Water treatment dosing online control method and system based on turbidity detection

Abstract

The invention relates to the technical field of water treatment, and discloses a water treatment dosing online control method and system based on turbidity detection, and the method comprises the following steps: dividing chemical sewage treatment into different stages according to water quality distribution characteristics, and optimizing the dosing path of each stage based on real-time water quality data, dynamically adjusting the initial dosage of each stage; constructing a game theory model based on the dosing nodes and solving a Nash equilibrium point to obtain the dosage of each stage after optimization; based on the optimized dosage of each stage, controlling each dosing node to perform dosing treatment; and acquiring real-time water quality data of the water outlet subjected to dosing treatment, and dynamically optimizing parameters of the game theory model according to a comparison result of the real-time water quality data and the target water quality data. The problems of low efficiency, insufficient global optimization, poor dynamic adaptability, high operation cost and the like in traditional water treatment dosing control can be effectively solved, and the comprehensive improvement of the water quality treatment effect and the operation cost is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of water treatment, and in particular to a water treatment dosing online control method and system based on turbidity detection. Background Art

[0002] Water treatment is an important part of ensuring compliance of wastewater discharge and recycling of water resources in chemical enterprises, especially in industrial wastewater treatment. With the rapid development of the chemical industry and the increasingly stringent emission standards, chemical enterprises are facing increasing pressure on water treatment. Wastewater contains a large amount of harmful substances, such as organic matter, heavy metals, particulate matter and other pollutants, and their concentration and composition are highly volatile, which puts higher requirements on water treatment processes to ensure water quality safety.

[0003] In particular, the removal of turbidity, particulate matter and harmful substances in sewage directly affects whether the treated water meets the discharge standards or can be reused. How to accurately control the dosage of reagents and improve the treatment effect during the water treatment process has become a key challenge in the current sewage treatment of chemical companies.

[0004] The traditional dosing process in wastewater treatment of chemical companies usually relies on manual experience or fixed-ratio dosing control methods. This method often cannot cope with the dynamic changes in wastewater quality, especially when the raw water quality fluctuates greatly. Changes in parameters such as turbidity, particle concentration, and flow rate make it impossible for the fixed-ratio dosing of chemicals to accurately match actual needs. This may not only lead to waste of chemicals, but also cause secondary pollution, and increase operating costs and environmental risks.

[0005] The wastewater treatment process of chemical enterprises usually involves multiple complex stages, such as coagulation, flocculation, sedimentation and filtration, and the physical and chemical processes in these stages interact closely with each other. Simply optimizing a certain stage often fails to achieve the best overall treatment efficiency, and often ignores the synergy between different stages, resulting in poor overall wastewater treatment effect.

[0006] With the continuous advancement of sewage treatment technology, the application of sensor-based real-time water quality monitoring technology in sewage treatment of chemical enterprises has gradually increased. By real-time monitoring of parameters such as turbidity, particle distribution, and flow rate in sewage, the dynamic changes of sewage can be more accurately grasped. However, how to effectively convert these dynamic monitoring data into dosing control strategies and achieve global optimization in multi-stage treatment systems is still a difficulty in current technical research and practice.

[0007] Therefore, there is an urgent need for an intelligent dosing control method that can adapt to changes in sewage quality in real time, optimize the dosing distribution in multiple treatment stages, and improve the overall operating efficiency of the sewage treatment system, so as to meet the needs of chemical companies for high efficiency, economy and environmental protection in the sewage treatment process. Therefore, the present invention proposes an online control method and system for water treatment dosing based on turbidity detection. Summary of the invention

[0008] In view of the problems in the related art, the present invention proposes an online control method and system for water treatment dosing based on turbidity detection to overcome the above technical problems existing in the existing related art.

[0009] To this end, the specific technical solution adopted by the present invention is as follows: According to one aspect of the present invention, there is provided a method for online control of water treatment dosing based on turbidity detection, comprising the following steps: S1. Divide chemical wastewater treatment into different stages according to water quality distribution characteristics, optimize the dosing path of each stage based on real-time water quality data of turbidity, flow rate and particle distribution, and dynamically adjust the initial dosing amount of each stage; S2. According to the initial dosage of each stage after adjustment, a game theory model based on the dosage nodes is constructed, and the Nash equilibrium point is solved by using the distributed optimization algorithm to optimize the global dosage distribution and obtain the dosage of each stage after optimization; S3. Based on the dosage of each stage after optimization, control each dosing node to perform dosing treatment; obtain the real-time water quality data of the outlet after dosing treatment, and dynamically optimize the parameters of the game theory model according to the comparison results of the real-time water quality data and the target water quality data; The process of dividing the chemical wastewater treatment into different stages according to the water quality distribution characteristics, optimizing the dosing path of each stage based on the real-time water quality data of turbidity, flow rate and particle distribution, and dynamically adjusting the initial dosing amount of each stage includes the following steps: S11. Divide the chemical wastewater treatment into coagulation stage, flocculation stage and sedimentation stage according to the water quality distribution characteristics, and obtain the real-time water quality data of turbidity, flow rate and particle distribution at the raw water inlet, each stage treatment node and the outlet; S12, using genetic algorithm to combine the diffusion time of the reagent, the water flow path and the reaction efficiency to determine the optimal dosing point position at each stage; S13. Calculate the initial dosage of each stage based on the acquired real-time turbidity, flow rate and particle distribution data.

