Membrane filtration self-adaptive water treatment system and control method thereof

Abstract

The application discloses a membrane filtration self-adaptive water treatment system and a control method thereof, and relates to the technical field of water treatment. The system comprises a membrane filtration module, a water quality detection module, a control module and a plurality of auxiliary equipment modules. The water quality detection module detects the water quality of the membrane filtration module, the control module identifies unqualified indexes in the produced water, generates a water treatment process scheme containing the auxiliary equipment modules to be put in, the input positions and the operation parameters according to the unqualified indexes and a preset process decision rule library, and generates corresponding control instructions to be output to the auxiliary equipment modules; the plurality of auxiliary equipment modules are respectively arranged on the upstream of the front-end pipeline and / or the downstream of the rear-end pipeline of the membrane filtration module, are connected to the pipeline at the corresponding input positions according to the control instructions and work according to the operation parameters. The application also provides a corresponding control method. The application can automatically match and combine corresponding auxiliary treatment modules according to the water quality of the produced water, and realizes self-adaptive on-demand regulation and control of the water treatment process.

Description

Technical Field

[0001] This application relates to the field of water treatment technology, specifically to a membrane filtration adaptive water treatment system and its control method. Background Technology

[0002] Membrane technology, due to its ability to efficiently remove particulate matter, colloids, bacteria, and most microorganisms from water, and its significant advantages such as small footprint, high degree of automation, and stable effluent quality, has gradually become an important component of the core treatment process in modern water plants. In existing technologies, membrane filtration water treatment systems generally adopt a fixed combined process design approach. This means that a fixed combined process flow is pre-set based on the long-term average or worst value of the raw water quality, such as "coagulation sedimentation + ultrafiltration" or "coagulation sedimentation + ozone activated carbon + ultrafiltration + nanofiltration." Raw water flows sequentially through each treatment unit according to the pre-set fixed flow, operating at full capacity regardless of the raw water quality.

[0003] The existing solutions described above suffer from rigid process flows and a lack of dynamic adjustment capabilities. Raw water quality dynamically changes with seasons, climate, and the environment. Even when water quality is good, the fixed process still operates at full capacity, resulting in unnecessary consumption of electricity, chemicals, and equipment. Furthermore, when water quality fluctuates, the system cannot automatically adjust its process configuration based on real-time effluent quality. Therefore, a water treatment technology solution that can overcome these problems is urgently needed. Summary of the Invention

[0004] The technical problem to be solved by this application is that the existing membrane filtration-based water treatment system adopts a fixed combination process, which cannot dynamically adjust the treatment process according to the quality of the produced water, resulting in energy waste and control lag. Therefore, this application provides a membrane filtration adaptive water treatment system and its control method.

[0005] The first aspect of this application provides a membrane filtration adaptive water treatment system, comprising: The membrane filtration module treats raw water through membrane filtration. A water quality testing module is connected to the product water side of the membrane filtration module and is used to test the product water quality. The control module is communicatively connected to the water quality detection module, identifies non-compliant indicators in the product water, and generates a water treatment process plan based on the non-compliant indicators and a preset process decision rule library. The water treatment process plan includes the auxiliary equipment modules to be put into operation, their locations, and operating parameters. The control module generates and outputs corresponding control commands based on the water treatment process plan. Multiple auxiliary equipment modules are respectively located upstream of the front end pipeline and / or downstream of the rear end pipeline of the membrane filtration module. Each of the auxiliary equipment modules is communicatively connected to the control module, and connects to the pipeline at the corresponding input position according to the control command and operates according to the operating parameters.

[0006] In some of the proposed membrane filtration adaptive water treatment systems, the process decision rule base includes a grouping rule table and an autonomous decision-making model. The grouping rule table records the mapping relationship between different water quality indicators and corresponding auxiliary equipment modules, as well as their deployment locations; The autonomous decision-making model determines the operating parameters of the auxiliary equipment module based on the non-compliance indicators.

[0007] Some of the membrane filtration adaptive water treatment systems described in the solutions include auxiliary equipment modules comprising: mechanical screening and filtration modules, adsorption modules, chemical reaction modules, and biological treatment modules; each module is equipped with: An electric valve is installed at the inlet and / or outlet of the module to control the water flow of the module according to the control command. An electric bypass valve is installed on the bypass pipeline between the inlet and outlet of the module, and is used to control the bypass on / off of the module according to the control command. A drive pump is installed on the inlet or outlet pipe of this module; The frequency converter is electrically connected to the drive pump and adjusts the speed of the drive pump according to the control command.

[0008] In some of the membrane filtration adaptive water treatment systems described in the solutions, the mapping relationships between different water quality indicators and corresponding auxiliary equipment modules include: The auxiliary equipment modules corresponding to organic matter indicators include: mechanical screening and filtration modules, adsorption modules, chemical reaction modules, and biological treatment modules; The auxiliary equipment modules corresponding to the ammonia nitrogen index include: mechanical screening and filtration modules and biological treatment modules; The auxiliary equipment modules corresponding to ion-related indicators include: mechanical sieving and filtration modules, adsorption modules, and chemical reaction modules; The auxiliary equipment modules corresponding to the new pollutant indicators include: mechanical screening and filtration modules, adsorption modules, and chemical reaction modules.

[0009] In some of the membrane filtration adaptive water treatment systems described, the grouping rule table also records the priority level of auxiliary equipment modules. The priority level is used to determine the order in which auxiliary equipment modules are put into operation when multiple auxiliary equipment modules can handle the same non-compliant index.

[0010] In some solutions, the membrane filtration adaptive water treatment system has the following priority order from high to low: mechanical screening and filtration modules, adsorption modules, chemical reaction modules, and biological treatment modules.

[0011] In some solutions, the membrane filtration adaptive water treatment system is described, and the control module is also used to perform comprehensive cost accounting for the auxiliary equipment modules when there are multiple auxiliary equipment modules with the same priority level and all of them can handle the same non-compliant index. The comprehensive cost includes one-time investment cost and long-term operating cost, and the auxiliary equipment module with the lowest comprehensive cost is selected for investment.

[0012] In some solutions, the membrane filtration adaptive water treatment system includes a control module that determines the type of contaminant based on the non-compliant indicators and determines the deployment location of corresponding auxiliary equipment modules based on the contaminant type, including: For each type of pollutant that causes membrane fouling, the corresponding auxiliary equipment module is controlled to be connected to the front end of the membrane filtration module; For each type of pollutant that is dissolved, a corresponding auxiliary equipment module is installed at the back end of the membrane filtration module. When multiple pollutants of different types are present, the corresponding auxiliary equipment modules are activated at the front and back ends of the membrane filtration module.

[0013] In some solutions, the membrane filtration adaptive water treatment system uses a machine learning algorithm to construct an autonomous decision-making model. The model is trained using non-compliant indicators from historical operating data and their corresponding auxiliary equipment module operating parameters as training samples. The machine learning algorithm learns the correlation between non-compliant indicators and auxiliary equipment module operating parameters, and the autonomous decision-making model is obtained after training.

[0014] In some of the membrane filtration adaptive water treatment systems described in the scheme, when the control module determines that all the product water indicators are qualified, a bypass control command is generated and sent to all the auxiliary equipment modules. Each of the auxiliary equipment modules is in bypass mode according to the bypass control command, and the permeate is directly output after being processed by the membrane filtration module.

[0015] A second aspect of this application provides a control method for a membrane filtration adaptive water treatment system, comprising: Obtain the water quality parameters of the membrane filtration permeate; The water quality parameters of the produced water are compared with preset water quality standard thresholds to identify unqualified indicators in the produced water. If at least one non-compliant indicator exists, the preset process decision rule library is retrieved, and a water treatment process plan is generated based on the non-compliant indicator. The water treatment process plan includes the auxiliary equipment modules to be invested, their locations, and operating parameters. The corresponding control instructions are generated and output according to the water treatment process scheme. The control instructions are used to control the corresponding auxiliary equipment modules to connect to the pipeline at the corresponding input position and operate according to the operating parameters.

[0016] The control methods for membrane filtration adaptive water treatment systems described in some solutions also include: If all indicators meet the standards, a bypass control command is generated and output. The bypass control command is used to control all auxiliary equipment modules to switch to bypass mode, and the permeate is directly output after being processed by the membrane filtration module.

[0017] The control method for the membrane filtration adaptive water treatment system described in some solutions further includes, before obtaining the water quality parameters of the membrane filtration permeate: The steps for constructing the process decision rule base include a grouping rule table and an autonomous decision model. The grouping rule table records the mapping relationship and deployment location between different water quality indicators and corresponding auxiliary equipment modules. The autonomous decision model determines the operating parameters of the deployed auxiliary equipment modules based on the non-compliant indicators.

[0018] The control method for the membrane filtration adaptive water treatment system described in some solutions includes the step of constructing the process decision rule base, which includes: Obtain water quality indicators and corresponding auxiliary equipment module operating parameters from historical operating data, establish a mapping relationship between different water quality indicators and corresponding auxiliary equipment modules, and generate the grouping rule table; Using the non-compliant indicators and their corresponding auxiliary equipment module operating parameters in the historical operating data as training samples, the initial model constructed using a machine learning algorithm is trained, so that the machine learning algorithm learns the correlation between the non-compliant indicators and the auxiliary equipment module operating parameters, and the autonomous decision-making model is obtained after the training is completed.

[0019] The control method for the membrane filtration adaptive water treatment system described in some schemes, after generating the grouping rule table, further includes: Priority levels are set according to the processing priority of each auxiliary equipment module, and the priority levels are recorded in the grouping rule table.

[0020] In some solutions, the control method for a membrane filtration adaptive water treatment system, wherein the step of generating a water treatment process plan based on the non-compliance indicators, the water treatment process plan including the auxiliary equipment modules to be invested, their locations, and operating parameters, includes: Based on the non-compliance indicators, the grouping rule table is retrieved, and the corresponding auxiliary equipment modules and their deployment positions are matched. The autonomous decision-making model is invoked based on the non-compliance indicators to determine the operating parameters of the auxiliary equipment modules to be deployed.

[0021] In some control methods for membrane filtration adaptive water treatment systems, the step of retrieving the grouping rule table based on the non-compliance indicators and matching the corresponding auxiliary equipment modules and their deployment positions includes: When multiple non-compliant indicators match the same auxiliary equipment module, the modules are merged and deduplicated to determine the auxiliary equipment module that needs to be deployed; or... When multiple non-compliant indicators map to multiple auxiliary equipment modules with the same priority level, a comprehensive cost calculation is performed on each candidate auxiliary equipment module. The comprehensive cost includes one-time investment cost and long-term operating cost, and the auxiliary equipment module with the lowest comprehensive cost is selected for investment.

