An accurate chemical dosing control system for water treatment

Abstract

The present invention discloses an accurate chemical dosing control system for water treatment, which relates to the technical field of chemical dosing control and is used to solve the problem of poor accuracy in chemical dosing control during the water treatment process. It includes a data acquisition module, a condition library construction module, a multi-objective processing module, and an optimization decision-making module. In the present invention, sensors are set in the water body to collect pollutant parameters and preprocess the data. The types and concentrations of pollutants are identified through a support vector machine model. After identification, the best treatment parameter combination is called from the removal condition library, and the condition library is dynamically updated using the Apriori algorithm. When multiple pollutants coexist, the removal conditions are automatically adjusted according to the interaction rules between pollutants to achieve synergistic removal. By constructing a multi-objective optimization model with the removal efficiency, chemical cost, treatment time, and environmental adaptability as the objectives, and combining the genetic algorithm and the particle swarm optimization algorithm to solve the optimal decision, intelligent chemical dosing control is realized, improving the accuracy and efficiency of water treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of dosing control, and more specifically, to an accurate dosing control system for water treatment. Background Art

[0002] With the development of sensor technology, automatic control technology, and data processing technology, accurate dosing control systems have gradually become an important innovation direction in the field of water treatment. Such systems dynamically adjust the chemical dosing amount by real-time monitoring of water quality parameters such as pH value, turbidity, conductivity, dissolved oxygen, etc., combined with advanced control algorithms and feedback adjustment mechanisms, to ensure the high efficiency, stability, and environmental friendliness of the water treatment process. This accurate dosing control is not only applicable to traditional urban sewage treatment and industrial wastewater treatment, but also can be widely applied to fields such as drinking water purification and agricultural water treatment.

[0003] Deficiencies of the prior art: Wastewater treatment technologies have deficiencies in co-treating multiple pollutants. Especially when the optimal dosing amounts and reaction conditions for different pollutants (such as organic matter, ammonia nitrogen, phosphorus, etc.) are different, it is difficult to achieve precise and efficient dosing control. Usually relying on fixed dosing ratios or simple control rules, it is difficult to respond in real time to the dynamic fluctuations of pollutant concentrations in wastewater, resulting in insufficient treatment of some pollutants or waste of chemicals. At the same time, the lack of full consideration of the mutual interference between chemicals may cause the weakening of the drug effect or the generation of by-products, further affecting the treatment effect. And due to the lack of adaptability, it is unable to flexibly adjust the dosing strategy according to real-time environmental changes, resulting in large fluctuations in the co-treatment effect. Due to the lack of multi-objective optimization ability, it is difficult to achieve a balance among the removal rates of various pollutants, chemical consumption, and operating costs, restricting both the wastewater treatment efficiency and economy. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, there are the following solutions to solve the problem of poor dosing control accuracy in the water treatment process in the above-mentioned background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An accurate dosing control system for water treatment, including a data acquisition module, a condition library construction module, a multi-objective processing module, and an optimization decision module, with signal connections between the modules;

[0007] The data acquisition module is used to set sensors in the water body to collect pollutant parameters, preprocess the collected data, identify the types of pollutants using a support vector machine classification model, and output the types and concentrations of pollutants;

[0008] The condition library construction module is used to construct a removal condition library after identifying the types and concentrations of pollutants, call condition combinations according to the removal conditions stored in the removal condition library for water quality treatment, and use the Apriori algorithm to update the condition combinations. For the coexistence of multiple pollutants, the removal conditions are automatically adjusted according to the interaction rules between pollutants;

[0009] The multi-objective processing module is used to determine the optimization objectives and constraint conditions of the water treatment process, integrate multiple objectives to obtain a multi-objective function, and construct a multi-objective optimization model for water treatment decision-making;

[0010] The optimization decision-making module is used to combine the genetic algorithm and the particle swarm optimization algorithm to determine the global optimal solution of multi-objective requirements during the water treatment decision-making process, and optimize the water treatment decision-making according to the global optimal solution.

[0011] In a preferred embodiment, it is used to set sensors in the water body to collect pollutant parameters, preprocess the collected data, use a support vector machine classification model to identify the types of pollutants, and output the types and concentrations of pollutants. The specific steps are as follows:

[0012] The pollutant parameters include conductivity, fluorescence intensity, turbidity, and redox potential;

[0013] Remove the noise in the collected data, identify and correct abnormal data points, and convert the preprocessed data into feature vectors;

[0014] Construct a feature library to store the feature ranges and classification labels of pollutants;

[0015] Based on the feature library and the collected data, use the radial basis kernel function to map the feature vectors of the collected data. According to the classification labels of pollutants in the feature library and the feature vectors of pollutants, use a support vector machine classification model to identify the types of pollutants, and obtain the classification results, confidence levels, and concentration values of pollutants.