[0010] Furthermore, the method of using a genetic algorithm to combine the diffusion time of the reagent, the water flow path and the reaction efficiency to determine the optimal dosing point position in each stage includes the following steps: S121. Use chromosomes to represent the distribution of dosing paths and dosing points, and define the gene sequence of each chromosome. p 1, p 2,…, p i ,… p n ],in, p i Indicates i The location of the dosing point in the stage, n Indicates the total number of stages; S122, randomly generating a number of chromosomes, wherein each chromosome represents a combination of dosing paths; constructing a fitness function based on the turbidity reduction effect, drug diffusion time, and drug cost; S123, using the fitness function to calculate the fitness value of each chromosome, selecting a number of chromosomes from the current population according to the fitness value calculation results, and performing crossover and mutation operations on the selected chromosomes to generate a new population; S124, repeat the iteration until the maximum number of iterations is reached or the change in the population fitness value is less than the preset threshold, and select the chromosome with the highest fitness value from the final population as the optimal solution [ p 1 * , p 2 * ,…, p i * ,… p n * ],in, p i * Indicates i The best dosing point position of each stage is determined according to the optimal solution.

[0011] Furthermore, the fitness function is expressed as: In the formula, F represents the fitness value, E Indicates the turbidity reduction effect, T Indicates the drug diffusion time, C represents the cost of the drug, They represent the weights of turbidity reduction effect, drug diffusion time and drug cost respectively.

[0012] Furthermore, the calculation formula for the initial dosage of coagulant is: The calculation formula for the initial dosage of flocculant is: The calculation formula for the initial dosage of the sedimentation aid is: In the formula, Q h , Q x , Q z Respectively represent the initial dosage of coagulant, flocculant and sedimentation aid, k 1 、k 2. k 3 respectively represent the dosing coefficients of coagulant, flocculant and sedimentation aid, L Indicates flow rate, D r Indicates the initial turbidity of raw water, Respectively represent the target effluent turbidity in the coagulation stage, flocculation stage and sedimentation stage, They represent the effluent turbidity after coagulation stage and flocculation stage respectively.

[0013] Furthermore, the game theory model based on the dosing nodes is constructed according to the initial dosing amount of each stage after adjustment, and the Nash equilibrium point is solved by using a distributed optimization algorithm to optimize the global dosing distribution, and the dosing amount of each stage after optimization is obtained, which includes the following steps: S21, taking each stage as a game participant, taking the dosage of each stage as a decision variable, and taking the value range of the initial dosage of each stage after adjustment as the strategy space; S22. Based on the turbidity reduction effect and the cost of the drug, the profit function of each stage is constructed, and the non-cooperative game is used as the game type; the initial dosage of each stage is initialized, and the initial dosage of each stage is broadcast to other stages; S23, in each iteration process, when the dosage of other stages is fixed, each stage updates the dosage of the stage by optimizing the benefit function; each stage updates its own dosage and exchanges information with other stages, and performs convergence judgment; S24: Analyze whether the profit function of all stages meets the Nash equilibrium condition under the current solution. If not, return to step S23 to continue optimization. If yes, take the current Nash equilibrium point as the optimal dosage of each stage.

[0014] Furthermore, the expression of the profit function of each stage is: In the formula, Indicates i The profit function of the stage, Q iIndicates i The dosage of the stage, Indicates that except for i The dosage collection of other stages outside the stage, Represent the weights of turbidity reduction effect and drug cost, respectively. Indicates i The turbidity reduction effect of the stage, C i ( Q i ) indicates the i The cost of the drug at each stage, c i Indicates i The unit cost of the stage potion.

[0015] Furthermore, the expression for updating the dosage of the drug in this stage by optimizing the profit function is: In the formula, Indicates i Stage 1 k +1 dosage in iterations, Indicates k The dosage of other stages in the iteration, Respectively represent i The lower limit and upper limit of the dosage of the stage.

[0016] Furthermore, after the dosage of the drug in this stage is updated by optimizing the benefit function, the method further includes: when the updated dosage exceeds a preset range, a constraint processing is performed, wherein the constraint processing expression is: In the formula, Indicates i Stage 1 k +1 dosage in iterations, Respectively represent i The lower limit and upper limit of the dosage of the stage.

[0017] Furthermore, the execution of convergence judgment includes: judging whether the change range of the dosage of a certain stage or all stages between two consecutive iterations is less than a preset threshold, if so, stopping the iteration, if not, returning to step S23.

[0018] According to another aspect of the present invention, there is provided a water treatment dosing online control system based on turbidity detection, the water treatment dosing online control system based on turbidity detection comprises an initial dosing amount determination module, a dosing amount optimization module and a dosing processing control module; The initial dosage determination module is used to divide the chemical wastewater treatment into different stages according to the water quality distribution characteristics, optimize the dosing path of each stage based on the real-time water quality data of turbidity, flow rate and particle distribution, and dynamically adjust the initial dosage of each stage; The dosage optimization module is used to construct a game theory model between dosage nodes according to the initial dosage of each stage after adjustment, and use a distributed optimization algorithm to solve the Nash equilibrium point, optimize the global dosage distribution, and obtain the dosage of each stage after optimization; The dosing treatment control module is used to control each dosing node to perform dosing treatment based on the dosing amount in each stage after optimization; obtain real-time water quality data of the water outlet after dosing treatment, and dynamically optimize the parameters of the game theory model according to the comparison results of the real-time water quality data and the target water quality data.

