Application evaluation method and system of water treatment agent under complex water quality condition

Abstract

The invention belongs to the technical field of environmental engineering, and provides an application evaluation method and system for a water treatment agent under a complex water quality condition, and the method comprises the steps: analyzing the components of the water treatment agent, and matching water quality parameters to classify the water treatment agent; the relative closeness degree of each water treatment agent and the ideal solution is obtained, and multiple experiments are carried out to evaluate the performance of the water treatment agent under the complex water quality. Components, functions and water quality conditions are comprehensively considered, a TOPSIS method is used for sorting water treatment agents, the water treatment agent most suitable for specific water quality is selected, and the best treatment effect is ensured; according to the method, the types, doses and process parameters of the agents can be accurately determined, efficient and stable treatment is guaranteed, the cost is reduced, secondary pollution is avoided, meanwhile, the heat energy transmission efficiency can be improved, the consumption of fuel or electric power can be reduced by applying the water treatment agent selected after evaluation, the operation cost is further reduced, and meanwhile, the environmental protection requirements of energy conservation and emission reduction are met.

Description

Technical Field

[0001] This invention belongs to the field of environmental engineering technology, specifically a method and system for evaluating the application of water treatment agents under complex water quality conditions. Background Technology

[0002] With the rapid advancement of industrialization and urbanization, the demand for water resources is constantly increasing. At the same time, large amounts of industrial wastewater, domestic sewage, and agricultural non-point source pollution are discharged into water bodies without proper treatment, leading to increasingly complex water quality. Traditional single-process water treatment methods and conventional water treatment agents are gradually revealing their shortcomings in dealing with complex water conditions. Complex water environments require water treatment agents to have more powerful and diverse functions, capable of simultaneously treating multiple types of pollutants, such as removing organic matter and heavy metals while inhibiting microbial growth, and adapting to different water quality conditions.

[0003] Therefore, the present invention provides a method and system for evaluating the application of water treatment agents under complex water quality conditions. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0005] The technical solution adopted by this invention to solve its technical problem is: a method for evaluating the application of water treatment agents under complex water quality conditions, including:

[0006] Step 1: Pre-treat and analyze the components of the water treatment agent; determine the content and proportion of each component in the water treatment agent; compare the content of each component in the water treatment agent with the content of each component in the reference table; calculate the content matching degree of each component in the water treatment agent; perform a weighted average of the content matching degrees to output the category matching degree of each water treatment agent; determine the dominant category of the water treatment agent based on the category matching degree; perform functional similarity analysis based on the function of the water treatment agent, classify the water treatment agent into functional categories, and construct a functional table based on the functional category classification results; combine the water treatment agent component table and functional table to obtain the water treatment agent classification table.

[0007] Step 2: Based on the water treatment agent classification table, select the complex water quality that matches the water treatment agent, and use the water treatment agent to test and treat the matched complex water quality. After the test and treatment, measure various parameters in the complex water quality; compare the water quality parameters before and after treatment to determine the target parameters; obtain the normal standard range of the target parameters, and mark the optimal and worst values ​​within the normal standard range; at the same time, construct a decision matrix A based on the water quality parameter matching degree of each water treatment agent, and perform normalization to obtain the decision matrix R.

[0008] Step 3: According to the optimal value, the worst value, and the decision matrix R within the normal standard range, calculate and process the output to obtain the positive ideal solution vector V+ and the negative ideal solution vector V-, and obtain the relative proximity degree value Ci of the water treatment agent to the positive ideal solution through the positive ideal solution vector V+ and the negative ideal solution vector V-;

[0009] Step 4: According to the relative proximity degree value Ci corresponding to the water treatment agent after N times of test treatment, construct a box plot, check for outliers and correct the outliers; and after correcting the outliers, calculate the stability coefficient CV according to the relative proximity degree value Ci, and judge the performance of the water treatment agent through the stability coefficient CV.

[0010] As a further technical solution of the present invention: The process of determining the dominant category of the water treatment agent is as follows:

[0011] Perform a content matching degree analysis on all components in the water treatment agent that have a corresponding relationship with the control group, calculate the category matching degree Yi of each water treatment agent for the four categories by using the weighted average value, sort Yi from largest to smallest, and select the category with the highest Yi as the dominant category of the water treatment agent.

