realizes dynamic optimization of the influent water quality and flow of the sewage treatment plant.

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

[0009] A sewage treatment biochemical unit pollutant residual capacity calculation system, comprising a monitoring system, a sewage biochemical data processing system, a sewage biochemical treatment unit capacity correction coefficient intelligent calculation system and an intelligent control system, the monitoring system, the sewage biochemical data processing system, the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system and the intelligent control system are connected in sequence.

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

[0011] Preferably, the monitoring system is connected with each monitoring instrument of the sewage treatment plant, for real-time monitoring and obtaining the operation 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 out of the biochemical unit of the sewage treatment plant, the sewage liquid level, the pollutant concentration and the operation process parameters of the biochemical unit.

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

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

[0014] Preferably, the sewage biochemical data processing system is connected with the monitoring system, for data preprocessing of the operation parameters of the sewage treatment plant obtained by the monitoring system.

[0015] Preferably, the data preprocessing includes removing garbled data and standard format conversion, by removing the garbled data in the operation parameters of the sewage treatment plant and converting into a preset standard format, the data-processed operation parameters of the sewage treatment plant 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 with the sewage biochemical data processing system, for calculating the sewage biochemical treatment unit capacity correction coefficient and the residual capacity of 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 with the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system, for generating an optimal control scheme for the inflow and outflow of the sewage treatment biochemical unit according to the residual capacity of the sewage treatment biochemical unit, the water quality and flow of the inflow and outflow of the sewage treatment biochemical unit.

[0018] Preferably, the intelligent management and control system is provided with a pre-warning and alarm prompt module, which is pre-set with pollutant capacity dynamic grading alarm standards and sewage capacity dynamic grading alarm standards, and is connected with a preset terminal, for timely pushing pre-warning and alarm information and the optimal control scheme for the inflow and outflow of the sewage treatment biochemical unit according to the residual capacity of the sewage treatment biochemical unit.

[0019] Preferably, the terminal is set 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 an optimal control scheme for the influent and effluent of the biochemical unit of the sewage treatment plant to the preset mobile terminal in the form of a short message.

[0021] A sewage treatment biochemical unit pollutant residual capacity calculation method adopts the sewage treatment biochemical unit pollutant residual capacity calculation system as described above, and specifically includes the following steps:

[0022] Step 1: Collecting monitoring data of each monitoring instrument in the sewage treatment plant by using the monitoring system, obtaining the operation parameters of the sewage treatment plant, transmitting the obtained operation parameters of the sewage treatment plant to the sewage biochemical data processing system for data preprocessing, and inputting to the pre-commissioned sewage biochemical treatment unit capacity correction coefficient intelligent calculation system;

[0023] Step 2: Calculating the sewage biochemical treatment unit capacity correction coefficient by using the sewage biochemical treatment unit capacity correction coefficient calculation model in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system;

[0024] Step 3: Based on the sewage biochemical treatment unit capacity correction coefficient, the sewage treatment biochemical unit pollutant residual capacity is calculated;

[0025] Step 4: The sewage biochemical treatment unit capacity correction coefficient intelligent calculation system transmits the calculated sewage treatment biochemical unit pollutant residual capacity to the intelligent control system, and the intelligent control system generates an optimal control scheme for the influent and effluent of the sewage treatment biochemical unit according to the sewage treatment biochemical unit pollutant residual capacity, the influent and effluent quality of the sewage treatment biochemical unit, and the influent and effluent flow;

[0026] Step 5: The intelligent control system generates early warning alarm information according to the preset pollutant capacity dynamic grading alarm standard and the sewage capacity dynamic grading alarm standard, transmits the early warning alarm information and the optimal control scheme for the influent and effluent of the sewage treatment biochemical unit to the preset terminal, and reminds the staff to dynamically adjust the influent and effluent of the sewage treatment generation unit.

[0027] Preferably, in the data preprocessing process, the garbled data in the operation parameters of the sewage treatment plant is first removed, and then each operation parameter of the sewage treatment plant is normalized to convert each operation parameter of the sewage treatment plant into a preset standard format.

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

[0029] Preferably, the commissioning process of the sewage biochemical treatment unit capacity correction coefficient intelligent computing system comprises a sample data set construction phase, a training phase and a testing phase.

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

[0031] A plurality of sewage treatment plant operating parameters are obtained and preprocessed. After removing the random code data in the sewage treatment plant operating parameters, the sewage treatment plant operating parameters are normalized and converted to a predetermined standard format. The sludge loss and sewage biochemical treatment unit capacity correction coefficient corresponding to each sewage treatment plant operating parameter are determined, and a plurality of sewage biochemical treatment unit capacity correction coefficient intelligent computing data samples are obtained.

[0032] The sewage biochemical treatment unit capacity correction coefficient intelligent computing data samples are randomly assigned to the training sample set and the testing sample set, and a sample data set comprising the training sample set and the testing sample set is generated, which is used to train and verify the sewage biochemical treatment unit capacity correction coefficient intelligent computing model in the sewage biochemical treatment unit capacity correction coefficient intelligent computing system.

[0033] Step 2.2, establishing a sewage biochemical treatment unit capacity correction coefficient intelligent computing model based on RBF artificial neural network;

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

[0035] Step 2.4, training the sewage biochemical treatment unit capacity correction coefficient intelligent computing model using the testing set to obtain a tested sewage biochemical treatment unit capacity correction coefficient intelligent computing model;

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

[0037] Preferably, the sludge loss is the ratio between the change value of all pollutant concentrations and the change value of sludge concentration during the process of sewage entering the sewage plant to being treated and discharged from the sewage treatment plant, which is used to represent the sewage treatment capacity.