[0019] The beneficial effects of the present invention are: 1) This invention combines genetic algorithms and game theory models to deeply integrate traditional water treatment processes with intelligent algorithms, which can effectively solve the problems of low efficiency, insufficient global optimization, poor dynamic adaptability, and high operating costs in traditional water treatment dosing control, and achieve a comprehensive improvement in water quality treatment effects and operating costs. At the same time, the closed-loop control mechanism based on real-time monitoring and dynamic optimization enhances the system's adaptability to complex water quality environments, providing important support for the intelligent development of water treatment processes.

[0020] 2) The present invention can not only utilize genetic algorithms in combination with the agent diffusion time, water flow path and reaction efficiency to accurately determine the optimal dosing point position in each stage to ensure uniform distribution and efficient utilization of the agent in the treatment unit, but also optimize the fitness function and comprehensively consider the turbidity reduction effect, agent diffusion time and agent cost, so as to significantly improve the agent utilization rate and avoid waste.

[0021] 3) The present invention can not only dynamically adjust the initial dosage of each stage based on the real-time monitored turbidity, flow rate and particle distribution data to adapt to water quality fluctuations, but also continuously optimize the parameters of the game theory model through a real-time feedback mechanism according to the comparison results of the water quality data of the outlet after the dosing treatment and the target water quality data, thereby further enhancing the adaptive ability of the system.

[0022] 4) The present invention can construct a multi-stage dosing optimization model based on game theory, characterize the mutual influence between different stages in the form of non-cooperative game, and use distributed optimization algorithm to solve the Nash equilibrium point, realize the global optimization distribution of dosing amount in each stage, and maximize the overall treatment effect.

[0023] 5) The present invention can not only optimize the dosing path and the dosage of the agent in the coagulation, flocculation and sedimentation stages respectively to ensure that the treatment effect of each stage is optimal, thereby effectively reducing the fluctuation range of turbidity at the outlet and ensuring that the water quality is stable and meets the standards, but also comprehensively consider the trade-off between the cost of the agent and the treatment effect, and reduce the dosage of the agent and the operating cost on a global scale through a distributed optimization algorithm, avoid secondary pollution and agent residue problems caused by excessive addition of the agent, and effectively ensure water quality safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 The present invention is a flowchart of an online control method for water treatment dosing based on turbidity detection according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0027] According to an embodiment of the present invention, a method and system for online control of water treatment dosing based on turbidity detection are provided.

[0028] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a water treatment dosing online control method based on turbidity detection is provided, comprising the following steps: S1. Divide chemical wastewater treatment into different stages according to water quality distribution characteristics, optimize the dosing path of each stage based on real-time water quality data of turbidity, flow rate and particle distribution, and dynamically adjust the initial dosing amount of each stage; The process of dividing the chemical wastewater treatment into different stages according to the water quality distribution characteristics, optimizing the dosing path of each stage based on the real-time water quality data of turbidity, flow rate and particle distribution, and dynamically adjusting the initial dosing amount of each stage includes the following steps: S11. According to the water quality distribution characteristics, the chemical wastewater treatment is divided into the coagulation stage, flocculation stage, and sedimentation stage, and the real-time water quality data of turbidity, flow rate, and particle distribution at the raw water inlet, each stage treatment node, and the outlet are obtained. The obtained data is cleaned (such as removing noise) and normalized to ensure that the data range is consistent; Specifically, according to the water quality distribution characteristics, chemical wastewater treatment can usually be divided into the following three main stages, and key water quality data (including turbidity, flow rate and particle distribution) are obtained in real time to support optimized dosing control.

[0029] 1. Coagulation stage: The coagulation stage is to add coagulants (such as aluminum sulfate or polyaluminum chloride) to destabilize the fine suspended particles (colloids) in the chemical wastewater and aggregate them into larger flocs.

[0030] Goal: To disrupt the charge balance of particles and promote particle aggregation.

[0031] Real-time water quality data acquisition: Raw water quality at the inlet: turbidity (reflects the concentration and size of suspended particles in the water), flow rate (determines the diffusion rate and reaction time of the agent), particle distribution (the number and particle size distribution of fine particles and colloids).

[0032] Node data of coagulation stage: turbidity after coagulation (used to evaluate the coagulation effect), flow rate change (due to the possible change of water flow dynamics due to the addition of reagents), particle distribution change (average particle size and distribution characteristics after coagulation reaction).

[0033] 2. Flocculation stage: In the flocculation stage, flocculants (such as polyacrylamide) are added to further grow the fine flocs formed by coagulation, thereby improving the sedimentation efficiency.

[0034] Objective: To make the particle flocs reach a sufficiently large particle size and strength.

[0035] Real-time water quality data acquisition: Output water quality in the coagulation stage: turbidity of the effluent after coagulation (basis of water quality entering the flocculation stage), particle distribution (initial size and distribution of flocs).

[0036] Flocculation node data: turbidity after flocculation (used to evaluate the flocculation effect), particle distribution change (recording the increase in particle size of flocs after flocculation), flow rate change (reflecting the impact of flocculant addition).

[0037] 3. Settlement stage: During the sedimentation stage, the flocs are accelerated to settle and clear water is separated through the action of sedimentation aids (such as high molecular weight polyelectrolytes).

[0038] Objective: To remove large flocs and ensure that the effluent turbidity meets the discharge or recycling standards.

