Optimization method and system for circulating water scale inhibitor formulation based on mixture design

Abstract

This invention relates to the field of scale inhibitor formulation optimization technology, and to a method and system for optimizing circulating water scale inhibitor formulations based on mixing design. The method includes: confirming the formulation optimization environment based on scale inhibitor formulation optimization instructions; performing simplex centroid design based on the effective raw material component set and initial constraint boundary set; mixing and preparing multiple sets of basic mixing formulations to obtain multiple sets of test samples; measuring the multiple sets of test samples separately using a scale inhibition performance testing unit; conducting water circulation experiments based on the theoretically optimal formulation; calculating the actual dynamic scale inhibition rate and actual dynamic corrosion inhibition rate based on dynamic scaling amount and coupon weight loss; and comparing the actual comprehensive performance index with the maximum performance index. This invention can improve the efficiency, comprehensive performance, and verification reliability of circulating water scale inhibitor formulation optimization.

Description

Technical Field

[0001] This invention relates to the field of scale inhibitor formulation optimization technology, and in particular to a method and system for optimizing circulating water scale inhibitor formulations based on mixing design. Background Technology

[0002] As modern industrial circulating cooling water systems develop towards higher concentration ratios, more complex water qualities, and harsher operating conditions, higher requirements are placed on the comprehensive performance of scale inhibitor formulations. They must have both high-efficiency scale inhibition and corrosion inhibition capabilities, as well as adaptability to operating conditions.

[0003] However, existing methods for designing scale inhibitor formulations have significant limitations: traditional single-factor rotation methods are not only inefficient but also fail to reveal the synergistic and antagonistic effects between different scale inhibitor components; while conventional orthogonal experiments or multi-factor optimization often only focus on the scale inhibition rate as a single optimization objective, neglecting systematic consideration of corrosion inhibition performance and adaptability to dynamic operating conditions, resulting in poor performance of the optimized formulation in actual circulating water systems. Therefore, there is an urgent need for a scale inhibitor formulation optimization method that can integrate intelligent component screening, multi-index balanced optimization, and dynamic operating condition system verification. Summary of the Invention

[0004] This invention provides a method and system for optimizing the formulation of scale inhibitors for circulating water based on mixing design. Its main purpose is to improve the efficiency, overall performance and verification reliability of the optimization of scale inhibitor formulations for circulating water.

[0005] To achieve the above objectives, the present invention provides a method for optimizing the formulation of a circulating water scale inhibitor based on mixing design, comprising:

[0006] Confirm receipt of scale inhibitor formulation optimization instruction, and confirm formulation optimization environment based on scale inhibitor formulation optimization instruction, wherein the formulation optimization environment includes formulation optimization system and basic chemical raw materials, and the formulation optimization system includes raw material pretreatment and proportioning unit, scale inhibition performance testing unit and multi-objective analysis and decision-making unit;

[0007] Obtain a water quality parameter report, and based on the water quality parameter report and the raw material pretreatment and proportioning unit, screen and set the basic chemical raw materials to obtain an effective raw material component set and an initial constraint boundary set. Based on the effective raw material component set and the initial constraint boundary set, perform simplex centroid design to obtain multiple sets of basic mixing formulations.

[0008] Based on the multiple sets of basic mixing formulas, multiple sets of test samples are prepared by mixing and formulation. The scale inhibition performance test unit is used to measure the multiple sets of test samples respectively to obtain scale inhibition rate dataset and corrosion inhibition rate dataset.

[0009] The theoretical comprehensive performance index is calculated based on the multi-objective analysis decision unit, scale inhibition rate dataset, and corrosion inhibition rate dataset, and the theoretical optimal formula and maximum performance index are obtained based on the theoretical comprehensive performance index.

[0010] Water circulation experiments were conducted based on the theoretically optimal formula to obtain the dynamic scaling amount and the weight loss value of the coupon. The actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate were calculated based on the dynamic scaling amount and the weight loss value of the coupon. The actual comprehensive performance index was calculated based on the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate.

[0011] The actual comprehensive performance index is compared with the maximum performance index to obtain the performance verification deviation value. If the performance verification deviation value is less than the preset deviation threshold, the theoretical optimal formula is confirmed as the target formula. Based on the target formula, the formulation optimization of the circulating water scale inhibitor of the basic chemical raw materials is realized.

[0012] Optionally, the step of obtaining the water quality parameter report, and screening and setting the basic chemical raw materials based on the water quality parameter report and the raw material pretreatment and proportioning unit to obtain an effective raw material component set and an initial constraint boundary set, includes:

[0013] The water quality parameter report is analyzed based on the raw material pretreatment and proportioning unit to obtain key water quality characteristics, including calcium ion concentration, total alkalinity, sulfate concentration and reference operating temperature.

[0014] Based on the calcium ion concentration, total alkalinity, and the basic chemical raw materials, an adaptation analysis is performed to obtain an effective raw material component set, wherein the effective raw material component set includes multiple effective raw material components;

[0015] For each effective raw material component in the set of effective raw material components, the following operation is performed:

[0016] Static scale inhibition and solubility tests were conducted on the effective raw material components using the key water quality characteristics to obtain the lowest synergistic concentration and the highest solubility limit.

[0017] An independent constraint interval is constructed by using the lowest synergistic concentration as the lower limit and the highest solubility limit as the upper limit;

[0018] By summing up the independent constraint intervals corresponding to the multiple effective raw material components, an initial constraint boundary set is obtained.

[0019] Optionally, the simplex centroid design based on the effective raw material component set and the initial constraint boundary set yields multiple sets of basic mixing formulations, including:

[0020] The sum of lower limits is calculated based on the lower limits of each independent constraint interval in the initial constraint boundary set, and the free allocation ratio margin is calculated based on the sum of the lower limits.

[0021] Based on the effective raw material component set, multiple arbitrary real proportions are obtained. Based on the free allocation ratio margin and the lower limit of each independent constraint interval in the initial constraint boundary set, a virtual variable transformation relationship is established. Based on the established virtual variable transformation relationship and multiple arbitrary real proportions, multiple virtual variables are constructed to obtain multiple virtual variables. Based on the multiple virtual variables, a virtual variable space is obtained.

[0022] An initial set of experimental points is obtained based on the virtual variable space, wherein the initial set of experimental points includes vertices, edge midpoints, and center points of the virtual variable space;

[0023] Based on the virtual variable transformation relationship, the initial experimental point set is inversely transformed to obtain multiple initial mixing formulations, wherein each of the multiple initial mixing formulations includes multiple effective raw material component mass fractions.

[0024] Based on the upper limit of each independent constraint interval in the initial constraint boundary set, the multiple initial mixing formulations are screened to obtain multiple sets of basic mixing formulations.

[0025] Optionally, the process involves mixing and formulating multiple sets of basic mixture formulations to obtain multiple sets of test samples. The scale inhibition performance testing unit then measures each set of test samples to obtain a scale inhibition rate dataset and a corrosion inhibition rate dataset, including:

[0026] Based on the aforementioned multiple sets of basic mixture formulations, the mixtures were weighed and proportioned to obtain multiple sets of test samples. The following operations were performed on each of the multiple sets of test samples:

[0027] Based on the scale inhibition performance testing unit, the multiple groups of test samples are added to the pre-constructed circulating water for reaction, and the circulating water is filtered to obtain filtrate.

[0028] The calcium ion concentration of the filtrate is measured to obtain the residual calcium ion concentration. The scale inhibition rate of a single group is calculated based on the preset initial calcium ion concentration and the residual calcium ion concentration. The scale inhibition rates of the single groups of samples to be tested are summarized to obtain the scale inhibition rate dataset.

[0029] The multiple sets of test samples were added to the circulating water, and polarization scanning was performed using a pre-constructed electrochemical workstation to obtain the corrosion current density.

[0030] Obtain the control corrosion current density, calculate the single-group corrosion inhibition rate based on the control corrosion current density and the corrosion current density, and summarize the single-group corrosion inhibition rates corresponding to the multiple groups of test samples to obtain the corrosion inhibition rate dataset.

[0031] Optionally, the step of calculating the theoretical comprehensive performance index based on the multi-objective analysis decision unit, the scale inhibition rate dataset, and the corrosion inhibition rate dataset, and obtaining the theoretically optimal formula and the maximum performance index based on the theoretical comprehensive performance index, includes:

[0032] Based on the multi-objective analysis and decision-making unit, multiple raw material energy consumption parameters are obtained. Based on the multiple sets of basic mixture formulations and the multiple raw material energy consumption parameters, multiple formulation energy consumptions are calculated. Based on the multiple formulation energy consumptions, the maximum energy consumption and minimum energy consumption are obtained. For each set of basic mixture formulations, the following operations are performed:

[0033] Based on the maximum energy consumption, minimum energy consumption, energy consumption of the formula corresponding to the basic mixture formula, scale inhibition rate of a single group, and corrosion inhibition rate of a single group, the theoretical comprehensive performance index is calculated as follows:

[0034]

[0035] in, Indicates the theoretical comprehensive performance index. Indicates the scale inhibition rate of a single group. Indicates the corrosion inhibition rate of a single group. This indicates the energy consumption of the basic mixture formulation. Indicates minimum energy consumption. Indicates maximum energy consumption. Indicates the energy consumption coefficient. , Indicates the weighting coefficient;

[0036] The maximum performance index is obtained based on the theoretical comprehensive performance index corresponding to each of the multiple sets of basic mixture formulations, and the basic mixture formulation corresponding to the maximum performance index is confirmed as the theoretically optimal formulation.