[0022] In some solutions, the control method for a membrane filtration adaptive water treatment system, wherein the step of generating a water treatment process plan based on the non-compliance indicators, the water treatment process plan including the auxiliary equipment modules to be invested, their locations, and operating parameters, includes: The type of pollutant is determined based on the aforementioned non-compliant indicators; When the type of pollutant corresponds to a pollutant that causes film-forming fouling, the corresponding auxiliary equipment module is controlled to be put into the front end of the membrane filtration module; When the pollutant type corresponds to a soluble pollutant, the corresponding auxiliary equipment module is controlled to be connected to the back end of the membrane filtration module; When the pollutant type includes both pollutants that cause film-forming fouling and soluble pollutants, the corresponding auxiliary equipment modules are controlled to be put into the front and back ends of the membrane filtration module, respectively.

[0023] A third aspect of this application provides a computer-readable storage medium storing program information, wherein a computer reads the program information and executes the steps of the control method for the membrane filtration adaptive water treatment system described in any of the second aspects.

[0024] The fourth aspect of this application provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the control method for the membrane filtration adaptive water treatment system according to any one of the second aspects.

[0025] The fifth aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the control method for the membrane filtration adaptive water treatment system according to any of the second aspects.

[0026] The technical solution provided in this application has the following technical effects compared with the prior art: The membrane filtration adaptive water treatment system and its control method provided in this application detect the quality of the permeate water after membrane filtration through a water quality detection module and identify unqualified indicators through a control module. When unqualified indicators are found in the permeate water, the control module automatically matches the corresponding auxiliary equipment module according to a preset process decision rule library and determines its input position and operating parameters. Multiple auxiliary equipment modules are respectively set at the front end and / or back end of the membrane filtration module, and are connected to the pipeline as needed according to control commands and operate according to the operating parameters. The water treatment system of this application can automatically form differentiated optimal process strategies under different water quality conditions, thereby realizing dynamic control and intelligent operation of the entire water treatment process according to water quality as needed. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the overall structure of the membrane filtration adaptive water treatment system according to one embodiment of this application; Figure 2 This is a schematic diagram of the connection between the auxiliary equipment module and the pipeline according to one embodiment of this application; Figure 3 This is a schematic diagram of the overall structure of the membrane filtration adaptive water treatment system according to another embodiment of this application; Figure 4 This is a schematic diagram of the standard interface and group connection of the auxiliary equipment module according to one embodiment of this application; Figure 5 This is a flowchart of a control method for a membrane filtration adaptive water treatment system according to one embodiment of this application; Figure 6 This is a flowchart illustrating the steps of generating a water treatment process scheme based on non-compliant indicators according to one embodiment of this application; Figure 7 This is a flowchart illustrating the matching auxiliary equipment module and deployment steps according to one embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device for performing the membrane filtration adaptive water treatment method according to an embodiment of this application. Detailed Implementation

[0028] The specific embodiments of this application will be further described below with reference to the accompanying drawings.

[0029] It is readily understood that, based on the technical solution of this application, various structural and implementation methods can be interchanged by those skilled in the art without altering the essential spirit of this application. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this application and should not be considered as the entirety of this application or as limitations or restrictions on the technical solution of the application.

[0030] This embodiment provides a membrane filtration adaptive water treatment system, such as Figure 1 As shown, it includes: The membrane filtration module 100 performs membrane filtration treatment on the raw water. Specifically, the membrane filtration module 100 is installed on the raw water delivery pipeline to perform membrane filtration treatment on the raw water, and the raw water is obtained after filtration by the membrane filtration module 100.

[0031] A water quality testing module 200, connected to the product water side of the membrane filtration module, is used to detect the product water quality. Specifically, the water quality indicators detected by the water quality testing module 200 include, but are not limited to, one or more of the following: permanganate index, color, odor, ammonia nitrogen, total dissolved solids, hardness, various inorganic ions, emerging pollutants, turbidity, and microorganisms. The water quality testing module 200 outputs the measured values ​​of each detected water quality indicator to the control module 300. The water quality testing module 200 collects water quality indicator data using either online real-time monitoring or offline sampling and testing methods.

[0032] The control module 300, communicatively connected to the water quality detection module, identifies non-compliant indicators in the produced water and generates a water treatment process plan based on the non-compliant indicators and a preset process decision rule base. The water treatment process plan includes the auxiliary equipment modules to be deployed, their deployment locations, and operating parameters. The control module generates and outputs corresponding control commands based on the water treatment process plan. Specifically, the control module 300 receives the measured values ​​of the water quality indicators and compares each measured value with a preset water quality standard threshold. If all water quality indicators do not exceed their corresponding preset thresholds, the produced water is deemed qualified; if at least one water quality indicator exceeds its corresponding preset threshold, that indicator is deemed non-compliant. When a non-compliant indicator is determined to exist, the control module 300 generates a water treatment process plan. The control commands generated by the control module 300 based on the water treatment process plan include the identification information, deployment location information, and operating parameter information of the auxiliary equipment modules to be deployed.

[0033] Multiple auxiliary equipment modules 400 are respectively installed upstream of the front end pipeline and / or downstream of the rear end pipeline of the membrane filtration module. Each auxiliary equipment module is communicatively connected to the control module. According to the control command, it is connected to the pipeline at the corresponding input position and operates according to the operating parameters to perform corresponding water treatment on the raw water or membrane permeate.

[0034] Furthermore, the membrane filtration module 100 described in this application uses a filtration membrane with a molecular weight cutoff of 5kDa to 500kDa and a pore size of 0.01μm to 0.5μm. In a preferred embodiment, the membrane filtration module 100 uses an ultrafiltration membrane assembly with a pore size of 0.01μm to 0.1μm and a molecular weight cutoff of 100kDa. The membrane material of the ultrafiltration membrane assembly is a polyvinylidene fluoride (PVDF) hollow fiber membrane, which has advantages such as chemical resistance, high mechanical strength, and high packing density. The membrane assembly is of hollow fiber type. Raw water flows through the outside or inside of the hollow fiber membrane fibers. Under pressure, water molecules permeate through the membrane wall into the inside or outside of the membrane fibers, while particulate matter, colloids, bacteria, and large organic molecules are retained, achieving solid-liquid separation. When the raw water quality is excellent, qualified product water can be obtained simply by passing through the membrane filtration module 100. In another embodiment, the membrane material of the membrane filtration module 100 may also be selected from polymeric organic membranes such as polyvinyl chloride (PVC), polyethersulfone (PES), polysulfone (PS), polyethylene (PE), and polytetrafluoroethylene (PTFE), or inorganic membranes such as ceramic membranes, metal membranes, and silicon carbide membranes, or organic-inorganic hybrid membranes. Membrane structures include hollow fiber, tubular, flat sheet, spiral wound, plate and frame, pleated, butterfly tube, or container submerged types. The above membrane materials and structures can be selected based on comprehensive factors such as raw water quality, treatment scale, and operating costs.

[0035] The membrane filtration module 100 is the basic treatment unit of the system, undertaking the primary filtration function of raw water. Through the interception effect of the membrane filtration module 100, turbidity, suspended solids, most microorganisms, and some large molecular organic matter in the raw water are removed. Regardless of whether auxiliary equipment modules 400 are subsequently added, all raw water must be treated by the membrane filtration module 100, which is the basic guarantee unit for the operation of the system.

[0036] The above-described solution provided in this embodiment is based on one operating cycle. In actual operation, the control module 300 repeatedly executes the above-mentioned water quality detection, non-compliance indicator identification, and process scheme generation steps according to the set control cycle. This allows the auxiliary equipment modules 400 to be dynamically adjusted according to the real-time changes in the product water quality, enabling the water treatment process scheme to be adaptively updated with water quality fluctuations, thus forming a closed-loop control.

[0037] In the above embodiment, the system uses a water quality detection module 200 to detect the quality of the permeate water after membrane filtration, and a control module 300 to identify unqualified indicators. When unqualified indicators are found in the permeate water, the control module 300 automatically matches the corresponding auxiliary equipment module 400 according to a preset process decision rule library and determines its input position and operating parameters. Multiple auxiliary equipment modules 400 are respectively set at the front end and / or the back end of the membrane filtration module, and are connected to the pipeline as needed according to control commands and work according to operating parameters. The water treatment system provided in this embodiment can automatically form differentiated optimal process strategies under different water quality conditions, thereby realizing dynamic control and intelligent operation of the entire water treatment process according to water quality.

[0038] Preferably, the process decision rule base preset in the control module 300 includes a grouping rule table and an autonomous decision model; the grouping rule table records the mapping relationship and deployment position between different water quality indicators and corresponding auxiliary equipment modules; the autonomous decision model determines the operating parameters of the auxiliary equipment modules to be deployed based on the non-compliant indicators.

[0039] Specifically, the grouping rule table pre-stores the correspondence between various water quality indicators (such as permanganate index, ammonia nitrogen, hardness, emerging pollutants, etc.) and one or more auxiliary equipment modules capable of treating those indicators, as well as the placement information of the auxiliary equipment module—whether it should be installed at the front or back end of the membrane filtration module. For example, for ammonia nitrogen, the grouping rule table records its corresponding mechanical sieving filtration module and biological treatment module, and these modules are installed at the front end of the membrane filtration module; for ion-related indicators, the grouping rule table records its corresponding mechanical sieving filtration module, adsorption module, and chemical reaction module, and these modules are installed at the back end of the membrane filtration module.

[0040] The autonomous decision-making model is used to determine the operating parameters of the auxiliary equipment module based on the non-compliance indicators. The operating parameters include, but are not limited to, the operating power, treatment dosage, flow rate, pressure, and contact time of the auxiliary equipment module.

[0041] In one specific embodiment, the autonomous decision-making model is constructed using the Random Forest Regression algorithm. Random forest, as an ensemble learning algorithm, constructs multiple decision trees and averages their predictions, exhibiting good generalization ability and resistance to overfitting. It is suitable for modeling the nonlinear relationship between water quality indicators and operating parameters in the water treatment field. In other embodiments, the autonomous decision-making model may also employ algorithms such as Gradient Boosting Decision Tree (GBDT), XGBoost, Support Vector Regression (SVR), or Deep Neural Network (DNN). Those skilled in the art can flexibly choose the appropriate algorithm based on the actual amount of data and the application scenario.