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

[0039] Step 2.3.1, setting the precision value of training;

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

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

[0042] Step 2.3.4, updating the weight value between the hidden layer and the output layer in the intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient to obtain the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, continuing to randomly select the intelligent calculation data sample of the sewage biochemical treatment unit capacity correction coefficient in the training set and inputting the same into the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient, calculating the sewage biochemical treatment unit capacity correction coefficient by using the updated intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient to obtain the calculated value of the sewage biochemical treatment unit capacity correction coefficient, and returning to step 2.3.3;

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

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

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

[0046] Step 2.4.2, the calculated wastewater biochemical treatment unit capacity correction coefficient is compared with the wastewater biochemical treatment unit capacity correction coefficient in the wastewater biochemical treatment unit capacity correction coefficient intelligent calculation data sample, the precision value of the wastewater biochemical treatment unit capacity correction coefficient intelligent calculation model is calculated, if the precision value of the wastewater biochemical treatment unit capacity correction coefficient intelligent calculation model is not less than the preset precision value, step 2.5 is entered; Otherwise, return to step 2.3, continue to train the wastewater biochemical treatment unit capacity correction coefficient intelligent calculation model by using the training set.

[0047] Preferably, in the step 2, the wastewater biochemical treatment unit capacity correction coefficient calculation model is constructed based on the 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 as X, the input variable X = [x1, x2, …, xm], xm is the mth input variable, the base width variable of the RBF artificial neural network is set as B, the base width variable B = [b1, b2, …, bm], bm is the mth base width variable.

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

[0051]

[0052] In the formula, 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 base width parameter of the node in the RBF artificial neural network.

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

[0054] ym(k) = wh = w1h1 + w2h2 + … + wmhm (2)

[0055] In the formula, w is the weight value between the hidden layer and the output layer, and ym(k) is the wastewater biochemical treatment unit capacity correction coefficient calculated by the wastewater biochemical treatment unit capacity correction coefficient calculation model at k moment.

[0056] Preferably, in the step 3, the calculation formula of the wastewater treatment biochemical unit pollutant residual capacity is:

[0057] R n = R xα (3)

[0058] In the formula, Rn is the residual capacity of pollutants in the biochemical unit of the wastewater treatment plant at the n time, R is the treatment scale in the design scheme of the wastewater treatment plant, and a is the capacity correction coefficient of the wastewater biochemical treatment unit.

[0059] The present application has the beneficial effects that:

[0060] The wastewater treatment biochemical unit pollutant residual capacity calculation system provided by the present application can realize real-time monitoring of the treatment condition of the wastewater treatment plant without adding or replacing complex monitoring instrument equipment, and can realize real-time calculation of the wastewater biochemical treatment unit capacity correction coefficient to determine the wastewater treatment biochemical unit pollutant residual capacity by combining the chemical wastewater control data with the artificial intelligence algorithm, form an abnormal water inflow early warning and regulation and control technology for the wastewater treatment plant, take the water quality and flow as the optimization target, update the wastewater treatment biochemical unit inflow and outflow optimization regulation and control scheme of the wastewater treatment plant in a timely manner, and realize digital intelligent management of the daily operation of the wastewater treatment plant.

[0061] The wastewater treatment biochemical unit pollutant residual capacity calculation method provided by the present application can realize accurate calculation of the wastewater biochemical treatment unit capacity correction coefficient by introducing the characteristic parameters into the wastewater biochemical treatment unit capacity correction coefficient intelligent calculation model based on the artificial neural network, greatly shorten the calculation time and calculation precision of the wastewater biochemical treatment unit capacity correction coefficient, and provide reliable data support for the wastewater treatment plant workers to provide accurate wastewater treatment biochemical unit inflow and outflow optimization regulation and control scheme. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 The present application is a flow chart of a wastewater treatment biochemical unit pollutant residual capacity calculation method. DETAILED DESCRIPTION

[0063] The present application will be further described in detail below in combination with the drawings and specific embodiments:

[0064] Embodiment 1

[0065] The present application is a wastewater treatment biochemical unit pollutant residual capacity calculation system, which comprises a monitoring system, a wastewater biochemical data processing system, a wastewater biochemical treatment unit capacity correction coefficient intelligent calculation system and an intelligent control system, and the monitoring system, the wastewater biochemical data processing system, the wastewater biochemical treatment unit capacity correction coefficient intelligent calculation system and the intelligent control system are connected in sequence through a wireless network or a wired network.

[0066] The monitoring system is connected with each monitoring instrument of the sewage treatment plant, and is used for real-time monitoring and obtaining operation parameters of the sewage treatment plant, including sewage flow and pollutant concentration entering a biochemical unit of the sewage treatment plant, sewage flow and pollutant concentration leaving the biochemical unit of the sewage treatment plant, sewage liquid level, pollutant concentration and operation process parameters of the biochemical unit.

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

[0068] The sewage biochemical data processing system is connected with the monitoring system, and is used for data preprocessing of the operation parameters of the sewage treatment plant obtained by the monitoring system.