[0016] In a preferred embodiment, it is used to construct a removal condition library after identifying the types and concentrations of pollutants, call condition combinations according to the removal conditions stored in the removal condition library for water quality treatment, and use the Apriori algorithm to update the condition combinations. The specific steps include:

[0017] Perform the initial construction of the removal condition library. According to the basic characteristics and treatment experience of pollutants, establish a pollutant removal condition library. For each pollutant, set the removal parameters required for removal. The removal parameters include the optimal pH value, suitable temperature, recommended types of chemicals, initial dosage, and reaction time;

[0018] After identifying the pollutants and their concentrations, automatically call the corresponding removal parameters in the combination of conditions in the removal condition library;

[0019] Embed the Apriori algorithm in the removal condition library, learn the correlation relationship between the removal conditions of different pollutants, determine the optimal treatment condition combination, and update the generated condition combination to the condition library.

[0020] In a preferred embodiment, for the coexistence of multiple pollutants, automatically adjust the removal conditions according to the interaction rules between pollutants. The specific steps are as follows:

[0021] Analyze the historical removal parameters, including removal effect, chemical dosage, temperature, pH value, and treatment time, and determine the average removal effect between the actual removal efficiency of each pollutant and the existing removal conditions;

[0022] Based on the relationship between conditions and removal effects, adjust the removal parameters of pollutants. Based on the analysis of removal parameters and optimization of removal effects, dynamically update the removal conditions of each pollutant in the condition library;

[0023] In the case of co-treatment of multiple pollutants, construct interaction rules to determine the interaction between pollutants, and optimize the selection and dosage of chemicals according to the interaction influence factors.

[0024] In a preferred embodiment, it is used to determine the optimization objectives and constraint conditions for the water treatment process, integrate multiple objectives to obtain a multi-objective function, and construct a multi-objective optimization model for water treatment decision-making, including the following steps:

[0025] The optimization objectives include maximizing removal efficiency, minimizing chemical cost, minimizing reaction time, and maximizing environmental condition adaptability;

[0026] The constraint conditions include minimum removal rate constraint, chemical dosage constraint, reaction time constraint, and environmental condition constraint;

[0027] Integrate and calculate according to the optimization objectives to form a multi-objective function;

[0028] Use the results of the multi-objective function, the optimization objectives, and the constraint conditions to jointly form a multi-objective optimization model and make decisions on water treatment.

[0029] In a preferred embodiment, it is used to combine the genetic algorithm and the particle swarm optimization algorithm to determine the global optimal solution of multi-objective requirements in the water treatment decision-making process, and optimize the water treatment decision-making according to the global optimal solution, including the following steps:

[0030] Use the initialized genetic algorithm for global search, randomly generate an initial population containing multiple groups of candidate solutions, and use the candidate solutions as the initial individuals of the genetic algorithm;

[0031] Calculate the fitness value of each initial individual according to the multi-objective function;

[0032] Use the roulette wheel or tournament selection method to select individuals from the population according to the fitness value as the parents of the next generation;

[0033] Perform crossover operations on the selected parent individuals to generate new offspring individuals, and perform mutation operations on the genes of the selected offspring individuals;

[0034] Repeat the fitness calculation, selection, crossover, and mutation operations, and perform multi-generation iterative evolution. The genetic algorithm will converge to a preliminary optimal solution as an excellent solution set after multiple iterations;

[0035] Use the excellent solution set obtained by the genetic algorithm as the initial particle swarm of the particle swarm optimization. The particle swarm optimization performs local fine search, and the specific steps are as follows:

[0036] Initialize the positions and velocities of the particle swarm. Use the excellent solution set output by the genetic algorithm as the initial positions of the particle swarm. Each particle represents a candidate solution, and set the velocities of the particles;

[0037] Calculate the fitness value of each particle to measure the quality of the current solution, and record the individual best positions of each particle itself and the global best position of the particle swarm;

[0038] Dynamically adjust the particle positions according to the velocity update formula of the particle swarm optimization algorithm;

[0039] Repeat the fitness value calculation, velocity update, and position update processes, and update the individual best positions and global best positions of the particles;

[0040] When the particle swarm reaches the maximum number of iterations or the global best position no longer changes in consecutive iterations, the algorithm terminates, and use the global best position in the particle swarm as the optimal solution of the multi-objective optimization model;

[0041] Output the final optimal solution, which includes the optimal chemical dosage, reaction time, temperature, and pH value.

[0042] The technical effects and advantages of an accurate chemical dosing control system for water treatment according to the present invention:

[0043] The present invention collects pollutant parameters (such as conductivity, fluorescence intensity, turbidity, and redox potential) by setting sensors in water bodies, preprocesses the data, and uses a support vector machine classification model to identify the types and concentrations of pollutants. After identifying the pollutants, the system calls the corresponding treatment parameter combinations from the removal condition library, updates the condition combinations through the Apriori algorithm, and dynamically optimizes the condition library. At the same time, when multiple pollutants coexist, the removal conditions are automatically adjusted according to the interaction rules between pollutants to ensure the co-removal effect of different pollutants.