[0012] The process of obtaining the content matching degree of each component in the water treatment agent is as follows:

[0013] Determine the component content value T of each component in the water treatment agent; divide the component content value T by the total mass of the water treatment agent; obtain the component proportion degree Tw of each component in the water treatment agent; count the number of component types N owned by each water treatment agent;

[0014] Establish a chemical property comparison table of the water treatment agent according to industry standards, and count the typical content intervals [Tj1, Tj2] of each component; compare the components of the tested water treatment agent with all components in the comparison table, select the corresponding specific components, and then compare the actual content of the specific components in the water treatment agent with their typical content intervals in the comparison table;

[0015] If the content of the specific component is within the typical interval of the corresponding component in the comparison table, through the formula obtain the content matching degree Yij; if it is outside the interval, when T < Tj1, the content matching degree Yij = 0; when T > Tj2, the content matching degree Yij = 1;

[0016] As a further technical solution of the present invention: The process of constructing a function table according to the division result of the functional category is as follows:

[0017] Place the water treatment agent under complex water quality conditions, record the functional trends of the water treatment agent, and analyze the types of complex water quality that can be effectively treated based on the component information corresponding to the main classification in the ingredient table. Test the functionality of the water treatment agent under applicable complex water quality conditions, count all functions, perform similarity analysis on the functions, group water treatment agents with similar functions into one category, and compile a water treatment agent function table.

[0018] A further technical solution of the present invention is as follows: the target parameter is obtained in the following way:

[0019] Obtain the treatment parameters of each water treatment agent for the complex water quality under applicable complex water quality conditions;

[0020] Compare the treatment parameters for complex water quality with the corresponding initial parameters;

[0021] If the treatment parameters for complex water quality are improved compared to the corresponding initial parameters, then the treatment parameters for complex water quality are marked as target parameters.

[0022] As a further technical solution of the present invention, the process of constructing the decision matrix is ​​as follows:

[0023] A decision matrix A for the TOPSIS method is constructed, using the water quality parameter matching degree N of each water treatment agent, the parameter value X of the target parameter after treatment, the consumption Y, and the amount of impurities generated and remaining after treatment Z as matrix elements, to construct an Mx4 decision matrix A; the elements aij in matrix A are normalized to obtain the normalized decision matrix R=(rij)mx4;

[0024] The process of obtaining the matching degree of water quality parameters for each water treatment agent is as follows:

[0025] The ratio of the total number of target parameters for each water treatment agent to the total number of parameters measured for complex water quality is calculated to obtain the water quality parameter matching degree N for each water treatment agent.

[0026] A further technical solution of the present invention is as follows: the positive ideal solution vector V+ and the negative ideal solution vector V- are obtained as follows:

[0027] pass Obtaining the positive ideal solution vector: Each element of the positive ideal solution vector V+ is the optimal value of each index in the decision matrix A, that is, the optimal value of the water quality parameter matching degree is 1, the optimal value of the target parameter within the normal standard range is Xbest, the optimal value of the water treatment agent consumption is the minimum value Ymin among all water treatment agents, and the optimal value of the amount of generated and residual impurities after treatment is 0;

[0028] pass Obtain the negative ideal solution vector: Each element of the negative ideal solution vector V- is the worst value of each index in the decision matrix A. The worst value of water quality parameter matching degree is 0. The worst value of the normal standard range of the target parameter is Xworst. The worst value of water treatment agent consumption is the maximum value Ymax among all water treatment agent consumption. The worst value of the amount of impurities generated and remaining after treatment is the maximum value Zmax among all water treatment agents generated and remaining after treatment.

[0029] A further technical solution of the present invention is as follows: the method for obtaining the relative closeness value Ci between the water treatment agent and the positive ideal solution is as follows:

[0030] For the i-th water treatment agent, the distance D+ between the water treatment agent and the positive ideal solution vector V+ is obtained using the Euclidean distance formula; and the distance D- between the water treatment agent and the negative ideal solution vector V- is obtained; where rij are elements in the normalized decision matrix R; V + j These are elements of the positive ideal solution vector V+; V - j These are elements of the positive ideal solution vector V-;

[0031] Use the formula Obtain the relative proximity value Ci between each water treatment agent and the ideal solution.

[0032] As a further technical solution of the present invention, the process of constructing the box plot and checking for outliers is as follows:

[0033] Box plots were constructed based on the relative similarity values ​​Ci from multiple experiments with water treatment agents. First, the relative similarity values ​​Ci of each water treatment agent were sorted from smallest to largest to obtain a sequence. The lower quartile Q1, median Q2, upper quartile Q3, and interquartile range were then obtained from the sequence. Then, through... and Obtain the lower beard endpoint r1 and the upper beard endpoint r2 of the box plot. Next, construct a rectangular box on the number axis, with the lower boundary at position Q1 and the upper boundary at position Q3. Draw a line segment inside the box to represent Q2. Draw line segments downwards and upwards from the lower and upper boundaries of the box to represent the lower beard endpoint r1 and the upper beard endpoint r2, respectively. Identify points outside the box that exceed the beard range as outliers and count the number P of outliers for each water treatment agent.