[0037] Optionally, the step of conducting a water circulation experiment based on the theoretically optimal formula to obtain the dynamic scaling amount and the weight loss value of the coupon includes:

[0038] Based on the theoretically optimal formula, a scale inhibitor solution was obtained, and a circulating cooling water test pipeline was obtained. The circulating cooling water test pipeline includes an inner wall of the test pipe and carbon steel plates. A circulating water experiment was conducted based on a preset operating cycle time, the circulating cooling water test pipeline, and the scale inhibitor solution to obtain the inner wall of the test pipe and the carbon steel plates after the experiment.

[0039] After the test, the attached dirt on the inner wall of the test tube was peeled off to obtain a collection liquid. The collection liquid was dried and weighed to obtain the dynamic scale amount. The carbon steel hanging plate after the test was weighed to obtain the weight loss value of the hanging plate.

[0040] Optionally, the calculation of the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate based on the dynamic scale amount and the weight loss value of the coupon includes:

[0041] Water circulation experiments were conducted based on a pre-constructed blank control group to obtain the blank scaling amount and blank coupon weight loss value.

[0042] The actual dynamic scale inhibition rate is calculated based on the dynamic scale amount and the blank scale amount.

[0043] The actual dynamic corrosion inhibition rate is calculated based on the weight loss value of the pre-coated piece and the weight loss value of the blank pre-coated piece.

[0044] Optionally, comparing the actual comprehensive performance index with the maximum performance index to obtain the performance verification deviation value includes:

[0045] Obtain the ambient temperature fluctuation parameters, and calculate the performance verification deviation value based on the actual comprehensive performance index, maximum performance index, ambient temperature fluctuation parameters, and reference operating temperature. The calculation formula is as follows:

[0046]

[0047] in, This indicates the performance verification deviation value. Indicates the actual overall performance index. Indicates the maximum performance index. This represents a parameter indicating ambient temperature fluctuations. Indicates the reference operating temperature. , All of these represent performance verification weighting coefficients.

[0048] Optionally, the step of confirming the theoretically optimal formula as the target formula if the performance verification deviation value is less than a preset deviation threshold includes:

[0049] Compare the performance verification deviation value with the deviation threshold. If the performance verification deviation value is greater than the deviation threshold, then obtain an updated experimental point set based on the virtual variable space, use the updated experimental point set as the initial experimental point set, and return to the step of performing an inverse transformation on the initial experimental point set based on the virtual variable transformation relationship until the performance verification deviation value is less than or equal to the deviation threshold. Then, the theoretically optimal formula is confirmed as the target formula.

[0050] If the performance verification deviation value is less than or equal to the deviation threshold, then the theoretically optimal formula is confirmed as the target formula.

[0051] To achieve the above objectives, the present invention also provides a circulating water scale inhibitor formulation optimization system based on mixing design, comprising:

[0052] The environmental verification module is used to verify the receipt of the scale inhibitor formulation optimization instruction and verify the formulation optimization environment based on the scale inhibitor formulation optimization instruction. The formulation optimization environment includes a formulation optimization system and basic chemical raw materials. The formulation optimization system includes a raw material pretreatment and proportioning unit, a scale inhibition performance testing unit, and a multi-objective analysis and decision-making unit.

[0053] The formulation design module is used to obtain water quality parameter reports, screen and set the basic chemical raw materials based on the water quality parameter reports and the raw material pretreatment and proportioning unit, obtain an effective raw material component set and an initial constraint boundary set, and perform simplex centroid design based on the effective raw material component set and the initial constraint boundary set to obtain multiple sets of basic mixing formulations.

[0054] The performance testing module is used to mix and prepare multiple sets of basic mixing formulas to obtain multiple sets of test samples. The scale inhibition performance testing unit measures the multiple sets of test samples to obtain scale inhibition rate dataset and corrosion inhibition rate dataset.

[0055] The theoretical comprehensive performance index is calculated based on the multi-objective analysis decision unit, scale inhibition rate dataset, and corrosion inhibition rate dataset, and the theoretical optimal formula and maximum performance index are obtained based on the theoretical comprehensive performance index.

[0056] Water circulation experiments were conducted based on the theoretically optimal formula to obtain the dynamic scaling amount and the weight loss value of the coupon. The actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate were calculated based on the dynamic scaling amount and the weight loss value of the coupon. The actual comprehensive performance index was calculated based on the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate.

[0057] The verification output module is used to compare the actual comprehensive performance index with the maximum performance index to obtain the performance verification deviation value. If the performance verification deviation value is less than the preset deviation threshold, the theoretical optimal formula is confirmed as the target formula. Based on the target formula, the formulation optimization of the circulating water scale inhibitor of the basic chemical raw materials is realized.

[0058] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0059] Memory, storing at least one instruction;

[0060] The processor executes the instructions stored in the memory to implement the above-described method for optimizing the formulation of circulating water scale inhibitors based on mixing design.

[0061] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the above-described method for optimizing the formulation of a circulating water scale inhibitor based on mixing design.

[0062] Beneficial Effects: To address the problems described in the background art, this invention confirms the receipt of scale inhibitor formulation optimization instructions and, based on these instructions, confirms the formulation optimization environment. This environment includes a formulation optimization system and basic chemical raw materials. The system comprises a raw material pretreatment and proportioning unit, a scale inhibition performance testing unit, and a multi-objective analysis and decision-making unit. Therefore, before optimizing the circulating water scale inhibitor formulation, this invention fully considers the applicability of basic chemical raw materials under different water quality conditions. Thus, a water quality parameter report is first obtained. Based on this report and the raw material pretreatment and proportioning unit, the basic chemical raw materials are screened and set to obtain an effective raw material component set and an initial constraint boundary set. Based on this set, a simplex centroid design is performed to obtain multiple basic mixing formulations, thereby avoiding the problems associated with traditional methods. The invention significantly improves the systematicness and efficiency of formulation design by eliminating the waste of raw materials and the blindness of experiments in the trial-and-error method. Based on the multiple sets of basic mixing formulations, multiple sets of test samples are prepared. The scale inhibition performance testing unit measures each of these samples to obtain scale inhibition rate and corrosion inhibition rate datasets. It is evident that after the initial formulation design, the invention simultaneously quantifies both scale inhibition rate and corrosion inhibition rate through the scale inhibition performance testing unit. Therefore, based on the multi-objective analysis and decision-making unit, the scale inhibition rate dataset, and the corrosion inhibition rate dataset, a theoretical comprehensive performance index is calculated. Based on this index, the theoretically optimal formulation and maximum performance index are obtained, thus resolving the contradiction that optimizing a single indicator can easily lead to a decrease in another performance. The multi-objective analysis and decision-making unit achieves a balanced and optimal performance for scale inhibition and corrosion inhibition, laying a reliable theoretical foundation for subsequent practical verification. Water circulation experiments were conducted based on the theoretically optimal formula to obtain the dynamic scaling amount and the weight loss value of the coupon. The actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate were calculated based on the dynamic scaling amount and the actual dynamic corrosion inhibition rate. The actual comprehensive performance index was calculated based on the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate. It can be seen that the embodiments of the present invention also consider the difference between laboratory static testing and industrial dynamic operating environment. Therefore, the theoretically optimal formula is dynamically verified through real water circulation experiments, and the actual comprehensive performance index is calculated, thereby ensuring the effectiveness of the theoretical results under actual working conditions. The actual comprehensive performance index is compared with the maximum performance index to obtain the performance verification deviation value. If the performance verification deviation value is less than the preset deviation threshold, the theoretically optimal formula is confirmed as the target formula. Based on the target formula, the formulation optimization of the circulating water scale inhibitor of the basic chemical raw materials is realized. It can be seen that after completing the theoretical optimization, the present invention introduces the key judgment mechanism of performance verification deviation value. Therefore, the target formula is confirmed only when the deviation between theory and reality is within a controllable range, thereby avoiding the risk of the theoretically optimal formula failing in industrial applications and realizing closed-loop control from "theoretically optimal" to "engineering usability".Therefore, the present invention can improve the accurate optimization and reliable application of scale inhibitor formulations for circulating water. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating a method for optimizing the formulation of a circulating water scale inhibitor based on mixing design, according to an embodiment of the present invention.

[0064] Figure 2 This is a functional block diagram of a circulating water scale inhibitor formulation optimization system based on mixing design, provided in an embodiment of the present invention.

[0065] Figure 3 This is a schematic diagram of an electronic device for implementing the method for optimizing the formulation of a circulating water scale inhibitor based on mixing design, according to an embodiment of the present invention.

[0066] Explanation of reference numerals in the attached figures:

[0067] 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0068] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0069] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0070] This application provides a method for optimizing the formulation of a circulating water scale inhibitor based on a mixing design. The execution entity of this method includes, but is not limited to, at least one electronic device that can be configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal device or a server device, and the software may be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0071] Reference Figure 1 The diagram shown is a flowchart illustrating a method for optimizing the formulation of a circulating water scale inhibitor based on mixing design, according to an embodiment of the present invention. In this embodiment, the method for optimizing the formulation of a circulating water scale inhibitor based on mixing design includes:

[0072] S1. Confirm receipt of scale inhibitor formulation optimization instruction, and confirm formulation optimization environment based on scale inhibitor formulation optimization instruction. The formulation optimization environment includes formulation optimization system and basic chemical raw materials. The formulation optimization system includes raw material pretreatment and proportioning unit, scale inhibition performance testing unit and multi-objective analysis and decision unit.