[0069] In the embodiment, the data preprocessing includes removing garbled data and standard format conversion. By removing the garbled data in the operation parameters of the sewage treatment plant and converting into a preset standard format, the operation parameters of the sewage treatment plant 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 with the sewage biochemical data processing system, and is used for calculating the sewage biochemical treatment unit capacity correction coefficient and the pollutant residual capacity of 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 with the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system, and is used for generating an in-out water optimization control scheme of the sewage treatment biochemical unit according to the pollutant residual capacity of the sewage treatment biochemical unit, in-out water quality and in-out water flow of the sewage treatment biochemical unit.

[0072] The intelligent management and control system is provided with a pre-warning alarm prompt module, and the pre-warning alarm prompt module is pre-set with pollutant capacity dynamic grading alarm standards and sewage capacity dynamic grading alarm standards. The pre-warning alarm prompt module is connected with a preset terminal, and is used for timely pushing pre-warning alarm information and the in-out water optimization control scheme of the sewage treatment biochemical unit according to the pollutant residual capacity of the sewage treatment biochemical unit.

[0073] In the embodiment, the terminal is set as a mobile terminal and a central control room, the pre-warning alarm module is wirelessly connected with the mobile terminal and the central control room, and the pre-warning alarm module sends the pre-warning alarm information and the in-out water optimization control scheme of the sewage treatment biochemical unit to the preset mobile terminal in the form of a short message.

[0074] Embodiment 2

[0075] A method for calculating the residual capacity of pollutants in a biochemical unit of a sewage treatment plant is provided in the embodiment. A sewage treatment biochemical unit pollutant residual capacity calculation system as described in Embodiment 1 is used, as shown in Figure 1 and specifically includes the following steps:

[0076] Step 1: Use the monitoring system to collect monitoring data of each monitoring instrument in the sewage treatment plant, obtain the operation parameters of the sewage treatment plant, transmit the obtained operation parameters of the sewage treatment plant to the sewage biochemical data processing system for data preprocessing, and input them into the pre-adjusted sewage biochemical treatment unit capacity correction coefficient intelligent calculation system.

[0077] In this embodiment, during the data preprocessing process, the garbled data in the operation parameters of the sewage treatment plant is first removed, and then each sewage treatment plant operation parameter is normalized to convert each sewage treatment plant operation parameter into a pre-set standard format.

[0078] Step 2: Calculate the sewage biochemical treatment unit capacity correction coefficient using the sewage biochemical treatment unit capacity correction coefficient calculation model in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system.

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

[0080] Step 2.1: Sample data set construction stage

[0081] Obtain multiple sets of sewage treatment plant operation parameters and preprocess them. After removing the garbled data in the sewage treatment plant operation parameters, normalize each sewage treatment plant operation parameter and convert it into a pre-set standard format, determine the corresponding sludge loss and sewage biochemical treatment unit capacity correction coefficient for each sewage treatment plant operation parameter, and obtain multiple sewage biochemical treatment unit capacity correction coefficient intelligent calculation data samples.

[0082] Randomly assign the sewage biochemical treatment unit capacity correction coefficient intelligent calculation data samples to the training sample set and the testing sample set, generate a sample data set including the training sample set and the testing sample set, and use it 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.

[0083] Step 2.2: Establish a sewage biochemical treatment unit capacity correction coefficient intelligent calculation model based on RBF artificial neural network.

[0084] Step 2.3.2, input the intelligent calculation data sample of the sewage biochemical treatment unit capacity correction coefficient in the training set into the intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient constructed in step 2.2, and calculate the intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient to obtain the calculation value of the sewage biochemical treatment unit capacity correction coefficient;

[0085] Step 2.3.1, set the precision value of the training;

[0086] Step 2.3.2, input the intelligent calculation data sample of the sewage biochemical treatment unit capacity correction coefficient in the training set into the intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient constructed in step 2.2, and calculate the intelligent calculation model of the sewage biochemical treatment unit capacity correction coefficient to obtain the calculation value of the sewage biochemical treatment unit capacity correction coefficient;

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

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

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

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

[0091] Step 2.4.1, input the intelligent calculation data sample of the sewage biochemical treatment unit capacity correction coefficient in the test set into the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, calculate the sewage biochemical treatment unit capacity correction coefficient by using the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, and obtain the calculation 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, calculate the precision value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, if the precision value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is not less than the preset precision value, then go to step 2.5; otherwise, return to step 2.3 and continue to train the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model using the training set.

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

[0094] Step 3, based on the sewage biochemical treatment unit capacity correction coefficient, calculate the residual capacity of the sewage treatment biochemical unit.

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

[0096] R n =R× α (3)

[0097] In the formula, Rn is the residual capacity of the sewage treatment plant biochemical unit at time n, R is the treatment scale in the design scheme of the sewage treatment plant, and a is the sewage biochemical treatment unit capacity correction coefficient.

[0098] Step 4, the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system transmits the calculated residual capacity of the sewage treatment biochemical unit to the intelligent management and control system, and the intelligent management and control system generates an optimal control scheme for the inflow and outflow of the sewage treatment biochemical unit according to the residual capacity of the sewage treatment biochemical unit, the inflow and outflow water quality of the sewage treatment biochemical unit, and the inflow and outflow water flow;

[0099] Step 5, the intelligent management and control system generates early warning information according to the preset pollutant capacity dynamic grading alarm standard and the sewage capacity dynamic grading alarm standard, transmits the early warning information and the optimal control scheme for the inflow and outflow of the sewage treatment biochemical unit to the preset terminal, and reminds the staff to dynamically adjust the inflow and outflow of the sewage treatment generation unit.