[0039] Real-time water quality data acquisition Output water quality in the flocculation stage: effluent turbidity after flocculation (reflecting the concentration of flocs entering the sedimentation stage), particle distribution (particle size and distribution of flocs).

[0040] Sedimentation node data: effluent turbidity after sedimentation (to evaluate sedimentation efficiency and water quality compliance), particle distribution (the number and distribution of residual particles after sedimentation).

[0041] Changes in flow rate during sedimentation: The addition of sedimentation aids may affect the flow state during the sedimentation stage and requires real-time monitoring and adjustment.

[0042] 4. Real-time acquisition technology of water quality data Sensor equipment: Turbidity sensor: installed at key nodes of raw water inlet, coagulation stage, flocculation stage and sedimentation stage for real-time monitoring of turbidity.

[0043] Flow rate sensor: Obtain flow rate data through electromagnetic or ultrasonic flow meter.

[0044] Particle size analyzer: monitors particle size and number distribution in real time using dynamic light scattering or laser particle size analysis techniques.

[0045] Data collection and transmission: Use a distributed data collection system (such as PLC or SCADA system) to centrally record and transmit water quality data. Use industrial Ethernet or wireless communication technology to achieve real-time synchronization of data at each node.

[0046] By real-time monitoring of water quality data (turbidity, flow rate and particle distribution) in the coagulation, flocculation and sedimentation stages, basic data support is provided for the optimization of dosing at each stage. Real-time water quality data can not only reflect the treatment effect at each stage, but also provide a basis for dynamically adjusting the dosing path and dosage, thereby significantly improving the operating efficiency and treatment effect of the chemical wastewater treatment system.

[0047] S12, using genetic algorithm to combine the diffusion time of the reagent, the water flow path and the reaction efficiency to determine the optimal dosing point position at each stage; Specifically, the method of using a genetic algorithm to combine the diffusion time of the reagent, the water flow path and the reaction efficiency to determine the optimal dosing point position in each stage includes the following steps: S121. Use chromosomes to represent the distribution of dosing paths and dosing points, and define the gene sequence of each chromosome. p 1, p 2,…, p i ,… pn ],in, p i Indicates i The location of the dosing point in the stage, n Indicates the total number of stages; S122. Randomly generate a number of chromosomes (e.g., 50-100), where each chromosome represents a combination of dosing paths, and ensure that the generated paths are feasible under physical conditions (e.g., the dosing point cannot exceed the range of the equipment); construct a fitness function based on the turbidity reduction effect, drug diffusion time, and drug cost; Specifically, the fitness function is expressed as: In the formula, F represents the fitness value, E Indicates the turbidity reduction effect, calculated using target value minus actual value. T Represents the drug diffusion time, which is calculated using the distance between the dosing point and the treatment node. C represents the cost of the drug, They respectively represent the weights of turbidity reduction effect, agent diffusion time and agent cost. In this embodiment, the weight of turbidity reduction effect is 0.6, the weight of agent diffusion time is 0.3, and the weight of agent cost is 0.1.

[0048] S123, using the fitness function to calculate the fitness value of each chromosome, selecting a number of chromosomes from the current population according to the fitness value calculation results, and performing crossover and mutation operations on the selected chromosomes to generate a new population; Selection operation: Roulette selection method is used to select chromosomes according to the fitness function value. The higher the fitness value, the greater the probability of the chromosome being selected.

[0049] Crossover operation: Select two chromosomes for single-point crossover or two-point crossover to generate new chromosomes. The crossover points are randomly selected to ensure the feasibility of the new chromosomes.

[0050] Mutation operation: mutate the chromosome with a certain probability (such as 0.01) to randomly change the value of a gene. The mutation operation introduces new solutions and increases population diversity.

[0051] New population generation: Generate a new generation of population through selection, crossover and mutation operations. Repeat iterations until the termination condition is met (such as the maximum number of iterations or fitness value convergence).

[0052] S124, repeat the iteration until the maximum number of iterations is reached or the change in the population fitness value is less than the preset threshold, and select the chromosome with the highest fitness value from the final population as the optimal solution [ p 1 * ,p 2 * ,…, p i * ,… p n * ],in, p i * Indicates i The best dosing point position of each stage is determined according to the optimal solution.

[0053] S13. Calculate the initial dosage of each stage based on the acquired real-time turbidity, flow rate and particle distribution data.

[0054] Specifically, the calculation formula for the initial dosage of coagulant is: The calculation formula for the initial dosage of flocculant is: The calculation formula for the initial dosage of the sedimentation aid is: In the formula, Q h , Q x , Q z Respectively represent the initial dosage of coagulant, flocculant and sedimentation aid, k 1 、k 2. k 3 represent the dosing coefficients of coagulant, flocculant and sedimentation aid, respectively, which are used to adjust the proportional relationship between the dosage and water quality parameters. The value is calibrated according to experimental or historical data, and the unit is usually dimensionless. In this embodiment, the dosing coefficient of coagulant is 0.05, the dosing coefficient of flocculant is 0.03, and the dosing coefficient of sedimentation aid is 0.02. L Indicates flow rate, D r Indicates the initial turbidity of raw water, Respectively represent the target effluent turbidity in the coagulation stage, flocculation stage and sedimentation stage, They represent the effluent turbidity after coagulation stage and flocculation stage respectively.