[0073] It should be explained that the scale inhibitor formulation optimization instruction refers to the instruction issued by personnel who want to optimize the formulation of circulating water scale inhibitors; the formulation optimization environment refers to the necessary environment for achieving formulation optimization; and the formulation optimization system refers to a system capable of optimizing the formulation of circulating water scale inhibitors. The formulation optimization system includes a raw material pretreatment and proportioning unit, a scale inhibition performance testing unit, and a multi-objective analysis and decision-making unit. For the specific application of these units, please refer to subsequent embodiments. The basic chemical raw materials refer to various chemical agents used in the preparation of scale inhibitors, such as polyaspartic acid, hydrolyzed polymaleic anhydride, polyepoxysuccinic acid, and organophosphonic acids. The purpose of this invention is to improve the screening efficiency, accuracy, and overall performance of optimal formulations for circulating water scale inhibitors.

[0074] For example, Xiao Zhang is a worker in a chemical reagent factory. In order to improve the screening efficiency, accuracy and overall performance of the optimal formula for circulating water scale inhibitors, Xiao Zhang issued a scale inhibitor formula optimization instruction and confirmed the formula optimization environment.

[0075] S2. Obtain water quality parameter reports, and screen and set the basic chemical raw materials based on the water quality parameter reports and the raw material pretreatment and proportioning unit to obtain an effective raw material component set and an initial constraint boundary set. Perform simplex centroid design based on the effective raw material component set and the initial constraint boundary set to obtain multiple sets of basic mixing formulas.

[0076] Furthermore, the process of obtaining a water quality parameter report, and based on the water quality parameter report and the raw material pretreatment and proportioning unit, screening and setting the basic chemical raw materials to obtain an effective raw material component set and an initial constraint boundary set, includes:

[0077] The water quality parameter report is analyzed based on the raw material pretreatment and proportioning unit to obtain key water quality characteristics, including calcium ion concentration, total alkalinity, sulfate concentration and reference operating temperature.

[0078] Based on the calcium ion concentration, total alkalinity, and the basic chemical raw materials, an adaptation analysis is performed to obtain an effective raw material component set, wherein the effective raw material component set includes multiple effective raw material components;

[0079] For each effective raw material component in the set of effective raw material components, the following operation is performed:

[0080] Static scale inhibition and solubility tests were conducted on the effective raw material components using the key water quality characteristics to obtain the lowest synergistic concentration and the highest solubility limit.

[0081] An independent constraint interval is constructed by using the lowest synergistic concentration as the lower limit and the highest solubility limit as the upper limit;

[0082] By summing up the independent constraint intervals corresponding to the multiple effective raw material components, an initial constraint boundary set is obtained.

[0083] It should be understood that the method for analyzing the water quality parameter report based on the raw material pretreatment and proportioning unit refers to using the raw material pretreatment and proportioning unit to extract, identify, and organize the data in the water quality parameter report to obtain key water quality characteristics. The raw material pretreatment and proportioning unit refers to a functional unit used to analyze the water quality parameter report and screen effective raw material components. Optionally, the raw material pretreatment and proportioning unit can be constructed using a programmable logic controller (PLC), an automatic batching control device, etc. The water quality test report refers to a water quality parameter file containing indicators such as calcium ion concentration, total alkalinity, sulfate concentration, and operating temperature, generated after water quality testing of circulating water (which can be done using a water quality analyzer). The circulating water refers to water samples recycled and reused in industrial production, which contain scale-forming ions such as calcium, alkalinity, and sulfate. The key water quality characteristics refer to the water quality data of the circulating water extracted from the circulating water quality test report, including calcium ion concentration, total alkalinity, sulfate concentration and reference operating temperature. The calcium ion concentration refers to the mass concentration of calcium ions in the circulating water, the total alkalinity refers to the total concentration of alkaline substances in the circulating water, the sulfate concentration refers to the mass concentration of sulfate ions in the circulating water, and the reference operating temperature refers to the standard operating temperature when the circulating water system is operating stably. The method for compatibility analysis based on the calcium ion concentration, total alkalinity, and basic chemical raw materials refers to using the calcium ion concentration and total alkalinity as the current water quality conditions, and performing a chemical compatibility assessment on each basic chemical raw material one by one. Raw materials that will not undergo severe precipitation reactions and can maintain their original chemical properties under the current water quality conditions are screened out. For example, if the calcium ion concentration is 400 mg / L and the total alkalinity is 250 mg / L, if the calcium tolerance of a certain basic chemical raw material (referring to the maximum allowable calcium ion concentration that the basic chemical raw material can maintain without forming precipitation or gelation with calcium ions in the water body, such as 500 mg / L) is higher than the current calcium ion concentration, and its alkalinity adaptation range (such as 100-400 mg / L) can cover the current total alkalinity, then it is determined to be compatible and retained. The effective raw material component set refers to the collection of multiple effective raw material components obtained after the above adaptation analysis. The multiple effective raw material components refer to a variety of basic chemical raw materials that, after the above adaptation analysis, are determined to not undergo serious precipitation reactions under the current water quality conditions (calcium ion concentration, total alkalinity) and can maintain their original chemical properties, such as polyaspartic acid and hydrolyzed polymaleic anhydride.The method for conducting static scale inhibition and solubility tests on the effective raw material components using the key water quality characteristics refers to adding each effective raw material component to circulating water under current water quality conditions according to a preset concentration gradient, and determining the maximum concentration at which the effective raw material component maintains chemical stability and does not precipitate calcium salts under the current water quality conditions, as well as the minimum concentration at which the effective raw material component can exert a scale inhibition effect. The minimum synergistic concentration and the maximum solubility limit refer to the maximum concentration at which the effective raw material component maintains chemical stability and does not precipitate calcium salts under the current water quality conditions, and the minimum concentration at which it can exert a scale inhibition effect, respectively. The method for constructing independent constraint intervals using the minimum synergistic concentration as the lower limit and the maximum solubility limit as the upper limit refers to using the minimum synergistic concentration and the maximum solubility limit as the upper and lower limits of the interval, respectively, to obtain an independent concentration constraint interval corresponding to a certain effective raw material component. The independent constraint interval refers to the concentration interval of the effective raw material component constructed with the minimum synergistic concentration as the lower limit and the maximum solubility limit as the upper limit. The initial constraint boundary set refers to the set of independent constraint intervals corresponding to multiple effective raw material components.

[0084] It should be explained that the simplex centroid design based on the effective raw material component set and the initial constraint boundary set yields multiple sets of basic mixing formulations, including:

[0085] The sum of lower limits is calculated based on the lower limits of each independent constraint interval in the initial constraint boundary set, and the free allocation ratio margin is calculated based on the sum of the lower limits.

[0086] Based on the effective raw material component set, multiple arbitrary real proportions are obtained. Based on the free allocation ratio margin and the lower limit of each independent constraint interval in the initial constraint boundary set, a virtual variable transformation relationship is established. Based on the established virtual variable transformation relationship and multiple arbitrary real proportions, multiple virtual variables are constructed to obtain multiple virtual variables. Based on the multiple virtual variables, a virtual variable space is obtained.

[0087] An initial set of experimental points is obtained based on the virtual variable space, wherein the initial set of experimental points includes vertices, edge midpoints, and center points of the virtual variable space;

[0088] Based on the virtual variable transformation relationship, the initial experimental point set is inversely transformed to obtain multiple initial mixing formulations, wherein each of the multiple initial mixing formulations includes multiple effective raw material component mass fractions.

[0089] Based on the upper limit of each independent constraint interval in the initial constraint boundary set, the multiple initial mixing formulations are screened to obtain multiple sets of basic mixing formulations.

[0090] Furthermore, the method of calculating the sum of lower limits based on the lower limits of each independent constraint interval in the initial constraint boundary set refers to adding the lower limits of each independent constraint interval in the initial constraint boundary set to obtain the sum of lower limits. The method of calculating the free allocation ratio margin based on the sum of lower limits refers to dividing the sum of lower limits by a preset benchmark dosage to obtain a mass fraction, and then subtracting the mass fraction from the total amount (i.e., 1) to obtain the free allocation ratio margin. The benchmark dosage refers to the total concentration value of all effective raw material components when preparing the test sample. For example, the minimum synergistic concentration of effective raw material component A is 8 mg / L, the minimum synergistic concentration of component B is 12 mg / L, and the preset benchmark dosage is 100 mg / L. The minimum synergistic concentrations of each component are added to obtain the sum of lower limits, and then the sum of lower limits is divided by the benchmark dosage to obtain a mass fraction of 0.2. Subtracting this mass fraction from the total amount 1 yields a free allocation ratio margin of 0.8. The method for obtaining multiple arbitrary true proportions based on the effective raw material component set refers to determining the addition ratio of multiple effective raw material components in the effective raw material component set based on a preset ratio. For example, for an effective raw material component set of three effective raw material components: A, B, and C, a preset ratio is set as 20%, 30%, and 50%. The ratio is arranged and combined to obtain combinations such as (20%, 30%, 50%), (20%, 50%, 30%), (30%, 20%, 50%), (30%, 50%, 20%), (50%, 20%, 30%), and (50%, 30%, 20%). These different arrangements are then allocated to the three effective raw material components A, B, and C respectively to obtain 6 proportions. The first group is: A ratio of 20%, B ratio of 30%, and C ratio of 40%, and so on. The arbitrary true proportion refers to a specific ratio composed of the mass fractions of multiple effective raw material components. The method of establishing a virtual variable transformation relationship based on the free allocation ratio margin and the lower limit of each independent constraint interval in the initial constraint boundary set, and constructing the system based on the established virtual variable transformation relationship and multiple arbitrary real proportions, refers to first using the lower limit of the independent constraint interval of each effective raw material component and the free allocation ratio margin to establish a mathematical transformation formula between the virtual variable and the virtual variable, i.e., the virtual variable transformation relationship, as shown below:

[0091]

[0092] in, Represents a dummy variable. This represents the proportion of a specific effective raw material component in any actual formula. It represents the lower limit (in mass fraction form) of a certain effective raw material component in any real proportion. This represents the remaining margin of the free allocation ratio. Next, select an arbitrary set of actual proportions, and substitute the proportions corresponding to different effective raw material components in these actual proportions into this conversion relationship (because the lower limits of different effective raw material components are different, the dummy variable conversion relationship is also different). Calculate these separately to obtain multiple dummy variables that correspond one-to-one with each effective raw material component. For example, the effective raw material component set includes components A, B, and C, with independent constraint intervals of 8 mg / L, 12 mg / L, and 10 mg / L for each component, and a baseline dosage of 100 mg / L. The calculated mass fractions of each component are 0.08, 0.12, and 0.1, and the remaining margin of the free allocation ratio is 0.7. For any actual proportions of 25%, 35%, and 40% (corresponding to components A, B, and C respectively), substitute these values ​​into the above formula to calculate the corresponding dummy variables for each component: 0.2429, 0.3286, and 0.4285 (0.2429 + 0.3286 + 0.4285 = 1). The dummy variable transformation relationship refers to the one-to-one mathematical mapping relationship between the quality score of the real component proportion and the dummy variable established through the above steps. The dummy variable refers to the proportion of each component in the real component proportion obtained by the above dummy variable transformation relationship. The method for obtaining the virtual variable space based on the multiple virtual variables refers to aggregating the calculated virtual variables (the virtual variables of each component corresponding to any real proportion are grouped together) and forming the feasible region (i.e., virtual variable space) of the simplex in the corresponding dimensional coordinate system (n effective raw material components correspond to n-1 dimensional space). The virtual variable space refers to the feasible region of the simplex constructed by the multiple virtual variables. The method for obtaining the initial experimental points based on the virtual variable space refers to selecting specific points of the simplex as the initial experimental point set. The initial experimental point set refers to each vertex of the simplex, the midpoint of each edge, and the overall geometric center point (each of the above points is a set of specific coordinate values ​​(i.e., a set of virtual variables) in the virtual variable space, and each set of virtual variables corresponds to a unique real mixing formula through subsequent inverse transformation). The method for performing an inverse transformation on the initial experimental point set based on the virtual variable transformation relationship to obtain multiple initial mixing formulas refers to performing an inverse transformation on the initial experimental point set using the above transformation formula to restore it to the real mass fraction of each effective raw material component. The initial mixing formula refers to the mass fraction of multiple effective raw material components obtained by the above restoration. The method of screening the multiple initial mixing formulations based on the upper limit of each independent constraint interval in the initial constraint boundary set refers to the fact that since the simplex experimental points mentioned above can only exclude formulations with the mass fraction of each component below the lower limit, and the upper limit of the mass fraction of each component is not verified, the upper limit corresponding to the effective raw material components is used to screen out the initial mixing formulations with the mass fraction of each component below the upper limit. The basic mixing formulation refers to the mass fraction of the multiple effective raw material components obtained through the above screening.For example, the mass fractions of each effective raw material component in the initial mixing formula are: A=0.25, B=0.35, C=0.4, and the upper limits of effective raw material components A, B, and C are 0.35, 0.4, and 0.6, respectively (obtained by dividing by the baseline total amount). If the mass fraction of each effective raw material component is less than the upper limit, then the initial mixing formula is confirmed as the basic mixing formula.

[0093] S3. Based on the multiple sets of basic mixing formulas, mix and prepare multiple sets of test samples. Measure the multiple sets of test samples using the scale inhibition performance testing unit to obtain scale inhibition rate datasets and corrosion inhibition rate datasets.

[0094] It should be explained that the process involves mixing and formulating multiple sets of basic mixtures to obtain multiple sets of test samples. The scale inhibition performance testing unit then measures each of these multiple sets of test samples to obtain scale inhibition rate datasets and corrosion inhibition rate datasets, including:

[0095] Based on the aforementioned multiple sets of basic mixture formulations, the mixtures were weighed and proportioned to obtain multiple sets of test samples. The following operations were performed on each of the multiple sets of test samples:

[0096] Based on the scale inhibition performance testing unit, the multiple groups of test samples are added to the pre-constructed circulating water for reaction, and the circulating water is filtered to obtain filtrate.

[0097] The calcium ion concentration of the filtrate is measured to obtain the residual calcium ion concentration. The scale inhibition rate of a single group is calculated based on the preset initial calcium ion concentration and the residual calcium ion concentration. The scale inhibition rates of the single groups of samples to be tested are summarized to obtain the scale inhibition rate dataset.

[0098] The multiple sets of test samples were added to the circulating water, and polarization scanning was performed using a pre-constructed electrochemical workstation to obtain the corrosion current density.

[0099] Obtain the control corrosion current density, calculate the single-group corrosion inhibition rate based on the control corrosion current density and the corrosion current density, and summarize the single-group corrosion inhibition rates corresponding to the multiple groups of test samples to obtain the corrosion inhibition rate dataset.

[0100] Furthermore, the method of obtaining multiple sets of test samples based on the multiple sets of basic mixture formulations involves accurately weighing each effective raw material component according to its mass fraction in the multiple sets of basic mixture formulations using an electronic balance, and dissolving them in a fixed volume of deionized water according to the specified proportions to prepare solutions with consistent concentrations. The multiple sets of test samples refer to scale inhibitor solution samples, each corresponding to a different basic mixture formulation, obtained through the above-mentioned weighing and proportioning. The method of adding the multiple sets of test samples to pre-constructed circulating water for reaction based on the scale inhibition performance testing unit, and then filtering the circulating water, involves adding each set of test samples to simulated circulating water (standard hard water containing known calcium ion concentrations, total alkalinity, and other scale-forming ions) at a fixed concentration, stirring and reacting under constant temperature water bath conditions for a period of time (e.g., 2 hours) to allow the scale inhibitor to fully react with the calcium ions, and then filtering through a 0.45μm filter membrane to remove the generated precipitate, obtaining a clear filtrate. The scale inhibition performance testing unit refers to a functional module specifically designed to evaluate the static scale inhibition performance (scale inhibition rate and corrosion inhibition rate) of scale inhibitors. Optionally, the scale inhibition performance testing unit can be constructed using devices such as a titration analyzer and a filter. The pre-constructed circulating water refers to simulated circulating cooling water containing scale-forming ions such as calcium ions and bicarbonate ions, prepared according to water quality parameter reports. The filtrate refers to the clear solution obtained after filtration following the reaction. The method for determining the calcium ion concentration in the filtrate refers to using EDTA complexometric titration to determine the concentration of residual calcium ions in the filtrate. The residual calcium ion concentration refers to the concentration of calcium ions in the filtrate that have not formed a precipitate after the reaction. The method for calculating the scale inhibition rate of a single group based on a preset initial calcium ion concentration and the residual calcium ion concentration refers to calculating the scale inhibition efficiency of each sample group according to the formula: Single group scale inhibition rate = [(initial calcium ion concentration - residual calcium ion concentration) / initial calcium ion concentration]. The single group scale inhibition rate refers to the percentage of scale inhibition corresponding to a single test sample. The scale inhibition rate dataset refers to the set of single group scale inhibition rates corresponding to all test samples. The method of adding the multiple sets of test samples to the circulating water and performing polarization scanning using a pre-constructed electrochemical workstation involves immersing a carbon steel electrode in the circulating water containing the test samples, and using the electrochemical workstation to perform potentiodynamic polarization curve scanning to obtain the corrosion current density value. The electrochemical workstation refers to an instrument used for electrochemical corrosion testing; optionally, a CHI660E or Gamry electrochemical workstation can be used. The corrosion current density is the value obtained by dividing the corrosion current obtained through polarization curve extrapolation by the electrode area, expressed in μA / cm². The method of obtaining the control corrosion current density involves performing the same polarization scanning test on the same carbon steel electrode in blank circulating water without any test samples added, obtaining the corrosion current density under blank conditions. The control corrosion current density refers to the corrosion current density measured in the blank experiment.The method for calculating the single-group corrosion inhibition rate based on the control corrosion current density and the corrosion current density refers to calculating the corrosion inhibition efficiency of each group of samples according to the formula: Single-group corrosion inhibition rate = [(control corrosion current density - corrosion current density)] / control corrosion current density]. The single-group corrosion inhibition rate refers to the percentage of corrosion inhibition corresponding to a single group of tested samples. The corrosion inhibition rate dataset refers to the collection of single-group corrosion inhibition rates corresponding to all tested samples.

[0101] For example, 27 basic mixture formulations are available. Each effective raw material component is weighed and prepared into 200 mL test samples. Each sample is added to simulated circulating water containing 500 mg / L calcium ions, reacted at a constant temperature of 60℃ for 3 hours, and then filtered. The remaining calcium ion concentration is measured, and the scale inhibition rate dataset {78.5%, 85.2%, …, 92.1%} is calculated. Simultaneously, samples are added to circulating water, and polarization scanning is performed using an electrochemical workstation to measure the corrosion current density of each sample. This value is compared with the blank control value (12.5 μA / cm²) to calculate the corrosion inhibition rate dataset {65.3%, 71.8%, …, 88.6%}.

[0102] S4. Calculate the theoretical comprehensive performance index based on the multi-objective analysis decision unit, scale inhibition rate dataset, and corrosion inhibition rate dataset, and obtain the theoretical optimal formula and maximum performance index based on the theoretical comprehensive performance index.

