CN111204867B

CN111204867B - Membrane Bioreactor-MBR Membrane Fouling Intelligent Decision-Making Method

Info

Publication number: CN111204867B
Application number: CN202010154288.3A
Authority: CN
Inventors: 韩红桂; 张会娟; 王盈旭; 郭民
Original assignee: Beijing University of Technology
Current assignee: Beijing University of Technology
Priority date: 2019-06-24
Filing date: 2020-03-07
Publication date: 2021-12-10
Anticipated expiration: 2040-03-07
Also published as: CN111204867A

Abstract

An intelligent decision-making method for membrane bioreactor-MBR membrane pollution belongs to the field of online early warning of water quality parameters in sewage treatment. Firstly, deeply analyzing a membrane pollution mechanism, constructing a membrane pollution prediction model based on a deep belief network, and realizing accurate prediction of membrane water permeability; secondly, fusion of a water permeability predicted value and related parameter variables is utilized, and intelligent early warning of membrane pollution is achieved based on comprehensive evaluation of membrane pollution; secondly, an intelligent decision-making model based on data and knowledge is established based on main factors of membrane pollution, decision support is provided for operators, harm caused by membrane pollution is reduced, the occurrence rate of membrane pollution is reduced, the safety of MBR sewage treatment process is guaranteed, and the efficient and stable operation of MBR sewage treatment plants is promoted.

Description

Membrane bioreactor-MBR membrane pollution intelligent decision-making method

Technical Field

The invention belongs to the field of online early warning of water quality parameters in sewage treatment, and provides an intelligent early warning system for membrane bioreactor-MBR membrane pollution for the first time. On the basis of real operation data of an MBR membrane sewage treatment process, extracting characteristic variables of MBR membrane water permeability by a characteristic analysis method, and establishing a soft measurement model by using a deep belief network to predict the membrane water permeability which is difficult to directly measure in the MBR membrane sewage treatment process; establishing a membrane pollution comprehensive evaluation model by utilizing the water permeability prediction value and combining other collectable process variables of a water plant, determining main factors of membrane pollution and realizing intelligent early warning of the membrane pollution; based on data and knowledge drive, an intelligent decision model is established, accurate identification of membrane pollution is realized, the incidence rate of membrane pollution is reduced, and the sewage treatment performance of the membrane is improved.

Background

MBR is a novel sewage treatment process combining membrane separation technology and biological treatment technology, has the obvious advantages of good solid-liquid separation effect, low sludge load, small occupied area and the like, and has great development potential. However, membrane fouling is a bottleneck problem in MBR wastewater treatment processes, which can lead to reduced effluent quality and increased operating costs, and even to a breakdown of the wastewater treatment process. Currently, the main approach to the membrane fouling problem is to periodically clean and replace the membranes. In the actual sewage treatment process, the cleaning and the replacement of the membrane do not have a strict, objective and quantitative standard and are mainly carried out according to human experience. However, frequent cleaning can lead to rupture and corrosion of membrane filaments, which can reduce membrane life, increase production energy consumption and operation cost, and greatly restrict popularization and application of MBR. Therefore, the method can accurately identify the membrane pollution, reduce the incidence rate of the membrane pollution, and become a key point for ensuring the stable operation of the MBR and popularizing the MBR technology. However, MBR sewage treatment process is complex and difficult to directly model, and the monitoring of pollution condition is a difficult problem in the current control field. Currently, an effective early warning decision system is not available in a membrane sewage treatment plant which is built and put into operation so as to realize intelligent early warning decision in the membrane sewage treatment process. Therefore, the research of new decision-making technology for solving the membrane pollution problem in the sewage treatment process becomes an important subject of research in the field of sewage control and has important practical significance.