[0034] As a further technical solution of the present invention, the process of obtaining the stability coefficient CV is as follows:

[0035] Through formula Obtain the standard deviation SD of the relative proximity value Ci for each water treatment agent; where Ci is the relative proximity value of the water treatment agent in each experiment. Ci is the average of the relative similarity values ​​of the water treatment agents under all experiments; X is the number of times the water treatment agent was tested repeatedly.

[0036] Through formula The stability coefficient CV of the relative similarity value Ci of the water treatment agent was obtained under multiple experiments. The obtained stability coefficient of the water treatment agent was compared with the predetermined stability threshold. If the stability coefficient CV of the water treatment agent is greater than the stability threshold, the water treatment agent has poor stability and large differences in treatment effect. If the stability coefficient CV of the water treatment agent is greater than the stability threshold, the water treatment agent has strong stability and stable treatment effect.

[0037] An evaluation system for the application of water treatment agents under complex water quality conditions, including:

[0038] The classification module performs pretreatment and component analysis on water treatment agents; determines the content and proportion of each component in the water treatment agent; compares and analyzes the content of each component in the water treatment agent with the content of each component in the reference table; calculates the content matching degree of each component in the water treatment agent; performs a weighted average of the content matching degrees to output the category matching degree of each water treatment agent; determines the dominant category of the water treatment agent based on the category matching degree; performs functional similarity analysis based on the function of the water treatment agent, classifies the water treatment agent into functional categories, and constructs a functional table based on the functional category classification results; combines the water treatment agent component table and functional table to obtain the water treatment agent classification table.

[0039] Matching Module: Based on the water treatment agent classification table, select the complex water quality that matches the water treatment agent, and use the water treatment agent to test and treat the matched complex water quality. After the test and treatment, measure various parameters in the complex water quality; compare the water quality parameters before and after treatment to determine the target parameters; obtain the normal standard range of the target parameters, and mark the optimal and worst values ​​within the normal standard range; at the same time, construct a decision matrix A based on the matching degree of water quality parameters for each water treatment agent, and perform normalization to obtain the decision matrix R.

[0040] Calculation module: Based on the optimal and worst values ​​marked within the normal standard range and the decision matrix R, the module calculates and processes the output to obtain the positive ideal solution vector V+ and the negative ideal solution vector V-. The module then processes the positive ideal solution vector V+ and the negative ideal solution vector V- to obtain the relative closeness value Ci between the water treatment agent and the positive ideal solution.

[0041] Evaluation module: Based on the relative proximity value Ci corresponding to the water treatment agent after N test treatments, a box plot is constructed to check for outliers and correct them; after correcting the outliers, the stability coefficient CV is calculated based on the relative proximity value Ci, and the performance of the water treatment agent is judged by the stability coefficient CV.

[0042] The beneficial effects of this invention are as follows: Through systematic component analysis, water quality matching tests, and multi-step data processing, the category and functional category of water treatment agents are accurately determined, and a comprehensive classification table is constructed, providing a precise basis for agent screening and reducing the risk of misselection. From the perspective of quantifying treatment effects, a decision matrix is ​​constructed, the ideal solution vector and the relative closeness value Ci are calculated, and the treatment effect is presented intuitively in a quantitative manner, facilitating the comparison of the effectiveness differences of different agents in complex water qualities. Regarding the accuracy of performance judgment, the stability coefficient CV is calculated after checking and correcting outliers using box plots, effectively eliminating interfering data, accurately judging performance, and facilitating the screening of high-quality agents. Overall, it can significantly improve water treatment efficiency and quality, ensure water quality compliance, and promote progress in the water treatment industry in agent research and development, process optimization, and other aspects. Attached Figure Description

[0043] The invention will now be further described with reference to the accompanying drawings.

[0044] Figure 1 This is a flowchart of the steps in Embodiment 1 of the present invention;

[0045] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. Detailed Implementation

[0046] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0047] Example 1

[0048] like Figure 1 As shown, the application evaluation method of the water treatment agent under complex water quality conditions according to the embodiments of the present invention includes:

[0049] Step 1: Pre-treat and analyze the components of the water treatment agent; determine the content and proportion of each component in the water treatment agent; compare the components of the water treatment agent with the reference table; obtain the content matching degree of each component in the water treatment agent; obtain the category matching degree of each water treatment agent by weighted average; determine the dominant category of the water treatment agent based on the category with the highest category matching degree; test the functionality of the water treatment agent and perform similarity analysis to classify functional categories and construct a functional table; combine the water treatment agent component table and functional table to obtain the water treatment agent classification table.

[0050] The carrier solution and fillers in the water treatment agent are pretreated; the quantity M of the treated water treatment agent is counted; and the components of the water treatment agent are analyzed using analytical methods such as chromatography, spectroscopy, and mass spectrometry.

