A method for optimizing control of drug consumption for sewage treatment

Abstract

This invention relates to the field of wastewater treatment technology, and more particularly to a method for optimizing and controlling chemical consumption in wastewater treatment. The method includes: conducting wastewater treatment tests on a wastewater sample based on several candidate treatment agents to determine the preferred treatment agent; adding the preferred treatment agent to a wastewater treatment tank based on initial agent dosing parameters; periodically acquiring key wastewater state parameters during the wastewater treatment process to determine floc formation nodes; determining the floc coagulation type based on the key wastewater state parameters at the floc formation nodes and wastewater state images; determining a wastewater treatment anomaly index based on changes in key wastewater state parameters within a preset time period before the floc formation nodes; and optimizing the initial agent dosing parameters or re-determining the preferred treatment agent based on an optimization adjustment coefficient. This invention enables dynamic optimization and control of chemical consumption during wastewater treatment, ensuring the stability of the optimized chemical consumption control.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a method for optimizing and controlling chemical consumption in wastewater treatment. Background Technology

[0002] In municipal and industrial wastewater treatment processes, chemical flocculation and sedimentation purification are core technological steps to ensure effluent quality meets standards and remove suspended pollutants, total phosphorus, and colloidal impurities. By adding coagulants, flocculants, coagulant aids, and phosphorus removal agents to wastewater, suspended particles, colloidal substances, and some dissolved pollutants in the wastewater can coagulate into flocs, which are then separated into solids and liquids through sedimentation or flotation, achieving the goal of purifying the water. The accuracy of chemical dosing and the suitability of the chemicals directly determine the wastewater treatment effect and the overall chemical consumption and operating costs. Currently, wastewater treatment chemical consumption control and chemical selection methods largely rely on the long-term work experience of operation and maintenance personnel, generally using fixed types of chemicals, fixed dosages, and fixed mixing process parameters for routine wastewater treatment. There is a lack of intelligent chemical screening and dynamic optimization control mechanisms based on real-time wastewater quality characteristics, making it difficult to achieve refined chemical consumption optimization control.

[0003] Chinese Patent Publication No. CN119668203A discloses a dosing control system for wastewater treatment, comprising: a data acquisition module for real-time acquisition of wastewater influent water quality data and influent flow rate, water quality data of each wastewater treatment process, and effluent water quality data; a data acquisition module for acquiring dosing data and detection data; a data preprocessing module for assigning values ​​to some water quality data based on preset judgment thresholds; a dosing prediction module for inputting real-time data into a self-attention dosing prediction model and outputting the dosing strategy for the next time; the self-attention dosing prediction model corrects the attention score matrix based on the chemical reaction relationship matrix; and a dosing control module for dosing chemicals according to the output dosing strategy.

[0004] Existing technologies have the following problems: relying solely on historical data regarding the compliance of effluent quality to determine dosing strategies fails to achieve dynamic optimization and control of chemical consumption during wastewater treatment. Summary of the Invention

[0005] Therefore, the present invention provides a method for optimizing and controlling chemical consumption in wastewater treatment, which overcomes the problem that the existing technology relies solely on historical data on the compliance of effluent quality to determine the dosing strategy, and cannot achieve dynamic optimization and control of chemical consumption in the wastewater treatment process.

[0006] To achieve the above objectives, the present invention provides a method for optimizing and controlling chemical consumption in wastewater treatment, comprising: Wastewater treatment tests were conducted on wastewater samples based on several candidate treatment agents to obtain the test treatment characteristics corresponding to each candidate treatment agent. Based on the test treatment characteristics corresponding to each of the candidate treatment agents, the wastewater treatment suitability of each candidate treatment agent is determined, so as to determine the preferred treatment agent for the wastewater to be treated. Based on the initial dosing parameters, the preferred treatment agent is added to the wastewater treatment tank, and key wastewater state parameters during the wastewater treatment process are periodically acquired to determine the floc formation node. The dosing parameters include the dosage of the agent and the stirring time. The floc condensation type is determined based on the key wastewater state parameters and wastewater state images of the floc forming node, and the wastewater treatment anomaly index is determined based on the changes in key wastewater state parameters within a preset time period before the floc forming node. The floc condensation type includes dense floc type and dispersed floc type. Based on the floc condensation type and the wastewater treatment anomaly index, an optimization adjustment coefficient is determined to optimize the initial reagent dosing parameters, or to re-determine the preferred treatment reagent.

[0007] Furthermore, the wastewater treatment test includes: The test reagent is added to the test wastewater sample according to the test reagent dosing parameters, and the state images and state parameters of the test wastewater are acquired in real time. The wastewater state parameters include wastewater turbidity, suspended particle concentration and wastewater transmittance. The starting and ending points of floc formation were determined based on images of the wastewater during the testing process. The test treatment characteristics corresponding to the test reagent are generated based on the wastewater state parameters at the coagulation initiation point and the wastewater state parameters at the coagulation endpoint.