[0103] It should be understood that the calculation of the theoretical comprehensive performance index based on the multi-objective analysis decision unit, the scale inhibition rate dataset, and the corrosion inhibition rate dataset, and the acquisition of the theoretical optimal formula and the maximum performance index based on the theoretical comprehensive performance index, includes:

[0104] Based on the multi-objective analysis and decision-making unit, multiple raw material energy consumption parameters are obtained. Based on the multiple sets of basic mixture formulations and the multiple raw material energy consumption parameters, multiple formulation energy consumptions are calculated. Based on the multiple formulation energy consumptions, the maximum energy consumption and minimum energy consumption are obtained. For each set of basic mixture formulations, the following operations are performed:

[0105] Based on the maximum energy consumption, minimum energy consumption, energy consumption of the formula corresponding to the basic mixture formula, scale inhibition rate of a single group, and corrosion inhibition rate of a single group, the theoretical comprehensive performance index is calculated as follows:

[0106]

[0107] in, Indicates the theoretical comprehensive performance index. Indicates the scale inhibition rate of a single group. Indicates the corrosion inhibition rate of a single group. This indicates the energy consumption of the basic mixture formulation. Indicates minimum energy consumption. Indicates maximum energy consumption. Indicates the energy consumption coefficient. , Indicates the weighting coefficient;

[0108] The maximum performance index is obtained based on the theoretical comprehensive performance index corresponding to each of the multiple sets of basic mixture formulations, and the basic mixture formulation corresponding to the maximum performance index is confirmed as the theoretically optimal formulation.

[0109] It should be explained that the method of obtaining multiple raw material energy consumption parameters based on the multi-objective analysis and decision-making unit refers to the multi-objective analysis and decision-making unit directly calling the energy consumption parameter value corresponding to each effective raw material component from the built-in database. The built-in database refers to the database used to pre-store relevant parameters. The raw material energy consumption parameter refers to the energy consumption value corresponding to each effective raw material component when used in the formulation. The multi-objective analysis and decision-making unit refers to the functional module used to calculate and screen the theoretically optimal formulation from multiple sets of basic mixture formulations. The method of calculating the energy consumption of multiple formulations based on the multiple sets of basic mixture formulations and the multiple raw material energy consumption parameters refers to multiplying the mass fraction of each effective raw material component in each set of basic mixture formulations by the corresponding raw material energy consumption parameter and then summing the results to obtain the total energy consumption value of that set of formulations. The formulation energy consumption refers to the total energy consumption value corresponding to a single set of basic mixture formulations. The method of obtaining the maximum and minimum energy consumption based on the multiple formulation energy consumptions refers to sorting the energy consumption of all sets of formulations and taking out the maximum and minimum values. The maximum energy consumption refers to the highest value among all formulation energy consumptions, and the minimum energy consumption refers to the lowest value among all formulation energy consumptions. The method for calculating the theoretical comprehensive performance index based on the maximum energy consumption, minimum energy consumption, the formula energy consumption corresponding to the basic mixture formula, the scale inhibition rate, and the corrosion inhibition rate of a single group refers to substituting the above parameters into the formula to calculate the D value; the weighting coefficient is the weighted positive contribution of the scale inhibition rate and the corrosion inhibition rate (e.g., 0.4, 0.6), and the energy consumption coefficient is a coefficient used to control the degree of reduction in the score of the high-energy-consumption basic mixture formula, for example, 0.5 or 2. The theoretical comprehensive performance index is a score that comprehensively considers the scale inhibition effect, corrosion inhibition effect, and energy consumption. As can be seen from the formula, the larger the single-group scale inhibition rate and the single-group corrosion inhibition rate, the larger the theoretical comprehensive performance index. Since the single-group scale inhibition rate and the single-group corrosion inhibition rate are positively correlated indicators that characterize the performance of the formula, the larger the value of the theoretical comprehensive performance index, the better the overall performance of the formula. The method of obtaining the maximum performance index based on the theoretical comprehensive performance index corresponding to each of the multiple sets of basic mixture formulations, and confirming the basic mixture formulation corresponding to the maximum performance index as the theoretically optimal formulation, refers to traversing all theoretical comprehensive performance indices, extracting the maximum value and its corresponding basic mixture formulation. The maximum performance index refers to the largest value among all theoretical comprehensive performance indices, and the theoretically optimal formulation refers to the basic mixture formulation (including the mass fraction of each effective raw material component) corresponding to the maximum performance index.For example, the energy consumption range of the 27 basic mixture formulations is calculated to be [2.85, 4.12]. For one of the formulations (scale inhibition rate 92.1%, corrosion inhibition rate 88.6%, formulation energy consumption 3.25), substituting into the formula (ω1=0.45, ω2=0.45, γ=0.2) yields D=0.872. After traversing all groups, the maximum performance index is 0.918, and the 14th basic mixture formulation is confirmed as the theoretically optimal formulation (polyaspartic acid 42.3%, hydrolyzed polymaleic anhydride 31.7%, polyepoxysuccinic acid 26.0%).

[0110] S5. Based on the theoretically optimal formula, a water circulation experiment is conducted to obtain the dynamic scaling amount and the weight loss value of the coupon. Based on the dynamic scaling amount and the weight loss value of the coupon, the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate are calculated. Based on the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate, the actual comprehensive performance index is calculated.

[0111] It should be understood that the water circulation experiment based on the theoretically optimal formula, to obtain the dynamic scaling amount and the weight loss value of the coupon, includes:

[0112] Based on the theoretically optimal formula, a scale inhibitor solution was obtained, and a circulating cooling water test pipeline was obtained. The circulating cooling water test pipeline includes an inner wall of the test pipe and carbon steel plates. A circulating water experiment was conducted based on a preset operating cycle time, the circulating cooling water test pipeline, and the scale inhibitor solution to obtain the inner wall of the test pipe and the carbon steel plates after the experiment.

[0113] After the test, the attached dirt on the inner wall of the test tube was peeled off to obtain a collection liquid. The collection liquid was dried and weighed to obtain the dynamic scale amount. The carbon steel hanging plate after the test was weighed to obtain the weight loss value of the hanging plate.

[0114] It should be explained that the method for obtaining the scale inhibitor solution based on the theoretically optimal formula refers to accurately weighing each component using an electronic balance according to the precise mass fraction of each effective raw material component in the theoretically optimal formula, dissolving them in deionized water, and preparing a uniform scale inhibitor solution of a specified concentration. The scale inhibitor solution refers to the working fluid of the scale inhibitor prepared from the theoretically optimal formula for dynamic verification. The method for obtaining the circulating cooling water test pipeline refers to preparing a closed-loop circulating pipeline from a laboratory or pilot-scale platform. The circulating cooling water test pipeline refers to a pipeline device simulating industrial circulating cooling water, composed of carbon steel pipes. The inner wall of the test pipe refers to the inner surface of the pipe section used for observing and collecting scale, and the carbon steel hanging plates refer to carbon steel test pieces suspended in the pipeline according to the standard (GB / T 18175) to simulate metal corrosion. The method for conducting circulating water experiments based on a preset operating cycle duration, circulating cooling water test pipeline, and scale inhibitor solution involves adding the scale inhibitor solution to the circulating cooling water at a fixed concentration and continuously circulating it under constant flow and temperature conditions for a preset operating cycle duration (e.g., 15 days) to simulate dynamic working conditions in an industrial setting. The operating cycle duration refers to the continuous operating time set for the experiment. The inner wall of the test pipe after the experiment refers to the inner wall of the test pipe section removed from the pipeline after the operation. The carbon steel hanging plate after the experiment refers to the carbon steel hanging plate removed from the pipeline after the operation. The method for removing the scale adhering to the inner wall of the test pipe after the experiment involves using a mechanical scraper or chemical cleaning agent to completely remove the scale layer adhering to the inner wall of the pipe and collecting it in a container to obtain a collection liquid containing the scale layer. The collection liquid refers to the mixture of the removed scale layer and the cleaning liquid. The method for drying and weighing the collection liquid to obtain the dynamic scale amount involves placing the collection liquid in a 105℃ oven to dry to constant weight and then weighing it; the obtained mass is the dynamic scale amount. The dynamic scaling amount refers to the total mass of the scale layer actually formed on the inner wall of the pipe under dynamic circulation conditions. The method for weighing the carbon steel pads after the test involves washing and drying the carbon steel pads with deionized water, weighing them, and comparing their mass with the mass before the test to obtain the mass reduction value. The weight loss value of the pads refers to the mass (in grams) lost by the carbon steel pads due to corrosion during the experiment.

[0115] Furthermore, the calculation of the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate based on the dynamic scale accumulation and the weight loss of the coupon includes:

[0116] Water circulation experiments were conducted based on a pre-constructed blank control group to obtain the blank scaling amount and blank coupon weight loss value.

[0117] The actual dynamic scale inhibition rate is calculated based on the dynamic scale amount and the blank scale amount.

[0118] The actual dynamic corrosion inhibition rate is calculated based on the weight loss value of the pre-coated piece and the weight loss value of the blank pre-coated piece.