[0042] Input features include the non-compliance indicator type code, measured value of the exceeding indicator, water quality standard threshold, raw water temperature, raw water pH value, previous dosage and previous removal rate. Output targets include powdered activated carbon dosage, contact time, ozone dosage, UV intensity and pump operating frequency. During initial system operation, the control module controls the auxiliary equipment module according to preset initial operating parameters. The water quality detection module detects the treated effluent quality and records the influent water quality indicators, exceeding indicators and their magnitude, auxiliary equipment module operating parameters and corresponding effluent water quality indicators for each operating cycle. Each record serves as a training sample. When the accumulated sample count reaches a preset threshold, model training is triggered. During training, a training sample set is extracted from the historical operating database. The input feature vectors are standardized to eliminate the influence between different units. A bootstrap sampling method is used to randomly sample K subsets with replacement. A decision regression tree is constructed for each subset. During the splitting process at each node, a subset of candidate features are randomly selected from all input features. The optimal feature is then selected for node splitting until the stopping condition is met. The average of the prediction results of the K decision trees is taken as the final output. After training, the model parameters are stored in the control module. In actual operation, the control module inputs the current non-compliant indicator into the autonomous decision model, and the model outputs the corresponding operating parameters within milliseconds. Taking the non-compliant indicator COD_Mn exceeding the standard as an example: when the water quality detection module 200 detects that the COD_Mn in the membrane permeate is 3.9 mg / L (exceeding the 3.0 mg / L standard), the control module 300 inputs the non-compliant indicator "COD_Mn exceeding the standard" into the autonomous decision model. The autonomous decision model outputs the corresponding operating parameters as "add super powdered carbon before the membrane, dosage 10 mg / L". The control module 300 generates corresponding control commands based on the operating parameters and outputs them to the super powdered carbon dosing device, controlling it to add powdered carbon to the raw water at the front end of the membrane filtration module at a dosage of 10 mg / L. After being adsorbed by the powdered carbon, the raw water enters the membrane filtration module 100, where the carbon powder and water are efficiently separated without the need for an additional sedimentation unit. The treated product water has a COD_Mn reduced to 2.2 mg / L, meeting the qualified standard.

[0043] When multiple non-compliant indicators correspond to the same auxiliary equipment module, the autonomous decision-making model takes all information of these indicators as input, uses the simultaneous compliance of all non-compliant indicators as a constraint, and aims to minimize operating costs. It outputs the minimum combination of operating parameters that satisfies these constraints. These constraints and the objective function are implicitly learned from historical sample data during model training, eliminating the need for online optimization during actual operation. The autonomous decision-making model also supports online updates. Each system cycle stores the influent water quality indicators, exceedances, actual added operating parameters, and treated effluent water quality as a new sample in the operating database. When the number of newly accumulated samples in the database reaches a preset threshold, an incremental model update is triggered. During incremental updates, a sliding window approach is used to select the most recent N samples, combining them with the historical training set for joint training to update the model parameters. The incremental update is executed asynchronously in the system background, without affecting normal system operation or the water treatment process. After the update, the new model parameters automatically replace the old parameters for subsequent cycle operating parameter decisions. Through the above model, the system can accurately determine the operating parameters according to the actual water quality, avoiding over-dosing or under-dosing caused by fixed parameter settings, and reducing chemical consumption while ensuring that the effluent meets the standards.

[0044] More preferably, the autonomous decision-making model also supports online updates. Each time the system runs for a cycle, it stores the influent water quality indicators, exceedances, actual added operating parameters, and treated effluent quality as a new sample in the operating database. When the number of newly accumulated samples in the database reaches a preset threshold, an incremental update of the model is triggered. During incremental updates, a sliding window approach is used to select the most recent N samples, which are then combined with the historical training set for joint training to update the model parameters. After the update, the new model parameters automatically replace the old parameters for use in subsequent cycles' operating parameter decisions. Taking COD_Mn exceedance conditions as an example, the system records the correspondence between the dosage of powdered activated carbon and the actual removal rate in multiple COD_Mn exceedance conditions. When the accumulated data reaches a preset condition, incremental learning is triggered to gradually optimize the dosage strategy. For example, in historical conditions, the powdered activated carbon dosage is gradually adjusted from a conservative 15 mg / L to a precise 10 mg / L, further reducing chemical consumption while ensuring effluent compliance. Incremental updates are executed asynchronously in the system background, without affecting the normal operation of the system and the water treatment process.

[0045] Furthermore, the auxiliary equipment module 400 includes: a mechanical sieving and filtration module, an adsorption module, a chemical reaction module, and a biological treatment module; such as Figure 2 As shown, each module is configured with: Electric valve 401 is installed at the inlet and / or outlet of the module (the figure shows an example where both the inlet and outlet are equipped with electric valves), and is used to control the water flow of the module according to the control command. When both electric valves 401 are open, raw water or membrane permeate enters the auxiliary equipment module 400 for processing. When both electric valves 401 are closed, the module is isolated from the main water flow and is in bypass mode.

[0046] An electric bypass valve 402 is installed on the bypass pipeline between the inlet and outlet of the module, and is used to control the bypass on / off of the module according to the control command. When the electric bypass valve 402 is open and the two electric valves 401 are closed, the water flows through the bypass pipeline, bypassing the auxiliary equipment module 400 and flowing directly to the next unit, and the module is in bypass state. When the electric bypass valve 402 is closed and the two electric valves 401 are open, the water flow is forced to pass through the auxiliary equipment module 400 for processing.

[0047] A drive pump, installed on the inlet or outlet pipe of the module, is used to provide power for water to flow through the module.

[0048] The frequency converter is electrically connected to the drive pump and adjusts the speed of the drive pump according to the control command, thereby adjusting the inlet or outlet water flow and operating pressure of the module.

[0049] The drive pump is an inherent component within the auxiliary equipment module (such as the high-pressure pump in nanofiltration equipment or the inlet pump in activated carbon filtration equipment), located inside the module's housing or frame. The frequency converter is an electrical control component, typically installed in the electrical control cabinet; the two are not located within the same module. Figure 2 The waterway connections are shown in the diagram.

[0050] In this solution, through the cooperation of electric valve 401 and electric bypass valve 402, each auxiliary equipment module 400 can independently switch between connected and bypass states: when the module needs to be put into operation, the control module 300 controls the electric valve 401 to open and the electric bypass valve 402 to close, allowing water to flow through the module for processing; when the module does not need to be put into operation, the control module 300 controls the electric valve 401 to close and the electric bypass valve 402 to open, allowing water to flow through the bypass pipeline and bypass the module. The connected states of each auxiliary equipment module 400 do not affect each other, realizing modular, on-demand combination.

[0051] This embodiment takes four auxiliary equipment modules 400 as an example, such as Figure 3As shown, the membrane filtration module 100 has a built-in membrane filtration assembly 101. Raw water is filtered through the membrane filtration assembly 101 to obtain membrane permeate. Multiple auxiliary equipment modules 400 are respectively located upstream of the front-end pipeline and downstream of the rear-end pipeline of the membrane filtration module 100. Each auxiliary equipment module 400 is equipped with an electric valve 401 and an electric bypass valve 402. A water quality detection module 200 is connected to the permeate side of the membrane filtration module 100 to detect the quality of the membrane permeate. A control module 300 is communicatively connected to the water quality detection module 200 and each auxiliary equipment module 400. Based on the non-compliant indicators identified by the water quality detection module 200, the control module 300 generates a water treatment process plan and generates corresponding control commands based on this plan, sending them to each auxiliary equipment module 400. Each auxiliary equipment module 400 independently controls the opening or closing of its electric valve 401 and electric bypass valve 402 according to the received control commands, switching between the module's access state and bypass state. Through the independent control of the electric valves 401 and electric bypass valves 402 of each auxiliary equipment module 400, diverse process combinations can be formed based on water quality test results.

[0052] Combination Figure 4 As shown, the auxiliary equipment modules 400 can be connected in parallel or in series. Each auxiliary equipment module 400 has an independent inlet 41, outlet 42, independent control system 43, and standard communication interface 44. When multiple auxiliary equipment modules 400 are connected in parallel, the inlets 41 of each auxiliary equipment module 400 are connected to the same main inlet pipe, and the outlets 42 are connected to the same main outlet pipe. Each module operates independently and in parallel. Each auxiliary equipment module 400 has an independent inlet 41 and outlet 42. Through the independent control of the electric valves 401 and electric bypass valves 402 of each module, one or more modules can be selected for operation as needed, and the modules do not interfere with each other. Parallel connection is suitable for scenarios where multiple modules need to operate simultaneously in the same processing stage, or where a backup module is required. When multiple auxiliary equipment modules 400 are connected in series, each auxiliary equipment module 400 is connected sequentially to the same pipeline, with the outlet 42 of the previous module connected to the inlet 41 of the next module, and the water flows through each module sequentially. Each auxiliary equipment module 400 has an independent inlet 41 and outlet 42. Through the independent control of the electric valve 401 and electric bypass valve 402 of each module, any module can be selectively connected or bypassed to achieve multi-stage series processing.

[0053] Each auxiliary equipment module 400 has an independent control system 43. The independent control system 43 communicates with the control module 300 via a standard communication interface 44, receiving control commands and independently controlling the operation of the electric valve 401, electric bypass valve 402, and frequency converter within its module according to the commands. Simultaneously, it feeds back the module's operating status to the control module 300. The inlet 41 and outlet 42 of each auxiliary equipment module 400 are standard interfaces, allowing for quick connection or disconnection from the piping system. The independent control system 43 and standard interfaces enable flexible integration of each auxiliary equipment module 400 into the system as needed, achieving plug-and-play modular combination.

[0054] Furthermore, in the above scheme, the mapping relationship between different water quality indicators and corresponding auxiliary equipment modules includes: The auxiliary equipment modules corresponding to organic matter indicators include: mechanical sieving and filtration modules, adsorption modules, chemical reaction modules, and biological treatment modules. Organic matter indicators include permanganate index, color, and odor. Among them, the mechanical sieving and filtration module uses physical sieving to retain soluble macromolecular organic matter and color- and odor-causing substances; the adsorption module uses porous structures to physically adsorb and remove organic matter and color- and odor-causing substances; the chemical reaction module uses advanced oxidation to generate hydroxyl radicals to oxidize and decompose recalcitrant organic matter, and can be used in conjunction with the adsorption module to adsorb oxidation intermediates; the biological treatment module relies on microorganisms to degrade organic pollutants. These four types of modules can be used individually or in any combination according to actual treatment needs.

[0055] The auxiliary equipment modules corresponding to the ammonia nitrogen index include: mechanical screening and filtration modules and biological treatment modules. The mechanical screening and filtration modules utilize membrane charge retention and screening to remove ammonium ions from the water. The biological treatment modules use porous materials as carriers to cultivate nitrifying bacteria, converting ammonia nitrogen into nitrate through biological nitrification, achieving harmless removal. These two types of modules can be used individually or in combination, depending on the extent of ammonia nitrogen exceedance.