[0055] S2. According to the initial dosage of each stage after adjustment, a game theory model based on the dosage nodes is constructed, and the Nash equilibrium point is solved by using the distributed optimization algorithm to optimize the global dosage distribution and obtain the dosage of each stage after optimization; Among them, the method of constructing a game theory model based on dosing nodes according to the initial dosing amount of each stage after adjustment, solving the Nash equilibrium point by using a distributed optimization algorithm, optimizing the global dosing distribution, and obtaining the dosing amount of each stage after optimization includes the following steps: S21, taking each stage as a game participant, taking the dosage of each stage as a decision variable, and taking the value range of the initial dosage of each stage after adjustment as the strategy space; S22. Based on the turbidity reduction effect and the cost of the drug, the profit function of each stage is constructed, and the non-cooperative game is used as the game type; the initial dosage of each stage is initialized, and the initial dosage of each stage is broadcast to other stages; Specifically, the expression of the profit function of each stage is: In the formula, Indicates i The profit function of the stage, Q i Indicates i The dosage of the stage, Indicates that except for i The dosage collection of other stages outside the stage, They represent the weights of the turbidity reduction effect and the cost of the agent, respectively. In this embodiment, the weight of the turbidity reduction effect is 0.8, and the weight of the agent cost is 0.2. Indicates i The turbidity reduction effect of the stage, C i ( Q i ) indicates the i The cost of the drug at each stage, c i Indicates i The unit cost of the stage potion.

[0056] S23, in each iteration process, when the dosage of other stages is fixed, each stage updates the dosage of the stage by optimizing the benefit function; each stage updates its own dosage and exchanges information with other stages, and performs convergence judgment; Specifically, the expression for updating the dosage of the drug in this stage by optimizing the profit function is: In the formula, Indicates i Stage 1 k +1 dosage in iterations, Indicates k The dosage of other stages in the iteration, Respectively represent i The lower limit and upper limit of the dosage of the stage.

[0057] Specifically, after the dosage of the drug in this stage is updated by optimizing the benefit function, the method further includes: when the updated dosage exceeds a preset range, a constraint processing is performed, wherein the constraint processing expression is: In the formula, Indicates i Stage 1 k +1 dosage in iterations, Respectively represent i The lower limit and upper limit of the dosage of the stage.

[0058] Specifically, the execution of convergence judgment includes: It is determined whether the change range of the dosage of a certain stage or all stages between two consecutive iterations is less than a preset threshold. If so, the iteration is stopped; if not, the process returns to step S23.

[0059] S24. Analyze whether the profit function of all stages meets the Nash equilibrium condition under the current solution (that is, when the strategies of other nodes are fixed, the node i Unable to improve the profit by changing its own strategy), if not, return to step S23 to continue optimization, if so, take the current Nash equilibrium point as the optimal dosage for each stage.

[0060] S3. Based on the dosage of each stage after optimization, control each dosing node to perform dosing treatment; obtain the real-time water quality data of the outlet after dosing treatment, and dynamically optimize the parameters of the game theory model according to the comparison results of the real-time water quality data and the target water quality data; Specifically, in the water treatment process, the optimized dosage is controlled, the water quality data of the water outlet is obtained in real time, and the parameters of the game theory model are dynamically adjusted to ensure that the system always operates in an efficient and stable state. The following are the detailed implementation steps and methods: 1. Control of dosage based on optimization: Obtaining optimized dosing: Optimization solutions obtained from game theory models and distributed optimization algorithms.

[0061] Control of dosing nodes: An automatic dosing device (such as a metering pump or valve) is installed at the optimal dosing point in each treatment stage; the optimized dosing amount is converted into a control signal to control the operation of the dosing device.

[0062] Dosing operation process: first receive the optimized dosage, then iThe control signal sends instructions to the dosing node, then monitors the operating status of the dosing device of each node in real time to ensure the dosing accuracy, and finally monitors the addition and diffusion of the agent in the system to avoid overdosage or underdosage.

[0063] 2. Obtain real-time water quality data: Outlet monitoring: Real-time collection of treated water quality data, including outlet turbidity, particle distribution and other parameters (such as pH value, color).

[0064] Node monitoring: turbidity, particle distribution and flow rate at key nodes in each stage for process monitoring.

[0065] Data transmission and processing: The real-time collected data is transmitted to the data processing center through the industrial control network (such as PLC / SCADA system), and processed using data analysis software or algorithm modules (such as Python or MATLAB) to calculate the deviation between the water quality and the target water quality.

[0066] 3. Dynamically optimize the parameters of the game theory model: Compare real-time water quality data with target water quality data: Define target water quality parameters (such as target effluent turbidity) according to design requirements or discharge standards, and compare real-time effluent turbidity with target turbidity; the definition of particle distribution deviation can be adjusted according to actual needs to ensure water quality safety.

[0067] Dynamically adjust game model parameters: When the turbidity deviation and particle distribution deviation exceed the allowable range, dynamically optimize the game model parameters, and the adjustment content is as follows: Weight adjustment: Increase the benefit weight when cost sensitivity decreases, and increase the cost weight when the total amount of system dosing exceeds the standard; Turbidity reduction effect correction: According to real-time monitoring data, adjust the model parameters in the turbidity reduction effect to make it closer to the actual situation to ensure water quality safety; Adjustment of strategy space: If the dosage deviation in a certain stage is large, the strategy space of that stage will be reduced.

[0068] Update the game model: Based on the adjusted parameters, re-solve the Nash equilibrium point and send the new optimization results to the dosing control system to ensure water quality safety.