The invention relates to an intelligent decision-making method for membrane bioreactor-MBR membrane pollution, which utilizes a characteristic analysis method to extract characteristic variables and establishes a soft measurement model of membrane water permeability based on a deep belief network to realize accurate prediction of the water permeability in the process of membrane sewage treatment, utilizes a water permeability prediction value to establish a membrane pollution comprehensive evaluation model in combination with other collectable process variables of a water plant, but an intelligent decision-making system for membrane pollution at home and abroad does not form a complete theoretical system, builds an intelligent decision-making method for MBR membrane pollution including soft and hard platforms based on an intelligent method, and has high development and application values in the aspects of filling technical blanks at home and abroad, integrating a sewage treatment industrial chain and the like.

Disclosure of Invention

The membrane bioreactor-MBR membrane pollution intelligent decision method comprises the steps of operation process data acquisition, operation process data preprocessing, membrane pollution intelligent prediction and membrane pollution intelligent decision, and specifically comprises the following steps:

(1) acquiring data of an operation process: the MBR membrane processing system is used as a research object, and the data of the operation process is collected through a collection instrument arranged on a process field, and the MBR membrane processing system comprises the following steps: the method comprises the following steps of (1) realizing data acquisition by using inlet water chemical oxygen demand, inlet water pH value, inlet water biological oxygen demand, outlet water chemical oxygen demand, outlet water pH value, outlet water biological oxygen demand, anaerobic zone oxidation-reduction potential, anoxic zone oxidation-reduction potential, aerobic zone nitrate, aerobic zone dissolved oxygen, water production pressure, water production turbidity, water production flow, sludge concentration and aeration quantity; the data collected by the instrument is transmitted to the programmable logic controller through a communication protocol, the programmable logic controller transmits the operation process data to the upper computer through the communication protocol, and the data in the upper computer is transmitted to the data processing server through the local area network;

(2) preprocessing operation process data: the operating data of the membrane pool is taken as a research object, a characteristic analysis model is established by utilizing a partial least square method, and 5 principal component variables are obtained, wherein the principal component variables are respectively as follows: the water production pressure, the water production turbidity, the water production flow, the sludge concentration and the aeration rate, and 5 main component variables are used as input variables of the membrane pollution intelligent prediction model block; the water permeability is used as an output variable of the membrane pollution intelligent prediction module;

(3) intelligent prediction of membrane pollution: the water permeability prediction is realized by intelligent prediction of membrane pollution, wherein: the water permeability is obtained by predicting a deep belief network-DBN, the DBN consists of 1 input layer, 2 hidden layers and 1 output layer, the number of neurons in the input layers is 5, the number of neurons in each hidden layer is M, M is a positive integer which is more than 2 and less than 30, and the number of neurons in the output layers is 1, namely the connection mode is 5-M-M-1; n groups of data are used as training samples of the soft measurement model; DBN input at time t is x (t) ═ x₁(t),…,x₅(t)]，x₁(t) value x representing water pressure at time t₂(t) value x representing turbidity of water produced at time t₃(t) value x representing the water production flow at time t₄(t) value x representing sludge concentration at time t₅(t) value representing aeration amount at time t, soft water permeability prediction based on DBNThe calculation mode of the measurement model is as follows in sequence:

inputting a layer: h is₀(t)＝x(t)

(1)

② first hidden layer:

third, the second hidden layer:

output layer:

wherein h is₀(t) represents the output vector of the input layer at time t, h₁(t) represents the output vector of the first hidden layer at time t, h₂(t) represents the output vector of the second hidden layer at time t, w_0,1(t) represents the weight vector between the input layer and the first hidden layer at time t, w_1,2(t) represents the weight vector between the first hidden layer and the second hidden layer at time t, w_2,3(t) represents the weight vector between the second hidden layer and the output layer at time t, b₁(t) denotes the bias vector of the first hidden layer at time t, b₂(t) represents the bias vector of the second hidden layer at time t, and y (t) represents the actual output of the DBN at time t;

the DBN training is divided into two processes: unsupervised pre-training and supervised weight fine tuning; setting the iteration number of each layer of pre-training as 100, the iteration number of a back propagation algorithm as 1000, the expected error as 0.01, and the initial weight and bias as 0.01; the specific training steps are as follows:

unsupervised pre-training: the update rule of the parameters obtained by the contrast divergence algorithm is as follows:

wherein, w_0,1(t +1) is the input layer and the first at time t +1Weight vectors between hidden layers, w_1,2(t +1) is the weight vector between the first hidden layer and the second hidden layer at time t +1, b₁(t +1) is the bias vector for the first hidden layer at time t +1, b₂(t +1) is the bias vector for the second hidden layer at time t +1, E_dataIs a expectation of training data, E_modelIs an expectation of model definition, μ_w1E (0,0.02) is the learning rate of the weights of the input layer and the first hidden layer, mu_w2E (0,0.02) is the learning rate of the weights of the first hidden layer and the second hidden layer, mu_b1E (0,0.01) is the learning rate of the first hidden layer, μ_b2E (0,0.01) is the learning rate of the second hidden layer;

secondly, adjusting the weight value by a back propagation algorithm: training layer by layer to obtain the initial value of the DBN parameter, then fine-tuning the weight value through a back propagation algorithm to obtain a better model effect, and adjusting the weight value by adopting an error back propagation method as follows:

wherein, w_2,3(t +1) is the weight vector between the second hidden layer and the output layer at time t +1, y_d(t) is the desired output of DBN at time t, η_outE (0,3) is the weight learning rate between the second hidden layer and the output layer, η₂E (0,3) is the weight learning rate between the first hidden layer and the second hidden layer, η₁E (0,3) is a weight learning rate between the input layer and the first hidden layer;

(4) intelligent decision making of membrane pollution: the membrane pollution intelligent decision is to establish a membrane pollution comprehensive evaluation model by utilizing a water permeability prediction value and combining other membrane pollution related variables, and provide a decision aiming at membrane pollution, and the process is as follows:

1) determining an evaluation index of membrane fouling, u ═ u { [ u ]₁,u₂,u₃,u₄,u₅,u₆}，u₁Is the value of the water production flow, u₂Is the value of the water production pressure u₃Is the value of turbidity of the produced water, u₄Is the value of the sludge concentration u₅Is aeratedValue sum u₆Is a predicted value of the water permeability;

2) establishing monitoring statistics, wherein the process variable is u epsilon R^6×nWherein 6 is a variable dimension, and n is the number of samples; and (3) carrying out independent component and principal component decomposition on the membrane pollution evaluation matrix u:

u＝As+Pt^T+f^T (7)

wherein A ∈ R^6×rFor the mixing matrix, s ∈ R^r×nFor independent matrices, t ∈ R^n×kAs principal component scoring matrix, load matrix P is formed by R^6×k，f∈R^n×6The final residual matrix is obtained, r represents the number of independent elements, and k is the number of selected principal elements; to estimate A and s, a unmixing matrix W is required to obtain a reconstructed signal

Is an estimated value of a source signal, and performs whitening processing on u to obtain a score vector:

Z＝Λ^-1/2H^Tu＝Bs (8)

wherein Λ is a diagonal matrix containing all eigenvalues, H is a corresponding eigenvector matrix and the unit is orthogonal, and B is a unit orthogonal matrix; the unmixing matrix is:

W＝B^TΛ^-1/2H^T (9)

decomposing the original data by independent components and principal components to obtain independent component matrix and principal component scoring matrix, and establishing I²And T²Statistics, namely establishing SPE statistics for the residual information; independent component vector is s_l＝[s_1l, s_2l,…,s_rl]^T∈R^r×1And a pivot score vector t_l＝[t_l1,t_l2,…,t_lk]^T∈R^k×1Residual vector f_l＝[f_l1, f_l2,…,f_l6]^T∈

R

^6×11,2, …, n (n is the number of samples); establishment of I²、T²And the SPE statistics are as follows:

wherein

Is the control limit for the SPE statistic,

λ_j(j ═ 1, 2., k) is the eigenvalue corresponding to each principal component covariance matrix, k is the number of principal components, F_k.(n-k),αThe upper limit value of F distribution with confidence coefficient alpha and degree of freedom k and (n-k); c. C_αCorresponding to the lower limit value of the normal distribution under the confidence coefficient alpha, determining I by adopting a nuclear density estimation method²Control limit of statistic, determining T based on F distribution²A control limit for the statistic;