[0056] The auxiliary equipment modules corresponding to ion-related indicators include: mechanical sieving and filtration modules, adsorption modules, and chemical reaction modules. Ion-related indicators include total dissolved solids, total hardness, and various inorganic ions (such as calcium, magnesium, sodium, fluorine, arsenic, etc.). Among them, the mechanical sieving and filtration module deeply removes dissolved salts and hardness substances from water using nanofiltration or reverse osmosis membranes; the adsorption module removes ions such as calcium, magnesium, sodium, fluorine, and arsenic through ion exchange resins or inorganic mineral adsorption; and the chemical reaction module removes hardness ions and dissolved salts through precipitation or complexation reactions using reagents. These three types of modules can be used individually or in combination depending on the type and extent of ion exceedances.

[0057] The auxiliary equipment modules corresponding to emerging pollutant indicators include: mechanical sieving and filtration modules, adsorption modules, and chemical reaction modules. Emerging pollutants include perfluorinated compounds, antibiotics, endocrine disruptors, and trace organic pollutants. Specifically, the mechanical sieving and filtration module uses nanofiltration or reverse osmosis membranes to retain molecular-level emerging pollutants; the adsorption module uses ultrafine activated carbon or specialized adsorption resins to deeply adsorb trace organic pollutants; and the chemical reaction module uses ozone, UV / H2O2, or UV / ozone systems to oxidize and decompose emerging organic pollutants, and can be used in conjunction with the adsorption module to adsorb residual intermediate products. These three types of modules can be used individually or in combination, depending on the type and concentration of the emerging pollutant.

[0058] The above mapping relationship is pre-stored in the grouping rule table. When the control module identifies one or more water quality indicators as non-compliant, it retrieves the corresponding auxiliary equipment module according to the mapping relationship and determines the final process combination scheme by combining the priority level of each module.

[0059] Preferably, the grouping rule table also records the priority levels of auxiliary equipment modules. These priority levels are used to determine the order in which auxiliary equipment modules are deployed when multiple auxiliary equipment modules can handle the same non-compliant indicator. Specifically, when a non-compliant indicator corresponds to multiple auxiliary equipment modules (for example, organic indicators correspond to four types of modules: mechanical screening and filtration, adsorption, chemical reaction, and biological treatment), the control module 300 selects the auxiliary equipment modules to be deployed from highest to lowest priority level according to the priority levels recorded in the grouping rule table. In this embodiment, as shown in Table 1, the priority levels are arranged from highest to lowest as follows: mechanical screening and filtration modules, adsorption modules, chemical reaction modules, and biological treatment modules. That is: the first priority is mechanical screening and filtration modules, the second priority is adsorption modules, the third priority is chemical reaction modules, and the fourth priority is biological treatment modules. The following example illustrates the situation with an organic indicator (such as permanganate index) exceeding the standard: Table 1 Priority hierarchy of auxiliary equipment modules

[0060] As shown in Table 1, when the permanganate index in the membrane permeate exceeds the standard, the candidate auxiliary equipment modules matched in the grouping rule table include four categories: mechanical screening and filtration modules, adsorption modules, chemical reaction modules, and biological treatment modules. Control module 300 screens modules according to their priority level from highest to lowest: the first priority is the mechanical screening and filtration module. It is determined whether the effluent meets the standard after the mechanical screening and filtration module is activated; if it does, only the mechanical screening and filtration module is activated, and no other lower-priority modules are activated; if it does not, the next lower priority module is activated sequentially until the effluent meets the standard. Therefore, when the permanganate index exceeds the standard by a small margin, activating the mechanical screening and filtration module alone is sufficient to achieve the standard, and operating only this module results in the lowest energy consumption and operating cost. When the permanganate index exceeds the standard by a large margin, the mechanical screening and filtration module cannot achieve the standard alone, and adsorption modules or chemical reaction modules are activated sequentially to form a combined process.

[0061] Through the above priority hierarchy setting, the system prioritizes physical treatment processes (mechanical screening and filtration modules and adsorption modules) that are low in energy consumption, simple to operate and maintain, and do not require the addition of chemical agents, while ensuring that the effluent quality meets the standards. Only when high-priority processes cannot meet the treatment requirements will higher-cost and more complex chemical or biological treatment processes be activated, thereby minimizing operating costs while ensuring treatment effectiveness. Additionally, as shown in Table 1: Mechanical filtration modules include ultrafiltration (microfiltration) equipment, nanofiltration equipment, reverse osmosis equipment, and forward osmosis equipment, corresponding to ultrafiltration membrane modules, microfiltration membrane modules, nanofiltration membrane modules, reverse osmosis membrane modules, and forward osmosis membrane modules, respectively. These modules use physical sieving to remove particulate matter, macromolecular organic matter, and dissolved substances of different sizes from the water. Ultrafiltration (microfiltration) is mainly used to remove particulate matter, colloids, and macromolecular organic matter; nanofiltration and reverse osmosis can further remove dissolved small molecule organic matter, ammonium ions, dissolved salts, and emerging pollutants.

[0062] Adsorption modules include: fixed-bed filter filled with adsorbent materials, fluidized-bed filter filled with adsorbent materials, adsorbent material addition, and resin. Fixed-bed filter filled with adsorbent materials such as granular activated carbon filters and inorganic mineral filter media filters, where water flows through the fixed bed and contacts the adsorbent materials. Fluidized-bed filter filled with adsorbent materials such as activated carbon fluidized beds, where water flow fluidizes the adsorbent materials to increase the contact area. Adsorbent addition, such as powdered activated carbon or ultrafine powdered activated carbon, involves directly adding the adsorbent material to the water for adsorption, followed by separation through subsequent membrane separation or sedimentation units. Resins include ion exchange resins and adsorption resins, which remove ions and organic pollutants from the water through ion exchange or adsorption. This type of module removes organic matter, color- and odor-causing substances, ions, and emerging pollutants from water through the physical adsorption or ion exchange of porous structures.

[0063] Chemical reaction modules include: ozone + activated carbon process, UV / H2O2 + activated carbon process, and UV / ozone + activated carbon process. These modules utilize advanced oxidation to generate hydroxyl radicals that oxidize and decompose recalcitrant organic matter. Residual pollutants and oxidation intermediates are then adsorbed by activated carbon, achieving deep removal of recalcitrant organic matter. Specifically, ozone + activated carbon combines ozone oxidation with activated carbon adsorption; UV / H2O2 + activated carbon combines ultraviolet catalytic hydrogen peroxide oxidation with activated carbon adsorption; and UV / ozone + activated carbon combines ultraviolet catalytic ozone oxidation with activated carbon adsorption. These modules may also include oxidant dosing equipment to oxidize iron, manganese, and other ions in the water.

[0064] Biological treatment modules include fixed-bed and fluidized-bed systems. These modules use porous carriers (such as activated carbon, ceramic packing materials, and polymer packing materials) as a substrate to construct a biofilm, relying on attached nitrifying bacteria, nitrite-oxidizing bacteria, and other microorganisms to degrade ammonia nitrogen and low-concentration organic matter in the water. In fixed-bed systems, the packing material remains stationary, and water flows through the packing layer to contact the biofilm. In fluidized-bed systems, the packing material is fluidized by the rising water flow, increasing the contact area between the biofilm and water and improving mass transfer efficiency. These modules are mainly used for biological nitrification treatment when ammonia nitrogen levels exceed standards.

[0065] In the above four priority levels, the first priority (mechanical screening and filtration) has the lowest energy consumption, requires no reagents, and is simple to operate and maintain; the second priority (adsorption) has mature technology, few byproducts, and wide applicability; the third priority (chemical reaction) has strong treatment capacity but higher operating costs; and the fourth priority (biological treatment) has low operating costs but is greatly affected by the environment. The system selects auxiliary equipment modules to be deployed sequentially according to this priority order, minimizing operating costs while ensuring that the effluent quality meets standards.

[0066] Furthermore, the control module 300 is also used to perform comprehensive cost accounting on auxiliary equipment modules when multiple auxiliary equipment modules 400 have the same priority level and can all handle the same non-compliant index. The comprehensive cost includes one-time investment cost and long-term operating cost, and the auxiliary equipment module with the lowest comprehensive cost is selected for operation. In specific implementation, one-time investment cost includes equipment purchase cost, installation cost, and commissioning cost, etc.; long-term operating cost includes electricity cost, reagent cost, consumable replacement cost, and labor cost, etc. The control module 300 calculates the comprehensive cost of each candidate auxiliary equipment module according to the preset cost parameters of each auxiliary equipment module, and selects the auxiliary equipment module with the lowest comprehensive cost for operation. Taking the permanganate index exceeding the standard as an example, when the matched auxiliary equipment modules all belong to the same priority level (for example, all belong to the second priority adsorption module), the control module 300 performs comprehensive cost accounting on each candidate adsorption module (such as granular activated carbon filter, powdered activated carbon dosing device, resin adsorption tank, etc.). Based on the pre-set cost parameters of different modules, the total cost of their one-time investment and long-term operation is calculated, and the module with the lowest overall cost is selected for operation. Through a cost verification mechanism, the scheme with the lowest overall cost is selected from multiple candidate modules of the same priority for operation, further optimizing the economic performance of the system.

[0067] Furthermore, in the membrane filtration adaptive water treatment system described above, the control module 300 determines the type of pollutant based on the non-compliant indicators and determines the corresponding auxiliary equipment module's deployment location based on the pollutant type, including: For the types of pollutants that cause membrane fouling (such as algae, macromolecular suspended solids, and colloidal pollutants), corresponding auxiliary equipment modules are controlled to be installed at the front end of the membrane filtration module. In this solution, the auxiliary equipment modules for removing these types of pollutants are set at the front end of the membrane filtration module, which can remove pollutants such as algae, macromolecular suspended solids, and colloids that easily cause membrane fouling before the raw water enters the membrane filtration module, thereby effectively slowing down the membrane fouling rate, extending the service life of the membrane and the chemical cleaning cycle, and reducing the maintenance cost of the membrane module.

[0068] For different types of dissolved pollutants (such as dissolved ions, small molecule organic matter, and residual trace pollutants), corresponding auxiliary equipment modules are installed at the downstream end of the membrane filtration module. In this solution, the auxiliary equipment module for removing this type of pollutant is placed at the downstream end of the membrane filtration module. This allows for targeted deep treatment of dissolved ions, small molecule organic matter, and trace pollutants after the membrane filtration module has retained particulate matter and macromolecules. Since dissolved substances cannot be effectively retained by the membrane filtration module, placing it at the downstream end ensures that it receives targeted treatment after membrane filtration.

[0069] When multiple pollutants of different types are present, the corresponding auxiliary equipment modules are activated at the front and rear ends of the membrane filtration module. The raw water then flows sequentially through the front-end auxiliary equipment module (removing pollutants that easily cause membrane fouling), the membrane filtration module (retaining residual particulate matter and macromolecules), and the rear-end auxiliary equipment module (removing dissolved substances), achieving the synergistic removal of complex pollutants.