[0051] Determine the content range of each component in each water treatment agent; take the median value of each component content range; mark it as the component content value T; divide the component content value T by the total mass of the water treatment agent; obtain the component proportion degree Tw of each component in the water treatment agent; count all the component types owned by each water treatment agent; record and mark it as N;

[0052] Establish a chemical property comparison table for water treatment agents according to industry standards; the comparison table statistics the typical content ranges corresponding to all components contained in the water treatment agent; the typical range of each component is [Tj1, Tj2];

[0053] The comparison table divides water treatment agents into four categories: inorganic, organic, microbial, and composite;

[0054] Compare the components contained in the tested water treatment agent with all the components recorded in the comparison table; select the specific components corresponding to the components owned by the water treatment agent in the comparison table; obtain the typical content range of the corresponding components according to the information in the comparison table; compare the actual content of the specific components in the water treatment agent with the typical content range of the corresponding components in the comparison table; the following is the detailed comparison process:

[0055] Obtain the content matching degree of the specific component in the water treatment agent and the typical content range of the corresponding component in the comparison table;

[0056] The value range of the content matching degree is limited to the interval [0, 1]; and 0 represents that the content of the specific component in the water treatment agent does not match the typical content range in the comparison table at all; that is, the actual content of the specific component is outside the typical content range and the deviation degree is large; while 1 means that the content of the specific component matches the typical content range perfectly; when the matching degree is between 0 and 1, the closer the value is to 1, the closer the content of the specific component in the water treatment agent is to the typical content range standard in the comparison table;

[0057] Obtained by comparing the actual content of the components matched in the water treatment agent with the typical content range of the corresponding components in the comparison table;

[0058] If the component content of the specific component is within the typical range of the corresponding component in the comparison table, then through the formula Obtain the content matching degree Yij of the specific component;

[0059] If the component content of the specific component is outside the typical content range of the corresponding component in the comparison table; and T < Tj1; then the content matching degree Yij of the specific component = 0;

[0060] If the component content of the specific component is outside the typical content range of the corresponding component in the comparison table; and T > Tj2; then the content matching degree Yij of the specific component = 1;

[0061] Content matching analysis was performed on all components in the water treatment agent that corresponded to the control group; the formula was used to... Calculate the weighted average; obtain the category matching degree Yi for each water treatment agent in the four categories; sort the category matching degree Yi of each water treatment agent from largest to smallest; select the category with the highest category matching degree Yi as the dominant category of the water treatment agent;

[0062] The composition of water treatment agents under the dominant category is statistically analyzed; the relevant information of each water treatment agent is categorized; and a water treatment agent composition table is constructed.

[0063] The water treatment agent was placed under complex water quality conditions, and its functional tendencies under these conditions were recorded. Based on the component information corresponding to the dominant category in the water treatment agent's ingredient list, the types of complex water quality that the water treatment agent can effectively treat were analyzed.

[0064] The water treatment agent is placed under conditions that allow it to effectively treat complex water quality; the functionality of the water treatment agent is tested; all the functions exhibited by the water treatment agent are statistically analyzed; the different functions exhibited by the water treatment agent are analyzed for similarity; water treatment agents with similar functions are grouped together; and a functional table of water treatment agents is compiled.

[0065] Data from the water treatment agent composition table and water treatment agent function table were integrated and organized to obtain a water treatment agent classification table that records various aspects of water treatment agent information, including but not limited to the composition, function and applicable water quality conditions of the water treatment agent.

[0066] Step 2: Measure various parameters in complex water quality; compare water quality parameters before and after treatment to determine target parameters; obtain the normal standard range of target parameters and mark the optimal and worst values; obtain the water quality parameter matching degree for each water treatment agent; construct decision matrix A and perform normalization to obtain decision matrix R;

[0067] Measurements were taken under complex water quality conditions; initial parameters of the complex water quality were obtained, including but not limited to: hardness, alkalinity, pH value, temperature, and microbial content; and the total number of parameters measured under complex water quality conditions was counted.

[0068] Obtain the treatment parameters of each water treatment agent after treating complex water under applicable complex water quality conditions. The treatment parameters include, but are not limited to, the hardness, alkalinity, pH value, temperature, and microbial content of the treated complex water.

[0069] Compare the treatment parameters for complex water quality with the corresponding initial parameters;

[0070] If the treatment parameters for complex water quality are improved compared to the corresponding initial parameters, then the treatment parameters for complex water quality are marked as target parameters.

[0071] If the treatment parameters for complex water quality do not improve the corresponding initial parameters, then the treatment parameters for complex water quality are marked as non-target parameters.