[0044] Furthermore, a multi-objective optimization model is constructed, with the removal efficiency, chemical cost, treatment time, and environmental adaptability as the optimization objectives. The multi-objective requirements are integrated to meet the overall requirements of water treatment. Through the combination of genetic algorithm and particle swarm optimization algorithm, the optimal solution is accurately searched globally and locally, and finally the global optimal solution of multi-objective optimization is determined to achieve accurate chemical dosing, cost control, and efficient treatment, thus significantly improving the intelligent level of water treatment and the dynamic adaptability of the treatment process, and enabling more accurate chemical dosing control during the water treatment process. Brief Description of the Drawings

[0045] Figure 1 It is a schematic structural diagram of an accurate chemical dosing control system for water treatment according to the present invention. Detailed Embodiments

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0047] To achieve the above objectives, Figure 1 A schematic structural diagram of an accurate chemical dosing control system for water treatment according to the present invention is given, which specifically includes a data acquisition module, a condition library construction module, a multi-objective processing module, and an optimization decision module. The modules are connected by signals;

[0048] The data acquisition module is used to set sensors in the water body to collect pollutant parameters, preprocess the collected data, use a support vector machine classification model to identify the types of pollutants, and output the types and concentrations of pollutants;

[0049] The condition library construction module is used to construct a removal condition library after identifying the types and concentrations of pollutants, call condition combinations according to the removal conditions stored in the removal condition library for water quality treatment, and use the Apriori algorithm to update the condition combinations. For the coexistence of multiple pollutants, the removal conditions are automatically adjusted according to the interaction rules between pollutants;

[0050] A multi-objective processing module, which is used to determine the optimization objectives and constraint conditions of the water treatment process, integrate multiple objectives to obtain a multi-objective function, and construct a multi-objective optimization model for water treatment decision-making;

[0051] An optimization decision-making module, which is used to combine the genetic algorithm and the particle swarm optimization algorithm to determine the global optimal solution of multi-objective requirements in the water treatment decision-making process, and optimize the water treatment decision according to the global optimal solution.

[0052] Step 1: Conduct pollutant identification and real-time concentration detection, identify and detect the pollutants in the water body to obtain real-time information on the types and concentrations of pollutants. The specific steps are as follows:

[0053] Carry out data collection and preprocessing. The basic parameters of pollutants are collected in real time through sensors installed in the water body, including conductivity (reflecting the ion concentration in water), fluorescence intensity (used to identify specific organic substances), turbidity (indicating the content of suspended particulate matter in water), redox potential (reflecting the oxidation-reduction property in water), pH, fluoride ion concentration, suspended solid concentration, total nitrogen, nitrate concentration, etc. The basic parameters of each pollutant will form a data set for subsequent processing;

[0054] After data collection, the system preprocesses the data, including removing noise in the data (for example, through a filtering algorithm) and identifying and correcting abnormal data points. Through data preprocessing, it is ensured that the parameters collected by the sensors can reflect the real pollutant situation of the water body and reduce errors in subsequent identification;

[0055] Construct a pollutant identification model. Based on the water body parameters collected in real time, identify the main pollutant types in the wastewater and classify the pollutants in combination with the pollutant feature library. The specific steps are as follows:

[0056] Convert the collected preprocessed data (such as conductivity C, fluorescence intensity F, turbidity T, redox potential R, etc.) into a multi-dimensional feature vector. Assuming that n pollutants are to be identified, the feature vector of each pollutant is defined as: Cross terms (such as C·F, T·R) and non-linear transformations (such as log(1 + C), sin(F)) can be used to enhance the expressiveness of features;

[0057] Conduct feature library matching. Construct a feature library to store the feature ranges and classification labels of common pollutants. For example, for a certain pollutant P i , the feature value range of its conductivity is defined as: C i ∈ [C i，min , C i，max , where C i，minDenote the minimum eigenvalue of the conductivity of pollutant i, C i，min is the maximum eigenvalue of the conductivity. Similarly for the eigenvalue ranges in fluorescence intensity, turbidity, and oxidation-reduction potential. When the parameter values in the feature vector collected in real time are all within the corresponding pollutant feature ranges, it can be preliminarily judged that it may belong to a certain type of pollutant;

[0058] Based on the feature library and the collected data, a support vector machine (SVM) classification model is used to identify the types of pollutants. The goal of SVM is to find a classification hyperplane to separate the data points of different pollutant types. The optimization objective of SVM is expressed as: where, is the weight vector, b is the bias term, is the classification label of the i-th pollutant, is the feature vector of the i-th pollutant; by adjusting the weight vector and the bias term, the model finds the optimal hyperplane so that each pollutant is separated in the feature space;

[0059] To improve the classification effect, a kernel function can be used to map the feature vector to a high-dimensional space. Commonly used kernel functions include polynomial kernel, radial basis kernel (RBF kernel), etc. For example, the radial basis kernel function is defined as: where γ is the adjustment parameter, is the feature vector of the j-th pollutant, which is used to control the range of the kernel function. Through the mapping of the kernel function, the model classifies in the high-dimensional space, improving the ability to identify complex pollutants;

[0060] Input the feature vector of the real-time collected data after processing into the model to obtain the classification result and confidence level of the pollutant. Assume that for a given feature vector, the pollutant type output by the classifier is P k , and the confidence level is η i is calculated by the following formula: where η i represents the confidence level of the model for the classification result. A value close to 1 indicates a high confidence level, and close to 0 indicates a low confidence level;

[0061] After the above steps, the recognition model can classify the pollutants in the water and calculate the confidence level. The classification result and confidence level of the model will be transmitted to the next step of concentration calculation, providing basic data for subsequent dosing control;

[0062] Directly perform weighted calculation according to the pollutant parameters (conductivity, fluorescence intensity, turbidity, oxidation-reduction potential, etc.) of each pollutant to quickly obtain the concentration value.

