A MABR optimal state control method and system

Abstract

The present invention proposes a MABR optimal control method and system, the method comprising continuously collecting perception parameters in the MABR, the perception parameters comprising biofilm parameters, environmental parameters and control parameters; constructing a water quality model, the water quality model comprising a mechanism model and a prediction model, the mechanism model being used to simulate the operation process of the MABR, the variables of the mechanism model being reactor parameters, the reactor parameters comprising perception parameters and efficiency parameters, the prediction model being used to predict the reactor parameters of the next time step based on the reactor parameters of the current time step; obtaining a first efficiency parameter of the current time step by using the mechanism model of the constructed water quality model, and obtaining a second efficiency parameter of the current time step by using the prediction model; respectively calculating the deviation of each parameter in the first efficiency parameter and the second efficiency parameter, and then obtaining a first optimized control parameter or a second optimized control parameter by using the mechanism model, the basic efficiency parameters in the optimal control reactor parameters, and the prediction model, so as to regulate the MABR.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to a MABR optimal state control method and system. Background Art

[0002] MABR (Membrane Aeration Bioreactor) is a technology that can simultaneously supply air and provide a carrier for microorganisms to attach and grow through a medium. It is essentially a biofilm reactor, and its ability to remove pollutants depends on the type, abundance, activity, state of the enriched microorganisms, and the exchange of substances with pollutants. Currently, MABR can only achieve the designed performance of the reaction unit through manual adjustments with high attention and frequency by experienced engineers at the initial startup and commissioning stage. However, once it is operated for a long time, senior engineers cannot provide long-term and high-attention continuous support on site, and the reactor performance begins to decline rapidly.

[0003] There are also some different means used in the prior art to ensure the sewage treatment effect. For example, some have adopted measures of additional fiber filaments. For example, the Chinese invention patent with application publication number CN115340178A adopts MABR aeration membrane filaments and fiber filaments to shuttle and cross-weave into membrane sheets (only applicable to MABR in the form of hollow fiber membranes), thereby providing a better attachment carrier for microorganisms, and controlling the height of the membrane surface by controlling the distance between the aeration membrane filaments and the fiber filaments to control the thickness of the biofilm (affecting the abundance and material exchange capacity) and the replacement of microorganisms; some have adopted the method of bottom perforated pipes or aeration boxes, using MABR aeration exhaust gas or a third-party air source to provide air scrubbing to affect the biofilm.

[0004] However, the above two methods have the following problems: the additional fiber filaments can indeed provide a better attachment carrier for microorganisms, but the additional fiber filaments are actually similar to the carriers such as biological ropes in the traditional biofilm method, which can only provide the function of attachment carriers, but cannot provide internal air supply at the same time. This environmental change will cause the microorganisms attached to it to be different from the microorganisms required by the MABR technology target. The use of additional fiber filaments and MABR aeration membrane filaments will reduce the proportion of MABR membrane filaments in the space, and will also reduce the competitiveness and abundance of the target microbial flora of MABR membrane filaments in the space, resulting in the actual treatment effect being less than expected. It is difficult to control the required biofilm thickness (for example, ≤0.5mm) by adjusting the height of the sheet membrane. Since there is a gap between the additional fiber filaments and the aeration membrane filaments, the staggered amplitude is created by extending the membrane along the fiber filament direction. The raised biofilm is actually the biofilm attached to the fiber filaments. The aeration intensity is small, and the impact of scouring on the target required biofilm thickness is not significant. If the aeration intensity is large, the staggered amplitude is meaningless, and the required biofilm thickness cannot be controlled by adjusting the aeration intensity to achieve the optimization effect. In addition, because the biofilm bacteria are wrapped with extracellular polymers (EPS), which contain polysaccharides, proteins, free nuclei and water. According to the shortcomings of the original biofilm method, the gaps between the fiber filaments and the aeration membrane will soon be blocked by the extracellular polymers, resulting in the blockage of the original MABR's unique mass transfer pathway, and finally returning to the traditional biofilm method with fiber filaments as the main body, resulting in a reduction in the effect of using MABR. The bottom perforated tube or aeration box method simply reuses the MABR tail gas, without a control plan and control parameters. In fact, it can neither solve the problem of actual degradation effect nor has universal applicability. Summary of the invention

[0005] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0006] To this end, the first object of the present invention is to propose a MABR optimal state control method to maintain the pollutant degradation capacity of the reactor at a better state.

[0007] The second object of the present invention is to provide a MABR optimal control system.

[0008] A third objective of the present invention is to provide an electronic device.

[0009] A fourth object of the present invention is to provide a computer-readable storage medium.

[0010] To achieve the above object, the first aspect of the present invention proposes a MABR optimal state control method, comprising:

[0011] Continuously collect the sensing parameters in the MABR reaction unit, wherein the sensing parameters include three types of parameters: biofilm parameters, environmental parameters and control parameters;

[0012] Constructing a water quality model, wherein the water quality model includes a mechanism model and a prediction model, wherein the mechanism model is used to simulate the operation process of the MABR, wherein the variables of the mechanism model are reactor parameters, wherein the reactor parameters include perception parameters and efficiency parameters, wherein the four types of parameters in the reactor parameters all include multiple parameters, and wherein the prediction model is used to predict the reactor parameters of the next time step based on the reactor parameters of the current time step;

[0013] Based on the perception parameters of the current time step, a mechanism model in the constructed water quality model is used to obtain a first efficiency parameter of the current time step, and based on the reactor parameters of the previous time step of the current time step, a prediction model in the constructed water quality model is used to predict the reactor parameters of the current time step to obtain a second efficiency parameter;

[0014] Calculate the deviations of the first performance parameter and each parameter in the second performance parameter respectively. If the deviation of at least one parameter is greater than or equal to the set deviation, obtain the first optimized control parameter by using the mechanism model and the basic performance parameter in the optimal control reactor parameter; if the deviations of all parameters are less than the set deviation, obtain the second optimized control parameter by using the prediction model based on the perception parameter of the current time step and the basic performance parameter;

[0015] The MABR is regulated based on the first optimized control parameter or the second optimized control parameter.

[0016] In the method of the first aspect of the present invention, the first optimized control parameter is obtained by utilizing the mechanism model and the basic performance parameters in the optimal control reactor parameters, including: obtaining the historical reactor parameters under the first set number of days, selecting a group of target historical reactor parameters corresponding to each day from the historical reactor parameters of each day, each group of target historical reactor parameters including target historical perception parameters and target historical performance parameters; if under the first consecutive number of days, at least one parameter in the target historical performance parameters of each day is respectively less than the corresponding parameter in the basic performance parameters, and the change of the at least one parameter is greater than or equal to the first set change, then determining the backtracking time, and determining the first optimized control parameter based on the perception parameters and basic performance parameters within the backtracking time.