[0072] It should be noted that the meaning of "improvement" is that after the water treatment agent treats complex water quality, the parameter values ​​of the target parameters in the complex water quality are closer to the normal standard range. For example, when treating industrial wastewater containing high concentrations of heavy metal ions and high hardness, the copper ion concentration before treatment is as high as 100 mg / L, far exceeding the industrial wastewater discharge standard of 0.5-2 mg / L. After adding heavy metal precipitants such as sodium sulfide, the copper ion concentration is significantly reduced to about 1 mg / L, which meets the normal standard range.

[0073] Obtain the normal standard range of the target parameter for each water treatment agent; mark the best value within the normal standard range as Xbest and the worst value as Xworst; where the normal standard range of the target parameter is obtained based on the water quality standard;

[0074] The ratio of the total number of target parameters corresponding to each water treatment agent to the total number of parameters measured for complex water quality is calculated to obtain the water quality parameter matching degree N for each water treatment agent.

[0075] Under applicable complex water quality conditions, each water treatment agent is added to the complex water quality separately until the target parameter value of the water treatment agent under the complex water quality reaches the normal standard range;

[0076] Record the parameter values ​​X corresponding to the target parameters of the water treatment agent under complex water quality; record the consumption Y of each water treatment agent to treat the target parameter values ​​to the normal standard range; record the amount of impurities generated and remaining after the water treatment agent is completed;

[0077] Construct a decision matrix A for the TOPSIS method; use the water quality parameter matching degree N, the parameter value X of the target parameter after treatment, the consumption Y, and the amount of impurities generated and remaining after treatment as matrix elements; construct an Mx4 decision matrix A; where rows represent different water treatment agents and columns correspond to four evaluation indicators respectively;

[0078] To ensure comparability between different indicators, each element aij in the decision matrix A is normalized; the normalized decision matrix R = (rij) is obtained after processing decision matrix A. mx4 ;

[0079] Step 3: Obtain the positive ideal solution vector V+ and the negative ideal solution vector V-; obtain the relative proximity value Ci between each water treatment agent and the positive ideal solution;

[0080] Through formula Calculate the positive ideal solution vector;

[0081] The elements of the positive ideal solution vector V+ are the optimal values ​​of each index in the decision matrix A; the optimal value of the water quality parameter matching degree for each water treatment agent is 1, indicating that all parameters measured in the water quality can perfectly match the water treatment agent; the optimal value of the normal standard range of the target parameter corresponding to each water treatment agent is Xbest; the optimal value of the consumption of each water treatment agent is the minimum value Ymin among all water treatment agent consumptions; and the optimal value of the amount of impurities Z generated and remaining after the water treatment agent is completed is 0.

[0082] Through formula Calculate the negative ideal vector;

[0083] The elements of the negative ideal solution vector V- are the worst values ​​of each index in the decision matrix A; the worst value of the water quality parameter matching degree of each water treatment agent is 0, indicating that all parameters measured in the water quality cannot be matched with the water treatment agent; the worst value of the normal standard range of the target parameter corresponding to each water treatment agent is Xworst; the worst value of the consumption of each water treatment agent is the maximum value Ymax among all water treatment agent consumption; the worst value of the amount of impurities Z generated and remaining after the water treatment agent is completed is the maximum value Zmax among all water treatment agent generated and remaining after the water treatment agent is completed.

[0084] For the i-th (i=1,2,3,...n) water treatment agent, use the Euclidean distance formula. The distance D+ between the water treatment agent and the positive ideal solution vector V+ is obtained; where rij are elements in the normalized decision matrix R; V + j These are elements of the positive ideal solution vector V+;

[0085] Through formula The distance D- between the water treatment agent and the negative ideal solution vector V- is obtained; where rij are elements in the normalized decision matrix R; V - j These are elements of the positive ideal solution vector V-;

[0086] Use the formula Obtain the relative proximity value Ci between each water treatment agent and the ideal solution;

[0087] The closer the relative proximity value Ci is to 1, the closer the water treatment agent is to the ideal solution under applicable complex water quality conditions; the better its comprehensive treatment performance under applicable complex water quality conditions. The closer the relative proximity value Ci is to 0, the further the water treatment agent is from the ideal solution under applicable complex water quality conditions; the worse its comprehensive treatment performance under applicable complex water quality conditions.

[0088] Step 4: Conduct repeated experiments on the water treatment agent and record the relative similarity value Ci for each experiment; construct a box plot and check for outliers; correct outliers; obtain the standard deviation SD and stability coefficient CV of the relative similarity value Ci for the water treatment agent.

