A multi-drug low-drug-consumption intelligent collaborative optimization method of a dosing model

Abstract

This invention discloses a multi-agent, low-consumption intelligent collaborative optimization method for a dosing model, belonging to the field of intelligent dosing optimization technology for water treatment. It is used to optimize the coordinated dosing of three types of agents—coagulants, flocculants aids, and pH adjusters—in water treatment systems, with the constraint of achieving effluent quality standards and the optimization objective of minimizing total agent cost. The method includes the following steps: Q1, real-time acquisition of influent parameters; Q2, construction of a total agent cost objective function; Q3, establishment of coupled models for effluent turbidity and effluent pH, combined with process boundary constraints to form an optimization equation set; Q4, using lightweight piecewise correction rules to solve for the optimal dosage of the three types of agents in a coordinated manner; Q5, distribution of the optimal dosage to the dosing actuator to complete automatic dosing. This invention uses linear, piecewise functions and simple calculation formulas throughout, without complex neural networks, deep learning, or other heavy algorithms, resulting in a low computational load and simple logic.

Description

Technical Field

[0001] This invention belongs to the field of intelligent dosing optimization technology for water treatment, specifically referring to an intelligent collaborative optimization method for multi-agent low-consumption dosing models. Background Technology

[0002] In water treatment processes such as municipal water supply, industrial wastewater, and sewage treatment, coagulation and sedimentation are the core water purification units. They typically require the simultaneous addition of three types of agents: coagulants, flocculants aids, and pH adjusters, working synergistically to ensure that water quality indicators such as turbidity and pH levels consistently meet standards. Currently, on-site chemical dosing methods in domestic water treatment mainly fall into two categories: manual, experience-based dosing and independent automatic dosing of single agents, both of which generally suffer from numerous technical shortcomings.

[0003] First, the dosing of chemicals lacks a synergistic mechanism. Most existing technologies control and adjust coagulants, coagulant aids, and pH adjusters individually, without considering the coupling effects between these three types of agents: coagulants are mostly iron and aluminum salts, which significantly lower the pH of the water; coagulant aids can amplify the flocculation effect of coagulants and reduce the amount of coagulant used, while also slightly disturbing the pH of the water; pH adjusters directly change the acidity or alkalinity of the water, which in turn affects the efficiency of coagulation and flocculation reactions. When each agent is adjusted individually, a vicious cycle of "adding acid and alkali agents is necessary after adding coagulant" easily occurs, resulting in high overall chemical consumption and continuously increasing operating costs.

[0004] Secondly, the dosing strategy is crude and lacks the support of a refined optimization model. Traditional manual dosing relies on the operator's on-site experience, and when influent turbidity, organic matter concentration, and pH fluctuate, it cannot quickly and accurately match the dosage. Conventional automatic dosing systems only use simple single-parameter closed-loop control and do not make segmented corrections for influent organic matter, turbidity, and other operating conditions. When the influent water quality changes abruptly, problems such as excessive effluent turbidity and pH exceeding the process allowable range are very likely to occur. In order to ensure water quality stability, the conservative approach of overdosing is often adopted on-site, further aggravating the waste of chemicals.

[0005] Third, existing intelligent optimization models are poorly implemented. Currently, some studies use complex machine learning and deep learning models to optimize dosing. These models have complex structures and numerous parameters, which require high computing power from low-end field equipment such as PLCs and embedded controllers. They are difficult to deploy and have high operating costs, making it difficult to promote them on a large scale in small and medium-sized water plants and decentralized sewage treatment sites. They cannot meet the actual field needs of low computing power, low cost, and low chemical consumption.

[0006] Fourth, there is a lack of a quantitative evaluation system for reducing consumption. Traditional dosing methods only focus on whether the effluent water quality meets the standards. There is no unified accounting for chemical consumption or evaluation indicators for the effect of reducing consumption. It is impossible to intuitively judge the economic efficiency of different dosing strategies, and it is also difficult to achieve dynamic iteration and continuous optimization of model parameters.

[0007] In summary, existing multi-drug dosing technologies suffer from problems such as unutilized drug coupling relationships, poor synergy, high drug consumption, complex and difficult-to-implement models, and inability to quantify optimization effects. Therefore, developing a lightweight, easily deployable, intelligently synergistic multi-drug dosing optimization method with the goal of minimizing drug consumption has become an urgent technical challenge to be solved in this field. Summary of the Invention

[0008] In response to the above situation and to overcome the shortcomings of the existing technology, this invention provides a multi-drug low-drug-consumption intelligent collaborative optimization method for dosing models, which effectively solves the problems currently on the market.