[0008] Further, determining the wastewater treatment suitability of any of the candidate treatment agents includes: The wastewater treatment suitability of the candidate treatment agent is determined based on the comparison results between the test treatment characteristics and the standard treatment characteristics corresponding to the candidate treatment agent.

[0009] Furthermore, the preferred treatment agents for the wastewater to be treated are determined, including: The preferred treatment agent for the wastewater to be treated is determined based on the comparison between the wastewater treatment suitability of each candidate treatment agent and the preset treatment suitability.

[0010] Further, the floc formation nodes are determined, including: Determine the floc coagulation characterization values ​​for each node based on key wastewater state parameters in the wastewater treatment process; Based on the changes in the floc coagulation characterization values, a floc change curve is constructed to determine the floc formation node.

[0011] Further, the type of floc condensation is determined, including: The first state characterization value is determined based on the key wastewater state parameters of the floc forming node; The second state characterization value is determined based on the wastewater state image of the floc forming node; The floc condensation type is determined based on the first state characterization value and the second state characterization value.

[0012] Furthermore, anomaly indices in wastewater treatment were determined, including: Several key nodes are determined based on the changes in key wastewater state parameters within a preset time period before the floc formation node. The wastewater treatment anomaly index is determined based on the comparison results of key wastewater state parameters of each key node.

[0013] Further, the optimization adjustment coefficients are determined, including: The basic adjustment index is determined based on the floc condensation type; An abnormal adjustment index is determined based on the comparison between the wastewater treatment abnormality index and the preset abnormality index. The optimized adjustment coefficient is determined based on the basic adjustment index and the abnormal adjustment index.

[0014] Furthermore, the initial drug delivery parameters are optimized, including: Under the first determination condition, the initial dosage of the drug is reduced based on the optimization adjustment coefficient to obtain the target dosage of the drug; Under the second determination condition, the initial stirring time is increased based on the optimization adjustment coefficient to obtain the target stirring time; The first determination condition is that the floc condensation type is the floc compaction type and the wastewater treatment abnormality index is less than the preset abnormality index. The second determination condition is that the floc condensation type is floc dispersion type and the wastewater treatment abnormality index is less than the preset abnormality index.

[0015] Furthermore, the preferred treatment agents were redefined, including: Under the third determination condition, the preferred treatment agent is re-determined based on the wastewater treatment suitability of each of the candidate treatment agents; The third determination condition is that the floc condensation type is floc dispersion type and the wastewater treatment abnormality index is greater than or equal to the preset abnormality index.

[0016] Compared with existing technologies, the advantages of this invention are as follows: By conducting wastewater treatment tests on multiple candidate treatment agents, the coagulation reaction characteristics of each candidate agent can be quantified, achieving an objective quantitative characterization of agent compatibility and improving the scientific and objective nature of agent selection. By testing treatment characteristics to determine the matching degree between different candidate treatment agents and the wastewater quality, the optimal treatment agent can be screened, achieving precise agent selection and laying the foundation for subsequent optimized control of agent consumption. By using initial agent dosing parameters as basic control parameters for standardized dosing, and simultaneously periodically and continuously collecting key wastewater state parameters, the changes in wastewater state parameters during the wastewater treatment process can be accurately captured, the floc formation node can be precisely located, and the time-series monitoring of the flocculation reaction process can be achieved. By integrating wastewater state parameters and wastewater state images to determine floc coagulation type, it can accurately distinguish between dense floc formation and dispersed floc formation. By quantitatively calculating the wastewater treatment anomaly index through the temporal changes of parameters in the early stages of floc formation, it can objectively characterize the temporal fluctuations in water quality and the transformation of floc micromorphology during wastewater treatment using selected treatment agents, achieving precise dual perception of floc state and operating conditions. By combining floc coagulation morphology and operating conditions to generate optimization adjustment coefficients, it is possible to adaptively and precisely correct agent dosing parameters, achieving dynamic optimization control of agent consumption during wastewater treatment and ensuring the stability of agent consumption optimization control.

[0017] Furthermore, this invention conducts parallel tests on different candidate reagents using standardized test reagent dosing parameters, ensuring that all candidate reagents are under the same testing conditions. Through wastewater state parameters and wastewater state images, the entire reagent reaction process can be recorded from two dimensions, enabling comprehensive perception of the microscopic changes in the flocculation reaction and the water purification status during the test process, providing data support for subsequent accurate identification of the floc coagulation process. By performing time-series analysis of the entire floc growth process using wastewater state images, the coagulation initiation point where flocs begin to aggregate and the coagulation endpoint where the floc morphology stabilizes and the structure no longer changes significantly can be accurately located. By comparing the wastewater state parameters at the coagulation initiation and endpoint, the water purification capacity and particle coagulation efficiency of the test reagent during wastewater treatment of the test wastewater sample can be quantitatively characterized, comprehensively reflecting the degree of matching between the test reagent and the test wastewater sample.