[0070] More preferably, the autonomous decision-making model is a model constructed using a machine learning algorithm. It is trained using non-compliant indicators from historical operating data and their corresponding auxiliary equipment module operating parameters as training samples. The machine learning algorithm learns the correlation between the non-compliant indicators and the auxiliary equipment module operating parameters, and the autonomous decision-making model is obtained after training. In specific implementation, this scheme collects various water quality indicator exceedance events recorded during the system's historical operation and their corresponding auxiliary equipment module operating parameters (including but not limited to dosage, operating power, flow rate, pressure, contact time, etc.) to form a training sample set. The non-compliant indicators are used as input features, and the corresponding operating parameters are used as labels to train the initial model constructed using the machine learning algorithm. The machine learning algorithm establishes the correlation between non-compliant indicators and operating parameters through iterative learning, and the autonomous decision-making model is obtained after training. In actual operation, the currently identified non-compliant indicators are input into the autonomous decision-making model, and the operating parameters that make the effluent meet the standards are output. Taking permanganate index exceeding the standard as an example, historical operating data of the system under multiple permanganate index exceeding the standard is collected. The permanganate index exceeding the standard and the corresponding powdered charcoal dosage and contact time are used as training samples to train the machine learning model. After training, when the control module identifies that the current permanganate index of the produced water is 3.9 mg / L, the exceeding index is input into the autonomous decision-making model. The model outputs the corresponding powdered charcoal dosage (e.g., 10 mg / L) and contact time (e.g., 30 min). Based on this, the control module generates the corresponding control command and outputs it to the super powdered charcoal dosing device.

[0071] Through the above methods, the autonomous decision-making model can accurately determine the operating parameters based on the actual water quality, avoiding over-dosing or under-dosing caused by fixed parameter settings, and reducing chemical consumption while ensuring that the effluent meets the standards.

[0072] In the above scheme, when the control module 300 determines that all product water indicators are qualified, it generates a bypass control command and sends it to all the auxiliary equipment modules 400. Each auxiliary equipment module 400 is in bypass mode according to the bypass control command, and the product water is directly output after being processed by the membrane filtration module 100. That is, when the raw water quality is good and the membrane filtration module 100 itself can meet the effluent water quality requirements, the system does not start any auxiliary equipment modules 400, only maintaining the operation of the membrane filtration module 100. At this time, all auxiliary equipment modules 400 are in bypass mode, consuming no electrical energy, no chemical reagents, and generating no equipment wear. The system operates in the lowest energy consumption and lowest operating cost mode, realizing on-demand treatment of raw water and avoiding the energy and resource waste caused by traditional fixed combined processes operating at full load even when the water quality is good.

[0073] Furthermore, the control module 300 also includes a human-machine interface. The human-machine interface is used to display influent and effluent water quality data, the water treatment process scheme, and the grouping status of each auxiliary equipment module 400. Specifically, maintenance personnel can use the human-machine interface to view in real time the measured values ​​of various water quality indicators detected by the water quality detection module 200 and their exceedance status, the water treatment process scheme currently generated by the control module 300 (including the auxiliary equipment modules to be deployed, their deployment locations, and operating parameters), and the current connection status (connection / bypass) and operating parameters of each auxiliary equipment module 400. In a preferred embodiment, the human-machine interface is a touch screen display, which uses a graphical method to display the connection relationship and operating status of each module. For example, the arrangement relationship between the membrane filtration module 100 and each auxiliary equipment module 400 is displayed in flowchart form, different colors are used to indicate the connection status of each module (green indicates connection, gray indicates bypass, and red indicates fault), and real-time water quality data and historical trend curves are displayed in numerical or graphical form. Maintenance personnel can also view system operation logs and alarm information through the human-machine interface, and view and modify preset water quality standard thresholds, mapping relationships and priority levels in the process decision rule base. In special circumstances, maintenance personnel can manually lock or unlock the automatic grouping permission of a specific auxiliary equipment module 400 through the human-machine interface: when a module is manually locked, the control module 300 does not send grouping commands to that module, and the module remains in its current state; when unlocked, the module resumes automatic control by the control module 300. In this solution, through the human-machine interface, maintenance personnel can intuitively understand the system's operating status and perform manual intervention when necessary, improving the system's operability and ease of maintenance.

[0074] This application also provides a control method for a membrane filtration adaptive water treatment system, applied in a control module, such as... Figure 5 As shown, the method includes: S100: Obtain water quality parameters of membrane filtration permeate; Specifically, the water quality parameters mentioned in this step include, but are not limited to, permanganate index, color, odor, ammonia nitrogen, total dissolved solids, hardness, various inorganic ions, emerging pollutants, turbidity, and microorganisms.

[0075] S200: Compare the product water quality parameters with preset water quality standard thresholds to identify unqualified indicators in the product water; Specifically, in this step, the measured values ​​of each water quality indicator are compared with the preset water quality standard thresholds one by one: if all water quality indicators do not exceed the corresponding preset thresholds, the produced water is deemed to be fully qualified, and step S500 is executed; if at least one water quality indicator exceeds the corresponding preset threshold, that indicator is deemed unqualified, and step S300 is executed. The water quality standard thresholds can be flexibly set according to the system application scenario and the intended use of the effluent.

[0076] S300: Retrieve the preset process decision rule library and generate a water treatment process plan based on the non-compliance indicators. The water treatment process plan includes the auxiliary equipment modules to be invested, their locations, and operating parameters.

[0077] In this step, upon identifying a non-compliant indicator, a dynamic process combination decision-making mechanism is triggered. Based on the non-compliant indicator, a grouping rule table is retrieved, and the corresponding auxiliary equipment module and its deployment location are matched. If multiple non-compliant indicators exist, the auxiliary equipment modules corresponding to each non-compliant indicator are retrieved separately and combined to form a process combination for the synergistic removal of complex pollutants. A water treatment process plan is generated based on the merged and deduplicated auxiliary equipment modules and their combination methods. This water treatment process plan includes the auxiliary equipment modules to be deployed, their deployment locations, and operating parameters.

[0078] S400: The processing scheme generates corresponding control instructions and outputs them. The control instructions are used to control the corresponding auxiliary equipment modules to connect to the pipeline at the corresponding input position and work according to the operating parameters.

[0079] In this step, the control command includes the identification information, placement location information, and operating parameter information of the auxiliary equipment module to be deployed, and is used to control the corresponding auxiliary equipment module to connect to the pipeline at the corresponding placement location and operate according to the operating parameters.

[0080] S500: Generate and output bypass control commands. The bypass control commands are used to control all auxiliary equipment modules to switch to bypass mode, and the permeate water is directly output after being processed by the membrane filtration module.

[0081] In this step, when all the permeate water meets the standards, the control module does not generate a water treatment process plan, but instead generates and outputs a bypass control command. This bypass control command is used to control all auxiliary equipment modules to switch to bypass mode, and the permeate water is directly output after being treated by the membrane filtration module.

[0082] It can be understood that S100 to S500 constitute one control cycle of the system. In actual operation, the system repeatedly executes the above steps according to the set control cycle, dynamically adjusting the connection status and operating parameters of each auxiliary equipment module based on real-time changes in the product water quality to achieve closed-loop control. Furthermore, when the product water remains substandard or the system operates abnormally, the control module triggers an alarm.

[0083] In the above scheme, preferably before S100, it also includes: S010: The step of constructing the process decision rule base, which includes a grouping rule table and an autonomous decision-making model; the grouping rule table records the mapping relationship and deployment location between different water quality indicators and corresponding auxiliary equipment modules; the autonomous decision-making model determines the operating parameters of the auxiliary equipment modules to be deployed based on the non-compliant indicators. This step can be completed in advance before the system runs, and the constructed process decision rule base is stored in the control module for subsequent steps to call.

[0084] More preferably, S010 includes: S011: Obtain water quality indicators and corresponding auxiliary equipment module operating parameters from historical operating data, establish a mapping relationship between different water quality indicators and corresponding auxiliary equipment modules, and generate the grouping rule table.

[0085] Specifically, the system acquires water quality index data and corresponding auxiliary equipment module operating parameters accumulated during historical operation. The water quality index data includes measured values ​​of various water quality indicators detected in each operating cycle and instances of exceedance. The auxiliary equipment module operating parameters include the operating power, treatment dosage, flow rate, pressure, and contact time of each auxiliary equipment module during operation. The historical data is analyzed to establish a mapping relationship between different water quality indicators and corresponding auxiliary equipment modules, determining which auxiliary equipment modules can process each water quality indicator and whether each auxiliary equipment module should be installed before or after the membrane filtration module. Based on the mapping relationship and deployment location, a grouping rule table is generated and stored in the control module.

[0086] S012: Using the non-conforming indicators and their corresponding auxiliary equipment module operating parameters in the historical operating data as training samples, train the initial model constructed using a machine learning algorithm, so that the machine learning algorithm learns the correlation between the non-conforming indicators and the auxiliary equipment module operating parameters, and obtain the autonomous decision-making model after training is completed.

[0087] Specifically, using the non-compliant indicators and their corresponding auxiliary equipment module operating parameters from the historical operating data obtained in S011 as training samples, an initial machine learning model is selected. The non-compliant indicators are used as input features, and the corresponding auxiliary equipment module operating parameters are used as prediction targets to train the initial model. The machine learning algorithm establishes the correlation between the non-compliant indicators and the auxiliary equipment module operating parameters through iterative learning, enabling the model to output corresponding operating parameters based on the input non-compliant indicators. After training, the autonomous decision-making model is obtained and stored in the control module. In subsequent operations, when the control module identifies a non-compliant indicator, it inputs the non-compliant indicator into the autonomous decision-making model, and the model outputs the corresponding operating parameters for use in generating water treatment process schemes.

[0088] Preferably, after generating the grouping rule table in S010, the method further includes: setting priority levels according to the processing priority of each auxiliary equipment module, and recording the priority levels in the grouping rule table. In this embodiment, the priority levels are arranged from high to low as follows: the first priority is mechanical sieving and filtering modules, the second priority is adsorption modules, the third priority is chemical reaction / advanced oxidation modules, and the fourth priority is biological treatment modules. According to the above arrangement, each auxiliary equipment module recorded in the grouping rule table is labeled with a corresponding priority level, and the priority level is recorded in the grouping rule table. For example, for organic indicators (permanganate index, color, odor), the auxiliary equipment modules matched in the grouping rule table include mechanical sieving and filtering modules (first priority), adsorption modules (second priority), chemical reaction modules (third priority), and biological treatment modules (fourth priority). The priority level of each module is labeled in its corresponding grouping rule table record. When organic matter levels exceed the standard, the mechanical screening and filtration module with the highest priority is activated first. If the water quality still fails to meet the standard after this module is activated, the next lower priority auxiliary equipment module is activated sequentially. By recording the priority hierarchy in the grouping rule table, the priority hierarchy of each auxiliary equipment module can be directly obtained when the grouping rule table is retrieved, eliminating the need to recalculate and sort for each decision, thereby improving decision-making efficiency.