[0063] It should be noted that the confidence value is used to evaluate the reliability of the recognition. If the confidence is lower than the set threshold, the system will mark the recognition result as an uncertain state and reclassify the sample or conduct a manual review; the weight coefficient is determined in advance through experimental data, and each coefficient represents the degree of influence of the corresponding parameter on the pollutant concentration. The magnitude of the weight is directly related to the sensitivity of the parameter to the pollutant concentration and is usually determined based on historical data and experiments at the initial stage of the system. For example, if organic pollutants are more sensitive to turbidity, the weight coefficient of turbidity will be higher, while heavy metal pollutants may be more sensitive to the oxidation-reduction potential, and the weight coefficient of the oxidation-reduction potential will be higher.

[0064] Step 2: Construct and update the pollutant removal condition library. That is, after identifying the types and concentrations of pollutants, the system needs to call the best removal conditions for each pollutant and update and optimize the condition library in real time according to the historical data and dynamic changes in the water treatment process, thereby improving the dynamic adaptability of the treatment parameters and enabling the system to have a higher response efficiency when dealing with different water quality fluctuations. The specific steps are as follows:

[0065] Conduct the initial construction of the removal condition library. At the initial stage of the system, based on the basic characteristics of pollutants and treatment experience, establish a pollutant removal condition library. For each pollutant P i , set the key removal parameters required for its removal, including: the optimal pH value pH i , the appropriate temperature T i , the recommended types of chemicals D i , the initial dosage Q i , the reaction time t i ;

[0066] After identifying the pollutant P i and its concentration C i , the system automatically calls the condition combination in the removal condition library to correspond to the removal parameters {pH i , T i , D i , Q i , t i} as the initial treatment conditions. If it is identified that multiple pollutants coexist, the system will make a preliminary adjustment of the treatment parameters based on their characteristics and treatment interactions. The specific expression is as follows: Among them, Q i，adj represents the adjusted dosage of pollutant P i , ρ ij is the interaction coefficient between pollutant P i and P j , which is obtained by training historical data and represents the interaction between multiple pollutants;

[0067] Embed an association rule mining algorithm (such as the Apriori algorithm) in the removal condition library to learn the association relationships between the removal conditions of different pollutants. For example, for the removal effects of co-existing pollutants during the multi-pollutant removal process, use the Apriori algorithm to mine the optimal treatment condition combinations. The specific steps are as follows:

[0068] Transactionize the historical treatment data to form a dataset containing pollutant types, removal conditions (pH value, temperature, dosage), and removal effects;

[0069] Apply the Apriori algorithm to extract high-frequency condition combinations (such as {pH = 7, T = 25°C, dosage = 50 mg / L}) and the association rules with the removal effects from the transactionized data;

[0070] The generated condition combinations (rules) will be automatically updated to the condition library to become a new set of recommended parameters.

[0071] Perform automatic update of the condition library and construction of interaction rules. Based on the historical data of wastewater treatment and the treatment effects of pollutants, dynamically adjust the optimal removal conditions of pollutants, and construct interaction rules according to the interactions between different pollutants. The specific steps are as follows:

[0072] The system regularly analyzes the historical treatment data (removal parameters), covering parameters such as removal effects, chemical dosage, temperature, pH value, treatment time, etc. By analyzing the historical data, identify the relationship between the actual removal efficiency of each pollutant and the existing removal conditions. Let R i，k represent the removal effect of pollutant P i in the k-th treatment. Then, for each pollutant, the system calculates the average removal effect of its condition library where N is the number of treatment times in the historical data. If the removal effect of a certain pollutant is significantly lower than the set threshold, the system will mark its removal conditions and automatically adjust the removal parameters (such as pH value, temperature, dosage, etc.) of this pollutant based on the relationship between the conditions and the removal effects to improve the removal efficiency;

[0073] Based on the analysis of historical data (removal parameters) and optimization of removal effects, the removal conditions of each pollutant are dynamically updated in the condition library. For example, if the optimal removal conditions (pH value, temperature, etc.) of a certain treatment are significantly different from the set conditions in the original condition library, the system will update this new optimal condition to the condition library and mark it as the preferred call condition. This update process ensures the adaptability of the condition library in actual wastewater treatment, enabling the system to optimize the removal parameters in real time according to water quality changes.