[0089] Perform X repeated experiments on the water treatment agent; record the relative similarity value Ci of the water treatment agent in each experiment;

[0090] A box plot was constructed based on the relative similarity values ​​Ci from multiple experiments with the water treatment agents; the relative similarity values ​​Ci for each water treatment agent were sorted from smallest to largest to form a sequence; the lower quartile Q1, median Q2, and upper quartile Q3 of the sequence were obtained; and the results were then analyzed using the formula... Obtain the interquartile range of the sequence; through The formula yields the lower beard endpoint r1 of the box plot; through The formula yields the lower beard endpoint r2 of the box plot;

[0091] Construct a rectangular box on the number axis; the lower boundary of the box is at position Q1; the upper boundary is at position Q3; draw a line segment inside the box to represent the median Q2; draw a line segment downward from the lower boundary of the box to represent the lower beard line; the endpoint of the lower beard line is r1; draw a line segment upward from the upper boundary of the box to represent the upper beard line; the endpoint of the upper beard line is r2; identify points outside the box that are outside the beard line range as outliers; outliers outside the box represent abnormal values ​​in the data sequence that deviate from the normal data distribution; count the number P of outliers for each water treatment agent;

[0092] Outliers should be corrected by replacing them with reasonable alternatives; for example, the mean or median can be used to replace outliers to reduce their impact on the overall analysis results. If outliers deviate significantly from the normal range and cannot be reasonably explained, they can be removed. After removing or correcting outliers, the data analysis and evaluation should be performed again.

[0093] Through formula Obtain the standard deviation SD of the relative proximity value Ci for each water treatment agent; where Ci is the relative proximity value of the water treatment agent in each experiment. Ci is the average of the relative similarity values ​​of the water treatment agents under all experiments; X is the number of times the water treatment agent was tested repeatedly.

[0094] Through formula The stability coefficient (CV) of the water treatment agent was obtained under multiple experiments, representing the relative similarity value Ci. This stability coefficient was then compared to a predetermined stability threshold. If the CV value was greater than the threshold, the water treatment agent exhibited poor stability and significant variations in treatment effectiveness. Conversely, if the CV value was greater than the threshold, the water treatment agent demonstrated strong stability and consistent treatment performance. A smaller CV value indicated more stable performance of the water treatment agent.

[0095] The technical solution of this invention is as follows: Water treatment agents undergo pretreatment and in-depth component analysis. The matching degree of each component's content is calculated by comparing it with a reference table and then weighted and averaged to determine the dominant category. Simultaneously, functional categories are divided according to function, and a functional table is constructed, ultimately integrating these into a water treatment agent classification table. Based on the classification table, suitable complex water qualities are selected for testing and treatment. Multiple water quality parameters are then measured before and after treatment to determine target parameters and clarify their normal standard range, optimal, and worst values. A decision matrix A is constructed and normalized. Based on the previous standard range values ​​and the decision matrix R, the positive ideal solution vector V+ and the negative ideal solution vector V- are accurately calculated, thus obtaining the relative proximity value Ci. A box plot is constructed based on the Ci values ​​after X tests of the water treatment agent. Outliers are carefully identified and corrected. The stability coefficient CV is then calculated based on the corrected Ci values, thereby accurately judging the performance of the water treatment agent and providing a scientific basis and effective method for the rational application and performance improvement of water treatment agents.

[0096] Example 2

[0097] like Figure 2 As shown in the embodiment of the present invention, the water treatment agent application evaluation system under complex water quality conditions includes:

[0098] Classification module: Performs principal component analysis on different water treatment agents; classifies them according to the properties of the principal components; selects suitable water qualities for water treatment agents based on the principal component table; tests the specific functions of water treatment agents and performs comprehensive classification;

[0099] Matching module: acquires various parameters of complex water quality and matches them with the water treatment agent classification table; evaluates water treatment agents using the TOPSIS method; constructs a decision matrix for the TOPSIS method;

[0100] Calculation module: Obtains the relative closeness of each water treatment agent to the ideal solution;

[0101] Evaluation module: Analyzes the distribution of relative similarity values; checks for outliers; evaluates the application of water treatment agents based on relative similarity values;