[0089] Furthermore, such as Figure 6 As shown, the process of generating a water treatment process based on the non-compliant indicators in S300 includes: S301: Retrieve the grouping rule table based on the non-compliance indicators, and match the corresponding auxiliary equipment modules and their deployment positions.

[0090] Specifically, the identified non-compliant indicators are used as indexes to retrieve the locally stored grouping rule table. This table records the mapping relationships and deployment locations between different water quality indicators and their corresponding auxiliary equipment modules. The system searches the grouping rule table for a record matching the current non-compliant indicator, and then retrieves the type of auxiliary equipment module corresponding to that indicator and its deployment location. If multiple non-compliant indicators exist, the system retrieves the corresponding auxiliary equipment module and its deployment location for each indicator.

[0091] S302: Based on the non-compliance indicators, invoke the autonomous decision-making model to determine the operating parameters of the auxiliary equipment modules to be deployed.

[0092] Specifically, the identified non-compliant indicators are input into the autonomous decision-making model. This model is pre-trained using machine learning algorithms, employing non-compliant indicators from historical operational data and their corresponding auxiliary equipment module operating parameters as training samples to learn the correlation between these indicators and the operating parameters. Upon receiving the non-compliant indicators, the autonomous decision-making model outputs the corresponding operating parameters based on the learned correlation, including but not limited to dosage, operating power, flow rate, pressure, and contact time. If multiple non-compliant indicators exist, the autonomous decision-making model outputs the operating parameters corresponding to each indicator separately. For example, when COD_Mn and ammonia nitrogen exceedances coexist and both correspond to the same auxiliary equipment module, the autonomous decision-making model uses both COD_Mn and ammonia nitrogen exceedances as input, with the constraint that all non-compliant indicators simultaneously meet the standards, and with the objective of minimizing operating costs, outputting a comprehensive combination of operating parameters that satisfies these constraints. These constraints and the objective function are implicitly learned from historical sample data during model training, eliminating the need for online optimization during actual operation. Through the above methods, the merged and deduplicated modules can simultaneously process multiple pollutants with a set of comprehensive operating parameters, achieving synergistic removal of multiple pollutants.

[0093] Through S301 and S302, auxiliary equipment modules are matched and operating parameters are determined sequentially based on the non-conforming indicators, forming a complete water treatment process solution.

[0094] Furthermore, such as Figure 7 As shown, in step S301, the step of retrieving the grouping rule table based on the non-conforming indicators and matching the corresponding auxiliary equipment modules and their deployment positions includes: S3011: When multiple non-compliant indicators match the same auxiliary equipment module, merge and deduplicate them to determine the auxiliary equipment module that needs to be put into use; Specifically, the grouping rule table is retrieved using the identified non-compliant indicators as indexes, and the corresponding auxiliary equipment modules are searched for each non-compliant indicator. When multiple non-compliant indicators exist, and two or more of them match the same auxiliary equipment module, the auxiliary equipment module is merged into one input item, eliminating duplicates. For example, when the permanganate index and emerging pollutants in the produced water simultaneously exceed the standards, and both indicators match adsorption modules and chemical reaction modules, then the adsorption module and the chemical module are each treated as a separate input item, avoiding duplicate input due to two indicators. Through the above merging and deduplication process, the auxiliary equipment modules that need to be input are determined, avoiding the repeated input of modules with overlapping functions, simplifying the process flow, reducing equipment footprint and energy consumption, and achieving synergistic removal of multiple pollutants.

[0095] S3012: When multiple non-compliant indicators are mapped to multiple auxiliary equipment modules with the same priority level, a comprehensive cost calculation is performed on each candidate auxiliary equipment module. The comprehensive cost includes one-time investment cost and long-term operating cost. The auxiliary equipment module with the lowest comprehensive cost is selected for investment.

[0096] Specifically, when multiple non-compliant indicators exist and the auxiliary equipment modules matched by these indicators have different priority levels, the order of deployment is determined according to the priority level from highest to lowest. However, when multiple non-compliant indicators match multiple auxiliary equipment modules with the same priority level, a comprehensive cost calculation is performed on each candidate auxiliary equipment module. For example, if a non-compliant indicator matches activated carbon filtration equipment and resin adsorption equipment in the adsorption module, both belonging to the same priority level, the comprehensive cost of activated carbon filtration equipment and resin adsorption equipment is calculated separately, including one-time investment costs (equipment purchase cost, installation cost, commissioning cost, etc.) and long-term operating costs (electricity cost, reagent cost, consumable replacement cost, labor cost, etc.). The auxiliary equipment module with the lowest comprehensive cost is selected for deployment. The comprehensive cost calculation is based on the cost parameters of each auxiliary equipment module pre-set in the control module. When multiple candidate combinations with the same process level and similar costs exist, the combination with the lowest comprehensive cost is selected as the final process scheme to achieve comprehensive optimization of treatment effect and economic benefits.

[0097] In the above schemes, during the deduplication process, the operating parameters of the auxiliary equipment modules are determined as follows, depending on the specific circumstances: When multiple non-compliant indicators are combined into the same auxiliary equipment module, these indicators are input into the autonomous decision-making model. The model then outputs a set of operating parameters based on the exceedance of each non-compliant indicator to achieve synergistic removal of multiple pollutants. For example, when permanganate index and ammonia nitrogen exceed the standards simultaneously, and both correspond to biological treatment modules, the autonomous decision-making model uses the measured values ​​of permanganate index (4.2 mg / L) and ammonia nitrogen (1.2 mg / L) as inputs. Based on historical data of similar operating conditions, the model outputs comprehensive operating parameters: dissolved oxygen concentration 2.5 mg / L (for ammonia nitrogen nitrification requirements) and hydraulic retention time extended to 8 hours (for organic matter degradation requirements). The control module then controls the operation of the biological treatment module according to these comprehensive operating parameters, enabling the simultaneous removal of both pollutants within the same module. For example, when both permanganate index and emerging pollutant exceedances coexist, and both correspond to chemical reaction modules, the autonomous decision-making model inputs both indicators together, using the indicator with the more stringent reaction conditions to determine the operating parameters: if the ozone dosage of 3.0 mg / L required to remove the emerging pollutant is higher than the ozone dosage of 2.0 mg / L required to remove the permanganate index, then the model outputs an ozone dosage of 3.0 mg / L, ensuring that both pollutants meet the standards simultaneously. Through this method, the merged and deduplicated modules can simultaneously treat multiple pollutants with a single set of comprehensive operating parameters, eliminating the need to set separate operating parameters for different indicators and then superimpose them.

[0098] When the same auxiliary equipment module processes multiple pollutants simultaneously, if the module's operating parameter is "contact time," the time required for the most stringent requirement should be taken as the comprehensive operating parameter. Taking the adsorption module simultaneously processing permanganate index and emerging pollutants as an example, when both permanganate index and emerging pollutants are matched to the adsorption module, they are merged and deduplicated to determine the adsorption module as a single input item, avoiding duplicate input. The operating parameters of this adsorption module are determined as follows: Based on the process requirements corresponding to each indicator in the grouping rule table, the required powdered carbon contact time for permanganate index is 20 minutes, and the required powdered carbon contact time for emerging pollutants is 30 minutes. Since the emerging pollutant has a more stringent contact time requirement, the control module sets the contact time of the adsorption module to 30 minutes to ensure that both pollutants meet the standards.

[0099] For operational parameters like dosage, the dosages are usually cumulative because each pollutant requires a dosage when the same module removes multiple pollutants simultaneously. Taking an ozone oxidation unit simultaneously treating permanganate index and algae as an example, if the ozone dosage required to treat permanganate index alone is 2.0 mg / L, and the ozone dosage required to treat algae alone is 3.0 mg / L, then the total ozone dosage required when both are present is 2.0 mg / L + 3.0 mg / L = 5.0 mg / L. The core decision-making logic is: each pollutant requires a dosage, and the sum of the required dosages for each indicator is taken as the comprehensive dosage.

[0100] In practice, for operating parameters with competitive consumption characteristics (such as dosage, reagent concentration, etc.), each pollutant requires the addition of the substance, and the sum of the required additions for each indicator is taken as the comprehensive addition amount; for operating parameters with shared occupancy characteristics (such as contact time, hydraulic retention time, etc.), the module operating time is uniform, and the maximum value of the time required for each indicator is taken as the comprehensive time.

[0101] Furthermore, S301 also includes: S3013: Determine the type of pollutant based on the aforementioned non-compliant indicators.

[0102] Specifically, based on the identified non-compliant indicators, the type of pollutant to which the non-compliant indicator belongs is determined by the water quality indicator classification information recorded in the grouping rule table. The water quality indicator classification includes, but is not limited to: organic matter indicators, ammonia nitrogen indicators, ionic indicators, and emerging pollutant indicators. Different types of pollutants exhibit different behavioral characteristics during the membrane filtration process, which determines the location of the corresponding auxiliary equipment modules.

[0103] S3014: When the pollutant type corresponds to a pollutant that causes film-forming fouling, control the corresponding auxiliary equipment module to be engaged at the front end of the membrane filtration module.

[0104] When the pollutant type corresponding to the non-compliant indicator is a pollutant that easily causes membrane fouling (such as algae, large molecular suspended solids, colloidal pollutants), the corresponding auxiliary equipment module is controlled to be put into the upstream of the front end pipeline of the membrane filtration module. This can remove pollutants that easily cause membrane fouling before the raw water enters the membrane filtration module, thereby effectively slowing down the membrane fouling rate and extending the membrane's service life and cleaning cycle.

[0105] S3015: When the pollutant type corresponds to a soluble pollutant, control the corresponding auxiliary equipment module to be connected to the back end of the membrane filtration module.

[0106] When the pollutant type corresponding to the non-compliant indicator is a dissolved substance (such as dissolved ions, small molecule organic matter, residual trace pollutants, etc.), the corresponding auxiliary equipment module is controlled to be put into the downstream pipeline of the membrane filtration module. After the membrane filtration module intercepts particulate matter and macromolecules, it can specifically treat the ions, small molecule organic matter and trace pollutants dissolved in the water for in-depth treatment, so as to achieve precise treatment.

[0107] S3016: When the pollutant type includes both pollutants that cause film-forming fouling and soluble pollutants, control the corresponding auxiliary equipment modules to be put into the front and back ends of the membrane filtration module respectively.