[0119] It should be understood that the method of obtaining the blank scaling amount and blank coupon weight loss value based on the pre-constructed blank control group water circulation experiment refers to conducting parallel experiments using only blank circulating water under the same conditions of circulating cooling water test pipeline and operating cycle duration, without adding any scale inhibitor solution, to obtain the scaling amount and coupon weight loss value under blank conditions; the blank control group refers to the control experimental group without adding scale inhibitor. The blank scaling amount refers to the mass of the scale layer formed on the inner wall of the test pipe in the blank experiment, and the blank coupon weight loss value refers to the mass lost by the carbon steel coupon in the blank experiment. The method of calculating the actual dynamic scale inhibition rate based on the dynamic scaling amount and the blank scaling amount refers to calculating according to the formula: actual dynamic scale inhibition rate = [(blank scaling amount - dynamic scaling amount)] / blank scaling amount]; the actual dynamic scale inhibition rate refers to the scale inhibition efficiency (percentage) actually performed by the scale inhibitor under dynamic circulation conditions. The method for calculating the actual dynamic corrosion inhibition rate based on the weight loss value of the coated plate and the weight loss value of the blank coated plate refers to the calculation according to the formula: Actual dynamic corrosion inhibition rate = [(weight loss value of blank coated plate - weight loss value of coated plate) / weight loss value of blank coated plate]. Considering the actual situation, since no scale inhibitor solution was added to the blank control group (scale inhibitors can prevent or interfere with the precipitation and scaling of sparingly soluble inorganic salts on the metal surface), the measured scale amount and weight loss value of the blank coated plate should be greater than the dynamic scale amount and weight loss value of the coated plate, respectively. That is, the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate are always greater than zero. The actual dynamic corrosion inhibition rate refers to the actual corrosion inhibition efficiency (percentage) achieved by the scale inhibitor under dynamic circulation conditions. The method for calculating the actual comprehensive performance index based on the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate is similar to the method for calculating the theoretical comprehensive performance index, and will not be elaborated here. The actual comprehensive performance index refers to the comprehensive performance score calculated based on the measured data of the above experiments, used for subsequent verification and comparison with the theoretical maximum performance index.

[0120] For example, a scale inhibitor solution was prepared using the theoretically optimal formula and added to a circulating cooling water test pipeline (test pipe inner diameter 20 mm, carbon steel fin area 50 cm²). A dynamic circulation experiment was conducted with an operating cycle of 14 days, a flow rate of 1.5 m / s, and a temperature of 45℃. After the experiment, the scale layer on the inner wall of the test pipe was peeled off, dried, and weighed to obtain a dynamic scale amount of 1.85 g / m². The dynamic scale amount of the blank control group was 12.6 g / m², and the actual dynamic scale inhibition rate was calculated to be 85.3%. The weight loss of the carbon steel fin was 0.012 g, and the weight loss of the blank fin was 0.085 g, and the actual dynamic corrosion inhibition rate was calculated to be 85.9%.

[0121] S6. The actual comprehensive performance index is compared with the maximum performance index to obtain the performance verification deviation value. If the performance verification deviation value is less than the preset deviation threshold, the theoretical optimal formula is confirmed as the target formula. Based on the target formula, the formulation optimization of the circulating water scale inhibitor of the basic chemical raw materials is realized.

[0122] It should be explained that the comparison of the actual comprehensive performance index with the maximum performance index to obtain the performance verification deviation value includes:

[0123] Obtain the ambient temperature fluctuation parameters, and calculate the performance verification deviation value based on the actual comprehensive performance index, maximum performance index, ambient temperature fluctuation parameters, and reference operating temperature. The calculation formula is as follows:

[0124]

[0125] in, This indicates the performance verification deviation value. Indicates the actual overall performance index. Indicates the maximum performance index. This represents a parameter indicating ambient temperature fluctuations. Indicates the reference operating temperature. , All of these represent performance verification weighting coefficients.

[0126] Furthermore, the method for obtaining the ambient temperature fluctuation parameter refers to the maximum fluctuation range of the ambient temperature recorded in real time during the aforementioned water circulation experiment. The ambient temperature fluctuation parameter refers to the maximum temperature value at which the circulating water temperature deviates from the baseline operating temperature during the experiment. The method for calculating the performance verification deviation value based on the actual comprehensive performance index, the maximum performance index, the ambient temperature fluctuation parameter, and the baseline operating temperature refers to substituting the actual comprehensive performance index, the maximum performance index, the ambient temperature fluctuation parameter, and the baseline operating temperature into the aforementioned formula for calculation. The performance verification deviation value is a quantitative indicator that comprehensively reflects the difference between theoretical static performance and actual dynamic performance, as well as the influence of ambient temperature; the smaller the value, the more reliable the theoretically optimal formula is under real-world conditions. The preset deviation threshold refers to the maximum acceptable deviation limit pre-set by the system (e.g., 0.05, determined according to engineering accuracy requirements).

[0127] It should be understood that the step of confirming the theoretically optimal formula as the target formula if the performance verification deviation value is less than a preset deviation threshold includes:

[0128] Compare the performance verification deviation value with the deviation threshold. If the performance verification deviation value is greater than the deviation threshold, then obtain an updated experimental point set based on the virtual variable space, use the updated experimental point set as the initial experimental point set, and return to the step of performing an inverse transformation on the initial experimental point set based on the virtual variable transformation relationship until the performance verification deviation value is less than or equal to the deviation threshold. Then, the theoretically optimal formula is confirmed as the target formula.

[0129] If the performance verification deviation value is less than or equal to the deviation threshold, then the theoretically optimal formula is confirmed as the target formula.

[0130] It should be explained that the method of comparing the performance verification deviation value and the deviation threshold refers to comparing the magnitude of the performance verification deviation value and the deviation threshold. If the deviation value is greater than the threshold, iterative optimization is performed; otherwise, the theoretically optimal formulation is directly confirmed as the target formulation. The method of obtaining an updated experimental point set based on the dummy variable space if the performance verification deviation value is greater than the deviation threshold refers to generating a new set of experimental points in the dummy variable space using Latin hypercube sampling or uniform design methods. The updated experimental point set refers to a newly generated set of experimental points with wider coverage or higher density. The method of using the updated experimental point set as the initial experimental point set and returning to the step of performing an inverse transformation of the initial experimental point set based on the dummy variable transformation relationship refers to substituting the new experimental point set back into the dummy variable transformation relationship for inverse transformation, generating a new basic mixing formulation, and re-executing subsequent steps until verification is successful. The method of confirming the theoretically optimal formulation as the target formulation refers to determining the formulation as the final applicable target formulation when the performance verification deviation value meets the conditions. The target formulation refers to the final optimized formulation that has been dynamically verified and confirmed, containing the precise mass fractions of each effective raw material component.

[0131] Beneficial Effects: To address the problems described in the background art, this invention confirms the receipt of scale inhibitor formulation optimization instructions and, based on these instructions, confirms the formulation optimization environment. This environment includes a formulation optimization system and basic chemical raw materials. The system comprises a raw material pretreatment and proportioning unit, a scale inhibition performance testing unit, and a multi-objective analysis and decision-making unit. Therefore, before optimizing the circulating water scale inhibitor formulation, this invention fully considers the applicability of basic chemical raw materials under different water quality conditions. Thus, a water quality parameter report is first obtained. Based on this report and the raw material pretreatment and proportioning unit, the basic chemical raw materials are screened and set to obtain an effective raw material component set and an initial constraint boundary set. Based on this set, a simplex centroid design is performed to obtain multiple basic mixing formulations, thereby avoiding the problems associated with traditional methods. The invention significantly improves the systematicness and efficiency of formulation design by eliminating the waste of raw materials and the blindness of experiments in the trial-and-error method. Based on the multiple sets of basic mixing formulations, multiple sets of test samples are prepared. The scale inhibition performance testing unit measures each of these samples to obtain scale inhibition rate and corrosion inhibition rate datasets. It is evident that after the initial formulation design, the invention simultaneously quantifies both scale inhibition rate and corrosion inhibition rate through the scale inhibition performance testing unit. Therefore, based on the multi-objective analysis and decision-making unit, the scale inhibition rate dataset, and the corrosion inhibition rate dataset, a theoretical comprehensive performance index is calculated. Based on this index, the theoretically optimal formulation and maximum performance index are obtained, thus resolving the contradiction that optimizing a single indicator can easily lead to a decrease in another performance. The multi-objective analysis and decision-making unit achieves a balanced and optimal performance for scale inhibition and corrosion inhibition, laying a reliable theoretical foundation for subsequent practical verification. Water circulation experiments were conducted based on the theoretically optimal formula to obtain the dynamic scaling amount and the weight loss value of the coupon. The actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate were calculated based on the dynamic scaling amount and the actual dynamic corrosion inhibition rate. The actual comprehensive performance index was calculated based on the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate. It can be seen that the embodiments of the present invention also consider the difference between laboratory static testing and industrial dynamic operating environment. Therefore, the theoretically optimal formula is dynamically verified through real water circulation experiments, and the actual comprehensive performance index is calculated, thereby ensuring the effectiveness of the theoretical results under actual working conditions. The actual comprehensive performance index is compared with the maximum performance index to obtain the performance verification deviation value. If the performance verification deviation value is less than the preset deviation threshold, the theoretically optimal formula is confirmed as the target formula. Based on the target formula, the formulation optimization of the circulating water scale inhibitor of the basic chemical raw materials is realized. It can be seen that after completing the theoretical optimization, the present invention introduces the key judgment mechanism of performance verification deviation value. Therefore, the target formula is confirmed only when the deviation between theory and reality is within a controllable range, thereby avoiding the risk of the theoretically optimal formula failing in industrial applications and realizing closed-loop control from "theoretically optimal" to "engineering usability".Therefore, the present invention can improve the accurate optimization and reliable application of scale inhibitor formulations for circulating water.

[0132] like Figure 2 The diagram shown is a functional block diagram of a circulating water scale inhibitor formulation optimization system based on mixing design, provided in an embodiment of the present invention.