[0069] In addition, in order to better understand the above technical solution, this embodiment uses the following application scenario for detailed description: Scenario description: The raw water inlet turbidity of a chemical enterprise's sewage treatment plant is 200 NTU. It needs to be treated through three stages of coagulation, flocculation and sedimentation to eventually reduce the effluent turbidity to below 5 NTU.

[0070] First, determine the water treatment stage division and real-time water quality data collection, and the specific implementation is as follows: 1) Stage division: Coagulation stage: By adding aluminum sulfate, the colloids in the water are destabilized and initial micro-flocculation is formed.

[0071] Flocculation stage: Add cationic polyacrylamide to promote the growth of micro-flocs and form large flocs that can settle.

[0072] Sedimentation stage: Use sedimentation aids (such as polyaluminium chloride) to accelerate the sedimentation of flocs and separate clear water.

[0073] 2) Real-time water quality data collection: Install monitoring equipment at key nodes: Raw water inlet: Install turbidity sensor, flow meter and particle distribution analyzer.

[0074] Coagulation tank outlet: monitor turbidity and particle distribution after coagulation.

[0075] Flocculation tank outlet: monitor the flocculation effect.

[0076] Sedimentation tank outlet (water outlet): monitor the final treatment effect.

[0077] Initial data: The raw water turbidity is 200NTU, and the particle distribution is mainly <2μm.

[0078] The outlet turbidity of the coagulation stage is 80NTU, and the particle size is mainly 5~10μm.

[0079] The outlet turbidity of the flocculation stage is 20NTU, and the particle size is mainly 20~50μm.

[0080] The outlet turbidity is 4.8NTU.

[0081] 3) Goal Setting: Treatment effect at each stage: Coagulation: turbidity reduced by 60%.

[0082] Flocculation: turbidity reduced by 75%.

[0083] Settling: The turbidity is reduced to the final target.

[0084] Then, based on the previously collected data, the dosage of each stage is optimized to achieve the best balance between the turbidity reduction effect and the cost of the agent. The specific implementation is as follows: 1) Game model establishment: Participants (stage): P1: coagulation stage.

[0085] P2: Flocculation stage.

[0086] P3: Sedimentation stage.

[0087] Strategy Space: Coagulant addition amount Q The value range of 1 is [50,150] mg / L.

[0088] Flocculant addition amount Q The value range of 2 is [20,60] mg / L.

[0089] Amount of sedimentation aid added Q The value range of 3 is [10,30] mg / L.

[0090] 2) Optimize calculation: Initial conditions: Q 1=100mg / L, Q 2=40mg / L, Q 3=20mg / L.

[0091] Iterative optimization through genetic algorithm: optimize the benefits according to the current strategy at each stage. Update the strategy space and exchange information.

[0092] Iterate until convergence and get the optimal solution: .

[0093] 3) Result analysis: After optimization, the dosage of each stage was: 120 mg / L of coagulant, 45 mg / L of flocculant, and 22 mg / L of sedimentation aid.

[0094] The total reagent cost was reduced by 10%, and the effluent turbidity was stabilized below 4.5 NTU, meeting the target requirements.

[0095] Finally, based on the optimized dosing amount, each dosing node is controlled for processing, and the water quality data of the outlet is obtained in real time to dynamically optimize the parameters of the game model. The specific implementation is as follows: 1) Control dosing nodes: Control signal calculation: According to the formula (in, S i Indicates i The pump speed of the stage, k i Indicates i The influence coefficient of stage pumping, in this embodiment k 1=10, k 2=12, k 3=8, Indicates i The pump speed of each stage was calculated based on the optimal dosage of each stage, and the corresponding pump speed for the coagulation stage was 1200rpm, the corresponding pump speed for the flocculation stage was 540rpm, and the corresponding pump speed for the sedimentation stage was 176rpm.

[0096] Dosing control: Send control signals to each dosing node to ensure accurate dosing of chemicals to ensure water quality safety.

[0097] 2) Obtain real-time water quality data: Outlet monitoring: The real-time turbidity of the outlet water is 4.5NTU; the proportion of particles with a diameter of <1μm is reduced to less than 5% to ensure water quality safety.

[0098] Node monitoring: The turbidity at the coagulation tank outlet was 78NTU, the turbidity at the flocculation tank outlet was 19NTU, and the turbidity at the sedimentation tank outlet was 4.5NTU.

[0099] 3) Compare with target water quality data: the target effluent turbidity is 4NTU, and the deviation is calculated to be 0.5NTU.

[0100] 4) Dynamically optimize game model parameters: Adjust the benefit weights: increase the weight of the turbidity reduction effect and reduce the weight of the dosing cost. Strategy space adjustment: Adjust the strategy space of coagulant to [110,130] mg / L to better ensure water quality safety.

[0101] 5) Optimization results update: Re-solve the optimal dosage: , in order to better ensure water quality safety.

[0102] 6) Closed-loop operation: The turbidity of the effluent water is reduced to 4.0 NTU, meeting the target water quality requirements and ensuring water quality safety; the system enters a stable state and is continuously monitored and optimized.

[0103] Through the above application scenarios, we can see the whole process from determining the water treatment stage division and real-time water quality data collection, to optimizing the dosing distribution, and then to dynamic control and real-time optimization. This series of steps ensures the efficient operation of the water treatment system and the compliance of water quality.