[0100] Therefore, the present application determines the residual capacity of pollutants in the biochemical unit of the wastewater treatment by implementing the calculation of the wastewater biochemical treatment unit capacity correction coefficient, timely grasps the biochemical disposal potential of the biochemical unit in the petrochemical wastewater treatment process, optimizes the regulation and control scheme of the wastewater treatment plant, and provides technical support for realizing the dynamic optimization of the influent water quality and flow of the wastewater treatment plant.

[0101] Embodiment 3

[0102] The present embodiment proposes a wastewater treatment biochemical unit pollutant residual capacity calculation method, which uses the wastewater treatment biochemical unit pollutant residual capacity calculation system as described in Embodiment 1. The wastewater treatment biochemical unit pollutant residual capacity calculation method specifically includes the following steps:

[0103] Step 1, using the monitoring system to collect the monitoring data of each monitoring instrument in the wastewater treatment plant, obtaining the wastewater treatment plant operation parameters, transmitting the obtained wastewater treatment plant operation parameters to the wastewater biochemical data processing system for data preprocessing, and inputting to the pre-commissioned wastewater biochemical treatment unit capacity correction coefficient intelligent calculation system.

[0104] In this embodiment, during the data preprocessing process, the garbled data in the wastewater treatment plant operation parameters is first removed, and then each wastewater treatment plant operation parameter is normalized to convert each wastewater treatment plant operation parameter into a pre-set standard format.

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

[0106] The debugging process of the wastewater biochemical treatment unit capacity correction coefficient intelligent calculation system includes a sample data set construction stage, a training stage and a test stage.

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

[0108] After obtaining multiple sets of wastewater treatment plant operation parameters and preprocessing, removing the garbled data in the wastewater treatment plant operation parameters, normalizing each wastewater treatment plant operation parameter and converting it into a pre-set standard format, determining the sludge loss amount and wastewater biochemical treatment unit capacity correction coefficient corresponding to each wastewater treatment plant operation parameter, and obtaining multiple wastewater biochemical treatment unit capacity correction coefficient intelligent calculation data samples.

[0109] The intelligent calculation data samples of the sewage biochemical treatment unit capacity correction coefficient are randomly allocated to the training sample set and the test sample set to generate 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.

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

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

[0112] Step 2.3.1, setting the precision value of the training;

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

[0114] 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 samples to calculate the precision value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, if the precision value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is less than the preset precision value, then entering step 2.3.4; otherwise, entering step 2.3.5;

[0115] Step 2.3.4, updating the weight value between the hidden layer and the output layer in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model to obtain the updated sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, and continuing to randomly select the sewage biochemical treatment unit capacity correction coefficient intelligent calculation data samples in the training set to input into the updated sewage biochemical treatment unit capacity correction coefficient intelligent calculation model, and calculating the sewage biochemical treatment unit capacity correction coefficient by using the updated sewage biochemical treatment unit capacity correction coefficient intelligent calculation model to obtain the calculated value of the sewage biochemical treatment unit capacity correction coefficient, and returning to step 2.3.3;

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

[0117] Step 2.4, the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is trained by using the test set, and a sewage biochemical treatment unit capacity correction coefficient intelligent calculation model after test is obtained, specifically including the following steps:

[0118] Step 2.4.1, the sewage biochemical treatment unit capacity correction coefficient intelligent calculation data sample in the test set is input into the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model after training, the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is used to calculate the sewage biochemical treatment unit capacity correction coefficient, and the calculation value of the sewage biochemical treatment unit capacity correction coefficient is obtained;

[0119] Step 2.4.2, the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is compared with the sewage biochemical treatment unit capacity correction coefficient in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation data sample, the precision value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is calculated, if the precision value of the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is not less than the preset precision value, step 2.5 is entered; otherwise, step 2.3 is returned, and the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model is trained by using the training set.

[0120] Step 2.5, the sewage biochemical treatment unit capacity correction coefficient intelligent calculation model after test is packaged in the sewage biochemical treatment unit capacity correction coefficient intelligent calculation system.

[0121] In the embodiment, the sewage biochemical treatment unit capacity correction coefficient calculation model 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 as X, the input variable X = [x1, x2, …, xm], xm is the mth input variable, the base width variable of the RBF artificial neural network is set as B, the base width variable B = [b1, b2, …, bm], bm is the mth base width variable;

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

[0125]

[0126] In the formula, X is an input variable, Cj is the center of a Gaussian function, j is the serial number of the center of the Gaussian function, j=1, 2, …, m, m is the total number of the center of the Gaussian function, ||X-Cj|| is the Euclidean norm, and b is the base width parameter of a node in the RBF artificial neural network.

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

[0128] ym(k)=wh=w1h1+w2h2+......+wmhm (2)

[0129] In the formula, w is the weight value between the hidden layer and the output layer, and ym(k) is the wastewater biochemical treatment unit capacity correction coefficient calculated by the wastewater biochemical treatment unit capacity correction coefficient calculation model at the k moment.

[0130] Step 3: Based on the wastewater biochemical treatment unit capacity correction coefficient, the residual capacity of the wastewater treatment biochemical unit for pollutants is calculated.

[0131] In this embodiment, the residual capacity of the wastewater treatment biochemical unit for pollutants is calculated according to the following formula:

[0132] Rn=R×α (3)

[0133] In the formula, Rn is the residual capacity of the wastewater treatment biochemical unit for pollutants at the n moment, R is the treatment scale in the design scheme of the wastewater treatment plant, and a is the wastewater biochemical treatment unit capacity correction coefficient.