[0009] The technical solution adopted in this invention is as follows: This invention proposes a multi-agent, low-consumption intelligent synergistic optimization method for dosing models, used for coagulants in water treatment systems. Coagulant The optimization of the synergistic dosing of three types of agents—pH adjuster, pH adjuster, and effluent—is constrained by achieving effluent quality standards and aims to minimize the total cost of the agents. The optimization includes the following steps:

[0010] Q1. Real-time collection of influent parameters: influent turbidity Water inlet Concentration of organic matter in influent Preset control threshold: upper limit of effluent turbidity Upper and lower limits of effluent pH , Upper and lower limits of dosage for each agent;

[0011] Q2. Construct the objective function for the total cost of the drug:

[0012] ;

[0013] In the formula: , , The unit prices are listed in order: coagulant, coagulant aid, and pH adjuster. The total cost of chemicals per unit of water body, the optimization objective is... ;

[0014] Q3. Establish a coupled model of effluent turbidity and a coupled model of effluent pH, and combine them with process boundary constraints to form an optimization equation set.

[0015] Q4. Using lightweight segmented correction rules to achieve the optimal dosage solution for three types of agents in a coordinated manner. , , ;

[0016] Q5. The optimal dosage is sent to the dosing execution unit to complete the automatic dosing.

[0017] Furthermore, the expression for the effluent turbidity coupling model in step 3 is as follows:

[0018] The constraints are satisfied. ;

[0019] In the formula: Turbidity of the effluent The turbidity removal coefficient of the coagulant. To enhance the coagulation efficiency coefficient, This is the correction factor for organic matter consumption.

[0020] Furthermore, the expression for the effluent pH coupling model in step 3 is as follows:

[0021] Constraints satisfied ;

[0022] In the formula: The pH of the effluent. The pH decay coefficient of the coagulant. This is the pH adjustment coefficient. This is the pH correction factor for the coagulant.

[0023] Furthermore, process constraints regarding the dosage of each agent:

[0024] , , ;

[0025] , Set upper and lower limits for coagulant dosage. , The upper and lower limits for the dosage of coagulant aids are set. , Set upper and lower limits for pH adjuster dosage.

[0026] Furthermore, step 4 sets a fixed ratio linkage rule for coagulant and coagulant aid, with the following ratio formula:

[0027] ;

[0028] To achieve the optimal ratio of chemical consumption determined on-site, the two agents are linked and bound together to realize the synchronous quantitative addition of the coagulant aid along with the coagulant.

[0029] Furthermore, the standard dosage of coagulant is adjusted in stages based on the amount of organic matter in the influent, and the calculation formula for each stage is as follows:

[0030] ;

[0031] This is the baseline dosage of coagulant. Based on the basic dosage, The critical concentration for segmented organic matter. , These are correction coefficients for different organic compound ranges.

[0032] Furthermore, the pH of the effluent fell below the lower limit. At that time, a fine-tuning formula involving both regulators and coagulants is used:

[0033] , ;

[0034] In the formula: The change in pH adjuster dosage This refers to the variation in coagulant dosage. This is the pH ratio adjustment coefficient. This is the concrete reduction coefficient. To control pH levels, the dosage of regulators is increased while the dosage of coagulants is reduced, achieving synergistic cost reduction.

[0035] Furthermore, the simultaneous model is used to solve for the optimal dosage. , , Solve the system of equations:

[0036] ;

[0037] , , The optimal dosage of three types of agents to meet the minimum chemical consumption under water quality constraints.

[0038] Furthermore, the total drug consumption level is calculated using the total drug consumption index, and the formula for calculating the total drug dosage is as follows:

[0039] ;

[0040] The total chemical consumption per unit of water body after optimization.

[0041] Furthermore, the optimization effect is quantified using the energy reduction rate, and the energy reduction rate is calculated using the following formula:

[0042] ;

[0043] This represents the total drug consumption for traditional independent drug administration. To determine the drug saving rate, the coefficients of each model are dynamically fine-tuned based on the consumption reduction rate.

[0044] The beneficial effects achieved by the present invention using the above structure are as follows:

[0045] (1) Establish effluent turbidity coupling model and effluent pH coupling model to accurately characterize the mutual influence relationship among coagulant, coagulant aid, and pH adjuster; by setting a fixed optimal ratio of coagulant and coagulant aid and a chemical linkage fine-tuning rule when pH deviates, the synergistic effect of coagulant aid is fully utilized and the vicious cycle of chemical addition is avoided. Taking the total chemical cost function as the core optimization objective, the optimal dosage is solved under the premise of ensuring water quality meets the standards. Combined with the consumption reduction rate formula, the chemical saving effect can be quantified, effectively solving the problem of excessive chemical dosage in the traditional independent dosing mode, and significantly reducing the procurement and operation costs of water treatment chemicals;

[0046] (2) The entire process adopts linear, piecewise functions and simple calculation formulas, without complex neural networks, deep learning and other heavy algorithms. The model has a small amount of computation and simple logic. It can be directly deployed and run in the existing PLC, embedded dosing controller and conventional industrial control equipment in water plants without the need for additional high-performance computing hardware. It is compatible with large, medium and small water treatment sites. Its versatility and applicability are far superior to traditional complex intelligent models.