[0017] In the method of the first aspect of the present invention, the first optimized control parameter is determined based on the perception parameters and basic performance parameters within the backtracking time, including: for each parameter of the biofilm parameters and environmental parameters within the backtracking time, any parameter is selected as an unknown parameter, and the optimal value of the unknown parameter is calculated based on the remaining parameters of the biofilm parameters and environmental parameters except the unknown parameters within the backtracking time, the control parameters and basic performance parameters within the backtracking time, and then the optimal value of each parameter of the biofilm parameters and environmental parameters within the backtracking time is obtained; based on the optimal value of each parameter and the value of the corresponding parameter in the current time step, the target parameter of the biofilm parameters and environmental parameters within the backtracking time is determined; a predefined mapping relationship is obtained, and the objects of the mapping relationship include each parameter of the biofilm parameters and environmental parameters and the corresponding parameter change, each parameter of the control parameter and the corresponding parameter control method; based on the target parameter, the target parameter change and the mapping relationship, at least one parameter of the control parameter is obtained, thereby obtaining the first optimized control parameter.

[0018] In the method of the first aspect of the present invention, the target parameters in the biofilm parameters and environmental parameters within the backtracking period are determined based on the optimal value of each parameter and the value of the corresponding parameter in the current time step, including: obtaining the target difference based on the optimal value of each parameter and the value of the corresponding parameter in the current time step, and obtaining the historical difference of each parameter of the biofilm parameters and environmental parameters in the second set number of days; determining the difference of each parameter based on the ratio of the target difference to the historical difference of each parameter, selecting the parameter corresponding to the preset number of items with the largest difference, thereby obtaining the target parameter.

[0019] In the method of the first aspect of the present invention, the first optimized control parameters are obtained by utilizing the mechanism model and the basic performance parameters in the optimal control reactor parameters, and also include: if in the second consecutive days, at least one parameter in the target historical performance parameters of each day is greater than the corresponding parameter in the basic performance parameters, and the average change of the at least one parameter is greater than or equal to the second set change, then compare the historical reactor parameters and the optimal control reactor parameters in the second consecutive days to update the optimal control reactor parameters.

[0020] In the method of the first aspect of the present invention, the biofilm parameters include biofilm biomass mass, biofilm thickness, biofilm biomass density, biofilm microbial population structure, and biofilm microbial activity; the environmental parameters include gas volume, air pressure, temperature, membrane surface tangential flow velocity, concentration levels of various pollutants, comprehensive water quality three-dimensional fluorescence spectrum, infrared spectrum, mass spectrum parameters, membrane gas transfer efficiency, membrane gas pressure drop, biofilm surface desorption rate, mass transfer resistance, and membrane gas oxygen consumption quality; the control parameters include air-water scrubbing parameters, brush plate scrubbing parameters, filler / granular medium friction scrubbing parameters, power source, and current, voltage, frequency, and valve opening parameters of instrument equipment.

[0021] In the method of the first aspect of the present invention, the performance parameters include multiple parameters such as the daily removal mass of different key pollutants per unit aeration membrane area of ​​the reactor and the mass of key pollutants that can be removed daily per unit biofilm mass of the reactor.

[0022] To achieve the above-mentioned purpose, the second aspect of the present invention proposes a MABR optimal control system, including a sensing system, a processing system and a control system;

[0023] The sensing system is used to continuously collect sensing parameters in the MABR reaction unit, wherein the sensing parameters include three types of parameters: biofilm parameters, environmental parameters and control parameters;

[0024] The treatment system includes a water quality model, which includes a mechanism model and a prediction model. The mechanism model is used to simulate the operation process of the MABR. The variables of the mechanism model are reactor parameters, which include perception parameters and efficiency parameters. The four types of parameters in the reactor parameters include multiple parameters. The prediction model is used to predict the reactor parameters of the next time step based on the reactor parameters of the current time step; the treatment system is used to obtain the first efficiency parameter of the current time step based on the perception parameters of the current time step using the mechanism model in the constructed water quality model, and predict the reactor parameters of the current time step based on the reactor parameters of the previous time step of the current time step using the prediction model in the constructed water quality model to obtain the second efficiency parameter, respectively calculate the deviation of each parameter in the first efficiency parameter and the second efficiency parameter, if the deviation of at least one parameter is greater than or equal to the set deviation, then use the mechanism model and the basic efficiency parameters in the optimal control reactor parameters to obtain the first optimized control parameter; if the deviation of all parameters is less than the set deviation, then use the prediction model based on the perception parameters of the current time step and the basic efficiency parameters to obtain the second optimized control parameter;

[0025] The control system is used to regulate the MABR based on the first optimized control parameter or the second optimized control parameter.

[0026] To achieve the above-mentioned purpose, the third aspect of the present invention proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method proposed in the first aspect of the present invention.

[0027] To achieve the above-mentioned purpose, the fourth aspect of the present invention proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method proposed in the first aspect of the present invention.

[0028] The MABR optimal control method, system, electronic device and storage medium provided by the present invention continuously collect the perception parameters in the MABR reaction unit, and the perception parameters include three types of parameters: biofilm parameters, environmental parameters and control parameters; construct a water quality model, and the water quality model includes a mechanism model and a prediction model. The mechanism model is used to simulate the MABR operation process. The variables of the mechanism model are reactor parameters. The reactor parameters include perception parameters and efficiency parameters. The four types of parameters in the reactor parameters include multiple parameters. The prediction model is used to predict the reactor parameters of the next time step based on the reactor parameters of the current time step; based on the perception parameters of the current time step, the mechanism model in the constructed water quality model is used to obtain the current time step. The first efficiency parameter of the reactor is obtained, and the reactor parameters of the previous time step of the current time step are predicted by the prediction model in the constructed water quality model to obtain the second efficiency parameter; the deviation of each parameter in the first efficiency parameter and the second efficiency parameter is calculated respectively, and if the deviation of at least one parameter is greater than or equal to the set deviation, the first optimized control parameter is obtained by using the mechanism model and the basic efficiency parameter in the optimal control reactor parameter; if the deviation of all parameters is less than the set deviation, the second optimized control parameter is obtained by using the prediction model based on the perception parameter and the basic efficiency parameter of the current time step; the MABR is regulated based on the first optimized control parameter or the second optimized control parameter. In this case, compared with the existing single control method, the method of the present invention integrates four types of data, namely biofilm parameters, environmental parameters, control parameters and performance parameters, and combines the mechanism model and the prediction model to obtain the first performance parameter and the second performance parameter of the current time step, and then calculates the deviation of each parameter in the first performance parameter and the second performance parameter, so as to respectively use the mechanism model and the basic performance parameters in the optimal control reactor parameters, or the prediction model and the basic performance parameters to obtain the corresponding optimized control parameters, and then regulate the MABR. In this way, the pollutant degradation capacity of the reactor can be maintained in a better state.

[0029] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0031] Figure 1 A schematic flow chart of a MABR optimal control method provided by an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of a specific flow chart of a process for obtaining a first optimization control parameter provided by an embodiment of the present invention;

[0033] Figure 3 A block diagram of a MABR optimal control system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0035] The MABR optimal control method and system according to an embodiment of the present invention will be described below with reference to the accompanying drawings.

[0036] Based on this, an embodiment of the present invention provides a MABR optimal state control method to maintain the pollutant degradation capacity of the reactor at a better state.

[0037] Figure 1 A flow chart of a MABR optimal control method provided in an embodiment of the present invention.