[0134] Step 4: The wastewater treatment biochemical unit residual capacity for pollutants calculated by the wastewater biochemical treatment unit capacity correction coefficient intelligent calculation system is transmitted to the intelligent management and control system, and the intelligent management and control system generates an optimized control scheme for the influent and effluent of the wastewater treatment biochemical unit according to the residual capacity of the wastewater treatment biochemical unit for pollutants, the influent and effluent quality of the wastewater treatment biochemical unit, and the influent and effluent flow.

[0135] Step 5: The intelligent management and control system generates early warning information according to the preset dynamic classification alarm standard of the pollutant capacity and the dynamic classification alarm standard of the wastewater capacity, transmits the early warning information and the optimized control scheme for the influent and effluent of the wastewater treatment biochemical unit to the preset terminal, and reminds the staff to dynamically adjust the influent and effluent of the wastewater treatment biochemical unit.

[0136] Therefore, the wastewater biochemical treatment unit capacity correction coefficient is calculated to determine the residual capacity of the wastewater treatment biochemical unit for pollutants, the biochemical disposal potential of the biochemical unit in the petrochemical wastewater treatment process is grasped in time, the control scheme of the wastewater treatment plant is optimized, and technical support is provided for realizing the dynamic optimization of the influent quality and flow of the wastewater treatment plant.

[0137] Example 4

[0138] The present embodiment takes a certain refinery wastewater treatment plant as an example. In the present embodiment, the wastewater treatment plant adopts A-O treatment process, and the design treatment scale is 400 t / h. The main design treatment of high-concentration wastewater mainly includes stripping purified water, alkali residue wastewater, and flue gas desulfurization salt-containing water. The design water quality of the wastewater treatment plant is shown in Table 1.

[0139] Table 1 Summary of design water quality of wastewater treatment plant

[0140]

[0141] The wastewater treatment biochemical unit pollutant residual capacity calculation system described in embodiment 1 and the wastewater treatment biochemical unit pollutant residual capacity calculation method described in embodiment 3 are used for processing. The monitoring system collects monitoring data of each monitoring instrument in the wastewater treatment plant, obtains the wastewater treatment plant operation parameters, transmits the obtained wastewater treatment plant operation parameters to the wastewater biochemical data processing system for data preprocessing, and inputs them into the pre-commissioned wastewater biochemical treatment unit capacity correction coefficient intelligent calculation system.

[0142] In the present embodiment, the wastewater treatment plant operation data from January 1, 2022 to April 31, 2022 in four months are selected as data samples. Among them, 69 groups of data samples from January 1, 2022 to March 10, 2022 (all data are selected as daily average data) are selected as training samples and are imported into the wastewater biochemical treatment unit capacity correction coefficient calculation model training module for training until the calculation accuracy of the wastewater biochemical treatment unit capacity correction coefficient calculation model reaches the expected requirement. A total of 20 groups of data from March 11, 2022 to March 20, 2022 and from April 1, 2022 to April 10, 2022 are selected as experimental data and are imported into the wastewater biochemical treatment unit capacity correction coefficient calculation model to further verify the calculation accuracy of the wastewater biochemical treatment unit capacity correction coefficient calculation model.

[0143] The input variables are divided into two groups. Among them, the A group data input variables include: wastewater pollutant concentration (COD, ammonia nitrogen, total nitrogen, total phosphorus, petroleum, volatile phenol, sulfide), process parameters of each device and each link in the wastewater treatment plant (mainly including dissolved oxygen, nitrification liquid reflux ratio, sludge reflux ratio, sludge age, sludge discharge amount, etc.), and a total of 12 input variables of characteristic parameters such as sludge loss amount. A 14-input 1-output RBF artificial neural network model is constructed.

[0144] The B group data input variables specifically include: sewage pollutant concentrations (COD, ammonia nitrogen, total nitrogen, total phosphorus, petroleum, volatile phenol, sulfide), process parameters of each device and each link in the sewage treatment plant (mainly including dissolved oxygen, nitrification liquid reflux ratio, sludge reflux ratio, sludge age, sludge discharge amount and other process parameters), a total of 11 input variables, and an intelligent calculation model of sewage biochemical treatment unit capacity correction coefficient is established based on a 13-input 1-output RBF artificial neural network.

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

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

[0147]

[0148]

[0149] As shown in Table 2, the sewage treatment biochemical unit pollutant residual capacity calculation system and method proposed in the application is suitable for A / O treatment process, the calculation accuracy of the A group data model of the feature parameter sludge loss amount is higher than that of the B group data model without using the feature parameter, and the calculation result is more stable.

[0150] Therefore, the sewage treatment biochemical unit pollutant residual capacity dynamic calculation method and system using the feature parameter sludge loss amount proposed in the application can better complete the calculation task of the sewage treatment biochemical unit residual capacity, realize the abnormal water inflow early warning and regulation of the sewage treatment plant, and provide a basis for guiding the water inflow and outflow regulation of the sewage treatment plant staff.

[0151] Example 5

[0152] In this embodiment, a certain refining enterprise sewage treatment plant is taken as an example, the sewage treatment plant in this embodiment adopts A2 / O treatment process, the design treatment scale is 1000 t / h, and mainly designs to treat wastewater containing high-concentration N, P and other pollutants. The design water quality of the sewage treatment plant is shown in Table 3.