[0018] Furthermore, this invention provides a unified and quantifiable reference benchmark for evaluating the performance of wastewater treatment agents by comparing the test treatment characteristics of each candidate agent with standard treatment characteristics. Through precise comparison of feature dimensions, it can comprehensively capture the performance differences of different candidate agents in the wastewater treatment process, achieving standardized and precise evaluation of agent-wastewater treatment suitability. This provides accurate and reliable quantitative data support for subsequent selection of suitable agents. By using the quantified wastewater treatment suitability as the core basis, combined with preset treatment suitability, agent screening can be completed quickly and accurately, identifying the preferred treatment agents that highly match the real-time water quality characteristics and pollutant features of the wastewater to be treated. This achieves intelligent and precise automatic selection of wastewater treatment agents, improving wastewater treatment efficiency.

[0019] Furthermore, this invention quantifies and calculates the floc coagulation characterization values ​​corresponding to each node in the wastewater treatment process using key wastewater state parameters, thereby constructing a floc change curve. This allows for precise location of floc formation nodes, providing a precise and standardized time-series benchmark for subsequent floc coagulation type identification, operational anomaly analysis, and reagent parameter optimization, thus improving the accuracy and stability of reagent consumption optimization control. By using a dual-dimensional fusion discrimination method combining the quantitative features of wastewater state parameters at floc formation nodes with wastewater state image features, first and second state characterization values ​​are generated respectively. This comprehensively determines the floc coagulation type, accurately and stably distinguishing between dense and dispersed floc types, providing a state-based basis for refined reagent consumption optimization. By extracting multiple key nodes from the wastewater state parameter changes over a preset time period before the floc formation node, and generating a wastewater treatment anomaly index through multi-node parameter deviation comparison, the temporal fluctuations in water quality and anomalies in floc coagulation state changes can be quantified, improving the accuracy of subsequent reagent consumption optimization control. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of the chemical consumption optimization control method for wastewater treatment according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the wastewater treatment test process according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the process of determining the floc forming node in an embodiment of the present invention; Figure 4 This is a schematic diagram of the process for determining the type of floc condensation in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0022] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] Please see Figure 1 The diagram shown is a flowchart illustrating the chemical consumption optimization control method for wastewater treatment according to an embodiment of the present invention. The present invention provides a chemical consumption optimization control method for wastewater treatment, comprising: Step S1: Based on several candidate treatment agents, wastewater treatment tests are conducted on the wastewater samples to be treated in order to obtain the test treatment characteristics corresponding to each candidate treatment agent. Specifically, in step S1, the wastewater treatment test includes: Step S11: The test reagent is added to the test wastewater sample according to the test reagent dosing parameters, and the test wastewater state image and test wastewater state parameters are acquired in real time. The wastewater state parameters include wastewater turbidity, suspended particle concentration and wastewater transmittance. Step S12: Determine the coagulation start point and coagulation end point of the test flocs based on the test wastewater state images during the test process; Step S13: Generate the test treatment characteristics corresponding to the test agent based on the wastewater state parameters at the coagulation start point and the wastewater state parameters at the coagulation end point.

[0024] In implementation, there are no restrictions on the specific equipment and structure for acquiring images and parameters of wastewater state. For example, high-resolution industrial cameras can be set directly above and to the side of the wastewater sample. The optical axis of the camera directly above is perpendicular to the liquid surface to acquire a top-down image of the wastewater's internal distribution, while the optical axis of the camera to the side is horizontal to the liquid surface to acquire a side-view image of the wastewater's internal structure. Both cameras should have a frame rate of no less than 30 frames per second (fps) and a resolution of no less than 1920×1080 pixels, and be equipped with a uniform backlight to eliminate shadow interference. The turbidity of the wastewater is measured by a turbidity meter, and the concentration of suspended particles (the concentration of suspended particles with a diameter greater than 1 μm) is indirectly determined by a laser scattering particle size analyzer or by weighing a filter membrane. The transmittance of the wastewater is measured by a transmission photometer. The selected treatment agents can be coagulants (such as polyaluminum chloride, aluminum sulfate, and polyferric sulfate), flocculants (such as polyacrylamide), or any agents that can be used in combination, to achieve wastewater treatment by coagulating flocs. The dosage ratio of each selected treatment agent to the wastewater to be treated is determined based on historical wastewater treatment data and wastewater treatment industry standard agent usage methods.