[0074] In the case of co-treatment of multiple pollutants, the removal effects of different pollutants may interact with each other. For example, some agents are effective for different pollutants simultaneously, while others may have their effects weakened due to chemical reactions between pollutants. The system constructs interaction rules to reflect the interactions between pollutants. The construction formula form of the interaction rules can be: where γ ij represents the interaction influence factor between pollutants P i and P j ; represents the removal effect when P i and P j coexist; is the removal effect when P i and P j are treated separately. When γ ij > 0, it indicates that there is a synergistic effect between the two pollutants. When γ ij < 0, it indicates that there is an inhibitory effect between them. The interaction influence factors are stored in the interaction rule table in the condition library, and these rules are applied in the actual treatment process to optimize the selection and dosage of agents;

[0075] It should be noted that the removal effect during separate treatment refers to the removal rate measured after adding agents when there is only one target pollutant in the wastewater. The measurement of this effect is used as the benchmark removal rate of the pollutant under ideal conditions and can be designed through separate treatment experiments of the pollutant. The removal effect during coexistence refers to the pollutant removal rate measured by the system during the actual treatment process under the condition that multiple pollutants coexist. This measurement is mainly used to evaluate the effect of co-treatment of multiple pollutants and can be determined through co-treatment experiments of multiple pollutants.

[0076] The formation and real-time update of the dynamic response mechanism ensure that the system can adjust the treatment conditions in real time according to the changes in pollutant concentrations in the wastewater, fluctuations in environmental conditions, and the latest parameters in the condition library, so as to improve the effect of co-removal of multiple pollutants. The specific steps are as follows:

[0077] After identifying the types and concentrations of pollutants, the system enables the dynamic response mechanism to call the latest removal conditions in the condition library, including the optimal pH value, temperature, types and dosages of agents. The dynamic response mechanism matches the removal conditions in the condition library with the current pollutant concentrations and decides whether to adjust the removal conditions according to the interaction rule table in the condition library. For example, it can be set that the concentration of pollutant P i in the current wastewater is C i , and the concentration of pollutant P j is C j . When the interaction influence factor γ ij < 0, the system adjusts the removal conditions through the following formula: where Qi，adj is the adjusted dosage of the reagent for suppressing interference, Q i is the pollutant P i is the initial dosage; by this adjustment, the adaptability to different pollutant removal conditions is improved, mutual interference is avoided, and the co-removal effect is enhanced;

[0078] The system continuously monitors the pollutant concentration, removal effect and environmental changes (such as temperature, pH, etc.) during the wastewater treatment process. When significant fluctuations in concentration or environmental parameters are detected, the dynamic response mechanism automatically updates the current treatment parameters, calls the latest combination of removal conditions in the condition library, and adjusts the reagent dosage and treatment conditions. For example, when the temperature drops by more than the preset threshold, the system automatically adjusts the reaction time and dosage to ensure the removal effect. The specific update formula is as follows: where Q i，new is the updated reagent dosage, T opt is the optimal treatment temperature of the pollutant, T curent is the current temperature, and δ is the adjustment coefficient used to control the influence of temperature on the dosage. When the removal parameters or environmental conditions in the condition library change, the system immediately updates the current treatment conditions to ensure that the treatment effect remains optimal in the changing water environment.

[0079] Step 3: Establish a multi-objective optimization model. After completing pollutant identification, concentration calculation and construction of the removal condition library, the system establishes a multi-objective optimization model to achieve the best balance among pollutant removal efficiency, reagent cost, reaction time and environmental condition adaptability. This multi-objective optimization model enables the system to dynamically select the optimal dosing strategy. The specific steps for establishing the multi-objective optimization model are as follows:

[0080] In the multi-objective optimization model to be established, determine the optimization objectives and constraints. The optimization objectives can be set as multiple independent sub-objectives according to actual operation requirements. The optimization objectives clarify the multi-dimensional objectives that the system needs to achieve during pollutant removal, such as removal efficiency, cost, treatment time, etc.; the constraints ensure the safety and stability of the optimization process. Common optimization objectives include:

[0081] Maximization of removal efficiency: Improve the removal efficiency of pollutants to ensure that the effluent quality meets the discharge standard. This optimization objective is mainly measured by the removal rate before and after treatment and is set as the objective to be maximized, that is, the higher the removal efficiency, the better;

[0082] Minimization of reagent cost: Reduce the usage of reagents to lower the operating cost of wastewater treatment. The reagent cost objective is mainly represented by the reagent dosage, that is, the lower the reagent cost, the better;

[0083] Minimization of reaction time: Shorten the treatment time to improve the processing efficiency of the system. The reaction time refers to the time required from the addition of the reagent until the effluent meets the standards. The shorter the reaction time, the better;

[0084] Maximization of environmental condition adaptability: Enhance the system's adaptability to fluctuations in environmental parameters such as temperature and pH value. This optimization goal is usually represented by the fluctuation range of environmental parameters. The higher the environmental condition adaptability, the better;

[0085] During the optimization process of water treatment, the following constraint conditions also need to be set:

[0086] Minimum removal rate constraint: The removal rate of each pollutant must reach the minimum discharge standard; Reagent dosage constraint: The reagent dosage must be controlled below the maximum dosage; Reaction time constraint: The reaction time must be within the specified time; Environmental condition constraint: The pH value and temperature need to be maintained within an appropriate range;

[0087] According to the above optimization goals, integrate multiple optimization goals into a multi-objective function, set weight coefficients for each sub-goal, and transform each goal into a term of the objective function. The form of the multi-objective function is as follows: Z = w1·f1(E i ) - w2·f2(Q i ) - w3·f3(t i ) + w4·f4(ΔpH, ΔT);

[0088] Among them, represents the removal efficiency objective term, C in，i , C out，i are the concentrations of the pollutant before and after treatment, respectively;

[0089] is the reagent cost objective term, defined as the sum of the reagent dosage multiplied by its cost. The lower, the better. Q i is the reagent dosage, and Price Di is the price per unit of reagent;

[0090] is the reaction time objective term, defined as the total reaction time of each pollutant. The shorter, the better. t i represents the reaction time;

[0091] is the environmental condition adaptability objective term, defined as the sum of the pH and temperature fluctuations. The lower the absolute value, the better. pH opt，i , T opt，i are the optimal pH value and temperature of the pollutant, respectively. pH current and T current are the pH value and temperature of the current treatment conditions;

[0092] The weight coefficients w1, w2, w3, and w4 are used to adjust the priorities of different optimization objectives. Based on the actual operating conditions, experimental data, and requirements analysis, the weight values are reasonably determined. According to the actual treatment situation, the weight coefficients for different treatment stages are dynamically adjusted. For example, when higher removal efficiency is required, the weight of the removal efficiency objective term can be increased; when cost is prioritized, the weight of the chemical agent cost objective term can be increased.

[0093] The multi-objective optimization model can analyze based on the optimization objectives, constraints, and the results of the multi-objective function, comprehensively considering multiple key indicators (such as removal efficiency, chemical agent cost, reaction time, environmental adaptability, etc.), and achieving a balance among conflicting objectives. For example, on the premise of ensuring the removal efficiency, the multi-objective optimization model can find the chemical agent usage amount to reduce the operating cost.

[0094] It should be noted that the multi-objective optimization model includes a multi-objective function, and on this basis, optimization objectives and constraints are added. The multi-objective optimization model defines the maximization or minimization method of the multi-objective function and stipulates various constraints in the optimization process (such as the minimum removal efficiency, the maximum chemical agent dosage, etc.) to improve the safety, practical feasibility, and stability of the optimization process.

[0095] After constructing the multi-objective function, an optimization algorithm is selected to solve the multi-objective optimization model to obtain the parameter combination that makes the multi-objective function Z optimal. The genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) are combined for the optimization strategy to determine the global optimal solution for multi-objective requirements in the wastewater treatment process. The following are the specific steps:

[0096] First, the genetic algorithm is used for preliminary global search to find potential excellent solutions, and the particle swarm optimization algorithm then performs local fine search based on the genetic algorithm to accelerate convergence to the global optimal solution. The specific steps are as follows:

[0097] Use the initialized genetic algorithm for global search to generate an initial population. Randomly generate an initial population containing multiple groups of candidate solutions. Each candidate solution represents a possible combination of treatment parameters (such as chemical agent dosage, reaction time, pH value, temperature, etc.). These solutions will serve as the initial individuals of the genetic algorithm.

[0098] Calculate the fitness value. Calculate the fitness value of each individual according to the multi-objective function (i.e., the value of the multi-objective function Z). The higher the fitness value, the closer the removal efficiency, chemical agent cost, reaction time, etc. of this combination scheme are to the optimal.

[0099] Use methods such as roulette wheel selection or tournament selection to select individuals with higher fitness from the current population as the parents of the next generation. Individuals with higher fitness have a higher probability of being selected and entering the next generation.

[0100] Perform crossover operations on the selected parent individuals to generate new offspring individuals. The crossover rate is generally set to 0.6 to 0.8 to ensure the diversity of the population. Mutate the genes of some offspring individuals (such as adjusting the value of a certain processing parameter). The mutation rate is generally set to 0.01 to 0.05 to prevent the algorithm from falling into a local optimum.

[0101] Repeat the fitness calculation, selection, crossover, and mutation operations for multiple generations of iterative evolution. The genetic algorithm will converge to a preliminary optimal solution after multiple iterations, that is, obtain a set of candidate solutions with higher fitness (i.e., excellent solution sets). These solutions have higher removal efficiency, lower chemical agent costs, and shorter processing times.

[0102] Use the excellent solution set obtained by the genetic algorithm as the initial particle swarm for particle swarm optimization. Particle swarm optimization performs local fine-grained search. The specific steps are as follows:

[0103] Initialize the positions and velocities of the particle swarm. Use the excellent solution set output by the genetic algorithm as the initial positions of the particle swarm. Each particle represents a candidate solution, and the velocity of the particle is initialized to a small value to make fine adjustments near the initial solution.