[0038] like Figure 1 As shown, the MABR optimal control method includes the following steps:

[0039] Step S101, continuously collect the sensing parameters in the MABR reaction unit, the sensing parameters include three types of parameters: biofilm parameters, environmental parameters and control parameters.

[0040] In step S101, the sensing parameters include three types of parameters: biofilm parameters, environmental parameters and control parameters.

[0041] Biofilm parameters include but are not limited to biofilm biomass mass, biofilm thickness, biofilm biomass density, biofilm microbial population structure, biofilm microbial activity and many other parameters.

[0042] Environmental parameters include but are not limited to gas volume, air pressure, temperature, tangential flow velocity on the membrane surface, concentration levels of various pollutants, comprehensive water quality three-dimensional fluorescence spectrum, infrared spectrum, mass spectrum parameters, membrane gas transfer efficiency, membrane gas pressure drop, biofilm surface desorption rate, mass transfer resistance, membrane gas oxygen consumption quality, etc. Among them, various pollutants include but are not limited to COD (chemical oxygen demand), ammonia nitrogen, TN (total nitrogen), nitrate nitrogen, DO (dissolved oxygen), dimethyl isoborneol, oxygen, carbon dioxide, etc.

[0043] The control parameters include but are not limited to parameters for air-water scrubbing (such as flow, pressure, temperature, duration, frequency, etc. under air scrubbing, water scrubbing or air-water combined scrubbing), parameters for brush plate scrubbing (such as fitting distance, brushing frequency, etc.), filler / granular medium friction scrubbing parameters and many other parameters.

[0044] In step S101, the sensing parameters can be obtained through the sensing system. The sensing system mainly collects environmental and microbial parameters in the MABR reaction unit through various sensors and corresponding information transmitters. Environmental and microbial parameters are also called sensing parameters.

[0045] In step S101, the sensing system includes but is not limited to a microbial quality sensing system (for example, for measuring the quality of attached and growing biofilms, etc.), a multi-parameter water quality automatic monitoring system (for example, including but not limited to the temperature at the reactor inlet and outlet and near the geometric center, COD, ammonia nitrogen, TN, nitrate nitrogen, DO, etc., automatic sampling and storage of ex situ measured comprehensive water quality three-dimensional fluorescence spectra, infrared spectra and mass spectrometry parameters, etc.), a membrane gas supply matrix utilization sensing system (for example, including but not limited to the gas volume, air pressure, temperature, oxygen and carbon dioxide concentrations, oxygen transfer efficiency, etc. at the front and rear ends of the MABR membrane gas supply), a membrane thickness sensing system (for example, including but not limited to using the diffusion rate of gases that cannot be used by microorganisms to calculate membrane thickness, using microelectrodes to puncture biofilms to calculate membrane thickness, etc.), membrane thickness and membrane metabolism control sensing system (for example, including but not limited to scrubbing parameters such as flow, pressure, temperature, brush plate fitting distance, etc.), biofilm microbial population and activity sensing system (including but not limited to using microelectrode in situ measurement methods, ex situ high-throughput sequencing methods, etc. to characterize the abundance, community structure and activity of microbial populations in biofilms, etc.), equipment status monitoring system (including but not limited to the current, voltage, frequency, valve opening, etc. of various instrument equipment in the MABR optimal control system).

[0046] In step S101, biofilm parameters can be obtained by a microbial quality sensing system, a film thickness sensing system, and a biofilm microbial population and activity sensing system.

[0047] When the microbial quality sensing system, membrane thickness sensing system and biofilm microbial population and activity sensing system collect corresponding parameters, the collection points should include but are not limited to key reference points such as near water inlet point, far water inlet point, biofilm near sewage point, biofilm near aeration membrane point, etc.

[0048] In step S101, environmental parameters can be obtained by the multi-parameter water quality automatic monitoring system and the membrane aeration matrix using the sensing system. When the multi-parameter water quality automatic monitoring system collects corresponding parameters, the collection points should include but are not limited to the reactor inlet, reactor outlet, reactor geometric center and other positions. When the membrane aeration matrix uses the sensing system to collect corresponding parameters, the collection points should include but are not limited to the comprehensive inlet and comprehensive outlet of the reactor aeration membrane and other key reference points.

[0049] In step S101, the control parameters can be obtained from the membrane thickness and membrane metabolism control sensing system and the equipment status monitoring system.

[0050] Step S102, constructing a water quality model, which includes a mechanism model and a prediction model. The mechanism model is used to simulate the operation process of the MABR. The variables of the mechanism model are reactor parameters. The reactor parameters include perception parameters and efficiency parameters. The four types of parameters in the reactor parameters all include multiple parameters. The prediction model is used to predict the reactor parameters of the next time step based on the reactor parameters of the current time step.

[0051] In step S102, the reactor parameters include perception parameters and performance parameters, that is, the reactor parameters include four types of parameters: biofilm parameters, environmental parameters, control parameters and performance parameters. Among them, the performance parameters include the daily removal quality of different key pollutants per unit aeration membrane area of ​​the reactor (key pollutants are determined by the application environment requirements of the reactor, such as the COD quality, ammonia nitrogen quality, TN quality, dimethylisoborneol quality, etc. removed per unit aeration membrane area per day), the quality of key pollutants that can be removed per unit biofilm mass of the reactor per day, and other parameters. It should be noted that the daily removal quality of different key pollutants per unit aeration membrane area of ​​the reactor and the quality of key pollutants that can be removed per unit biofilm mass of the reactor are the corresponding performance parameters under a certain set of biofilm parameters, environmental parameters, and control parameters. Different performance parameters can be used for different biofilm parameters, environmental parameters, and control parameters.

[0052] In step S102, when the biofilm parameters, environmental parameters and control parameters are determined, the mechanism model can be used to calculate the performance parameters.

[0053] Specifically, in step S102, a water quality model is constructed using the perception parameters obtained from the perception system and the calculated efficiency parameters. The water quality model includes a mechanism model and a prediction model. The mechanism model is used to simulate the MABR operation process. The MABR operation process includes but is not limited to the mass transfer and conversion process of different component matrices in the suspension, biofilm and aeration membrane, and the growth and desorption process of different microorganisms in the suspension and biofilm. Different processes are generally in different regions, so the mechanism model can divide the reactor into different regions, assuming that each region only bears different parts of a process control, each region as a separate completely mixed phase, and is assumed to be a steady state. The mechanism model is essentially composed of a large number of determined mathematical equations, and the composition of the equations, boundary conditions, and some assumed quantities or constant values ​​(such as microbial specific growth rate, substrate utilization, surface fluid shear force, mass transfer boundary layer thickness, etc.) can be determined by small tests or theoretical equations. The mechanism model simulates the corresponding process through different mathematical equations. The variables of the entire mechanism model are reactor parameters, that is, the variables of all equations included in the mechanism model include four categories, namely biofilm parameters, environmental parameters, control parameters and efficiency parameters. Since each type of parameter includes multiple parameters, each type of variable is composed of multiple sub-variables, and each sub-variable corresponds to a parameter. If three types of variables are determined, the mechanism model can be used to calculate the remaining type of variables. For example, when the biofilm parameters, environmental parameters, and control parameters are determined, the mechanism model can be used to calculate the efficiency parameters.