[0025] Understandably, several wastewater samples to be treated are taken as test wastewater samples. The volume of the test wastewater samples can be set to 500mL to 2000mL to ensure the representativeness and operability of the test results. Each test wastewater sample is placed in a beaker of a standard six-unit stirring test apparatus. The beaker is equipped with a constant temperature water bath jacket to maintain the test temperature consistent with the actual temperature of the wastewater to be treated, eliminating the influence of temperature on flocculation kinetics. At the same time, each candidate treatment agent is added to the beaker containing each test wastewater sample according to the test dosage, and stirring is carried out for the test stirring time. The stirring speed can be set to 150r / min to 200r / min. The minimum dosage ratio is determined based on the dosage ratio of the candidate treatment agent to the wastewater to be treated, and the test dosage is set in combination with the volume of the test wastewater samples. The test stirring time can be set to 5min to 10min.

[0026] It is understandable that the spacing between suspended particles inside the floc is less than the preset spacing and the equivalent diameter of the floc (converting the actual irregular contour of the floc into the diameter of a standard circle with the same projected area) is greater than the first preset diameter. Generally, the preset spacing is set to a range of 15μm to 20μm, and the first preset diameter is set to a range of 30μm to 50μm. The test wastewater state image is preprocessed, including: grayscale conversion, Gaussian filtering for noise reduction, and contrast enhancement. Adaptive threshold segmentation or a semantic segmentation model based on deep learning (such as U-Net) is used to extract the floc region. For any frame of the test wastewater state image, several floc regions are extracted from the image. For any floc region, the area change rate (area change per unit time) of the floc region at each node during the test process is constructed. The coagulation start point is the time node corresponding to any floc region with the earliest coagulation start time, and the coagulation end point is the time node corresponding to the floc region with an area change rate less than the preset area change rate and the longest coagulation time. Preferably, the preset area change rate is set to 1 / 2 to 1 / 3 of the average area change rate during the coagulation process of the floc region.

[0027] Understandably, the test treatment characteristics include test turbidity characterization value, test concentration characterization value, and test transmittance characterization value. The ratio of the turbidity of the wastewater at the end of condensation to the turbidity of the wastewater at the beginning of condensation is determined as the test turbidity characterization value, the ratio of the suspended particle concentration at the end of condensation to the suspended particle concentration at the beginning of condensation is determined as the test concentration characterization value, and the ratio of the transmittance of the wastewater at the beginning of condensation to the transmittance of the wastewater at the end of condensation is determined as the test transmittance characterization value.

[0028] Specifically, this invention conducts parallel tests on different candidate reagents using standardized reagent dosing parameters, ensuring that all candidate reagents are under the same testing conditions. Through wastewater state parameters and images, the entire reagent reaction process can be recorded from two dimensions, enabling comprehensive perception of the microscopic changes in the flocculation reaction and the water purification status during the test process. This provides data support for subsequent accurate identification of the floc coagulation process. Time-series analysis of the entire floc growth process using wastewater state images allows for precise location of the coagulation initiation point, where flocs begin to aggregate, and the coagulation endpoint, where the floc morphology stabilizes and the structure no longer changes significantly. Comparison of wastewater state parameters at the coagulation initiation and endpoint quantifies the water purification capacity and particle coagulation efficiency of the tested reagent in treating the test wastewater sample, comprehensively reflecting the degree of matching between the tested reagent and the test wastewater sample.

[0029] Step S2: Determine the wastewater treatment suitability of each candidate treatment agent based on the test treatment characteristics corresponding to each candidate treatment agent, so as to determine the preferred treatment agent for the wastewater to be treated; Specifically, in step S2, determining the wastewater treatment suitability of any of the candidate treatment agents includes: The wastewater treatment suitability of the candidate treatment agent is determined based on the comparison results between the test treatment characteristics and the standard treatment characteristics corresponding to the candidate treatment agent.

[0030] In implementation, standard treatment characteristics can be set using pre-calibrated optimal test treatment characteristics of the corresponding reagent. Alternatively, multiple batches of historical samples of the same type of wastewater can be selected, and conventional, mature treatment reagents with the best flocculation effect, lowest reagent consumption, and dense flocs, verified through engineering, can be used. Multiple parallel wastewater treatment tests can be repeatedly conducted under the same reagent dosing parameters and environmental conditions as the candidate reagent. For each test, the coagulation initiation and end points are identified in the same way, and the corresponding turbidity, concentration, and transmittance values ​​are calculated. The arithmetic mean of the multiple valid test results is then calculated to obtain the standard treatment characteristics. For any candidate reagent, its test treatment characteristics are Y=(Y1, Y2, Y3) and E=(E1, E2, E3), then the wastewater treatment suitability DP for that candidate reagent is DP=(Y1×E1+Y2×E2+Y3×E3) / (sqrt((Y1))). 2 +(Y2) 2 +(Y3) 2 )×sqrt((E1) 2 +(E2) 2 +(E3) 2 )), where sqrt() is the preset square root determination function.