[0104] Calculate the fitness value of each particle (i.e., the value of the multi-objective function Z) to measure the quality of the current solution, and record the individual best position (personal best position p best and the global best position of the particle swarm (global best position g best );

[0105] According to the velocity update formula of PSO, dynamically adjust the particle positions to make them gradually approach the global optimal solution. The velocity update formula is: v i = ω·v i + c1·r1·(p best - x i ) + c2·r2·(g best - x i ), where ω is the inertia weight, which controls the influence degree of the current velocity and usually gradually decreases with the number of iterations; c1 and c2 are acceleration factors, which are used to adjust the velocities of the particles moving towards the individual best and global best directions; r1 and r2 are random numbers to ensure the randomness of the particles during the search process and avoid falling into a local optimum; x i represents the current position;

[0106] Perform iterative updates and approach the global optimum. Repeat the fitness calculation, velocity update, and position update processes, continuously update the individual best position and global best position of the particles. As the number of iterations increases, the particle swarm gradually converges to the global optimal solution.

[0107] When the particle swarm reaches the maximum number of iterations, or when the global optimal position does not change for several consecutive iterations, the algorithm terminates. At this time, the global optimal position g in the particle swarm best is the optimal solution of the multi-objective optimization model;

[0108] After PSO ends, the final optimal solution is output, including the optimal chemical dosage, reaction time, temperature, pH value, etc. This solution meets the requirements of the multi-objective optimization model (such as high removal efficiency, low cost, short treatment time, strong environmental adaptability, etc.);

[0109] Apply the optimal solution obtained by hybrid optimization to the actual wastewater treatment process to verify whether the key indicators such as removal efficiency, cost, and time in actual operation meet the expectations; if the actual operation results deviate from the expected values of the optimization model, the inertia weight of PSO and the crossover and mutation rates of GA can be further adjusted, and the solution process of the algorithm can be optimized to improve the convergence and accuracy of the hybrid algorithm.

[0110] By combining the genetic algorithm and particle swarm optimization, the system can perform global and local fine searches in multi-objective optimization to achieve fast and accurate optimal solution solving. The genetic algorithm is responsible for the initial global search to find a set of excellent solutions, and the particle swarm optimization algorithm further optimizes through local search on this basis, and finally obtains the global optimal solution of multi-objective optimization, providing accurate and economical treatment parameters for the water treatment process.

[0111] Apply the optimal solution obtained by hybrid optimization to actual wastewater treatment, measure the key indicators such as its removal rate, chemical cost, and treatment time, and ensure that they meet the expected requirements. If the actual effect deviates greatly, the parameters of GA and PSO can be adjusted. Through this optimization method, the system realizes efficient, economical, and highly adaptable chemical dosing control for wastewater treatment, improving the accuracy and stability of water treatment.

[0112] It should be noted that the threshold information related to this embodiment is set in advance by professionals and will not be explained in detail here. There are some cases where the English letters of some parameters are the same in this embodiment, but different meanings are explained when used, and they will not be explained one by one here.

[0113] In the present invention, sensors are set in the water body to collect pollutant parameters (such as conductivity, fluorescence intensity, turbidity, and redox potential), and the data is preprocessed. The support vector machine classification model is used to identify the types and concentrations of pollutants. After identifying the pollutants, the system calls the corresponding treatment parameter combinations from the removal condition library, and updates the condition combinations through the Apriori algorithm to dynamically optimize the condition library. At the same time, when multiple pollutants coexist, the removal conditions are automatically adjusted according to the interaction rules between pollutants to ensure the co-removal effect of different pollutants.

[0114] Furthermore, a multi-objective optimization model is constructed with the optimization objectives of removing efficiency, chemical cost, treatment time, and environmental adaptability. The multi-objective requirements are integrated to meet the overall requirements of water treatment. By combining the genetic algorithm and the particle swarm optimization algorithm, the optimal solution is accurately searched globally and locally. Finally, the global optimal solution of multi-objective optimization is determined to achieve precise chemical dosing, cost control, and efficient treatment, thus significantly improving the intelligent level of water treatment and the dynamic adaptability of the treatment process, and enabling more accurate chemical dosing control during the water treatment process.