[0054] In step S102, the prediction model is used to predict the reactor parameters of the next time step based on the reactor parameters of the current time step. Specifically, the prediction model of the present invention is a data model, which adopts the Transformer deep learning model architecture and introduces a self-attention mechanism so that the model can simultaneously consider all positions in the input sequence, rather than processing step by step like a recurrent neural network or a convolutional neural network. For each parameter input into the water quality model from the perception system, the position encoding is to use the mechanism model to correct the relationship between the parameters. The multi-head attention mechanism is introduced for multi-parameter parallel calculation, and the residual neural network is introduced to prevent overfitting and network degradation. The input data of the prediction model are the reactor parameters of the previous time step in the adjacent time step, and the output data of the prediction model are the reactor parameters of the next time step in the adjacent time step. For example, if the input data of the prediction model are the biofilm parameters, environmental parameters, control parameters and efficiency parameters of the previous time step of the current time step, the output data of the prediction model are the biofilm parameters, environmental parameters, control parameters and efficiency parameters of the current time step. If the input data of the prediction model are the biofilm parameters, environmental parameters, control parameters and performance parameters of the current time step, the output data of the prediction model are the biofilm parameters, environmental parameters, control parameters and performance parameters of the next time step of the current time step.

[0055] In step S102, the prediction model is trained using historical reactor parameters to obtain a trained prediction model. The constructed water quality model includes a mechanism model and a trained prediction model.

[0056] Step S103, based on the perception parameters of the current time step, the mechanism model in the constructed water quality model is used to obtain the first efficiency parameter of the current time step, and based on the reactor parameters of the previous time step of the current time step, the prediction model in the constructed water quality model is used to predict the reactor parameters of the current time step to obtain the second efficiency parameter.

[0057] Specifically, in step S103, the perception parameters of the current time step (i.e., the biofilm parameters, environmental parameters, and control parameters of the current time step) are obtained, and the perception parameters of the current time step are input into the mechanism model in the constructed water quality model to obtain the efficiency parameters of the current time step, which can be regarded as the first efficiency parameters (also called calculation efficiency parameters).

[0058] In step S103, the reactor parameters of the previous time step of the current time step are obtained, and the reactor parameters of the previous time step of the current time step are input into the prediction model in the constructed water quality model to output the reactor parameters of the current time step, wherein the efficiency parameters included in the reactor parameters of the current time step can be regarded as the second efficiency parameters (also called simulation efficiency parameters).

[0059] In step S103, the item type, unit and limit range of the first performance parameter and the second performance parameter (i.e., the calculated performance parameter and the simulated performance parameter) are consistent with the basic performance parameter, and only the specific value of the performance parameter changes. Among them, the basic performance parameter refers to the performance parameter in the project benchmark parameter (i.e., the optimal control reactor parameter).

[0060] The process of obtaining the project benchmark parameters is: after the MABR reactor is debugged, the biofilm parameters, environmental parameters, and control parameters collected by the sensing system of the reactor within a certain time range after the debugging are selected, and the efficiency parameters are calculated, and the collected biofilm parameters, environmental parameters, control parameters, and the calculated efficiency parameters are used as the project benchmark parameters under optimal control (i.e., optimal control reactor parameters), and are entered into the optimal control benchmark database. Among them, the value of each parameter in the four types of parameters can be a selected real value, or it can be the average, median, or other calculated confidence number of the values ​​collected from the same project within the time range. Preferably, it is recommended to use the average of continuous data with an overall deviation rate of no more than 5% within the minimum generation time range of autotrophic nitrifying bacteria as the value of each parameter in each type of parameter. The biofilm parameters in the project benchmark parameters are called basic biofilm parameters, the environmental parameters in the project benchmark parameters are called basic environmental parameters, the control parameters in the project benchmark parameters are called basic control parameters, and the efficiency parameters in the project benchmark parameters are called basic efficiency parameters. It should be noted that for each project starting from the commissioning period, there is only one set of project baseline parameters in the initial operation, and the subsequent project baseline parameters will be updated.

[0061] Step S104, respectively calculate the deviation of each parameter in the first efficiency parameter and the second efficiency parameter. If the deviation of at least one parameter is greater than or equal to the set deviation, the first optimized control parameter is obtained by using the mechanism model and the basic efficiency parameters in the optimal control reactor parameters; if the deviation of all parameters is less than the set deviation, the second optimized control parameter is obtained by using the prediction model based on the perception parameters and basic efficiency parameters of the current time step.

[0062] Specifically, in step S104, since the performance parameter includes multiple parameters, when calculating the deviation between the first performance parameter and the second performance parameter, the deviation is calculated for the parameters of the same item in the first performance parameter and the second performance parameter, so as to obtain the deviation of the multiple parameters, and the deviation of each parameter is compared with the set deviation, for example, 5%. If the deviation of at least one parameter is ≥5%, the mechanism model control logic is adopted, that is, the first optimized control parameter is obtained by using the mechanism model and the basic performance parameters in the optimal control reactor parameters; if the deviation of all parameters is less than 5%, the data model control logic is adopted, that is, the second optimized control parameter is obtained by using the prediction model based on the perception parameters and basic performance parameters of the current time step. Among them, the deviation can be determined, for example, based on the difference between the parameters of the same item in the first performance parameter and the second performance parameter and the ratio of the parameter in the first performance parameter.

[0063] Figure 2 The following is a schematic diagram of a specific process of obtaining the first optimization control parameter provided by an embodiment of the present invention. Figure 2As shown, the first optimized control parameter is obtained by using the mechanism model and the basic performance parameters in the optimal control reactor parameters, including: obtaining the historical reactor parameters under the first set number of days, selecting a group of target historical reactor parameters corresponding to each day from the historical reactor parameters of each day, and each group of target historical reactor parameters includes target historical perception parameters and target historical performance parameters (step S1041); if under the first consecutive number of days, at least one parameter in the target historical performance parameters of each day is respectively less than the corresponding parameter in the basic performance parameters, and the change amount of the at least one parameter is greater than or equal to the first set change amount, then determine the backtracking time, and determine the first optimized control parameter based on the perception parameters and basic performance parameters within the backtracking time (step S1042). The first set number of days is greater than the first consecutive number of days. The first set number of days is greater than the second consecutive number of days.

[0064] In step S1041, the first set number of days is, for example, greater than or equal to a microbial generation cycle (e.g., 8 days), and the historical reactor parameters under the first set number of days are obtained. For each day, a group of historical reactor parameters is selected from the historical reactor parameters of that day, and the group of historical reactor parameters is used as the target historical reactor parameters corresponding to that day (i.e., one group of target historical reactor parameters corresponds to one day). The process of determining the target historical reactor parameters of that day includes: obtaining all groups of historical reactor parameters of that day; comparing the biofilm parameters and environmental parameters in each group of historical reactor parameters with the basic biofilm parameters and basic environmental parameters in the project benchmark parameters respectively; and selecting a group of historical reactor parameters with the lowest weighted average change rate of each parameter as the target historical reactor parameters. Each group of target historical reactor parameters includes target historical perception parameters and target historical efficiency parameters. The target historical efficiency parameters are calculated based on the target historical perception parameters using a mechanism model.