[0102] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the application of water treatment agents under complex water quality conditions, characterized in that: include: Step 1: Pre-treat and analyze the components of the water treatment agent; determine the content and proportion of each component in the water treatment agent; compare the content of each component in the water treatment agent with the content of each component in the reference table; calculate the content matching degree of each component in the water treatment agent; perform a weighted average of the content matching degrees to output the category matching degree of each water treatment agent; determine the dominant category of the water treatment agent based on the category matching degree; perform functional similarity analysis based on the function of the water treatment agent, classify the water treatment agent into functional categories, and construct a functional table based on the functional category classification results; combine the water treatment agent component table and functional table to obtain the water treatment agent classification table. Step 2: Based on the water treatment agent classification table, select the complex water quality that matches the water treatment agent, and use the water treatment agent to test and treat the matched complex water quality. After the test and treatment, measure various parameters in the complex water quality; compare the water quality parameters before and after treatment to determine the target parameters; obtain the normal standard range of the target parameters, and mark the optimal and worst values ​​within the normal standard range; at the same time, construct a decision matrix A based on the water quality parameter matching degree of each water treatment agent, and perform normalization to obtain the decision matrix R. Step 3: Based on the optimal and worst values ​​and the decision matrix R within the normal standard range, calculate the positive ideal solution vector V+ and the negative ideal solution vector V-, and then process the positive ideal solution vector V+ and the negative ideal solution vector V- to obtain the relative closeness value Ci between the water treatment agent and the positive ideal solution. Step 4: Based on the relative proximity value Ci corresponding to the water treatment agent after N test treatments, construct a box plot, check for outliers and correct them; after correcting the outliers, calculate the stability coefficient CV based on the relative proximity value Ci, and judge the performance of the water treatment agent by the stability coefficient CV.

2. The method for evaluating the application of the water treatment agent under complex water quality conditions according to claim 1, characterized in that: The process for determining the dominant category of water treatment agents is as follows: Content matching degree analysis was performed on all components in the water treatment agent that corresponded to the control group. The category matching degree Yi of each water treatment agent to the four categories was calculated using the weighted average. Yi was sorted from largest to smallest, and the category with the highest Yi was selected as the dominant category of the water treatment agent. The process of obtaining the content matching degree of each component in the water treatment agent is as follows: Determine the content value T of each component in the water treatment agent; divide the content value T by the total mass of the water treatment agent; obtain the proportion Tw of each component in the water treatment agent; count the number of component types N in each water treatment agent; A chemical property comparison table for water treatment agents was established based on industry standards, and the typical content ranges of each component [Tj1, Tj2] were statistically analyzed. The components of the tested water treatment agents were compared with all the components in the comparison table. The corresponding specific components were selected, and then the actual content of the specific components in the water treatment agents was compared with their typical content ranges in the comparison table. If the content of a specific component is within the typical range of the corresponding component in the comparison table, the content matching degree Yij is obtained through the formula ; if it is outside the range, when T < Tj1, the content matching degree Yij = 0; when T > Tj2, the content matching degree Yij = 1.

3. The method for evaluating the application of the water treatment agent under complex water quality conditions according to claim 1, characterized in that: The process of constructing the function table based on the functional category division results is as follows: Place the water treatment agent under complex water quality conditions, record the functional trends of the water treatment agent, and analyze the types of complex water quality that can be effectively treated based on the component information corresponding to the main classification in the ingredient table. Test the functionality of the water treatment agent under applicable complex water quality conditions, count all functions, perform similarity analysis on the functions, group water treatment agents with similar functions into one category, and compile a water treatment agent function table.

4. The method for evaluating the application of the water treatment agent under complex water quality conditions according to claim 1, characterized in that: The target parameters are obtained as follows: Obtain the treatment parameters of each water treatment agent for the complex water quality under applicable complex water quality conditions; Compare the treatment parameters for complex water quality with the corresponding initial parameters; If the treatment parameters for complex water quality are improved compared to the corresponding initial parameters, then the treatment parameters for complex water quality are marked as target parameters.

5. The method for evaluating the application of the water treatment agent under complex water quality conditions according to claim 1, characterized in that: The process of constructing the decision matrix is ​​as follows: Construct a decision matrix A for the TOPSIS method, using the matching degree N of water quality parameters of each water treatment agent, the parameter value X of the target parameter after treatment, the consumption Y, and the amount of impurities generated and remaining after treatment Z as matrix elements to construct an Mx4 decision matrix A; Normalize each element aij in matrix A to obtain the normalized decision matrix R = (rij)mx4; The process of obtaining the matching degree of water quality parameters for each water treatment agent is as follows: The ratio of the total number of target parameters for each water treatment agent to the total number of parameters measured for complex water quality is calculated to obtain the water quality parameter matching degree N for each water treatment agent.

6. The method for evaluating the application of the water treatment agent under complex water quality conditions according to claim 5, characterized in that: The positive ideal solution vector V+ and the negative ideal solution vector V- are obtained as follows: pass Obtaining the positive ideal solution vector: Each element of the positive ideal solution vector V+ is the optimal value of each index in the decision matrix A, that is, the optimal value of the water quality parameter matching degree is 1, the optimal value of the target parameter within the normal standard range is Xbest, the optimal value of the water treatment agent consumption is the minimum value Ymin among all water treatment agents, and the optimal value of the amount of generated and residual impurities after treatment is 0; pass Obtain the negative ideal solution vector: Each element of the negative ideal solution vector V- is the worst value of each index in the decision matrix A. The worst value of water quality parameter matching degree is 0. The worst value of the normal standard range of the target parameter is Xworst. The worst value of water treatment agent consumption is the maximum value Ymax among all water treatment agent consumption. The worst value of the amount of impurities generated and remaining after treatment is the maximum value Zmax among all water treatment agents generated and remaining after treatment.