[0031] Specifically, in step S2, the preferred treatment agent for the wastewater to be treated is determined, including: The preferred treatment agent for the wastewater to be treated is determined based on the comparison between the wastewater treatment suitability of each candidate treatment agent and the preset treatment suitability.

[0032] In practice, the wastewater treatment suitability of each candidate treatment agent is sorted from largest to smallest. If the largest wastewater treatment suitability is greater than the preset treatment suitability, the candidate treatment agent corresponding to the largest wastewater treatment suitability is determined as the preferred treatment agent. If the largest wastewater treatment suitability is less than or equal to the preset treatment suitability, a new candidate treatment agent is selected for testing.

[0033] Specifically, this invention provides a unified and quantifiable reference benchmark for evaluating the performance of wastewater treatment agents by comparing the test treatment characteristics of each candidate agent with standard treatment characteristics. Through precise comparison of feature dimensions, it can comprehensively capture the performance differences of different candidate agents in the wastewater treatment process, achieving standardized and precise evaluation of agent-wastewater treatment suitability. This provides accurate and reliable quantitative data support for subsequent selection of suitable agents. By using the quantified wastewater treatment suitability as the core basis, combined with preset treatment suitability, agent screening can be completed quickly and accurately, identifying the preferred treatment agents that highly match the real-time water quality characteristics and pollutant features of the wastewater to be treated. This enables intelligent and precise automatic selection of wastewater treatment agents, improving wastewater treatment efficiency.

[0034] Step S3: Based on the initial reagent dosing parameters, the preferred treatment reagent is added to the wastewater treatment tank, and key wastewater state parameters during the wastewater treatment process are periodically acquired to determine the floc formation node. The reagent dosing parameters include the reagent dosage and stirring time. Specifically, in step S3, determining the floc forming node includes: Step S31: Determine the floc coagulation characterization values ​​corresponding to each node based on the key wastewater state parameters in the wastewater treatment process; Step S32: Construct a floc change curve based on the changes in the floc coagulation characterization value to determine the floc formation node.

[0035] In implementation, the dosage range of the preferred treatment agent to the wastewater to be treated is determined based on the optimal ratio and the total amount of wastewater to be treated in the wastewater treatment tank. This range includes both the minimum and maximum dosage. The average of the minimum and maximum dosages is set as the initial dosage. For example, if the optimal ratio is 1 / 10 to 1 / 20 and the total amount of wastewater to be treated in the tank is 1000L, then the minimum dosage of the preferred treatment agent is 100L, and the maximum dosage is 200L. Therefore, the initial dosage is (100+200) / 2=150L. The ratio of the initial dosage to the dosage of the preferred treatment agent during the wastewater treatment test is determined as the adjustment ratio. The product of 0.7 to 0.8 times the adjustment ratio and the test stirring time is determined as the initial stirring time. The parameter acquisition cycle is set to 10s to 15s.

[0036] Understandably, by defining the wastewater state parameters at the coagulation endpoint of the preferred treatment agent in wastewater treatment testing as the comparison wastewater state parameters, for any given node, the ratio of the comparison wastewater turbidity to the key wastewater turbidity is defined as the key turbidity characterization value; the ratio of the comparison suspended particle concentration to the key suspended particle concentration is defined as the key concentration characterization value; the ratio of the key wastewater transmittance to the comparison wastewater transmittance is defined as the key transmittance characterization value; and the average of the key turbidity characterization value, key concentration characterization value, and key transmittance characterization value is defined as the floc coagulation characterization value corresponding to that node. A larger floc coagulation characterization value at that node indicates better flocculation and a higher degree of wastewater treatment completion. Using time as the independent variable and the floc coagulation characterization value as the dependent variable, a floc change curve is constructed. The slope of each position on the curve is calculated, and the time node with the largest slope is defined as the floc formation node. If multiple time nodes with the largest slope exist, the last time node with the largest slope is taken as the floc formation node.

[0037] Step S4: Determine the floc coagulation type based on the key sewage state parameters and sewage state image of the floc forming node, and determine the sewage treatment anomaly index based on the changes in key sewage state parameters within a preset time period before the floc forming node. The floc coagulation type includes dense floc type and dispersed floc type. Specifically, in step S4, determining the type of floc condensation includes: Step S41: Determine the first state characterization value based on the key wastewater state parameters of the floc forming node; Step S42: Determine the second state characterization value based on the wastewater state image of the floc forming node; Step S43: Determine the floc coagulation type based on the first state characterization value and the second state characterization value.