[0104] According to another embodiment of the present invention, there is provided a water treatment dosing online control system based on turbidity detection, the water treatment dosing online control system based on turbidity detection comprises an initial dosing amount determination module, a dosing amount optimization module and a dosing processing control module; The initial dosage determination module is used to divide the chemical wastewater treatment into different stages according to the water quality distribution characteristics, optimize the dosing path of each stage based on the real-time water quality data of turbidity, flow rate and particle distribution, and dynamically adjust the initial dosage of each stage; The dosage optimization module is used to construct a game theory model between dosage nodes according to the initial dosage of each stage after adjustment, and use a distributed optimization algorithm to solve the Nash equilibrium point, optimize the global dosage distribution, and obtain the dosage of each stage after optimization; The dosing treatment control module is used to control each dosing node to perform dosing treatment based on the dosing amount in each stage after optimization; obtain real-time water quality data of the water outlet after dosing treatment, and dynamically optimize the parameters of the game theory model according to the comparison results of the real-time water quality data and the target water quality data.

[0105] In summary, with the help of the above technical solutions of the present invention, the present invention combines genetic algorithms and game theory models to deeply integrate traditional water treatment processes with intelligent algorithms, which can effectively solve the problems of low efficiency, insufficient global optimization, poor dynamic adaptability, and high operating costs in traditional water treatment dosing control, and achieve a comprehensive improvement in water quality treatment effects and operating costs. At the same time, the closed-loop control mechanism based on real-time monitoring and dynamic optimization enhances the system's adaptability to complex water quality environments, providing important support for the intelligent development of water treatment processes.

[0106] In addition, the present invention can not only utilize genetic algorithms in combination with the agent diffusion time, water flow path and reaction efficiency to accurately determine the optimal dosing point position in each stage to ensure the uniform distribution and efficient utilization of the agent in the treatment unit, but also optimize the fitness function and comprehensively consider the turbidity reduction effect, agent diffusion time and agent cost, so as to significantly improve the agent utilization rate and avoid waste.

[0107] In addition, the present invention can not only dynamically adjust the initial dosage of each stage based on the real-time monitored turbidity, flow rate and particle distribution data to adapt to water quality fluctuations, but also continuously optimize the parameters of the game theory model through a real-time feedback mechanism according to the comparison results of the water quality data of the outlet after the dosage treatment and the target water quality data, thereby further enhancing the system's adaptive ability.

[0108] In addition, the present invention can construct a multi-stage dosing optimization model based on game theory, characterize the mutual influence between different stages in the form of non-cooperative game, and use distributed optimization algorithm to solve the Nash equilibrium point, so as to achieve the global optimization distribution of the dosing amount in each stage and maximize the overall treatment effect.

[0109] In addition, the present invention can not only optimize the dosing path and the dosage of the agent in the coagulation, flocculation and sedimentation stages respectively to ensure that the treatment effect of each stage is optimal, thereby effectively reducing the fluctuation range of turbidity at the outlet and ensuring that the water quality is stable and meets the standards, but also comprehensively consider the trade-off between the agent cost and the treatment effect, and reduce the agent dosage and operating cost on a global scale through a distributed optimization algorithm, avoid secondary pollution and agent residue problems caused by excessive addition of drugs, and effectively ensure water quality safety.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A water treatment dosing online control method based on turbidity detection, characterized in that: The following steps are involved: S1. Divide chemical wastewater treatment into different stages according to water quality distribution characteristics, optimize the dosing path of each stage based on real-time water quality data of turbidity, flow rate and particle distribution, and dynamically adjust the initial dosing amount of each stage; S2. According to the initial dosage of each stage after adjustment, a game theory model based on the dosage nodes is constructed, and the Nash equilibrium point is solved by using the distributed optimization algorithm to optimize the global dosage distribution and obtain the dosage of each stage after optimization; S3. Based on the dosage of each stage after optimization, control each dosing node to perform dosing treatment; obtain the real-time water quality data of the outlet after dosing treatment, and dynamically optimize the parameters of the game theory model according to the comparison results of the real-time water quality data and the target water quality data; The process of dividing the chemical wastewater treatment into different stages according to the water quality distribution characteristics, optimizing the dosing path of each stage based on the real-time water quality data of turbidity, flow rate and particle distribution, and dynamically adjusting the initial dosing amount of each stage includes the following steps: S11. Divide the chemical wastewater treatment into coagulation stage, flocculation stage and sedimentation stage according to the water quality distribution characteristics, and obtain the real-time water quality data of turbidity, flow rate and particle distribution at the raw water inlet, each stage treatment node and the outlet; S12, using genetic algorithm to combine the diffusion time of the reagent, the water flow path and the reaction efficiency to determine the optimal dosing point position at each stage; S13. Calculate the initial dosage of each stage based on the acquired real-time turbidity, flow rate and particle distribution data.

2. The method for online control of water treatment dosing based on turbidity detection according to claim 1 is characterized in that: The method of using a genetic algorithm to combine the diffusion time of the reagent, the water flow path and the reaction efficiency to determine the optimal dosing point position at each stage includes the following steps: S121. Use chromosomes to represent the distribution of dosing paths and dosing points, and define the gene sequence of each chromosome. p 1, p 2,…, p i ,… p n ],in, p i Indicates i The location of the dosing point in the stage, n Indicates the total number of stages; S122, randomly generating a number of chromosomes, wherein each chromosome represents a combination of dosing paths; constructing a fitness function based on the turbidity reduction effect, drug diffusion time, and drug cost; S123, using the fitness function to calculate the fitness value of each chromosome, selecting a number of chromosomes from the current population according to the fitness value calculation results, and performing crossover and mutation operations on the selected chromosomes to generate a new population; S124, repeat the iteration until the maximum number of iterations is reached or the change in the population fitness value is less than the preset threshold, and select the chromosome with the highest fitness value from the final population as the optimal solution [ p 1 * , p 2 * ,…, p i * ,… p n * ],in, p i * Indicates i The best dosing point position of each stage is determined according to the optimal solution.