[0065] In step S1042, the first consecutive number of days is, for example, 2 days, and the first set change amount is, for example, 5%. That is, if at least one parameter of the target historical performance parameter calculated using the mechanism model is less than the corresponding parameter in the basic performance parameter for 2 consecutive days, and the change amount is ≥5%, then the backtracking time is determined. The change amount of each parameter can be obtained, for example, by calculating the difference between each parameter in the target historical performance parameter and the corresponding parameter in the basic performance parameter, and calculating the ratio of each difference to the corresponding parameter in the basic performance parameter. The determined backtracking time can be to use the microbial generation cycle related to the relevant performance parameter before and after the decline as the backtracking time period benchmark, and backtrack 0.5 generation cycle time.

[0066] In step S1042, the first optimized control parameter is determined based on the perception parameters and basic performance parameters within the backtracking time, including: for each parameter of the biofilm parameters and environmental parameters within the backtracking time, any parameter is selected as the unknown parameter, and the optimal value of the unknown parameter is calculated based on the remaining parameters of the biofilm parameters and environmental parameters except the unknown parameters within the backtracking time, the control parameters and basic performance parameters within the backtracking time, and then the optimal value of each parameter of the biofilm parameters and environmental parameters within the backtracking time is obtained; based on the optimal value of each parameter and the value of the corresponding parameter in the current time step, the target parameter of the biofilm parameters and environmental parameters within the backtracking time is determined; a predefined mapping relationship is obtained, and the objects of the mapping relationship include each parameter of the biofilm parameters and environmental parameters and the corresponding parameter change, each parameter of the control parameter and the corresponding parameter control method; at least one parameter of the control parameter is obtained based on the target parameter, the target parameter change and the mapping relationship, so as to obtain the first optimized control parameter.

[0067] Among them, based on the optimal value of each parameter and the value of the corresponding parameter in the current time step, the target parameters in the biofilm parameters and environmental parameters within the backtracking time are determined, including: obtaining the target difference based on the optimal value of each parameter and the value of the corresponding parameter in the current time step, and obtaining the historical difference of each parameter of the biofilm parameters and environmental parameters in the second set number of days; determining the difference of each parameter based on the ratio of the target difference and the historical difference of each parameter, selecting the parameter corresponding to the preset number of items with the largest difference, so as to obtain the target parameter.

[0068] Taking the biofilm parameters and environmental parameters within the retrospective time including biofilm thickness, biofilm density, biofilm surface fluid flow rate, biofilm surface desorption rate, mass transfer resistance, aeration membrane oxygen supply quality (i.e., membrane gas supply oxygen consumption quality) as an example (it should be noted that the multiple parameters are only examples, and the biofilm parameters and environmental parameters include parameter items not limited to this), the process of obtaining the first optimized control parameters is as follows:

[0069] 1) Select any one of the parameters of biofilm thickness, biofilm density, fluid flow rate on the biofilm surface, desorption rate on the biofilm surface, mass transfer resistance, and oxygen supply quality of the aeration membrane as an unknown parameter, and determine the numerical values ​​of the remaining parameters of the biofilm parameters and environmental parameters other than the unknown parameters within the backtracking time, and bring the remaining parameters, control parameters within the backtracking time, and basic performance parameters into the mechanism model to calculate the optimal value of the unknown parameter, thereby obtaining the optimal values ​​corresponding to the biofilm thickness, biofilm density, fluid flow rate on the biofilm surface, desorption rate on the biofilm surface, mass transfer resistance, and oxygen supply quality of the aeration membrane;

[0070] 2) Compare the optimal values ​​corresponding to the biofilm thickness, biofilm density, biofilm surface fluid flow rate, biofilm surface desorption rate, mass transfer resistance, and aeration membrane oxygen supply quality with the biofilm thickness, biofilm density, biofilm surface fluid flow rate, biofilm surface desorption rate, mass transfer resistance, and aeration membrane oxygen supply quality collected in the current time step, respectively, and calculate the difference between the optimal value of each parameter and the value of the corresponding parameter in the current time step, which is the target difference corresponding to each parameter, and obtain the historical difference corresponding to each parameter (the historical difference may refer to the difference between the maximum and minimum values ​​of each parameter within a certain number of days in the past), determine the difference of each parameter based on the ratio of the target difference and the historical difference of each parameter, select the item parameter corresponding to the preset number of items (for example, 3 items) with the largest difference, and thus obtain the target item parameter;

[0071] 3) Obtaining a predefined mapping relationship, the objects of the mapping relationship include each parameter of the biofilm parameter and the environmental parameter and the corresponding parameter change, each parameter of the control parameter and the corresponding parameter control method; the mapping relationship includes, for example, the following control methods:

[0072] 31) Reduce / increase the membrane thickness and the start-up time of the membrane metabolism control system. The reduction / increase percentage is consistent with the change rate of the comparison output and the original parameter: the comparison output of the biofilm thickness is greater than / less than the original parameter.

[0073] 32) Reduce / increase the power source output (gas flow, water flow, agitator frequency, etc.) of the microbial attachment and material exchange control system. The reduction / increase percentage is consistent with the change rate of the comparison output and the original parameters: the comparison output of the biofilm density is less than / greater than the original parameters, the fluid flow rate on the biofilm surface is less than / greater than the original parameters; the comparison output of the desorption rate on the biofilm surface is less than / greater than the original parameters.

[0074] 33) Reduce / increase the membrane thickness and medium strength of the membrane metabolic control system (change the flow rate for fluid media and change the distance for mechanical media). The reduction / increase percentage is consistent with the rate of change of the comparison output and the original parameters: the comparison output of the biofilm thickness is greater than / less than the original parameters; the comparison output of the mass transfer resistance is greater than / less than the original parameters.

[0075] 34) Reduce / increase the aeration membrane gas supply flow rate, the reduction / increase percentage and the comparison output are consistent with the change rate of the original parameters: the aeration membrane oxygen supply mass comparison output is less than / greater than the original parameters.

[0076] The output of the above comparison is the optimal value of the corresponding parameter, and the corresponding original parameter is the value of the corresponding parameter in the current time step.

[0077] 4) After determining the target item parameter, calculate the target item parameter change, obtain at least one parameter involved in the control parameter according to the acquired mapping relationship, and all the parameters involved in the control parameter constitute the first optimized control parameter. Among them, since the control methods corresponding to different item parameters may be repeated, the control method corresponding to the item with a higher parameter change is selected at this time.

[0078] In step S104, the first optimized control parameters are obtained using the mechanism model and the basic performance parameters in the optimal control reactor parameters, and the method also includes: if on the second consecutive days, at least one parameter in the target historical performance parameters of each day is greater than the corresponding parameter in the basic performance parameters, and the average change of the at least one parameter is greater than or equal to the second set change, then the historical reactor parameters and the optimal control reactor parameters on the second consecutive days are compared to update the optimal control reactor parameters.