3) intelligent decision making: obtaining the membrane pollution evaluation index u (t) at the time t on line, and monitoring the statistic I at the time t²、T²And SPE is beyond the control limit, and the occurrence of membrane pollution is determined; in order to determine the main factors of membrane pollution, measures are taken aiming at the determination factors, and a multi-classifier based on a kernel function is established for distinguishing the membrane pollution factors, and the method mainly comprises the following steps: flow of produced water reaches peak value of 460m³H, the pressure of produced water is lower than 20kp, and the aeration quantity is lower than 2400m³(ii) the sludge concentration is higher than 13000mg/l, (ii) the water production pressure is lower than 20kp, the water permeability is lower than 30LMH/bar, (iii) the water production pressure is lower than 20kp, the water permeability is lower than 60LMH/bar, and the turbidity of the produced water is higher than 5 NUT; combining 6 binary classifiers into a multi-classifier, wherein for a first binary classifier, the data labels not belonging to the first class are-1, the data labels belonging to the first class are +1, for a second binary classifier, the data labels not belonging to the second class are-1, the data labels belonging to the second class are +1, for a third binary classifier, the data labels not belonging to the third class are-1, the data labels belonging to the third class are +1, and for a fourth binary classifier, the data labels not belonging to the third class are +1The classifier labels-1 for data not belonging to the fourth class, +1 for data belonging to the fourth class, -1 for data not belonging to the fifth class, +1 for data belonging to the fifth class, -1 for data not belonging to the sixth class, and +1 for data belonging to the sixth class, and the q-th classifier (q ═ 1,2, …,6) optimizes the objective function as:

wherein C ∈ (0,5) is a penalty coefficient, w^qIs the connection weight of the q-th classifier, T is the transpose symbol, ξ_p ^qRelaxation variables for the p-th data sample of the q-th classifier, b^qFor the bias of the q-th classifier,

is a Gaussian kernel function, σ ∈ (0,1) is the width of the kernel function, u_p ^qP-th data sample, u, of the q-th classifier, respectively_v ^qIs the v-th data sample of the q-th classifier (v ═ 1,2, …, n), y_pIs the category of the p sample; introducing a Lagrange multiplier, and converting the optimization problem into:

updating parameters:

wherein, y_vIs the class of the v-th sample, α_pThe glanz multiplier for the p-th data sample,α_vfor the evaluation index u, the lagrangian multiplier for the v-th data sample is as follows:

f(u)＝sgn(α_py_pK(u,u_p ^q)+b^q) (15)

wherein sgn represents a sign function, the input is positive, the output is 1, and the output is-1 when the input is negative; the output value can be obtained by inputting the sample to be detected into the decision function, and the category corresponding to the function with the maximum value is the category to which the sample to be detected belongs; the 6-type corresponding operation suggestions comprise: running for 4 hours in the state is not suitable, and the water yield is reduced to 260m³Lower than h, reducing the water yield of the membrane pool to 260m³Per hour, controlling transmembrane pressure difference to be less than 40kPa, increasing aeration to 3000m³Fourthly, controlling the sludge concentration of the membrane tank to be 8000- & lt12000 mg/L, & ltv & gt, adjusting the operation parameters, and reducing the water yield to 260m³H, or increasing aeration amount to 5000m³Over per hour, performing online physical cleaning and online chemical cleaning within 24 hours; and applying the trained decision model to a sewage treatment process, carrying out characteristic matching on fault data by the model, outputting fault categories, carrying out operation suggestions corresponding to the fault categories, and providing decision support for the production process.