3. The method for online control of water treatment dosing based on turbidity detection according to claim 2 is characterized in that: The expression of the fitness function is: In the formula, F represents the fitness value, E Indicates the turbidity reduction effect, T Indicates the drug diffusion time, C represents the cost of the drug, They represent the weights of turbidity reduction effect, drug diffusion time and drug cost respectively.

4. The method for online control of water treatment dosing based on turbidity detection according to claim 1 is characterized in that: The calculation formula for the initial dosage of coagulant is: The calculation formula for the initial dosage of flocculant is: The calculation formula for the initial dosage of the sedimentation aid is: In the formula, Q h , Q x , Q z Respectively represent the initial dosage of coagulant, flocculant and sedimentation aid, k 1 、k 2. k 3 respectively represent the dosing coefficients of coagulant, flocculant and sedimentation aid, L Indicates flow rate, D r Indicates the initial turbidity of raw water, Respectively represent the target effluent turbidity in the coagulation stage, flocculation stage and sedimentation stage, They represent the effluent turbidity after coagulation stage and flocculation stage respectively.

5. The method for online control of water treatment dosing based on turbidity detection according to claim 1 is characterized in that: The method of constructing a game theory model based on dosing nodes according to the adjusted initial dosing amount in each stage, solving the Nash equilibrium point by using a distributed optimization algorithm, optimizing the global dosing distribution, and obtaining the optimized dosing amount in each stage includes the following steps: S21, taking each stage as a game participant, taking the dosage of each stage as a decision variable, and taking the value range of the initial dosage of each stage after adjustment as the strategy space; S22. Based on the turbidity reduction effect and the cost of the drug, construct the profit function of each stage, and use the non-cooperative game as the game type; initialize the initial dosage of each stage, and broadcast the initial dosage of each stage to other stages; S23. In each iteration, when the dosage of other stages is fixed, each stage updates the dosage of the stage by optimizing the benefit function; each stage updates its own dosage and exchanges information with other stages, and performs convergence judgment; S24: Analyze whether the profit function of all stages meets the Nash equilibrium condition under the current solution. If not, return to step S23 to continue optimization. If yes, take the current Nash equilibrium point as the optimal dosage of each stage.

6. The method for online control of water treatment dosing based on turbidity detection according to claim 5 is characterized in that: The expression of the profit function of each stage is: In the formula, Indicates i The profit function of the stage, Q i Indicates i The dosage of the stage, Indicates that except for i The dosage collection of other stages outside the stage, Represent the weights of turbidity reduction effect and drug cost, respectively. Indicates i The turbidity reduction effect of the stage, C i ( Q i ) indicates the i The cost of the drug at each stage, c i Indicates i The unit cost of the stage potion.

7. The method for online control of water treatment dosing based on turbidity detection according to claim 6 is characterized in that: The expression for updating the dosage of the drug in this stage by optimizing the profit function is: In the formula, Indicates i Stage 1 k +1 dosage in iterations, Indicates k The dosage of other stages in the iteration, Respectively represent i The lower limit and upper limit of the dosage of the stage.

8. The method for online control of water treatment dosing based on turbidity detection according to claim 5 is characterized in that: After the dosage of the drug in this stage is updated by optimizing the benefit function, the method further includes: when the updated dosage exceeds a preset range, a constraint processing is performed, wherein the constraint processing expression is: In the formula, Indicates i Stage 1 k +1 dosage in iterations, Respectively represent i The lower limit and upper limit of the dosage of the stage.

9. The method for online control of water treatment dosing based on turbidity detection according to claim 5, characterized in that: The execution convergence judgment comprises: It is determined whether the change range of the dosage of a certain stage or all stages between two consecutive iterations is less than a preset threshold. If so, the iteration is stopped; if not, the process returns to step S23.

10. A water treatment dosing online control system based on turbidity detection, used to implement the steps of the water treatment dosing online control method based on turbidity detection according to any one of claims 1 to 9, characterized in that: The water treatment dosing online control system based on turbidity detection includes an initial dosing amount determination module, a dosing amount optimization module and a dosing treatment control module; The initial dosage determination module is used to divide the chemical wastewater treatment into different stages according to the water quality distribution characteristics, optimize the dosing path of each stage based on the real-time water quality data of turbidity, flow rate and particle distribution, and dynamically adjust the initial dosage of each stage; The dosage optimization module is used to construct a game theory model between dosage nodes according to the initial dosage of each stage after adjustment, and use a distributed optimization algorithm to solve the Nash equilibrium point, optimize the global dosage distribution, and obtain the dosage of each stage after optimization; The dosing treatment control module is used to control each dosing node to perform dosing treatment based on the dosing amount in each stage after optimization; obtain real-time water quality data of the water outlet after dosing treatment, and dynamically optimize the parameters of the game theory model according to the comparison results of the real-time water quality data and the target water quality data.

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