[0079] In step S104, the second consecutive number of days is, for example, 5 days, and the second set change is, for example, 5%. If at least one parameter of the target historical performance parameter calculated using the mechanism model is greater than the corresponding parameter in the basic performance parameter for 5 consecutive days, and the average change is ≥5%, the data within the 5 days are entered into the optimal control benchmark database with reference to the entry standard of the project benchmark parameter after debugging. If the data within the 5 days conflict with the original project benchmark parameters in the database, the group with the higher performance parameter value among the data within the 5 days and the basic performance parameters is selected as the new project benchmark parameter (i.e., the new optimal control reactor parameter).

[0080] In step S104, if the deviation of all parameters is less than the set deviation, the perception parameters and basic performance parameters of the current time step are input into the prediction model to predict the required reactor parameters, and the control parameters in the required reactor parameters are the second optimized control parameters.

[0081] Step S105, regulating the MABR based on the first optimization control parameter or the second optimization control parameter.

[0082] In step S105, the MABR is regulated by using the membrane thickness and membrane metabolism control system and the microbial attachment and material exchange control system based on the first optimized control parameter or the second optimized control parameter.

[0083] Among them, the membrane thickness and membrane metabolism control system mainly uses physical methods such as air scrubbing, water scrubbing, air-water combined scrubbing, brush plate / filler / granular medium friction scrubbing, or a combination of several methods to rub the microorganisms and extracellular polymers (EPS) attached to the surface of the aeration membrane. The equipment parameters in the system are controlled by sensing the input of parameters in the system and the output of parameters in the water quality model (i.e., the generated first optimization control parameters or the second optimization control parameters) to control the microbial attachment quality, generation replacement time, microbial species (for example, scrubbing away unnecessary metazoans such as red worms with weak adhesion), and biofilm thickness (thereby affecting the diffusion rate, diffusion resistance and diffusion time of multiple components such as matrix substrate and metabolites in the space of gas-solid / aeration membrane-membrane / microbial cluster-water). Among them, the main control parameters of the air / water / air-water combined scrubbing system are the air / water / air-water combined scrubbing intensity (air / water flow, pressure, temperature), the air / water / air-water combined scrubbing duration (within a certain period of time, such as the total duration of air / water / air-water combined scrubbing in one day), and the air / water / air-water combined scrubbing frequency (the interval between the opening of air / water / air-water combined scrubbing). The main control parameters of the brush plate friction scrubbing are the brush plate scrubbing position (if there is path control), the brush plate scrubbing duration, and the brush plate scrubbing frequency. The main control parameters of the filler / granular medium friction scrubbing are the output power of the filler / granular medium fluidization power source (for example, if gas stirring is used, it is the gas flow pressure, etc.; if mechanical stirring is used, it is the agitator frequency and power, etc.; if water flow stirring is used, it is the water flow pressure, etc.), duration, frequency, etc.

[0084] The microbial attachment and material exchange control system mainly uses different power sources (including but not limited to air lift circulation pumps using fans as power sources, hydraulic circulation pumps and hydrocyclones using water pumps as power sources, mechanical agitators using agitators as power sources, etc.) to cause changes in the flow state and flow rate of the liquid phase and boundary layer on the surface of the biofilm, thereby affecting the changes in the matrix concentration distribution of the liquid phase and the multi-component concentration difference of the microbial film-boundary layer-liquid phase. It can also cause changes in the surface shear force, thereby changing the adsorption and desorption rate of the microorganisms under normal conditions, thereby affecting the attachment structure of the microorganisms (the microbial film under high flow rate and high shear force will be denser).

[0085] In step S105, when the MABR is regulated by the membrane thickness and membrane metabolism control system and the microbial attachment and material exchange control system based on the first optimized control parameter, the adjustment period is generally 0.25 to 1.5 generation periods of key microorganisms. The process control measures are no longer adjusted repeatedly during the adjustment period, but the sensing system continues to collect data.

[0086] In step S105, when the MABR is regulated by the membrane thickness and membrane metabolism control system and the microbial attachment and material exchange control system based on the second optimized control parameters, the second optimized control parameters are used to directly intervene in the control of the valves of each device in the membrane thickness and membrane metabolism control system and the microbial attachment and material exchange control system. The adjustment cycle of the predicted model control is 2 hours. Through the correspondence between the iterative control parameters and the post-feedback, the overall control effect will continue to approach the optimal solution.

[0087] In an embodiment of the present invention, the MABR optimal control method further includes: if an uncontrollable external environment undergoes a significant change that cannot be perceived by the original method, a new perception system and parameter input system are introduced based on the new variables.

[0088] In an embodiment of the present invention, the MABR optimal control method further includes: if the deviation between the first performance parameter and the second performance parameter is ≥50%, an alarm is issued to indicate the possibility of a fault in the instrument monitoring.

[0089] In order to implement the above embodiments, the present invention also proposes a MABR optimal control system.

[0090] Figure 3 A block diagram of a MABR optimal control system provided in an embodiment of the present invention.

[0091] like Figure 3 As shown, the MABR optimal control system includes a sensing system 11, a processing system 12 and a control system 13, wherein:

[0092] The sensing system 11 is used to continuously collect the sensing parameters in the MABR reaction unit, and the sensing parameters include three types of parameters: biofilm parameters, environmental parameters and control parameters;

[0093] The processing system 12 includes a water quality model, which includes a mechanism model and a prediction model. The mechanism model is used to simulate the operation process of the MABR. The variables of the mechanism model are reactor parameters. The reactor parameters include perception parameters and efficiency parameters. The four types of parameters in the reactor parameters include multiple parameters. The prediction model is used to predict the reactor parameters of the next time step based on the reactor parameters of the current time step; the processing system is used to obtain the first efficiency parameter of the current time step based on the perception parameters of the current time step using the mechanism model in the constructed water quality model, and predict the reactor parameters of the current time step based on the reactor parameters of the previous time step of the current time step using the prediction model in the constructed water quality model to obtain the second efficiency parameter, and respectively calculate the deviation of each parameter in the first efficiency parameter and the second efficiency parameter. If the deviation of at least one parameter is greater than or equal to the set deviation, the first optimized control parameter is obtained by using the mechanism model and the basic efficiency parameter in the optimal control reactor parameter; if the deviation of all parameters is less than the set deviation, the second optimized control parameter is obtained by using the prediction model based on the perception parameters and the basic efficiency parameters of the current time step;

[0094] The control system 13 is used to regulate the MABR based on the first optimized control parameter or the second optimized control parameter.

[0095] Furthermore, in a possible implementation of an embodiment of the present invention, the sensing system 11 includes a microbial quality sensing system, a membrane thickness sensing system, a biofilm microbial population and activity sensing system, a multi-parameter water quality automatic monitoring system, a membrane gas supply matrix utilization sensing system, a membrane thickness and membrane metabolism control sensing system and an equipment status monitoring system.