[0047] (3) Simultaneously setting three rigid constraints—effluent turbidity limit, effluent pH range, and reagent dosage range—combined with segmented correction rules for influent organic matter concentration, allows for dynamic adjustment of reagent dosage based on real-time fluctuations in influent turbidity, organic matter content, and influent pH. This avoids the risk of water quality exceeding standards and eliminates the industry-wide problem of "conservative overdosing," achieving the dual goals of stable water quality compliance and low reagent consumption, thereby improving the operational reliability of the water treatment system. Attached Figure Description

[0048] Figure 1 This is the overall process of the intelligent collaborative optimization method for multi-drug low-drug consumption dosing model proposed in this invention;

[0049] Figure 2 This is a drug linkage calculation sub-process of a multi-drug low-drug-consumption intelligent collaborative optimization method for a dosing model proposed in this invention;

[0050] Figure 3 This invention provides an evaluation of the effectiveness and parameter iteration of a multi-drug, low-drug-consumption intelligent collaborative optimization method for a dosing model.

[0051] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0052] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0053] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0054] like Figures 1-3 As shown, this invention proposes a multi-agent, low-consumption intelligent collaborative optimization method for dosing models, applicable to coagulants in water treatment systems. Coagulant The optimization of the synergistic dosing of three types of agents—pH adjuster, pH adjuster, and effluent—is constrained by achieving effluent quality standards and aims to minimize the total cost of the agents. The optimization includes the following steps:

[0055] Q1. Real-time collection of influent parameters: influent turbidity Water inlet Concentration of organic matter in influent Preset control threshold: upper limit of effluent turbidity Upper and lower limits of effluent pH , Upper and lower limits of dosage for each agent;

[0056] Q2. Construct the objective function for the total cost of the drug:

[0057] ;

[0058] In the formula: , , The unit prices are listed in order: coagulant, coagulant aid, and pH adjuster. The total cost of chemicals per unit of water body, the optimization objective is... ;

[0059] Q3. Establish a coupled model of effluent turbidity and a coupled model of effluent pH, and combine them with process boundary constraints to form an optimization equation set.

[0060] Q4. Using lightweight segmented correction rules to achieve the optimal dosage solution for three types of agents in a coordinated manner. , , ;

[0061] Q5. The optimal dosage is sent to the dosing execution unit to complete the automatic dosing.

[0062] The expression for the effluent turbidity coupling model in step 3 is:

[0063] The constraints are satisfied. ;

[0064] In the formula: Turbidity of the effluent The turbidity removal coefficient of the coagulant. To enhance the coagulation efficiency coefficient, This is the correction factor for organic matter consumption.

[0065] The expression for the effluent pH coupling model in step 3 is as follows:

[0066] Constraints satisfied ;

[0067] In the formula: The pH of the effluent. The pH decay coefficient of the coagulant. This is the pH adjustment coefficient. This is the pH correction factor for the coagulant.

[0068] Process constraints on the dosage of each agent:

[0069] , , ;

[0070] , Set upper and lower limits for coagulant dosage. , The upper and lower limits for the dosage of coagulant aids are set. , Set upper and lower limits for pH adjuster dosage.

[0071] Step 4: Set up a fixed ratio linkage rule for coagulant and coagulant aid, with the following ratio formula:

[0072] ;

[0073] To achieve the optimal ratio of chemical consumption determined on-site, the two agents are linked and bound together to realize the synchronous quantitative addition of the coagulant aid along with the coagulant.

[0074] The standard dosage of modified coagulant for influent organic matter is calculated in stages, using the following formula:

[0075] ;

[0076] This is the baseline dosage of coagulant. Based on the basic dosage, The critical concentration for segmented organic matter. , These are correction coefficients for different organic compound ranges.

[0077] The pH of the effluent is below the lower limit. At that time, a fine-tuning formula involving both regulators and coagulants is used:

[0078] , ;

[0079] In the formula: The change in pH adjuster dosage This refers to the variation in coagulant dosage. This is the pH ratio adjustment coefficient. This is the concrete reduction coefficient. To control pH levels, the dosage of regulators is increased while the dosage of coagulants is reduced, achieving synergistic cost reduction.

[0080] Solving for the optimal dosage using a simultaneous model , , Solve the system of equations:

[0081] ;

[0082] , , The optimal dosage of three types of agents to meet the minimum chemical consumption under water quality constraints.