Drawings

FIG. 1 is a diagram of a deep belief network architecture;

FIG. 2 is a diagram of 10-step prediction results of a water outlet water permeability soft measurement model, wherein black line circles are actual water permeability values, and black line points are predicted values of a deep belief network soft measurement model;

FIG. 3 is a diagram showing the MBR membrane contamination early warning result, wherein (a) is a non-Gaussian statistic I²Changing result, wherein the black line points are statistics change curve, the black line is control limit, and the graph (b) is Gaussian statistic T²Changing results, wherein black line band points are a statistic change curve, black lines are control limits, and a graph (c) is a non-Gaussian statistic SPE changing result, wherein the black line band points are the statistic change curve, and the black lines are the control limits;

FIG. 4 is a plot of MBR membrane fouling decision results, wherein black circles are the decision model outputs and black stars are the actual decision suggestion categories;

FIG. 5 is a data flow direction indicating diagram of the MBR membrane pollution intelligent early warning system;

Detailed Description

(1) Specific implementation of membrane pollution intelligent decision system design and software and hardware function integration

The hardware platform environment built in an actual sewage treatment plant is shown in fig. 2. Collecting operation process data through a collection instrument installed in a process site; data collected by the instrument is transmitted to the PLC through a Modbus communication protocol, the PLC transmits operation process data to the upper computer through an RS232 communication protocol, and the data in the upper computer is transmitted to the data processing server through the local area network; issuing operation process data to a water plant work manager through a Web Server in a Browser/Server mode, and displaying the prediction of the water permeability and the early warning result of membrane pollution in a Client/Server mode; the main functions of the developed MBR membrane pollution intelligent early warning system can be realized by firstly inquiring the operation parameters of the membrane pool, secondly predicting the water permeability on line, thirdly early warning the membrane pollution and fourthly carrying out intelligent decision on the membrane pollution.

The invention adopts component technology in software industry to package the membrane pollution data preprocessing module, the membrane pollution intelligent prediction module, the membrane pollution intelligent early warning module and the membrane pollution intelligent decision-making module into functional modules, thereby enhancing the reusability of the model and making up the blank of popularization of MBR membrane pollution intelligent decision-making technology to human-computer interaction interface in actual system operation at home and abroad; the NET platform is adopted for software development, so that an ActiveX control is conveniently created, and the usable environment range of software is expanded; a field bus technology is adopted to establish a full-flow system communication network to realize information transmission among modules; meanwhile, the MBR membrane pollution intelligent decision-making system provided by the invention realizes the connection of a central control room and each on-site data acquisition point, forms a centralized management early warning system, is easy to expand, has independent functions of each part, can add software and hardware modules according to actual prediction requirements and is fused with other systems, can realize the stability and reliability of the system and ensures the early warning precision of membrane pollution.

(2) Specific implementation of film pollution intelligent decision method research

The invention obtains an MBR membrane pollution intelligent decision-making method; the method is characterized in that characteristic variables of MBR membrane water permeability are obtained through characteristic analysis, a soft measurement model of MBR membrane water permeability is established by utilizing a deep belief network, intelligent detection of MBR membrane water permeability is realized, a membrane pollution comprehensive evaluation model is established by combining a predicted value of the membrane water permeability with other collectable process variables of a water plant, identification of membrane pollution and identification of main factors are realized, the intelligent level of the sewage treatment plant is improved, and normal operation of a sewage treatment process is guaranteed.

Firstly, input variables are collected through an online detection instrument arranged on a process field, the variables to be collected comprise 5 types, and parameter information and collection positions are shown in a table 1.

TABLE 1 Process variable types collected

Secondly, establishing a soft measurement model by using a deep belief network, and realizing 510-step prediction of the membrane water permeability based on a multi-step prediction strategy. And training and testing the deep belief network by adopting the data acquired in real time. 80 sets of data were selected for testing. The data collected are shown in table 2.

And thirdly, performing comprehensive evaluation on membrane pollution by using the predicted value of the water permeability and current values of other related collected variables (water production flow, water production pressure, aeration amount and sludge concentration) so as to judge whether membrane pollution occurs.

And fourthly, making a membrane pollution operation decision according to the relevant variables of the membrane pollution.