[0153] Table 3 Summary table of design water quality of the sewage treatment plant

[0154]

[0155] The sewage treatment biochemical unit pollutant residual capacity calculation system described in Example 1 and the sewage treatment biochemical unit pollutant residual capacity calculation method described in Example 3 are used for processing, the monitoring system is used to collect the monitoring data of each monitoring instrument in the sewage treatment plant, the sewage treatment plant operation parameters are obtained, the obtained sewage treatment plant operation parameters are transmitted to the sewage biochemical data processing system for data preprocessing, and 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 from April 1, 2022 to July 31, 2022 in four months are taken as data samples, wherein 71 groups of data samples (all data are selected as daily average data) from April 1, 2022 to June 10, 2022 are taken as training samples and introduced 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 reaches the expected requirement, and 20 groups of data from June 11, 2022 to June 20, 2022 and from July 11 to July 20 are selected as experimental data and introduced 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, wherein the A group data input variables include: sewage pollutant concentration (COD, ammonia nitrogen, total nitrogen, petroleum, total phosphorus, sulfide, volatile phenol), process parameters of each device and each link in the sewage treatment plant (mainly including dissolved oxygen, nitrification liquid reflux ratio, sludge reflux ratio, sludge age, sludge discharge amount and other process parameters), and characteristic parameter sludge loss amount, totaling 12 input variables, and an RBF artificial neural network model with 12 inputs and 1 output is constructed.

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

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

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

[0161]

[0162]

[0163] As shown in Table 4, the sewage treatment biochemical unit pollutant residual capacity calculation system and method proposed in the application is suitable for A2 / O treatment process, the calculation accuracy of the A group data model of the characteristic parameter sludge loss amount is higher than that of the B group data model without the characteristic parameter, and the calculation result is more stable.

[0164] Therefore, the sewage treatment biochemical unit pollutant residual capacity dynamic calculation method and system proposed in the application can better complete the calculation task of the sewage treatment biochemical unit residual capacity, realize the abnormal water inflow early warning and regulation of the sewage treatment plant, and provide a basis for guiding the water inflow and outflow regulation of the sewage treatment plant personnel.

[0165] In the description of the present application, it should be noted that, unless otherwise explicitly specified and limited, the terms used herein are only for describing the 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, and in addition, it should be understood that when the terms "comprise" and / or "include" are used in the specification, they indicate the presence of the features, steps, operations, devices, components and / or their combinations.

[0166] In the present application, the terms such as "upper", "lower", "bottom", "top" and the like indicate the orientation or positional relationship shown in the drawings, which is only a relationship word determined for the purpose of describing the structural relationship of the components or elements of the present application, and is not specific to any component or element in the present application, and cannot be understood as a limitation of the present application.

[0167] In the present application, the terms such as "connected", "connected" and the like should be understood broadly, which means that it can be fixedly connected, integrally connected or detachably connected, and can be directly connected or indirectly connected through an intermediate medium. For related researchers or technicians in the art, the specific meaning of the above terms in the present application can be determined according to the specific circumstances, and should not be understood as a limitation of the present application.

[0168] Of course, the above description is not a limitation of the present application, and the present application is also not limited to the above examples, and the changes, modifications, additions or replacements made by the skilled in the art within the essential scope of the present application should also belong to the protection scope of the present application.

Claims

1. A system for calculating the remaining pollutant capacity of a wastewater treatment biochemical unit, characterized in that, It includes a monitoring system, a wastewater biochemical data processing system, a wastewater biochemical treatment unit capacity correction coefficient intelligent calculation system, and an intelligent control system, which are connected in sequence. Multiple sets of sewage treatment plant operating parameters are acquired and preprocessed. After removing garbled data from the sewage treatment plant operating parameters, the operating parameters of each sewage treatment plant are normalized and converted into a preset standard format. The sludge loss and sewage biochemical treatment unit capacity correction coefficient corresponding to each sewage treatment plant operating parameter are determined, and multiple intelligent calculation data samples of sewage biochemical treatment unit capacity correction coefficients are obtained. The intelligent calculation data samples of the capacity correction coefficient of the wastewater biochemical treatment unit are randomly assigned to the training sample set and the test sample set to generate a sample dataset including the training sample set and the test sample set, which is used to train and verify the intelligent calculation model of the capacity correction coefficient of the wastewater biochemical treatment unit in the intelligent calculation system. An intelligent calculation model for the capacity correction coefficient of wastewater biochemical treatment units is established based on the RBF artificial neural network. The RBF artificial neural network is configured 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, where X = [x1, x2, ..., xm] and xm is the m-th input variable. The base width variable of the RBF artificial neural network is set to B, where B = [b1, b2, ..., bm] and bm is the m-th base width variable. The RBF artificial neural network uses a Gaussian function as the radial basis function, as shown in formula (1): （1） In the formula, X is the input variable, Cj is the center of the Gaussian function, j is the index of the Gaussian function center, j=1,2,…,m, and m is the total number of Gaussian function centers. is the Euclidean norm, and b is the basis width parameter of a node in an RBF artificial neural network; The formula for calculating the capacity correction coefficient of the wastewater biochemical treatment unit in the RBF artificial neural network is as follows: （2） In the formula, w represents the weights between the hidden layer and the output layer. The capacity correction coefficient for the wastewater biochemical treatment unit is calculated by the calculation model for the capacity correction coefficient of the wastewater biochemical treatment unit at time k.

2. The wastewater treatment biochemical unit pollutant residual capacity calculation system according to claim 1, characterized in that, The monitoring system, wastewater biochemical data processing system, wastewater biochemical treatment unit capacity correction coefficient intelligent calculation system, and intelligent control system are connected via wireless or wired networks.