[0038] In implementation, the floc coagulation characterization value corresponding to the floc forming node is determined as the first state characterization value. The equivalent diameter of each floc in the image is determined based on the sewage state image of the floc forming node. The ratio of the standard deviation to the mean of the equivalent diameter of each floc is determined as the second state characterization value. If the first state characterization value is less than the first preset characterization value and the second state characterization value is less than the second preset characterization value, the floc coagulation type is determined as the floc compact type. If the first state characterization value is greater than or equal to the first preset characterization value, or the second state characterization value is greater than or equal to the second preset characterization value, the floc coagulation type is determined as the floc dispersion type.

[0039] Understandably, a first preset characterization value is set based on the average of the first state characterization values ​​calculated from historical data of wastewater treatment tanks where the effluent quality meets industry standards and the floc coagulation type is dense floc. A second preset characterization value is set based on the average of the second state characterization values ​​calculated from historical data of wastewater treatment tanks where the effluent quality meets industry standards and the floc coagulation type is dense floc.

[0040] Specifically, in step S4, the wastewater treatment anomaly index is determined, including: Step S44: Determine several key nodes based on the changes in key wastewater state parameters within a preset time period before the floc formation node; Step S45: Determine the wastewater treatment anomaly index based on the comparison results of the key wastewater state parameters of each key node.

[0041] In implementation, for any node within a preset time period before the floc formation node, the floc coagulation characterization value corresponding to that node is calculated. If the floc coagulation characterization value corresponding to that node is greater than the preset coagulation characterization value, then that node is determined as a critical node. Preferably, the preset coagulation characterization value is set to 1 / 2 to 2 / 3 of the floc coagulation characterization value corresponding to the floc formation node. If the floc formation node has not yet been determined, the maximum floc coagulation characterization value within the previous 0.5 hours is used as a reference. The preset time period is set to 1 / 2 to 2 / 3 of the time from the reagent delivery node to the floc formation node, with the floc formation node as the endpoint of the preset time period. For any critical node, the critical wastewater state parameters of that critical node are compared with those of the floc-forming node. The ratio of the critical wastewater turbidity of that critical node to that of the floc-forming node is determined as the turbidity characterization value of that critical node. The ratio of the critical suspended particle concentration of that critical node to that of the floc-forming node is determined as the concentration characterization value of that critical node. The ratio of the critical wastewater transmittance of the floc-forming node to that of the critical wastewater is determined as the transmittance characterization value of that critical node. The average of the turbidity characterization value, concentration characterization value, and transmittance characterization value of that critical node is determined as the wastewater treatment anomaly characterization value corresponding to that critical node. The wastewater treatment anomaly characterization values ​​corresponding to each critical node are compared, and the minimum and maximum wastewater treatment anomaly characterization values ​​are determined as the wastewater treatment anomaly index.

[0042] Specifically, this invention quantifies and calculates the floc coagulation characterization values ​​corresponding to each node in the wastewater treatment process using key wastewater state parameters. This constructs a floc change curve, enabling precise location of floc formation nodes. This provides a precise and standardized time-series benchmark for subsequent floc coagulation type identification, operational anomaly analysis, and reagent parameter optimization, improving the accuracy and stability of reagent consumption optimization control. By fusing the quantitative features of wastewater state parameters at floc formation nodes with wastewater state image features in a dual-dimensional fusion discrimination method, first and second state characterization values ​​are generated respectively. This comprehensively determines the floc coagulation type, accurately and stably distinguishing between dense and dispersed floc types, providing a state-based basis for refined reagent consumption optimization. Multiple key nodes are extracted by analyzing the changes in wastewater state parameters over a preset time period before the floc formation node. The wastewater treatment anomaly index generated by comparing multi-node parameter deviations quantifies water quality temporal fluctuations and abnormal changes in floc coagulation state, improving the accuracy of subsequent reagent consumption optimization control.

[0043] Step S5: Determine the optimization adjustment coefficient based on the floc coagulation type and the wastewater treatment anomaly index to optimize the initial reagent dosing parameters, or to re-determine the preferred treatment reagent.

[0044] Specifically, in step S5, the optimization adjustment coefficients are determined, including: Step S51: Determine the basic adjustment index based on the floc condensation type; Step S52: Determine the abnormal adjustment index based on the comparison result between the sewage treatment abnormal index and the preset abnormal index; Step S53: Determine the optimized adjustment coefficient based on the basic adjustment index and the abnormal adjustment index.