[0133] The circulating water scale inhibitor formulation optimization system 100 based on mixing design described in this invention can be installed in an electronic device. Depending on the functions implemented, the circulating water scale inhibitor formulation optimization system 100 based on mixing design may include an environmental validation module 101, a formulation design module 102, a performance testing module 103, and a verification output module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and is stored in the memory of the electronic device.

[0134] The environmental confirmation module 101 is used to confirm the receipt of the scale inhibitor formulation optimization instruction and confirm the formulation optimization environment based on the scale inhibitor formulation optimization instruction. The formulation optimization environment includes a formulation optimization system and basic chemical raw materials. The formulation optimization system includes a raw material pretreatment and proportioning unit, a scale inhibition performance testing unit, and a multi-objective analysis and decision-making unit.

[0135] The formulation design module 102 is used to obtain water quality parameter reports, screen and set the basic chemical raw materials based on the water quality parameter reports and the raw material pretreatment and proportioning unit, obtain an effective raw material component set and an initial constraint boundary set, and perform simplex centroid design based on the effective raw material component set and the initial constraint boundary set to obtain multiple sets of basic mixing formulations.

[0136] The performance testing module 103 is used to mix and prepare multiple sets of basic mixing formulas to obtain multiple sets of test samples, and to measure the multiple sets of test samples respectively based on the scale inhibition performance testing unit to obtain scale inhibition rate dataset and corrosion inhibition rate dataset.

[0137] The theoretical comprehensive performance index is calculated based on the multi-objective analysis decision unit, scale inhibition rate dataset, and corrosion inhibition rate dataset, and the theoretical optimal formula and maximum performance index are obtained based on the theoretical comprehensive performance index.

[0138] Water circulation experiments were conducted based on the theoretically optimal formula to obtain the dynamic scaling amount and the weight loss value of the coupon. The actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate were calculated based on the dynamic scaling amount and the weight loss value of the coupon. The actual comprehensive performance index was calculated based on the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate.

[0139] The verification output module 104 is used to compare the actual comprehensive performance index with the maximum performance index to obtain a performance verification deviation value. If the performance verification deviation value is less than a preset deviation threshold, the theoretical optimal formula is confirmed as the target formula. Based on the target formula, the formulation optimization of the circulating water scale inhibitor of the basic chemical raw materials is realized.

[0140] In detail, the modules in the circulating water scale inhibitor formulation optimization system 100 based on mixing design described in this embodiment of the invention adopt the same approach as described above during use. Figure 1 The method described herein is the same as the method for optimizing the formulation of circulating water scale inhibitors based on mixing design, and can produce the same technical effect, so it will not be repeated here.

[0141] like Figure 3 The diagram shown is a schematic diagram of an electronic device for implementing a method for optimizing the formulation of a circulating water scale inhibitor based on mixing design, according to an embodiment of the present invention.

[0142] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a method program for optimizing the formulation of a circulating water scale inhibitor based on mixing design.

[0143] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as the portable hard drive of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a circulating water scale inhibitor formulation optimization method program based on mixing design, but also to temporarily store data that has been output or will be output.

[0144] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a program for optimizing the formulation of a circulating water scale inhibitor based on mixing design), and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0145] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0146] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0147] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0148] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0149] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0150] The program for optimizing the formulation of a circulating water scale inhibitor based on mixing design, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0151] Confirm receipt of scale inhibitor formulation optimization instruction, and confirm formulation optimization environment based on scale inhibitor formulation optimization instruction, wherein the formulation optimization environment includes formulation optimization system and basic chemical raw materials, and the formulation optimization system includes raw material pretreatment and proportioning unit, scale inhibition performance testing unit and multi-objective analysis and decision-making unit;

[0152] Obtain a water quality parameter report, and based on the water quality parameter report and the raw material pretreatment and proportioning unit, screen and set the basic chemical raw materials to obtain an effective raw material component set and an initial constraint boundary set. Based on the effective raw material component set and the initial constraint boundary set, perform simplex centroid design to obtain multiple sets of basic mixing formulations.

[0153] Based on the multiple sets of basic mixing formulas, multiple sets of test samples are prepared by mixing and formulation. The scale inhibition performance test unit is used to measure the multiple sets of test samples respectively to obtain scale inhibition rate dataset and corrosion inhibition rate dataset.

[0154] The theoretical comprehensive performance index is calculated based on the multi-objective analysis decision unit, scale inhibition rate dataset, and corrosion inhibition rate dataset, and the theoretical optimal formula and maximum performance index are obtained based on the theoretical comprehensive performance index.

[0155] Water circulation experiments were conducted based on the theoretically optimal formula to obtain the dynamic scaling amount and the weight loss value of the coupon. The actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate were calculated based on the dynamic scaling amount and the weight loss value of the coupon. The actual comprehensive performance index was calculated based on the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate.

[0156] The actual comprehensive performance index is compared with the maximum performance index to obtain the performance verification deviation value. If the performance verification deviation value is less than the preset deviation threshold, the theoretical optimal formula is confirmed as the target formula. Based on the target formula, the formulation optimization of the circulating water scale inhibitor of the basic chemical raw materials is realized.

[0157] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0158] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0159] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0160] Confirm receipt of scale inhibitor formulation optimization instruction, and confirm formulation optimization environment based on scale inhibitor formulation optimization instruction, wherein the formulation optimization environment includes formulation optimization system and basic chemical raw materials, and the formulation optimization system includes raw material pretreatment and proportioning unit, scale inhibition performance testing unit and multi-objective analysis and decision-making unit;

[0161] Obtain a water quality parameter report, and based on the water quality parameter report and the raw material pretreatment and proportioning unit, screen and set the basic chemical raw materials to obtain an effective raw material component set and an initial constraint boundary set. Based on the effective raw material component set and the initial constraint boundary set, perform simplex centroid design to obtain multiple sets of basic mixing formulations.

[0162] Based on the multiple sets of basic mixing formulas, multiple sets of test samples are prepared by mixing and formulation. The scale inhibition performance test unit is used to measure the multiple sets of test samples respectively to obtain scale inhibition rate dataset and corrosion inhibition rate dataset.

[0163] The theoretical comprehensive performance index is calculated based on the multi-objective analysis decision unit, scale inhibition rate dataset, and corrosion inhibition rate dataset, and the theoretical optimal formula and maximum performance index are obtained based on the theoretical comprehensive performance index.

[0164] Water circulation experiments were conducted based on the theoretically optimal formula to obtain the dynamic scaling amount and the weight loss value of the coupon. The actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate were calculated based on the dynamic scaling amount and the weight loss value of the coupon. The actual comprehensive performance index was calculated based on the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate.

[0165] The actual comprehensive performance index is compared with the maximum performance index to obtain the performance verification deviation value. If the performance verification deviation value is less than the preset deviation threshold, the theoretical optimal formula is confirmed as the target formula. Based on the target formula, the formulation optimization of the circulating water scale inhibitor of the basic chemical raw materials is realized.

[0166] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0167] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0168] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0169] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the formulation of a circulating water scale inhibitor based on mixing design, characterized in that, The method includes: Confirm receipt of scale inhibitor formulation optimization instruction, and confirm formulation optimization environment based on scale inhibitor formulation optimization instruction, wherein the formulation optimization environment includes formulation optimization system and basic chemical raw materials, and the formulation optimization system includes raw material pretreatment and proportioning unit, scale inhibition performance testing unit and multi-objective analysis and decision-making unit; Obtain a water quality parameter report, and based on the water quality parameter report and the raw material pretreatment and proportioning unit, screen and set the basic chemical raw materials to obtain an effective raw material component set and an initial constraint boundary set. Based on the effective raw material component set and the initial constraint boundary set, perform simplex centroid design to obtain multiple sets of basic mixing formulations. Based on the aforementioned multiple sets of basic mixing formulations, multiple sets of test samples are prepared. The scale inhibition performance testing unit measures each of the multiple sets of test samples to obtain scale inhibition rate datasets and corrosion inhibition rate datasets. Specifically, the scale inhibition rate corresponding to each of the multiple sets of test samples is summarized to obtain a scale inhibition rate dataset, and the corrosion inhibition rate corresponding to each of the multiple sets of test samples is summarized to obtain a corrosion inhibition rate dataset. The theoretical comprehensive performance index is calculated based on the multi-objective analysis decision unit, scale inhibition rate dataset, and corrosion inhibition rate dataset, and the theoretical optimal formula and maximum performance index are obtained based on the theoretical comprehensive performance index. The step of calculating the theoretical comprehensive performance index based on the multi-objective analysis decision unit, the scale inhibition rate dataset, and the corrosion inhibition rate dataset, and obtaining the theoretical optimal formula and the maximum performance index based on the theoretical comprehensive performance index, includes: Based on the multi-objective analysis and decision-making unit, multiple raw material energy consumption parameters are obtained. Based on the multiple sets of basic mixture formulations and the multiple raw material energy consumption parameters, multiple formulation energy consumptions are calculated. Based on the multiple formulation energy consumptions, the maximum energy consumption and minimum energy consumption are obtained. For each set of basic mixture formulations, the following operations are performed: Based on the maximum energy consumption, minimum energy consumption, energy consumption of the formula corresponding to the basic mixture formula, scale inhibition rate of a single group, and corrosion inhibition rate of a single group, the theoretical comprehensive performance index is calculated as follows: in, Indicates the theoretical comprehensive performance index. Indicates the scale inhibition rate of a single group. Indicates the corrosion inhibition rate of a single group. This indicates the energy consumption of the basic mixture formulation. Indicates minimum energy consumption. Indicates maximum energy consumption. Indicates the energy consumption coefficient. , Indicates the weighting coefficient; The maximum performance index is obtained based on the theoretical comprehensive performance index corresponding to each of the multiple sets of basic mixture formulations, and the basic mixture formulation corresponding to the maximum performance index is confirmed as the theoretical optimal formulation. Water circulation experiments were conducted based on the theoretically optimal formula to obtain the dynamic scaling amount and the weight loss value of the coupon. The actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate were calculated based on the dynamic scaling amount and the weight loss value of the coupon. The actual comprehensive performance index was calculated based on the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate. The actual comprehensive performance index is compared with the maximum performance index to obtain the performance verification deviation value. If the performance verification deviation value is less than the preset deviation threshold, the theoretical optimal formula is confirmed as the target formula. Based on the target formula, the formulation optimization of the circulating water scale inhibitor of the basic chemical raw materials is realized.