TABLE 2 Soft measurement model test data

Claims

1. The membrane bioreactor-MBR membrane pollution intelligent decision-making method is characterized by comprising the following steps:

(1) acquiring data of an operation process: the MBR membrane treatment system is used as a research object, and the data of the operation process is collected through a collection instrument arranged on a process field, and the MBR membrane treatment system comprises the following steps: the method comprises the following steps of (1) realizing data acquisition by using inlet water chemical oxygen demand, inlet water pH value, inlet water biological oxygen demand, outlet water chemical oxygen demand, outlet water pH value, outlet water biological oxygen demand, anaerobic zone oxidation-reduction potential, anoxic zone oxidation-reduction potential, aerobic zone nitrate, aerobic zone dissolved oxygen, water production pressure, water production turbidity, water production flow, sludge concentration and aeration quantity; the data collected by the instrument is transmitted to the programmable logic controller through a communication protocol, the programmable logic controller transmits the operation process data to the upper computer through the communication protocol, and the data in the upper computer is transmitted to the data processing server through the local area network;

(3) intelligent prediction of membrane pollution: the water permeability prediction is realized by intelligent prediction of membrane pollution, wherein: the water permeability is obtained by predicting a deep belief network-DBN, the DBN consists of 1 input layer, 2 hidden layers and 1 output layer, the number of neurons of the input layer is 5, the number of neurons of each hidden layer is M, M is a positive integer larger than 2 and smaller than 30, and the number of neurons of the output layer is less than or equal to the number of neurons of each hidden layerThe number of warp elements is 1, namely the connection mode is 5-M-M-1; n groups of data are used as training samples of the soft measurement model; DBN input at time t is x (t) ═ x₁(t),…,x₅(t)]，x₁(t) value x representing water pressure at time t₂(t) value x representing turbidity of water produced at time t₃(t) value x representing the water production flow at time t₄(t) value x representing sludge concentration at time t₅(t) represents the value of aeration at the time t, and the calculation modes of the soft measurement model for predicting the water permeability based on the DBN sequentially comprise the following steps:

inputting a layer: h is₀(t)＝x(t) (1)

② first hidden layer:

third, the second hidden layer:

output layer:

wherein, w_0,1(t +1) is the weight vector between the input layer and the first hidden layer at time t +1, w_1,2(t +1) is the weight vector between the first hidden layer and the second hidden layer at time t +1, b₁(t +1) is the bias vector for the first hidden layer at time t +1, b₂(t +1) is the bias vector for the second hidden layer at time t +1, E_dataIs a expectation of training data, E_modelIs an expectation of model definition, μ_w1E (0,0.02) is the learning rate of the weights of the input layer and the first hidden layer, mu_w2E (0,0.02) is the learning rate of the weights of the first hidden layer and the second hidden layer, mu_b1E (0,0.01) is the learning rate of the first hidden layer, μ_b2E (0,0.01) is the learning rate of the second hidden layer;

secondly, adjusting the weight value by a back propagation algorithm: training layer by layer to obtain the initial value of the DBN parameter, then finely adjusting the weight value through a back propagation algorithm to obtain a better model effect, and adjusting the weight value by adopting an error back propagation method to be as follows:

1) determining an evaluation index of membrane fouling, u ═ u { [ u ]₁,u₂,u₃,u₄,u₅,u₆}，u₁Is the value of the water production flow, u₂Is the value of the water production pressure u₃Is the value of turbidity of the produced water, u₄Is the value of the sludge concentration u₅Is the value of aeration amount and u₆Is a predicted value of the water permeability;

u＝As+Pt^T+f^T (7)

wherein A ∈ R^6×rFor the mixing matrix, s ∈ R^r×nFor independent matrices, t ∈ R^n×kFor the principal component scoring matrix, the load matrix P is the same as R⁶ ^×k，f∈R^n×6The final residual matrix is r, the number of independent elements is represented by r, and k is the number of selected principal elements; to estimate A and s, a unmixing matrix W is required to obtain a reconstructed signal

Z＝Λ^-1/2H^Tu＝Bs (8)