[0108] When multiple non-compliant indicators exist, and these indicators belong to two different types of pollutants that easily cause membrane fouling: pollutants and dissolved substances, the corresponding auxiliary equipment modules are activated at the front and back ends of the membrane filtration module, respectively. At this point, the raw water flows sequentially through the front-end auxiliary equipment module (removing pollutants that easily cause membrane fouling), the membrane filtration module (retaining residual particulate matter and macromolecules), and the back-end auxiliary equipment module (removing dissolved substances), forming a multi-stage process link of front-end pretreatment, membrane filtration, and back-end advanced treatment, achieving the synergistic removal of complex pollutants.

[0109] The effects of the system and method described above in this application are illustrated below with specific examples: Example 1: Excellent raw water quality - membrane filtration module only in operation This embodiment uses water from a lake as the treatment target. The raw water quality is good, the water quality is stable all year round, the pollutant content is low, and there are no problems with excessive algae, ions, or new pollutants.

[0110] Raw water directly enters the membrane filtration module 100 for treatment. The membrane filtration module 100 uses a hollow fiber ultrafiltration membrane module made of PVDF, with a molecular weight cutoff of 100 kDa and a pore size of 0.02 micrometers. Raw water flows through the ultrafiltration membrane module under pressure. Water molecules permeate through the membrane wall, while particulate matter, colloids, bacteria, and large organic molecules are retained, resulting in membrane-permeable water.

[0111] The water quality testing module 200 tests the membrane permeate water, including turbidity, permanganate index, ammonia nitrogen, total hardness, color, odor, and emerging pollutants. The test results show that turbidity is <0.1 NTU, permanganate index is 2.2 mg / L, ammonia nitrogen is 0.1 mg / L, total hardness is 140 mg / L, and color, odor, and emerging pollutants were not detected. All water quality indicators meet the limits specified in the "Standards for Drinking Water Quality" (GB 5749-2022).

[0112] The water quality testing module 200 sends the test results to the control module 300. The control module 300 compares the measured values ​​of each water quality indicator with the preset water quality standard thresholds one by one, and determines that all produced water is qualified. The control module 300 does not generate a water treatment process plan, but directly generates bypass control commands and sends them to all auxiliary equipment modules 400. Each auxiliary equipment module 400 controls its electric valves to close and electric bypass valves to open according to the bypass control commands, switching all to bypass mode.

[0113] At this time, the raw water is directly output as system product water after being filtered only by the membrane filtration module 100. All auxiliary equipment modules 400 do not participate in the operation, and the system operates in the mode of minimum energy consumption and minimum operating cost.

[0114] In this embodiment, the water quality detection module 200 detects the quality of the produced water in real time and the control module 300 automatically determines the quality. When the produced water meets the standards, the system automatically bypasses all auxiliary equipment modules 400, realizing true "on-demand processing" and avoiding the waste of energy and resources caused by the traditional fixed combined process running at full load when the water quality is good.

[0115] Example 2: Permanganate Index Exceeds Standard - Front-End Superimposed Adsorption Module This embodiment uses surface water from a reservoir as the treatment target. During the rainy season, the natural organic matter in the water increases due to surface runoff, causing the permanganate index of the membrane permeate to exceed the standard, while other indicators are normal.

[0116] After the raw water enters the membrane filtration module 100 for treatment, the water quality testing module 200 tests the membrane permeate. The test results show: turbidity < 0.1 NTU, permanganate index 3.6 mg / L, ammonia nitrogen 0.2 mg / L, total hardness 140 mg / L, and no color, odor, or emerging pollutants detected. Except for the permanganate index (threshold 3.0 mg / L), all other water quality indicators meet the national standard limits.

[0117] The water quality testing module 200 sends the test results to the control module 300. The control module 300 compares the measured values ​​of each water quality indicator with the preset threshold and identifies the unqualified indicator as "permanganate index".

[0118] The control module 300 retrieves the grouping rule table from the preset process decision rule base. The mapping relationship recorded in the grouping rule table shows that the permanganate index corresponds to four types of auxiliary equipment modules: mechanical screening and filtration modules, adsorption modules, chemical reaction / advanced oxidation modules, and biological treatment modules. Based on the pollutant type (organic matter, belonging to dissolved substances) corresponding to the permanganate index, its input location is the rear end of the membrane filtration module 100. The control module 300 further reads the priority level of each auxiliary equipment module in the grouping rule table: mechanical screening and filtration modules are the first priority, adsorption modules are the second priority, chemical reaction / advanced oxidation modules are the third priority, and biological treatment modules are the fourth priority.

[0119] Control module 300 selects modules according to priority level from highest to lowest: the first priority is mechanical screening and filtration modules (such as nanofiltration equipment), but after cost verification and processing capacity assessment, although nanofiltration equipment can achieve the permanganate index standard, its overall cost is relatively high; the second priority is adsorption modules (such as activated carbon filtration and adsorption equipment), which can also achieve the permanganate index standard after implementation, and the overall cost is lower than that of nanofiltration equipment. Control module 300 performs a comprehensive cost calculation on the two candidate solutions, including the one-time investment cost and long-term operating cost, and selects the adsorption module with the lowest overall cost.

[0120] The control module 300 generates a water treatment process plan: An adsorption module (activated carbon filtration adsorption equipment) is activated at the rear end of the membrane filtration module 100. The operating parameters are determined by the autonomous decision-making model based on the current permanganate index exceedance, with a 10-minute empty bed contact time. The control module 300 generates corresponding control commands based on this plan and outputs them to the adsorption module located at the rear end of the membrane filtration module 100. Upon receiving the control commands, the module's electric valve opens and its electric bypass valve closes, allowing the membrane permeate to flow through the activated carbon filtration adsorption equipment, where organic matter is adsorbed and removed.

[0121] The treated water from the adsorption module was then tested again by the water quality detection module 200. The permanganate index dropped to 2.1 mg / L, meeting the qualification standard. The system continued to run, and the control module 300 maintained the current process scheme unchanged.

[0122] In this embodiment, for cases where the permanganate index exceeds the standard, the system automatically matches the corresponding auxiliary equipment module based on the process decision rule library, selects the optimal solution according to priority level and cost accounting, and puts the adsorption module into the back end of the membrane filtration module, thereby achieving precise removal of dissolved organic matter and avoiding over-processing.

[0123] Example 3: Multiple indicators exceeding the standard - front-end and back-end synchronous superposition process This embodiment uses seasonally polluted surface water as the treatment target. The raw water simultaneously presents problems such as increased membrane fouling risk due to algal growth, as well as excessive dissolved organic matter and ammonia nitrogen. The permeate treated by the membrane filtration module 100 was tested by the water quality testing module 200. The results showed: turbidity 0.2 NTU (compliant), permanganate index 4.2 mg / L (exceeding standard, threshold 3.0 mg / L), ammonia nitrogen 1.2 mg / L (exceeding standard, threshold 0.5 mg / L), algal count 200 cells / mL (exceeding standard, threshold 100 cells / mL), and other indicators were normal.

[0124] The water quality testing module 200 sends the test results to the control module 300, which identifies the non-compliant indicators as: permanganate index, ammonia nitrogen, and algae.

[0125] The control module 300 retrieves the grouping rule table from the preset process decision rule library. The mapping relationships recorded in the grouping rule table show that: permanganate index corresponds to mechanical sieving and filtration modules, adsorption modules, chemical reaction / advanced oxidation modules, and biological treatment modules; ammonia nitrogen index corresponds to mechanical sieving and filtration modules and biological treatment modules; and algae correspond to mechanical sieving and filtration modules and chemical reaction / advanced oxidation modules.

[0126] Among the three non-compliant indicators, there were duplicate modules: permanganate index and algae both matched to chemical reaction / advanced oxidation modules (such as ozone oxidation equipment). Control module 300 was merged and deduplicated, treating this module as a single input item to avoid duplicate input. Similarly, permanganate index and ammonia nitrogen both matched to biological treatment modules, and were also merged and deduplicated, identifying them as a single input item.

[0127] The control module 300 further reads the priority level of each auxiliary equipment module in the grouping rule table: mechanical screening and filtration modules are the first priority, adsorption modules are the second priority, chemical reaction / advanced oxidation modules are the third priority, and biological treatment modules are the fourth priority.

[0128] The placement location is determined based on the type of pollutant: algae are pollutants that easily cause membrane fouling (algae, macromolecular suspended matter, colloids), and the corresponding auxiliary equipment module should be placed at the front end of the membrane filtration module 100; permanganate index and ammonia nitrogen are soluble substances, and the corresponding auxiliary equipment modules should be placed at the rear end of the membrane filtration module 100.

[0129] The control module 300 generates a water treatment process plan according to priority levels and deployment locations: ozone oxidation equipment (third priority, used for algae removal) is deployed at the front end; nanofiltration equipment (first priority, used for permanganate index removal) and biological treatment modules (fourth priority, used for ammonia nitrogen removal) are deployed at the back end. Based on a deduplication logic, each module is treated as a separate input item. Operating parameters are determined by the autonomous decision-making model based on the current exceedance range of each non-compliant indicator.

[0130] The control module 300 generates corresponding control commands based on the scheme and outputs them to each auxiliary equipment module. The electric valve of the ozone oxidation device located at the front end of the membrane filtration module 100 opens and the electric bypass valve closes, allowing raw water to enter the membrane filtration module 100 after ozone oxidation removes algae. The electric valves of the nanofiltration device and the biological treatment module located at the rear end of the membrane filtration module 100 open and the electric bypass valve closes, allowing membrane permeate to pass through the nanofiltration device to remove permanganate index and organic matter, and through the biological treatment module to remove ammonia nitrogen before being output. The raw water flows sequentially through the front-end ozone oxidation device → membrane filtration module 100 → rear-end nanofiltration device → rear-end biological treatment module, forming a multi-stage barrier process link of "front-end pretreatment + membrane filtration + rear-end deep treatment".

[0131] The treated wastewater was tested again by the water quality testing module 200, and all indicators met the standards. The system achieves synergistic removal of complex pollutants through a front-end algae-removing protective film filtration module and a back-end removal of dissolved organic matter and ammonia nitrogen.

[0132] In this embodiment, for the condition of multiple indicators exceeding the standard, the system automatically retrieves the grouping rule table to match the auxiliary equipment modules corresponding to each non-compliant indicator. By merging and deduplicating, duplicate input is avoided. The synchronous layout of the front end and the back end is determined according to the type of pollutant, forming a multi-level barrier process link, and realizing the comprehensive removal of composite pollutants.

[0133] This application also provides a computer-readable storage medium storing program information. After reading the program information, the computer executes the steps of the control method for the membrane filtration adaptive water treatment system described in any of the above method embodiments.

[0134] This application also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implements the steps of the control method for the membrane filtration adaptive water treatment system described in any of the above method embodiments.