[0045] In implementation, if the floc formation type is dense, it indicates a good match between the current reagent dosage and stirring time, with a small optimization range; in this case, the basic adjustment coefficient is set to 0.4. If the floc formation type is dispersed, it indicates insufficient adaptability of the current reagent dosage or stirring process parameters, with a large optimization range; in this case, the basic adjustment coefficient is set to 0.6. The ratio of the wastewater treatment anomaly index to the preset anomaly index is determined as the anomaly adjustment index. If the wastewater treatment anomaly index is less than the preset anomaly index, it indicates that the water quality parameters fluctuate little within the preset time period before the floc formation node, the operating conditions are weak, the wastewater treatment operation is stable, and the degree of deviation from the normal steady state is low; therefore, a smaller anomaly adjustment index is set. If the wastewater treatment anomaly index is greater than or equal to the preset anomaly index, it indicates that the water quality parameters fluctuate drastically within the preset time period before the floc formation node, the influent operating conditions have significant impact disturbances, and the degree of deviation from the normal steady state is high; therefore, a larger anomaly adjustment index is set. The product of the basic adjustment coefficient and the anomaly adjustment coefficient is determined as the optimization adjustment coefficient. The implementers can set an anomaly adjustment index based on the average value of the sewage treatment anomaly index calculated from historical data of sewage treatment tanks where the effluent quality meets industry standards and the floc coagulation type is dense floc.

[0046] Specifically, in step S5, the initial drug delivery parameters are optimized, including: Under the first determination condition, the initial dosage of the agent is reduced based on the optimization adjustment coefficient to obtain the target dosage of the agent. In actual application, the product of the optimization adjustment coefficient and the initial dosage of the agent is determined as the dosage reduction amount, the difference between the initial dosage of the agent and the dosage reduction amount is determined as the target dosage of the agent, and the initial stirring time is determined as the target stirring time. Under the second determination condition, the initial stirring time is increased based on the optimization adjustment coefficient to obtain the target stirring time. In practical application, the product of the optimization adjustment coefficient and the initial stirring time is determined as the time increment, the sum of the initial stirring time and the time increment is determined as the target stirring time, and the initial dosage is determined as the target dosage. Under the third determination condition, the preferred treatment agent is re-determined based on the wastewater treatment suitability of each of the candidate treatment agents. In actual application, the wastewater treatment suitability of each candidate treatment agent is sorted from largest to smallest. If the wastewater treatment suitability of the second-ranked agent is greater than the preset treatment suitability, the candidate treatment agent corresponding to the second-ranked agent is determined as the preferred treatment agent. If the wastewater treatment suitability of the second-ranked agent is less than or equal to the preset treatment suitability, a new candidate treatment agent is selected for testing. Under the fourth determination condition, the initial stirring time is reduced based on the optimization adjustment coefficient to obtain the target stirring time. In practical application, the product of the optimization adjustment coefficient and the initial stirring time is determined as the time reduction amount, the difference between the initial stirring time and the time reduction amount is determined as the target stirring time, and the initial dosage is determined as the target dosage. The first determination condition is that the floc condensation type is the floc compaction type and the wastewater treatment abnormality index is less than the preset abnormality index. The second determination condition is that the floc condensation type is floc dispersion type and the wastewater treatment abnormality index is less than the preset abnormality index; The third determination condition is that the floc condensation type is floc dispersion type and the wastewater treatment abnormality index is greater than or equal to the preset abnormality index. The fourth determination condition is that the floc coagulation type is the dense floc type and the sewage treatment abnormality index is greater than or equal to the preset abnormality index.

[0047] This invention employs multiple candidate treatment agents to test wastewater samples, quantifying the coagulation reaction characteristics of each agent. This allows for objective quantitative characterization of agent compatibility, improving the scientific rigor and objectivity of agent selection. By testing treatment characteristics, the matching degree between different candidate agents and the wastewater quality is determined, enabling the selection of optimal agents and laying the foundation for subsequent optimized control of reagent consumption. Standardized dosing using initial agent dosage parameters as basic control parameters, coupled with periodic and continuous collection of key wastewater state parameters, accurately captures changes in wastewater state parameters during treatment, precisely pinpoints floc formation nodes, and achieves time-series monitoring of the flocculation reaction process. By integrating wastewater state parameters and wastewater state images to determine floc coagulation type, it can accurately distinguish between dense floc formation and dispersed floc formation. By quantitatively calculating the wastewater treatment anomaly index through the temporal changes of parameters in the early stages of floc formation, it can objectively characterize the temporal fluctuations in water quality and the transformation of floc micromorphology during wastewater treatment using selected treatment agents, achieving precise dual perception of floc state and operating conditions. By combining floc coagulation morphology and operating conditions to generate optimization adjustment coefficients, it is possible to adaptively and precisely correct agent dosing parameters, achieving dynamic optimization control of agent consumption during wastewater treatment and ensuring the stability of agent consumption optimization control.