W＝B^TΛ^-1/2H^T (9)

raw data is passed through independent components and principal componentsAfter decomposition, obtaining independent component matrix and principal component scoring matrix, and establishing I²And T²Statistics, namely establishing SPE statistics for the residual information; independent component vector is s_l＝[s_1l,s_2l,…,s_rl]^T∈R^r×1And a pivot score vector t_l＝[t_l1,t_l2,…,t_lk]^T∈R^k×1Residual vector f_l＝[f_l1,f_l2,…,f_l6]^T∈R^6×11,2, …, n (n is the number of samples); establishment of I²、T²And the SPE statistics are as follows:

wherein

Is the control limit for the SPE statistic,

λ_jeigenvalues corresponding to the covariance matrix of each principal component, where j is 1, 2. k is the number of pivot elements, F_k.(n-k),αIs the upper limit value of F distribution with confidence coefficient alpha and freedom degrees k and (n-k); c. C_αCorresponding to the lower limit value of normal distribution under the confidence coefficient alpha, determining I by adopting a nuclear density estimation method²Control limit of statistic, determining T based on F distribution²A control limit for the statistic;

3) intelligent decision making: obtaining the membrane pollution evaluation index u (t) at the time t on line, and monitoring the statistic I at the time t²、T²And SPE is beyond the control limit, and the occurrence of membrane pollution is determined; in order to determine the main factors of membrane pollution, measures are taken aiming at the determination factors, and a multi-classifier based on a kernel function is established for distinguishing the membrane pollution factors, and the method mainly comprises the following steps: flow of produced water reaches peak value of 460m³H, the water production pressure is lower than 20kPa, and the aeration quantity is lower than 2400m³High sludge concentrationAt 13000mg/l, water pressure lower than 20kPa and water permeability lower than 30LMH/bar, etc.; combining 6 binary classifiers into a multi-classifier, wherein for a first binary classifier, data not belonging to the first class is labeled with-1, data belonging to the first class is labeled with +1, for a second binary classifier, data not belonging to the second class is labeled with-1, data belonging to the second class is labeled with +1, for a third binary classifier, data not belonging to the third class is labeled with-1, data belonging to the third class is labeled with +1, for a fourth binary classifier, data not belonging to the fourth class is labeled with-1, data belonging to the fourth class is labeled with +1, for a fifth binary classifier, data not belonging to the fifth class is labeled with-1, data belonging to the fifth class is labeled with +1, for a sixth binary classifier, data not belonging to the sixth class is labeled with-1, the data belonging to the sixth class is labeled +1, and the q-th classifier (q 1,2, …,6) optimizes the objective function as:

wherein C ∈ (0,5) is a penalty coefficient, w^qIs the connection weight of the q-th classifier, T is the transposed symbol, xi_p ^qRelaxation variables for the p-th data sample of the q-th classifier, b^qFor the bias of the q-th classifier,

updating parameters:

wherein, y_vIs the class of the v-th sample, α_pIs the Grenarian multiplier, alpha, of the p-th data sample_vFor the evaluation index u, the lagrangian multiplier for the v-th data sample is as follows:

f(u)＝sgn(α_py_pK(u,u_p ^q)+b^q) (15)

wherein sgn represents a sign function, the input is positive, the output is 1, and the output is-1 when the input is negative; the output value can be obtained by inputting the sample to be detected into the decision function, and the category corresponding to the function with the maximum value is the category to which the sample to be detected belongs; the 6-type corresponding operation suggestions comprise: running in this state should not exceed 4h, and the water yield is reduced to 260m³Lower than h, reducing the water yield of the membrane pool to 260m³Per hour, controlling transmembrane pressure difference to be less than 40kPa, increasing aeration to 3000m³Fourthly, controlling the sludge concentration of the membrane tank to be 8000- & lt12000 mg/L, & ltv & gt, adjusting the operation parameters, and reducing the water yield to 260m³H, or increasing aeration amount to 5000m³Over per hour, performing online physical cleaning and online chemical cleaning within 24 hours; and applying the trained decision model to a sewage treatment process, performing feature matching on fault data by using the model, outputting fault categories, performing operation suggestions corresponding to the fault categories, and providing decision support for the production process.