[0083] The total drug consumption level is calculated using the total drug consumption index. The formula for calculating the total drug dosage is as follows:

[0084] ;

[0085] The total chemical consumption per unit of water body after optimization.

[0086] The optimization effect is quantified using the energy reduction rate. The energy reduction rate is calculated using the following formula:

[0087] ;

[0088] This represents the total drug consumption for traditional independent drug administration. To determine the drug saving rate, the coefficients of each model are dynamically fine-tuned based on the consumption reduction rate.

[0089] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0090] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0091] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A multi-drug, low-drug-consumption intelligent collaborative optimization method for a dosing model, characterized in that, Coagulant for water treatment systems Coagulant The optimization of the synergistic dosing of three types of agents—pH adjuster, pH adjuster, and effluent—is constrained by achieving effluent quality standards and aims to minimize the total cost of the agents. The optimization includes the following steps: Q1. Real-time collection of influent parameters: influent turbidity , water inlet Concentration of organic matter in influent Preset control threshold: upper limit of effluent turbidity Upper and lower limits of effluent pH , Upper and lower limits of dosage for each agent; Q2. Construct the objective function for the total cost of the drug: ； In the formula: , , The unit prices are listed in order: coagulant, coagulant aid, and pH adjuster. The total cost of chemicals per unit of water body, the optimization objective is... ; Q3. Establish a coupled model of effluent turbidity and a coupled model of effluent pH, and combine them with process boundary constraints to form an optimization equation set. Q4. Using lightweight segmented correction rules to achieve the optimal dosage solution for three types of agents in a coordinated manner. , , ; Q5. The optimal dosage is sent to the dosing execution unit to complete the automatic dosing.

2. The intelligent collaborative optimization method for multi-drug low-drug consumption dosing model according to claim 1, characterized in that: The expression for the effluent turbidity coupling model in step 3 is: The constraints are satisfied. ; In the formula: Turbidity of the effluent The turbidity removal coefficient of the coagulant. To enhance the coagulation efficiency coefficient, This is the correction factor for organic matter consumption.

3. The intelligent collaborative optimization method for multi-agent low-consumption dosing models according to claim 2, characterized in that: The expression for the effluent pH coupling model in step 3 is as follows: Constraints satisfied ; In the formula: The pH of the effluent. The pH decay coefficient of the coagulant. This is the pH adjustment coefficient. This is the pH correction factor for the coagulant.

4. The intelligent collaborative optimization method for multi-agent low-consumption dosing model according to claim 3, characterized in that: Process constraints on the dosage of each agent: ， ， ； , Set upper and lower limits for coagulant dosage. , The upper and lower limits for the dosage of coagulant aids are set. , Set upper and lower limits for pH adjuster dosage.

5. The intelligent collaborative optimization method for multi-agent low-consumption dosing models according to claim 4, characterized in that: Step 4: Set up a fixed ratio linkage rule for coagulant and coagulant aid, with the following ratio formula: ； To achieve the optimal ratio of chemical consumption determined on-site, the two agents are linked and bound together to realize the synchronous quantitative addition of the coagulant aid along with the coagulant.

6. The intelligent collaborative optimization method for multi-agent low-consumption dosing models according to claim 5, characterized in that: The standard dosage of modified coagulant for influent organic matter is calculated in stages, using the following formula: ； This is the baseline dosage of coagulant. Based on the basic dosage, The critical concentration for each segment of organic matter. , These are correction coefficients for different organic compound ranges.

7. The intelligent collaborative optimization method for multi-agent low-consumption dosing models according to claim 6, characterized in that: The pH of the effluent is below the lower limit. At that time, a fine-tuning formula involving both regulators and coagulants is used: ， ； In the formula: The change in pH adjuster dosage The change in coagulant dosage. This is the pH ratio adjustment coefficient. This is the concrete reduction coefficient. To control pH levels, the dosage of regulators is increased while the dosage of coagulants is reduced, achieving synergistic cost reduction.

8. The intelligent collaborative optimization method for multi-agent low-drug-consumption dosing model according to claim 7, characterized in that: Solving for the optimal dosage using a simultaneous model , , Solve the system of equations: ； , , The optimal dosage of three types of agents to meet the minimum chemical consumption under water quality constraints.

9. The intelligent collaborative optimization method for multi-agent low-consumption dosing model according to claim 8, characterized in that: The total drug consumption level is calculated using the total drug consumption index. The formula for calculating the total drug dosage is as follows: ； The total chemical consumption per unit of water body after optimization.

10. The intelligent collaborative optimization method for multi-agent low-consumption dosing models according to claim 9, characterized in that: The optimization effect is quantified using the energy reduction rate. The energy reduction rate is calculated using the following formula: ； This represents the total drug consumption for traditional independent drug administration. To calculate the drug saving rate, the coefficients of each model are dynamically adjusted based on the consumption reduction rate.

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