7. The method for evaluating the application of the water treatment agent under complex water quality conditions according to claim 1, characterized in that: The method for obtaining the relative closeness value Ci between the water treatment agent and the positive ideal solution is as follows: For the i-th water treatment agent, the distance D+ between the water treatment agent and the positive ideal solution vector V+ is obtained using the Euclidean distance formula; and the distance D- between the water treatment agent and the negative ideal solution vector V- is obtained; where rij are elements in the normalized decision matrix R; V + j These are elements of the positive ideal solution vector V+; V - j These are elements of the positive ideal solution vector V-; Use formula Obtain the relative proximity value Ci between each water treatment agent and the ideal solution.

8. The method for evaluating the application of the water treatment agent under complex water quality conditions according to claim 1, characterized in that: The process of constructing box plots and checking for outliers is as follows: Box plots were constructed based on the relative similarity values ​​Ci from multiple experiments with water treatment agents. First, the relative similarity values ​​Ci of each water treatment agent were sorted from smallest to largest to obtain a sequence. The lower quartile Q1, median Q2, upper quartile Q3, and interquartile range were then obtained from the sequence. Then, through... and Obtain the lower beard endpoint r1 and the upper beard endpoint r2 of the box plot; construct a rectangular box on the number axis, with the lower boundary at position Q1 and the upper boundary at position Q3. Draw a line segment inside the box to represent Q2. Draw line segments downward and upward from the lower and upper boundaries of the box, respectively, as the lower beard endpoint r1 and the upper beard endpoint r2. Identify points outside the box that exceed the beard range as abnormal points, and count the number P of abnormal points for each water treatment agent.

9. The method for evaluating the application of the water treatment agent under complex water quality conditions according to claim 1, characterized in that: The process of obtaining the stability coefficient CV is as follows: Through formula Obtain the standard deviation SD of the relative proximity value Ci for each water treatment agent; where Ci is the relative proximity value of the water treatment agent in each experiment. Ci is the average of the relative similarity values ​​of the water treatment agents under all experiments; X is the number of times the water treatment agent was tested repeatedly. Through formula The stability coefficient CV of the relative similarity value Ci of the water treatment agent was obtained under multiple experiments. The obtained stability coefficient of the water treatment agent was compared with the predetermined stability threshold. If the stability coefficient CV of the water treatment agent is greater than the stability threshold, the water treatment agent has poor stability and large differences in treatment effect. If the stability coefficient CV of the water treatment agent is greater than the stability threshold, the water treatment agent has strong stability and stable treatment effect.

10. An application evaluation system for water treatment agents under complex water quality conditions, characterized in that: include: The classification module performs pretreatment and component analysis on water treatment agents; determines the content and proportion of each component in the water treatment agent; compares and analyzes the content of each component in the water treatment agent with the content of each component in the reference table; calculates the content matching degree of each component in the water treatment agent; performs a weighted average of the content matching degrees to output the category matching degree of each water treatment agent; determines the dominant category of the water treatment agent based on the category matching degree; performs functional similarity analysis based on the function of the water treatment agent, classifies the water treatment agent into functional categories, and constructs a functional table based on the functional category classification results; combines the water treatment agent component table and functional table to obtain the water treatment agent classification table. Matching Module: Based on the water treatment agent classification table, select the complex water quality that matches the water treatment agent, and use the water treatment agent to test and treat the matched complex water quality. After the test and treatment, measure various parameters in the complex water quality; compare the water quality parameters before and after treatment to determine the target parameters; obtain the normal standard range of the target parameters, and mark the optimal and worst values ​​within the normal standard range; at the same time, construct a decision matrix A based on the matching degree of water quality parameters for each water treatment agent, and perform normalization to obtain the decision matrix R. Calculation module: Based on the optimal and worst values ​​marked within the normal standard range and the decision matrix R, the module calculates and processes the output to obtain the positive ideal solution vector V+ and the negative ideal solution vector V-. The module then processes the positive ideal solution vector V+ and the negative ideal solution vector V- to obtain the relative closeness value Ci between the water treatment agent and the positive ideal solution. Evaluation module: Based on the relative proximity value Ci corresponding to the water treatment agent after N test treatments, a box plot is constructed to check for outliers and correct them; after correcting the outliers, the stability coefficient CV is calculated based on the relative proximity value Ci, and the performance of the water treatment agent is judged by the stability coefficient CV.

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