[0096] Furthermore, in a possible implementation of an embodiment of the present invention, in the sensing system 11, biofilm parameters include biofilm biomass mass, biofilm thickness, biofilm biomass density, biofilm microbial population structure, and biofilm microbial activity; environmental parameters include gas volume, air pressure, temperature, membrane surface tangential flow velocity, concentration levels of various pollutants, comprehensive water quality three-dimensional fluorescence spectrum, infrared spectrum, mass spectrum parameters, membrane gas transfer efficiency, membrane gas pressure drop, biofilm surface desorption rate, mass transfer resistance, and membrane gas oxygen consumption quality; control parameters include air-water scrubbing parameters, brush plate scrubbing parameters, filler / granular medium friction scrubbing parameters, power source, and current, voltage, frequency, and valve opening parameters of instrument equipment.

[0097] Furthermore, in a possible implementation of the embodiment of the present invention, in the treatment system 12, the performance parameters include multiple parameters such as the daily removal mass of different key pollutants per unit aeration membrane area of ​​the reactor and the mass of key pollutants that can be removed per unit biofilm mass of the reactor.

[0098] Furthermore, in a possible implementation of an embodiment of the present invention, in the processing system 12, a first optimized control parameter is obtained by using a mechanism model and basic performance parameters in the optimal control reactor parameters, including: obtaining historical reactor parameters under a first set number of days, selecting a group of target historical reactor parameters corresponding to each day from the historical reactor parameters of each day, each group of target historical reactor parameters including target historical perception parameters and target historical performance parameters; if under a first consecutive number of days, at least one parameter in the target historical performance parameters of each day is respectively less than the corresponding parameter in the basic performance parameters, and the change in the at least one parameter is greater than or equal to the first set change, then determining the backtracking period, and determining the first optimized control parameter based on the perception parameters and basic performance parameters within the backtracking period.

[0099] Further, in a possible implementation of an embodiment of the present invention, in the processing system 12, a first optimized control parameter is determined based on the perception parameters and basic performance parameters within the backtracking time, including: for each parameter of the biofilm parameters and environmental parameters within the backtracking time, any parameter is selected as an unknown parameter, and the optimal value of the unknown parameter is calculated based on the remaining parameters of the biofilm parameters and environmental parameters except the unknown parameters within the backtracking time, the control parameters within the backtracking time, and the basic performance parameters, so as to obtain the optimal value of each parameter of the biofilm parameters and environmental parameters within the backtracking time; based on the optimal value of each parameter and the value of the corresponding parameter in the current time step, the target parameter of the biofilm parameters and environmental parameters within the backtracking time is determined; a predefined mapping relationship is obtained, and the objects of the mapping relationship include each parameter of the biofilm parameters and environmental parameters and the corresponding parameter change, each parameter of the control parameter and the corresponding parameter control method; at least one parameter of the control parameter is obtained based on the target parameter, the target parameter change and the mapping relationship, so as to obtain the first optimized control parameter.

[0100] Furthermore, in a possible implementation of an embodiment of the present invention, in the processing system 12, based on the optimal value of each parameter and the value of the corresponding parameter in the current time step, the target item parameters in the biofilm parameters and environmental parameters within the backtracking period are determined, including: obtaining the target difference based on the optimal value of each parameter and the value of the corresponding parameter in the current time step, and obtaining the historical difference of each parameter of the biofilm parameters and environmental parameters in the second set number of days; determining the difference of each parameter based on the ratio of the target difference and the historical difference of each parameter, selecting the item parameter corresponding to the preset number of items with the largest difference, thereby obtaining the target item parameter.

[0101] Furthermore, in a possible implementation of an embodiment of the present invention, in the processing system 12, the first optimized control parameters are obtained by using the mechanism model and the basic performance parameters in the optimal control reactor parameters, and it also includes: if on the second consecutive days, at least one parameter in the target historical performance parameters of each day is greater than the corresponding parameter in the basic performance parameters, and the average change of the at least one parameter is greater than or equal to the second set change, then the historical reactor parameters and the optimal control reactor parameters on the second consecutive days are compared to update the optimal control reactor parameters.

[0102] Furthermore, in a possible implementation of the embodiment of the present invention, the control system 13 includes a membrane thickness and membrane metabolism control system and a microorganism attachment and material exchange control system.

[0103] It should be noted that the aforementioned explanation of the MABR optimal control method embodiment is also applicable to the MABR optimal control system of this embodiment and will not be repeated here.

[0104] In the embodiment of the present invention, the perception parameters in the MABR reaction unit are continuously collected, and the perception parameters include three types of parameters: biofilm parameters, environmental parameters, and control parameters; a water quality model is constructed, and the water quality model includes a mechanism model and a prediction model. The mechanism model is used to simulate the operation process of the MABR. The variables of the mechanism model are reactor parameters. The reactor parameters include perception parameters and efficiency parameters. The four types of parameters in the reactor parameters include multiple parameters. The prediction model is used to predict the reactor parameters of the next time step based on the reactor parameters of the current time step; based on the perception parameters of the current time step, the mechanism model in the constructed water quality model is used to obtain the first efficiency parameter of the current time step, and based on The reactor parameters of the previous time step of the current time step are predicted by the prediction model in the constructed water quality model to obtain the second efficiency parameter; the deviation of each parameter in the first efficiency parameter and the second efficiency parameter is calculated respectively. If the deviation of at least one parameter is greater than or equal to the set deviation, the first optimized control parameter is obtained by using the mechanism model and the basic efficiency parameter in the optimal control reactor parameter; if the deviation of all parameters is less than the set deviation, the second optimized control parameter is obtained by using the prediction model based on the perception parameter and the basic efficiency parameter of the current time step; the MABR is regulated based on the first optimized control parameter or the second optimized control parameter. In this case, compared with the existing single control method, the method of the present invention integrates four types of data, namely biofilm parameters, environmental parameters, control parameters and performance parameters, and combines the mechanism model and the prediction model to obtain the first performance parameter and the second performance parameter of the current time step, and then calculates the deviation of each parameter in the first performance parameter and the second performance parameter, so as to respectively use the mechanism model and the basic performance parameters in the optimal control reactor parameters, or the prediction model and the basic performance parameters to obtain the corresponding optimized control parameters, and then regulate the MABR. In this way, the pollutant degradation capacity of the reactor can be maintained in a better state.

[0105] The method and system of the present invention ensure that the ability of the MABR reaction unit to degrade pollutants is always in a better state by constructing a perception system, an algorithm system, a membrane thickness and membrane metabolism control system, and a microbial attachment and material exchange control system. Providing a complete set of perception and control systems for the MABR reactor itself can help the reactor control the above-mentioned influencing factors to be maintained in a better range for a long time, not just in the startup and debugging stage, so that its pollutant degradation ability is maintained in a better state. The deviation rate, generation cycle length, backtracking time and other parameter data used in the basic logic involved in the present invention are all exemplary, and do not mean that only the parameter data can be used. Based on the basic logic, the parameter data can be increased or decreased and optimized according to different implementation sites and needs. The present invention has a complete set of systems and logics that can help the reactor control the above-mentioned influencing factors to be maintained in a better range for a long time, not just in the startup and debugging stage, and it is not a single control logic that cannot be applied to complex practical problems, so that its pollutant degradation ability is maintained in a better state, and it has a certain universality.