[0048] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for reagent consumption optimization control for wastewater treatment, characterized by, include: Wastewater treatment tests were conducted on wastewater samples based on several candidate treatment agents to obtain the test treatment characteristics corresponding to each candidate treatment agent. Based on the test treatment characteristics corresponding to each of the candidate treatment agents, the wastewater treatment suitability of each candidate treatment agent is determined, so as to determine the preferred treatment agent for the wastewater to be treated. Based on the initial dosing parameters, the preferred treatment agent is added to the wastewater treatment tank, and key wastewater state parameters during the wastewater treatment process are periodically acquired to determine the floc formation node. The dosing parameters include the dosage of the agent and the stirring time. The floc condensation type is determined based on the key wastewater state parameters and wastewater state images of the floc forming node, and the wastewater treatment anomaly index is determined based on the changes in key wastewater state parameters within a preset time period before the floc forming node. The floc condensation type includes dense floc type and dispersed floc type. Based on the floc condensation type and the wastewater treatment anomaly index, an optimization adjustment coefficient is determined to optimize the initial reagent dosing parameters, or to re-determine the preferred treatment reagent.

2. The method for reagent consumption optimization control for wastewater treatment according to claim 1, characterized in that, The wastewater treatment test includes: The test reagent is added to the test wastewater sample according to the test reagent dosing parameters, and the state images and state parameters of the test wastewater are acquired in real time. The wastewater state parameters include wastewater turbidity, suspended particle concentration and wastewater transmittance. The starting and ending points of floc formation were determined based on images of the wastewater during the testing process. The test treatment characteristics corresponding to the test reagent are generated based on the wastewater state parameters at the coagulation initiation point and the wastewater state parameters at the coagulation endpoint.

3. The method for reagent consumption optimization control for wastewater treatment according to claim 2, characterized in that, Determining the wastewater treatment suitability of any of the candidate treatment agents includes: The wastewater treatment suitability of the candidate treatment agent is determined based on the comparison results between the test treatment characteristics and the standard treatment characteristics corresponding to the candidate treatment agent.

4. The method for reagent consumption optimization control for wastewater treatment according to claim 3, characterized in that, Determine the preferred treatment agents for the wastewater to be treated, including: The preferred treatment agent for the wastewater to be treated is determined based on the comparison between the wastewater treatment suitability of each candidate treatment agent and the preset treatment suitability.

5. The method for reagent consumption optimization control for wastewater treatment according to claim 4, characterized in that, Determine the floc formation nodes, including: Determine the floc coagulation characterization values ​​for each node based on key wastewater state parameters in the wastewater treatment process; Based on the changes in the floc coagulation characterization values, a floc change curve is constructed to determine the floc formation node.

6. The method for reagent consumption optimization control for wastewater treatment according to claim 5, wherein, Determine the type of floc condensation, including: The first state characterization value is determined based on the key wastewater state parameters of the floc forming node; The second state characterization value is determined based on the wastewater state image of the floc forming node; The floc condensation type is determined based on the first state characterization value and the second state characterization value.

7. The method for reagent consumption optimization control for wastewater treatment according to claim 6, characterized in that, Determine the abnormal indices of wastewater treatment, including: Several key nodes are determined based on the changes in key wastewater state parameters within a preset time period before the floc formation node. The wastewater treatment anomaly index is determined based on the comparison results of key wastewater state parameters of each key node.

8. The method for reagent consumption optimization control for wastewater treatment according to claim 7, characterized in that, Determine the optimization adjustment coefficients, including: The basic adjustment index is determined based on the floc condensation type; An abnormal adjustment index is determined based on the comparison between the wastewater treatment abnormality index and the preset abnormality index. The optimized adjustment coefficient is determined based on the basic adjustment index and the abnormal adjustment index.

9. The method for optimizing and controlling chemical consumption in wastewater treatment according to claim 8, characterized in that, Optimizing the initial drug delivery parameters includes: Under the first determination condition, the initial dosage of the drug is reduced based on the optimization adjustment coefficient to obtain the target dosage of the drug; Under the second determination condition, the initial stirring time is increased based on the optimization adjustment coefficient to obtain the target stirring time; The first determination condition is that the floc condensation type is the floc compaction type and the wastewater treatment abnormality index is less than the preset abnormality index. The second determination condition is that the floc condensation type is floc dispersion type and the wastewater treatment abnormality index is less than the preset abnormality index.

10. The method for reagent consumption optimization control for wastewater treatment according to claim 9, wherein, The preferred treatment agents have been redefined, including: Under the third determination condition, the preferred treatment agent is re-determined based on the wastewater treatment suitability of each of the candidate treatment agents; The third determination condition is that the floc condensation type is floc dispersion type and the wastewater treatment abnormality index is greater than or equal to the preset abnormality index.

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