[0135] This application also provides an electronic device, such as... Figure 8As shown, the electronic device includes at least one processor 801 and at least one memory 802. The memory 802 stores program information. After reading the program information, the processor 801 executes the control method of the membrane filtration adaptive water treatment system described in any of the above embodiments. The device may further include an input device 803 and an output device 804. The processor 801, memory 802, input device 803, and output device 804 are communicatively connected. The memory 802, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The processor 801 executes various functional applications and data processing by running the non-volatile software programs, instructions, and modules stored in the memory 802, thereby implementing the control method of the membrane filtration adaptive water treatment system provided in any of the above embodiments. The memory 802 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the control method of the membrane filtration adaptive water treatment system. Furthermore, memory 802 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some embodiments, memory 802 may optionally include memory remotely located relative to processor 801, and these remote memories may be connected via a network to means of performing the control method of the membrane filtration adaptive water treatment system. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. Input device 803 may receive user clicks and generate signal inputs related to user settings and function control of the control method of the membrane filtration adaptive water treatment system. Output device 804 may include a display device such as a display screen. When the one or more modules are stored in memory 802 and are run by the one or more processors 801, the control method of the membrane filtration adaptive water treatment system in any of the above method embodiments is executed.

[0136] As needed, the above technical solutions can be combined to achieve the best technical effect.

[0137] The above are merely the principles and preferred embodiments of this application. It should be noted that, for those skilled in the art, several other modifications can be made based on the principles of this application, and these modifications should also be considered within the scope of protection of this application.

Claims

1. A membrane filtration adaptive water treatment system, characterized in that, include: The membrane filtration module treats raw water through membrane filtration. A water quality testing module is connected to the product water side of the membrane filtration module and is used to test the product water quality. The control module is communicatively connected to the water quality detection module, identifies non-compliant indicators in the product water, and generates a water treatment process plan based on the non-compliant indicators and a preset process decision rule library. The water treatment process plan includes the auxiliary equipment modules to be put into operation, their locations, and operating parameters. The control module generates and outputs corresponding control commands based on the water treatment process plan. Multiple auxiliary equipment modules are respectively located upstream of the front end pipeline and / or downstream of the rear end pipeline of the membrane filtration module. Each of the auxiliary equipment modules is communicatively connected to the control module, and connects to the pipeline at the corresponding input position according to the control command and operates according to the operating parameters.

2. The membrane filtration adaptive water treatment system according to claim 1, characterized in that: The process decision rule base includes a grouping rule table and an autonomous decision-making model; The grouping rule table records the mapping relationship between different water quality indicators and corresponding auxiliary equipment modules, as well as their deployment locations; The autonomous decision-making model determines the operating parameters of the auxiliary equipment module based on the non-compliance indicators.

3. The membrane filtration adaptive water treatment system according to claim 2, characterized in that: The auxiliary equipment modules include: mechanical sieving and filtration modules, adsorption modules, chemical reaction modules, and biological treatment modules; each module is equipped with: An electric valve is installed at the inlet and / or outlet of the module to control the water flow of the module according to the control command. An electric bypass valve is installed on the bypass pipeline between the inlet and outlet of the module, and is used to control the bypass on / off of the module according to the control command. A drive pump is installed on the inlet or outlet pipe of this module; The frequency converter is electrically connected to the drive pump and adjusts the speed of the drive pump according to the control command.

4. The membrane filtration adaptive water treatment system according to claim 3, characterized in that, The mapping relationships between different water quality indicators and corresponding auxiliary equipment modules include: The auxiliary equipment modules corresponding to organic matter indicators include: mechanical screening and filtration modules, adsorption modules, chemical reaction modules, and biological treatment modules; The auxiliary equipment modules corresponding to the ammonia nitrogen index include: mechanical screening and filtration modules and biological treatment modules; The auxiliary equipment modules corresponding to ion-related indicators include: mechanical sieving and filtration modules, adsorption modules, and chemical reaction modules; The auxiliary equipment modules corresponding to the new pollutant indicators include: mechanical screening and filtration modules, adsorption modules, and chemical reaction modules.

5. The membrane filtration adaptive water treatment system according to claim 4, characterized in that: The grouping rule table also records the priority level of the auxiliary equipment modules. The priority level is used to determine the order of deployment of the auxiliary equipment modules when multiple auxiliary equipment modules can handle the same non-compliant index.

6. The membrane filtration adaptive water treatment system according to claim 5, characterized in that: The priority levels, arranged from highest to lowest, are: mechanical sieving and filtration modules, adsorption modules, chemical reaction modules, and biological treatment modules.

7. The membrane filtration adaptive water treatment system according to claim 5, characterized in that: The control module is also used to perform comprehensive cost accounting on auxiliary equipment modules when there are multiple auxiliary equipment modules with the same priority level and all of them can handle the same non-conforming index. The comprehensive cost includes one-time investment cost and long-term operating cost, and the auxiliary equipment module with the lowest comprehensive cost is selected for investment.

8. The membrane filtration adaptive water treatment system according to claim 2, characterized in that: The control module determines the type of pollutant based on the non-compliant indicators, and determines the deployment location of the corresponding auxiliary equipment module based on the type of pollutant, including: For each type of pollutant that causes membrane fouling, the corresponding auxiliary equipment module is controlled to be connected to the front end of the membrane filtration module; For each type of pollutant that is dissolved, a corresponding auxiliary equipment module is installed at the back end of the membrane filtration module. When multiple pollutants of different types are present, the corresponding auxiliary equipment modules are activated at the front and back ends of the membrane filtration module.

9. The membrane filtration adaptive water treatment system according to claim 2, characterized in that: The autonomous decision-making model is a model constructed using machine learning algorithms. It is trained using non-compliant indicators in historical operating data and their corresponding auxiliary equipment module operating parameters as training samples. The machine learning algorithm learns the correlation between non-compliant indicators and auxiliary equipment module operating parameters, and the autonomous decision-making model is obtained after training is completed.

10. The membrane filtration adaptive water treatment system according to any one of claims 1-9, characterized in that: When the control module determines that all the water production indicators are qualified, it generates a bypass control command and sends it to all the auxiliary equipment modules. Each of the auxiliary equipment modules is in bypass mode according to the bypass control command, and the permeate is directly output after being processed by the membrane filtration module.

11. A control method for a membrane filtration adaptive water treatment system, characterized in that, include: Obtain the water quality parameters of the membrane filtration permeate; The water quality parameters of the produced water are compared with preset water quality standard thresholds to identify unqualified indicators in the produced water. If at least one non-compliant indicator exists, the preset process decision rule library is retrieved, and a water treatment process plan is generated based on the non-compliant indicator. The water treatment process plan includes the auxiliary equipment modules to be invested, their locations, and operating parameters. The corresponding control instructions are generated and output according to the water treatment process scheme. The control instructions are used to control the corresponding auxiliary equipment modules to connect to the pipeline at the corresponding input position and operate according to the operating parameters.

12. The control method for the membrane filtration adaptive water treatment system according to claim 11, characterized in that, Also includes: If all indicators meet the standards, a bypass control command is generated and output. The bypass control command is used to control all auxiliary equipment modules to switch to bypass mode, and the permeate is directly output after being processed by the membrane filtration module.

13. The control method for the membrane filtration adaptive water treatment system according to claim 12, characterized in that, Before obtaining the water quality parameters of the membrane filtration permeate, the following steps are also included: The steps for constructing the process decision rule base include a grouping rule table and an autonomous decision model. The grouping rule table records the mapping relationship and deployment location between different water quality indicators and corresponding auxiliary equipment modules. The autonomous decision model determines the operating parameters of the deployed auxiliary equipment modules based on the non-compliant indicators.

14. The control method for the membrane filtration adaptive water treatment system according to claim 13, characterized in that, The step of constructing the process decision rule base includes: Obtain water quality indicators and corresponding auxiliary equipment module operating parameters from historical operating data, establish a mapping relationship between different water quality indicators and corresponding auxiliary equipment modules, and generate the grouping rule table; Using the non-compliant indicators and their corresponding auxiliary equipment module operating parameters in the historical operating data as training samples, the initial model constructed using a machine learning algorithm is trained, so that the machine learning algorithm learns the correlation between the non-compliant indicators and the auxiliary equipment module operating parameters, and the autonomous decision-making model is obtained after the training is completed.

15. The control method for the membrane filtration adaptive water treatment system according to claim 14, characterized in that, After generating the grouping rule table, the following is also included: Priority levels are set according to the processing priority of each auxiliary equipment module, and the priority levels are recorded in the grouping rule table.

16. The control method for the membrane filtration adaptive water treatment system according to claim 15, characterized in that, The step of generating a water treatment process plan based on the non-compliant indicators, wherein the water treatment process plan includes the auxiliary equipment modules to be invested, their locations, and operating parameters, includes: Based on the non-compliance indicators, the grouping rule table is retrieved, and the corresponding auxiliary equipment modules and their deployment positions are matched. The autonomous decision-making model is invoked based on the non-compliance indicators to determine the operating parameters of the auxiliary equipment modules to be deployed.

17. The control method for the membrane filtration adaptive water treatment system according to claim 15, characterized in that, In the step of retrieving the grouping rule table based on the non-conforming indicators and matching the corresponding auxiliary equipment modules and their deployment positions: When multiple non-compliant indicators match the same auxiliary equipment module, the modules are merged and deduplicated to determine the auxiliary equipment module that needs to be deployed; or... When multiple non-compliant indicators map to multiple auxiliary equipment modules with the same priority level, a comprehensive cost calculation is performed on each candidate auxiliary equipment module. The comprehensive cost includes one-time investment cost and long-term operating cost, and the auxiliary equipment module with the lowest comprehensive cost is selected for investment.

18. The control method for the membrane filtration adaptive water treatment system according to claim 15, characterized in that, The step of generating a water treatment process plan based on the non-compliant indicators, wherein the water treatment process plan includes the auxiliary equipment modules to be invested, their locations, and operating parameters, includes: The type of pollutant is determined based on the aforementioned non-compliant indicators; When the type of pollutant corresponds to a pollutant that causes film-forming fouling, the corresponding auxiliary equipment module is controlled to be put into the front end of the membrane filtration module; When the pollutant type corresponds to a soluble pollutant, the corresponding auxiliary equipment module is controlled to be connected to the back end of the membrane filtration module; When the pollutant type includes both pollutants that cause film-forming fouling and soluble pollutants, the corresponding auxiliary equipment modules are controlled to be put into the front and back ends of the membrane filtration module, respectively.

19. A computer-readable storage medium, characterized in that, The storage medium stores program information, and after the computer reads the program information, it executes the steps of the control method of the membrane filtration adaptive water treatment system according to any one of claims 11-18.

20. A computer program product comprising a computer program / instructions, characterized in that, When executed by a processor, the computer program / instructions implement the steps of the control method for the membrane filtration adaptive water treatment system according to any one of claims 11-18.

21. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the control method for the membrane filtration adaptive water treatment system according to any one of claims 11-18.

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