[0106] In order to implement the above embodiments, the present invention also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0107] In order to implement the above embodiments, the present invention further proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0108] In order to implement the above embodiments, the present invention further provides a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0109] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0110] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0111] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0112] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0113] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0114] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0115] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0116] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A MABR optimal state control method, characterized in that: include: Continuously collect the sensing parameters in the MABR reaction unit, wherein the sensing parameters include three types of parameters: biofilm parameters, environmental parameters and control parameters; Constructing a water quality model, wherein the water quality model includes a mechanism model and a prediction model, wherein the mechanism model is used to simulate the operation process of the MABR, wherein the variables of the mechanism model are reactor parameters, wherein the reactor parameters include perception parameters and efficiency parameters, wherein the four types of parameters in the reactor parameters all include multiple parameters, and wherein the prediction model is used to predict the reactor parameters of the next time step based on the reactor parameters of the current time step; Based on the perception parameters of the current time step, a mechanism model in the constructed water quality model is used to obtain a first efficiency parameter of the current time step, and based on the reactor parameters of the previous time step of the current time step, a prediction model in the constructed water quality model is used to predict the reactor parameters of the current time step to obtain a second efficiency parameter; Calculate the deviations of the first performance parameter and each parameter in the second performance parameter respectively. If the deviation of at least one parameter is greater than or equal to the set deviation, obtain the first optimized control parameter by using the mechanism model and the basic performance parameter in the optimal control reactor parameter; if the deviations of all parameters are less than the set deviation, obtain the second optimized control parameter by using the prediction model based on the perception parameter of the current time step and the basic performance parameter; The MABR is regulated based on the first optimized control parameter or the second optimized control parameter.

2. The MABR optimal state control method according to claim 1, characterized in that: The method of obtaining the first optimized control parameter by using the mechanism model and the basic performance parameters in the optimal control reactor parameters includes: Obtaining historical reactor parameters for a first set number of days, and selecting a set of target historical reactor parameters corresponding to each day from the historical reactor parameters of each day, each set of target historical reactor parameters including a target historical perception parameter and a target historical performance parameter; If, in the first consecutive number of days, at least one parameter of the target historical performance parameters of each day is smaller than the corresponding parameter of the basic performance parameters, and the change of the at least one parameter is greater than or equal to the first set change, the backtracking period is determined, and the first optimization control parameter is determined based on the perception parameters and the basic performance parameters within the backtracking period.

3. The MABR optimal state control method according to claim 2, characterized in that: The determining of the first optimization control parameter based on the perception parameter and the basic performance parameter within the backtracking time period includes: For each parameter of the biofilm parameters and environmental parameters within the retrospective time, any parameter is selected as an unknown parameter, and the optimal value of the unknown parameter is calculated based on the remaining parameters of the biofilm parameters and environmental parameters except the unknown parameters within the retrospective time, the control parameters within the retrospective time, and the basic performance parameters, and then the optimal value of each parameter of the biofilm parameters and environmental parameters within the retrospective time is obtained; Based on the optimal value of each parameter and the value of the corresponding parameter in the current time step, the target parameters of the biofilm parameters and environmental parameters within the backtracking time are determined; Obtaining a predefined mapping relationship, wherein the objects of the mapping relationship include each parameter of the biofilm parameter and the environmental parameter and the corresponding parameter change amount, each parameter of the control parameter and the corresponding parameter control method; At least one parameter of the control parameters is obtained based on the target item parameter, the target item parameter change amount and the mapping relationship, so as to obtain a first optimized control parameter.

4. The MABR optimal state control method according to claim 3, characterized in that: The method of determining the target parameters of the biofilm parameters and environmental parameters within the backtracking time based on the optimal value of each parameter and the value of the corresponding parameter in the current time step includes: Obtaining a target difference based on the optimal value of each parameter and the value of the corresponding parameter in the current time step, and obtaining a historical difference of each parameter of the biofilm parameter and the environmental parameter in the second set number of days; The difference of each parameter is determined based on the ratio of the target difference to the historical difference of each parameter, and the item parameter corresponding to the preset number of items with the largest difference is selected to obtain the target item parameter.

5. The MABR optimal state control method according to claim 2, characterized in that: The method of obtaining the first optimized control parameter by using the mechanism model and the basic performance parameters in the optimal control reactor parameters also includes: If, on the second consecutive days, at least one parameter of the target historical performance parameters for each day is greater than the corresponding parameter in the basic performance parameters, and the average change of the at least one parameter is greater than or equal to the second set change, then the historical reactor parameters and the optimal control reactor parameters on the second consecutive days are compared to update the optimal control reactor parameters.

6. The MABR optimal state control method according to claim 1, characterized in that: The biofilm parameters include biofilm biomass quality, biofilm thickness, biofilm biomass density, biofilm microbial population structure, and biofilm microbial activity. The environmental parameters include gas volume, gas pressure, temperature, membrane surface tangential flow velocity, concentration levels of various pollutants, comprehensive water quality three-dimensional fluorescence spectrum, infrared spectrum, mass spectrum parameters, membrane gas transfer efficiency, membrane gas pressure drop, biofilm surface desorption rate, mass transfer resistance, membrane gas oxygen consumption quality and other parameters; The control parameters include parameters for air-water scrubbing, parameters for brush plate scrubbing, parameters for filler / granular medium friction scrubbing, power source, and multiple parameters of current, voltage, frequency, and valve opening of instrument equipment.

7. The MABR optimal state control method according to claim 1, characterized in that: The performance parameters include multiple parameters such as the daily removal mass of different key pollutants per unit aeration membrane area of ​​the reactor and the mass of key pollutants that can be removed per unit biofilm mass of the reactor.

8. A MABR optimal control system, characterized in that: Including perception system, processing system and control system; The sensing system is used to continuously collect sensing parameters in the MABR reaction unit, wherein the sensing parameters include three types of parameters: biofilm parameters, environmental parameters and control parameters; The treatment system includes a water quality model, which includes a mechanism model and a prediction model. The mechanism model is used to simulate the operation process of the MABR. The variables of the mechanism model are reactor parameters, which include perception parameters and efficiency parameters. The four types of parameters in the reactor parameters include multiple parameters. The prediction model is used to predict the reactor parameters of the next time step based on the reactor parameters of the current time step; the treatment system is used to obtain the first efficiency parameter of the current time step based on the perception parameters of the current time step using the mechanism model in the constructed water quality model, and predict the reactor parameters of the current time step based on the reactor parameters of the previous time step of the current time step using the prediction model in the constructed water quality model to obtain the second efficiency parameter, respectively calculate the deviation of each parameter in the first efficiency parameter and the second efficiency parameter, if the deviation of at least one parameter is greater than or equal to the set deviation, then use the mechanism model and the basic efficiency parameters in the optimal control reactor parameters to obtain the first optimized control parameter; if the deviation of all parameters is less than the set deviation, then use the prediction model based on the perception parameters of the current time step and the basic efficiency parameters to obtain the second optimized control parameter; The control system is used to regulate the MABR based on the first optimized control parameter or the second optimized control parameter.

9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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