3. The wastewater treatment biochemical unit pollutant residual capacity calculation system according to claim 1, characterized in that, The monitoring system is connected to various monitoring instruments in the wastewater treatment plant to monitor and acquire the plant's operating parameters in real time, including the wastewater flow rate and pollutant concentration entering the biochemical unit of the wastewater treatment plant, the wastewater flow rate and pollutant concentration exiting the biochemical unit of the wastewater treatment plant, the wastewater level, pollutant concentration, and operating process parameters of the biochemical unit.

4. The wastewater treatment biochemical unit pollutant residual capacity calculation system according to claim 3, characterized in that, The operating parameters of the biochemical unit include dissolved oxygen, nitrification liquor return ratio, sludge return ratio, sludge concentration, sludge age, and sludge discharge.

5. The wastewater treatment biochemical unit pollutant residual capacity calculation system according to claim 3, characterized in that, The pollutants include COD, ammonia nitrogen, total nitrogen, total phosphorus, petroleum hydrocarbons, volatile phenols, and sulfides.

6. The wastewater treatment biochemical unit pollutant residual capacity calculation system according to claim 1, characterized in that, The wastewater biochemical data processing system is connected to the monitoring system and is used to preprocess the wastewater treatment plant operating parameters acquired by the monitoring system.

7. The wastewater treatment biochemical unit pollutant residual capacity calculation system according to claim 6, characterized in that, The data preprocessing includes removing garbled data and converting to a standard format. By removing garbled data from the sewage treatment plant's operating parameters and converting them to a preset standard format, the processed sewage treatment plant operating parameters are obtained and sent to the intelligent calculation system for sewage biochemical treatment based on capacity correction coefficients.

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

9. The wastewater treatment biochemical unit pollutant residual capacity calculation system according to claim 1, characterized in that, The intelligent control system is connected to the intelligent calculation system for the capacity correction coefficient of the wastewater biochemical treatment unit. It is used to generate an optimized control scheme for the influent and effluent of the wastewater biochemical treatment unit based on the remaining pollutant capacity of the wastewater biochemical treatment unit, the influent and effluent water quality of the wastewater biochemical treatment unit, and the influent and effluent water flow.

10. The wastewater treatment biochemical unit pollutant residual capacity calculation system according to claim 9, characterized in that, The intelligent control system is equipped with an early warning and alarm module. The early warning and alarm module has preset dynamic classification alarm standards for pollutant capacity and dynamic classification alarm standards for wastewater capacity. The early warning and alarm module is connected to a preset terminal and is used to promptly push early warning and alarm information and optimized control schemes for influent and effluent of the wastewater treatment biochemical unit based on the remaining pollutant capacity of the wastewater treatment biochemical unit.

11. The wastewater treatment biochemical unit pollutant residual capacity calculation system according to claim 10, characterized in that, The terminal is configured as a mobile device and a central control room, and the early warning alarm module is wirelessly connected to the mobile device and the central control room.

12. The wastewater treatment biochemical unit pollutant residual capacity calculation system according to claim 11, characterized in that, The early warning and alarm module sends early warning and alarm information and the wastewater treatment biochemical unit influent and effluent optimization and control scheme to a preset mobile terminal via SMS.

13. A method for calculating the remaining pollutant capacity of a wastewater treatment biochemical unit, characterized in that, The wastewater treatment biochemical unit pollutant residual capacity calculation system according to any one of claims 1 to 12 specifically includes the following steps: Step 1: Use the monitoring system to collect monitoring data from various monitoring instruments in the wastewater treatment plant, obtain the operating parameters of the wastewater treatment plant, transmit the obtained operating parameters of the wastewater treatment plant to the wastewater biochemical data processing system for data preprocessing, and input them into the pre-calibrated intelligent calculation system for the capacity correction coefficient of the wastewater biochemical treatment unit. Step 2: Calculate the wastewater biochemical treatment unit capacity correction coefficient using the wastewater biochemical treatment unit capacity correction coefficient intelligent calculation system. Step 3: Calculate the remaining pollutant capacity of the wastewater treatment biochemical unit based on the capacity correction coefficient of the wastewater biochemical treatment unit; Step 4: The intelligent calculation system for the capacity correction coefficient of the wastewater biochemical treatment unit transmits the calculated remaining pollutant capacity of the wastewater biochemical treatment unit to the intelligent control system. The intelligent control system generates an optimized control scheme for the influent and effluent of the wastewater biochemical treatment unit based on the remaining pollutant capacity of the wastewater biochemical treatment unit, the influent and effluent water quality, and the influent and effluent flow rate. Step 5: The intelligent control system generates early warning alarm information based on the preset dynamic classification alarm standards for pollutant capacity and dynamic classification alarm standards for sewage capacity. The early warning alarm information and the optimized control scheme for influent and effluent of the sewage treatment biochemical unit are transmitted to the preset terminal to remind staff to dynamically adjust the influent and effluent of the sewage treatment generation unit.

14. The method for calculating the remaining pollutant capacity of a wastewater treatment biochemical unit according to claim 13, characterized in that, In the data preprocessing process, garbled data in the operating parameters of the sewage treatment plants are first removed, and then the operating parameters of each sewage treatment plant are normalized to convert them into a preset standard format.