[0115] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0116] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0117] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0118] In addition, in each embodiment of this application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0119] As mentioned above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0120] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A precise dosing control system for water treatment, characterized in that: It includes data acquisition module, condition library construction module, multi-objective processing module and optimization decision module, and each module is connected through signals; The data acquisition module is used to set sensors in the water body to collect pollutant parameters, pre-process the collected data, use the support vector machine classification model to identify the type of pollutants, and output the type and concentration of pollutants; The condition library construction module is used to construct a removal condition library after identifying the type and concentration of pollutants, call the condition combination for water quality treatment according to the removal conditions stored in the removal condition library, and use the Apriori algorithm to update the condition combination. In the case of coexistence of multiple pollutants, the removal conditions are automatically adjusted according to the interaction rules between pollutants; The multi-objective processing module is used to determine the optimization objectives and constraints of the water treatment process, integrate multiple objectives to obtain a multi-objective function, and construct a multi-objective optimization model for water treatment decision-making; The optimization decision module is used to combine the genetic algorithm and the particle swarm optimization algorithm to determine the global optimal solution of multi-objective requirements in the water treatment decision-making process, and optimize the water treatment decision according to the global optimal solution; For the coexistence of multiple pollutants, the removal conditions are automatically adjusted according to the interaction rules between pollutants. The specific steps are as follows: Analyze the historical removal parameters, including removal effect, reagent dosage, temperature, pH value, and treatment time, to determine the actual removal efficiency of each pollutant and the average removal effect between the existing removal conditions; Based on the relationship between conditions and removal effects, pollutant removal parameters are adjusted. Based on removal parameter analysis and removal effect optimization, the removal conditions for each pollutant are dynamically updated in the condition library. In the case of multi-pollutant coordinated treatment, interactive rules are constructed to determine the interaction between pollutants, and the selection and dosage of reagents are optimized according to the interaction factors; It is used to determine the optimization objectives and constraints of the water treatment process, integrate multiple objectives to obtain a multi-objective function, and construct a multi-objective optimization model for water treatment decision-making, including the following steps: The optimization objectives include maximizing removal efficiency, minimizing reagent costs, minimizing reaction time, and maximizing adaptability to environmental conditions; The constraints include minimum removal rate constraint, reagent dosage constraint, reaction time constraint and environmental condition constraint; Integrate the calculations according to the optimization objectives to form a multi-objective function; The multi-objective function results, optimization objectives and constraints are combined to form a multi-objective optimization model, and water treatment decisions are made; It is used to combine the genetic algorithm and the particle swarm optimization algorithm to determine the global optimal solution of multi-objective requirements in the water treatment decision-making process, and optimize the water treatment decision according to the global optimal solution, including the following steps: Use the initialized genetic algorithm to perform global search, randomly generate an initial population containing multiple sets of candidate solutions, and use the candidate solutions as the initial individuals of the genetic algorithm; Calculate the fitness value of each initial individual according to the multi-objective function; Use roulette or tournament selection methods to select individuals from the population based on their fitness values ​​to serve as parents for the next generation; Perform a crossover operation on the selected parent individuals to generate new offspring individuals, and perform a mutation operation on the genes of the selected offspring individuals; Repeat the fitness calculation, selection, crossover and mutation operations for multiple generations of iterative evolution. The genetic algorithm will converge to the initial optimal solution as the excellent solution set after multiple iterations. The excellent solution set obtained by the genetic algorithm is used as the initial particle swarm of particle swarm optimization, and the particle swarm optimization performs local fine search. The specific steps are as follows: Initialize the position and speed of the particle swarm, use the excellent solution set output by the genetic algorithm as the initial position of the particle swarm, each particle represents a candidate solution, and set the particle speed; Calculate the fitness value of each particle to measure the quality of the current solution, and record the individual optimal position of each particle and the global optimal position of the particle swarm; According to the speed update formula of the particle swarm optimization algorithm, the particle position is dynamically adjusted; Repeat the fitness value calculation, velocity update and position update process to update the individual optimal position and global optimal position of the particle; When the particle swarm reaches the maximum number of iterations or the global optimal position no longer changes in consecutive iterations, the algorithm terminates and the global optimal position in the particle swarm is taken as the optimal solution of the multi-objective optimization model; Output the final optimal solution, which includes the optimal dosage of reagent, reaction time, temperature and pH value.

2. The precise dosing control system for water treatment according to claim 1, characterized in that: It is used to set the sensor to collect pollutant parameters in the water body, pre-process the collected data, use the support vector machine classification model to identify the pollutant types, and output the pollutant types and concentrations. The specific steps are as follows: Pollutant parameters include conductivity, fluorescence intensity, turbidity, and redox potential; Remove noise from collected data, identify and correct abnormal data points, and convert preprocessed data into feature vectors; Build a feature library to store the feature ranges and classification labels of pollutants; Based on the feature library and collected data, the radial basis kernel function is used to map the feature vectors of the collected data. According to the classification labels of pollutants in the feature library and the feature vectors of pollutants, the support vector machine classification model is used to identify the types of pollutants, and the classification results, confidence levels and concentration values ​​of pollutants are obtained.

3. The precise dosing control system for water treatment according to claim 2, characterized in that: After identifying the type and concentration of pollutants, a removal condition library is constructed, and a condition combination is called for water quality treatment according to the removal conditions stored in the removal condition library, and the condition combination is updated using the Apriori algorithm. The specific steps include: Initial construction of the removal condition library: According to the basic characteristics of pollutants and treatment experience, a pollutant removal condition library is established. For each pollutant, the removal parameters required for removal are set. The removal parameters include the optimal pH value, suitable temperature, recommended reagent type, initial dosage, and reaction time. After identifying the pollutants and their concentrations, the corresponding removal parameters in the condition combination in the removal condition library are automatically called; The Apriori algorithm is embedded in the removal condition library to learn the correlation between different pollutant removal conditions, determine the best treatment condition combination, and update the generated condition combination to the condition library.

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