2. The method for optimizing the formulation of a circulating water scale inhibitor based on mixing design as described in claim 1, characterized in that, The process of obtaining water quality parameter reports involves screening and setting the basic chemical raw materials based on these reports and the raw material pretreatment and proportioning unit, resulting in an effective raw material component set and an initial constraint boundary set, including: The water quality parameter report is analyzed based on the raw material pretreatment and proportioning unit to obtain key water quality characteristics, including calcium ion concentration, total alkalinity, sulfate concentration and reference operating temperature. Based on the calcium ion concentration, total alkalinity, and the basic chemical raw materials, an adaptation analysis is performed to obtain an effective raw material component set, wherein the effective raw material component set includes multiple effective raw material components; For each effective raw material component in the set of effective raw material components, the following operation is performed: Static scale inhibition and solubility tests were conducted on the effective raw material components using the key water quality characteristics to obtain the lowest synergistic concentration and the highest solubility limit. An independent constraint interval is constructed by using the lowest synergistic concentration as the lower limit and the highest solubility limit as the upper limit; By summing up the independent constraint intervals corresponding to the multiple effective raw material components, an initial constraint boundary set is obtained.

3. The method for optimizing the formulation of a circulating water scale inhibitor based on mixing design as described in claim 2, characterized in that, The simplex centroid design based on the effective raw material component set and the initial constraint boundary set yields multiple sets of basic mixing formulations, including: The sum of lower limits is calculated based on the lower limits of each independent constraint interval in the initial constraint boundary set, and the free allocation ratio margin is calculated based on the sum of the lower limits. Based on the effective raw material component set, multiple arbitrary real proportions are obtained. Based on the free allocation ratio margin and the lower limit of each independent constraint interval in the initial constraint boundary set, a virtual variable transformation relationship is established. Based on the established virtual variable transformation relationship and multiple arbitrary real proportions, multiple virtual variables are constructed to obtain multiple virtual variables. Based on the multiple virtual variables, a virtual variable space is obtained. An initial set of experimental points is obtained based on the virtual variable space, wherein the initial set of experimental points includes vertices, edge midpoints, and center points of the virtual variable space; Based on the virtual variable transformation relationship, the initial experimental point set is inversely transformed to obtain multiple initial mixing formulations, wherein each of the multiple initial mixing formulations includes multiple effective raw material component mass fractions. Based on the upper limit of each independent constraint interval in the initial constraint boundary set, the multiple initial mixing formulations are screened to obtain multiple sets of basic mixing formulations.

4. The method for optimizing the formulation of a circulating water scale inhibitor based on mixing design as described in claim 3, characterized in that, The process involves mixing and formulating multiple sets of basic mixing formulations to obtain multiple sets of test samples. The scale inhibition performance testing unit then measures each set of test samples to obtain scale inhibition rate datasets and corrosion inhibition rate datasets, including: Based on the multiple sets of basic mixture formulations, the proportions and weights were measured to obtain multiple sets of test samples. The following operations were performed on each of the multiple sets of test samples: Based on the scale inhibition performance testing unit, the multiple groups of test samples are added to the pre-constructed circulating water for reaction, and the circulating water is filtered to obtain filtrate. The calcium ion concentration of the filtrate is measured to obtain the residual calcium ion concentration. The scale inhibition rate of a single group is calculated based on the preset initial calcium ion concentration and the residual calcium ion concentration. The scale inhibition rates of the single groups of samples to be tested are summarized to obtain the scale inhibition rate dataset. The multiple sets of test samples were added to the circulating water, and polarization scanning was performed using a pre-constructed electrochemical workstation to obtain the corrosion current density. Obtain the control corrosion current density, calculate the single-group corrosion inhibition rate based on the control corrosion current density and the corrosion current density, and summarize the single-group corrosion inhibition rates corresponding to the multiple groups of test samples to obtain the corrosion inhibition rate dataset.

5. The method for optimizing the formulation of a circulating water scale inhibitor based on mixing design as described in claim 4, characterized in that, The water circulation experiment based on the theoretically optimal formula was conducted to obtain the dynamic scaling amount and the weight loss value of the coupon, including: Based on the theoretically optimal formula, a scale inhibitor solution was obtained, and a circulating cooling water test pipeline was obtained. The circulating cooling water test pipeline includes an inner wall of the test pipe and carbon steel plates. A circulating water experiment was conducted based on a preset operating cycle time, the circulating cooling water test pipeline, and the scale inhibitor solution to obtain the inner wall of the test pipe and the carbon steel plates after the experiment. After the test, the attached dirt on the inner wall of the test tube was peeled off to obtain a collection liquid. The collection liquid was dried and weighed to obtain the dynamic scale amount. The carbon steel hanging plate after the test was weighed to obtain the weight loss value of the hanging plate.

6. The method for optimizing the formulation of a circulating water scale inhibitor based on mixing design as described in claim 5, characterized in that, The calculation of the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate based on the dynamic scaling amount and the weight loss value of the coupon includes: Water circulation experiments were conducted based on a pre-constructed blank control group to obtain the blank scaling amount and blank coupon weight loss value. The actual dynamic scale inhibition rate is calculated based on the dynamic scale amount and the blank scale amount. The actual dynamic corrosion inhibition rate is calculated based on the weight loss value of the pre-coated piece and the weight loss value of the blank pre-coated piece.

7. The method for optimizing the formulation of a circulating water scale inhibitor based on mixing design as described in claim 6, characterized in that, The step of comparing the actual comprehensive performance index with the maximum performance index to obtain the performance verification deviation value includes: Obtain the ambient temperature fluctuation parameters, and calculate the performance verification deviation value based on the actual comprehensive performance index, maximum performance index, ambient temperature fluctuation parameters, and reference operating temperature. The calculation formula is as follows: in, This indicates the performance verification deviation value. Indicates the actual overall performance index. Indicates the maximum performance index. This represents a parameter indicating ambient temperature fluctuations. Indicates the reference operating temperature. , All of these represent performance verification weighting coefficients.

8. The method for optimizing the formulation of a circulating water scale inhibitor based on mixing design as described in claim 7, characterized in that, If the performance verification deviation value is less than a preset deviation threshold, then the theoretically optimal formula is confirmed as the target formula, including: Compare the performance verification deviation value with the deviation threshold. If the performance verification deviation value is greater than the deviation threshold, then obtain an updated experimental point set based on the virtual variable space, use the updated experimental point set as the initial experimental point set, and return to the step of performing an inverse transformation on the initial experimental point set based on the virtual variable transformation relationship until the performance verification deviation value is less than or equal to the deviation threshold. Then, the theoretically optimal formula is confirmed as the target formula. If the performance verification deviation value is less than or equal to the deviation threshold, then the theoretically optimal formula is confirmed as the target formula.

9. A circulating water scale inhibitor formulation optimization system based on mixing design, applied to the circulating water scale inhibitor formulation optimization method based on mixing design as described in any one of claims 1-8, characterized in that, The system includes: The environmental verification module is used to verify the receipt of the scale inhibitor formulation optimization instruction and verify the formulation optimization environment based on the scale inhibitor formulation optimization instruction. The formulation optimization environment includes a formulation optimization system and basic chemical raw materials. The formulation optimization system includes a raw material pretreatment and proportioning unit, a scale inhibition performance testing unit, and a multi-objective analysis and decision-making unit. The formulation design module is used to obtain water quality parameter reports, screen and set the basic chemical raw materials based on the water quality parameter reports and the raw material pretreatment and proportioning unit, obtain an effective raw material component set and an initial constraint boundary set, and perform simplex centroid design based on the effective raw material component set and the initial constraint boundary set to obtain multiple sets of basic mixing formulations. The performance testing module is used to mix and prepare multiple sets of basic mixing formulas to obtain multiple sets of test samples. The scale inhibition performance testing unit measures the multiple sets of test samples to obtain scale inhibition rate dataset and corrosion inhibition rate dataset. The theoretical comprehensive performance index is calculated based on the multi-objective analysis decision unit, scale inhibition rate dataset, and corrosion inhibition rate dataset, and the theoretical optimal formula and maximum performance index are obtained based on the theoretical comprehensive performance index. Water circulation experiments were conducted based on the theoretically optimal formula to obtain the dynamic scaling amount and the weight loss value of the coupon. The actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate were calculated based on the dynamic scaling amount and the weight loss value of the coupon. The actual comprehensive performance index was calculated based on the actual dynamic scale inhibition rate and the actual dynamic corrosion inhibition rate. The verification output module is used to compare the actual comprehensive performance index with the maximum performance index to obtain the performance verification deviation value. If the performance verification deviation value is less than the preset deviation threshold, the theoretical optimal formula is confirmed as the target formula. Based on the target formula, the formulation optimization of the circulating water scale inhibitor of the basic chemical raw materials is realized.

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