15. The method for calculating the remaining pollutant capacity of a wastewater treatment biochemical unit according to claim 14, characterized in that, The operating parameters of the wastewater treatment plant do not include the operating data of the wastewater treatment plant during shutdown periods.

16. The method for calculating the remaining pollutant capacity of a wastewater treatment biochemical unit according to claim 13, characterized in that, The debugging process of the intelligent calculation system for the capacity correction coefficient of the wastewater biochemical treatment unit includes the sample dataset construction stage, the training stage, and the testing stage. Step 2.1, Sample Dataset Construction Phase; Multiple sets of sewage treatment plant operating parameters are acquired and preprocessed. After removing garbled data from the sewage treatment plant operating parameters, the operating parameters of each sewage treatment plant are normalized and converted into a preset standard format. The sludge loss and sewage biochemical treatment unit capacity correction coefficient corresponding to each sewage treatment plant operating parameter are determined, and multiple intelligent calculation data samples of sewage biochemical treatment unit capacity correction coefficients are obtained. The intelligent calculation data samples of the capacity correction coefficient of the wastewater biochemical treatment unit are randomly assigned to the training sample set and the test sample set to generate a sample dataset including the training sample set and the test sample set, which is used to train and verify the intelligent calculation model of the capacity correction coefficient of the wastewater biochemical treatment unit in the intelligent calculation system. Step 2.2: Establish an intelligent calculation model for the capacity correction coefficient of the wastewater biochemical treatment unit based on the RBF artificial neural network; Step 2.3: Use the training set to train the intelligent calculation model of the capacity correction coefficient of the wastewater biochemical treatment unit, and obtain the trained intelligent calculation model of the capacity correction coefficient of the wastewater biochemical treatment unit. Step 2.4: Use the test set to train the intelligent calculation model of the capacity correction coefficient of the wastewater biochemical treatment unit, and obtain the intelligent calculation model of the capacity correction coefficient of the wastewater biochemical treatment unit after testing. Step 2.5: The intelligent calculation model of the capacity correction coefficient of the wastewater biochemical treatment unit after testing is encapsulated in the intelligent calculation system of the capacity correction coefficient of the wastewater biochemical treatment unit.

17. The method for calculating the remaining pollutant capacity of a wastewater treatment biochemical unit according to claim 16, characterized in that, The sludge loss is the ratio between the change in the concentration of all pollutants and the change in the sludge concentration during the process from when the wastewater enters the wastewater treatment plant to when it is discharged from the wastewater treatment plant. It is used to characterize the wastewater treatment capacity.

18. The method for calculating the remaining pollutant capacity of a wastewater treatment biochemical unit according to claim 16, characterized in that, Step 2.3 specifically includes the following steps: Step 2.3.1, set the training precision value; Step 2.3.2: Input the intelligent calculation data samples of the wastewater biochemical treatment unit capacity correction coefficient from the training set into the intelligent calculation model of the wastewater biochemical treatment unit capacity correction coefficient constructed in Step 2.2, and use the intelligent calculation model of the wastewater biochemical treatment unit capacity correction coefficient to calculate the calculated value of the wastewater biochemical treatment unit capacity correction coefficient. Step 2.3.3: Compare the wastewater biochemical treatment unit capacity correction coefficient calculated by the intelligent calculation model with the wastewater biochemical treatment unit capacity correction coefficient in the intelligent calculation data sample. Calculate the accuracy value of the intelligent calculation model. If the accuracy value of the intelligent calculation model is less than the preset accuracy value, 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 for the capacity correction coefficient of the wastewater biochemical treatment unit to obtain the updated intelligent calculation model for the capacity correction coefficient of the wastewater biochemical treatment unit. Continue to randomly select data samples for the intelligent calculation of the capacity correction coefficient of the wastewater biochemical treatment unit from the training set and input them into the updated intelligent calculation model for the capacity correction coefficient of the wastewater biochemical treatment unit. Calculate the capacity correction coefficient of the wastewater biochemical treatment unit using the updated intelligent calculation model for the capacity correction coefficient of the wastewater biochemical treatment unit. After obtaining the calculated value of the capacity correction coefficient of the wastewater biochemical treatment unit, return to step 2.3.

3. Step 2.3.5: Complete the training of the intelligent calculation model for the capacity correction coefficient of the wastewater biochemical treatment unit, and obtain the trained intelligent calculation model for the capacity correction coefficient of the wastewater biochemical treatment unit.

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

20. The method for calculating the remaining pollutant capacity of a wastewater treatment biochemical unit according to claim 16, characterized in that, In step 2, the calculation model for the capacity correction coefficient of the wastewater biochemical treatment unit 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, where X = [x1, x2, ..., xm] and xm is the m-th input variable. The base width variable of the RBF artificial neural network is set to B, where B = [b1, b2, ..., bm] and bm is the m-th base width variable. The RBF artificial neural network uses a Gaussian function as the radial basis function, as shown in formula (1): （1） In the formula, X is the input variable, Cj is the center of the Gaussian function, j is the index of the Gaussian function center, j=1,2,…,m, and m is the total number of Gaussian function centers. is the Euclidean norm, and b is the basis width parameter of a node in an RBF artificial neural network; The formula for calculating the capacity correction coefficient of the wastewater biochemical treatment unit in the RBF artificial neural network is as follows: （2） In the formula, w represents the weights between the hidden layer and the output layer. The capacity correction coefficient for the wastewater biochemical treatment unit is calculated by the calculation model for the capacity correction coefficient of the wastewater biochemical treatment unit at time k.

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

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