Diversified analysis-based wastewater treatment process optimization control system

Abstract

The invention discloses a wastewater treatment process optimization control system based on diversified analysis. The system comprises a wastewater segmented treatment module, a multi-source data acquisition module, a multi-target analysis decision module and a terminal regulation and control module. The method comprises the following steps: monitoring a plurality of processes such as pH regulation, Fenton reaction, neutralization reaction and flocculation precipitation reaction in series, obtaining an initial dosing rate of a medicament according to feed-forward water inlet data before reaction of each process, analyzing a process treatment effect based on feedback monitoring data in the reaction process, judging whether wastewater treatment of each process reaches the standard or not, and if so, judging whether the wastewater treatment reaches the standard or not. If the wastewater does not reach the standard, obtaining an agent adding correction value based on deviation analysis of feedback monitoring data, and based on a composite control mechanism of feedforward setting reference and feedback real-time fine adjustment, not only can cope with complex and changeable water inlet conditions in different wastewater treatment processes, but also can perform dynamic feedback optimization based on water quality fluctuation and reaction conditions; the intelligent dynamic dosing of the medicament is realized.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment control technology, and more specifically, to a wastewater treatment process optimization control system based on multivariate analysis. Background Technology

[0002] Many industrial wastewaters (such as chemical, pharmaceutical, and dyeing wastewater) are not only highly concentrated, but also contain substances that inhibit or poison microorganisms, rendering direct treatment by biochemical methods ineffective. Therefore, the Fenton oxidation process is a key and irreplaceable technology for treating high-concentration, recalcitrant organic wastewater. The Fenton process is essentially an advanced oxidation process that uses hydrogen peroxide under acidic conditions to catalyze the generation of hydroxyl radicals with extremely strong oxidizing power to attack and degrade pollutants.

[0003] The traditional Fenton process has the following problems: the reagent dosage ratio is fixed and mainly relies on manual experience. It is not only difficult to cope with the complex and ever-changing influent conditions in different wastewater treatment processes, but also cannot dynamically optimize the process according to water quality fluctuations and reaction conditions. This leads to reagent waste or substandard treatment results. In particular, excessive reagent addition not only increases treatment costs, but also increases the salinity of the effluent, affecting subsequent processes.

[0004] Therefore, in response to the actual technical shortcomings, a wastewater treatment process optimization and control system based on multi-dimensional analysis is proposed. Summary of the Invention

[0005] The purpose of this invention is to address practical technical deficiencies, and a wastewater treatment process optimization and control system based on multivariate analysis is provided.

[0006] The objective of this invention can be achieved through the following technical solution: a wastewater treatment process optimization and control system based on multi-dimensional analysis, including a wastewater segmentation treatment module, a multi-source data acquisition module, a multi-objective analysis and decision-making module, and a terminal control module; The wastewater segmented treatment module includes a pH adjustment submodule, a Fenton reaction submodule, a neutralization reaction submodule, and a flocculation and sedimentation submodule. Each submodule corresponds to the pH adjustment process, Fenton reaction process, neutralization reaction process, and flocculation and sedimentation process in the wastewater treatment process, respectively. The multi-source data acquisition module is used to acquire feedforward water data before each process reaction and feedback monitoring data during the reaction, and sends the feedforward water data and feedback monitoring data to the multi-objective analysis and decision module. The multi-objective analysis and decision-making module performs wastewater pollution analysis based on feedforward influent data to obtain the initial dosing rate of the reagents. It also performs process treatment effect analysis based on feedback monitoring data to determine whether the wastewater treatment of each process meets the standards. If the standards are not met, it generates a reagent dosing correction signal for the corresponding process and calculates the deviation based on the feedback monitoring data to obtain the reagent dosing correction value for the corresponding process. Finally, it sends the initial dosing rate of the reagents and the reagent dosing correction value to the terminal control module.

[0007] Furthermore, the process for determining whether the wastewater treatment in the pH adjustment process meets the standards includes: The pH adjustment submodule is used to acquire influent data, pH stability value, and target pH range. Influent data serves as feedforward influent data, including influent flow rate, influent pH value, and influent COD value. pH stability value and target pH range serve as feedback monitoring data. Based on the influent data and the acid dosing prediction model, the initial acid dosing rate is obtained. During the pH adjustment process, a pH change curve is established based on the continuously acquired pH value. The pH value that is in a stable state in the pH change curve is marked as the pH stability value. When the pH stability value is not within the target pH range, it is determined that the wastewater pH adjustment treatment is not up to standard, and a corresponding acid dosing correction signal for the pH adjustment process is generated.

[0008] Furthermore, in response to the acid dosing correction signal, the pH deviation value is calculated by comparing the stable pH value with the target pH range. Based on the pH deviation value and the acid dosing correction model, the acid dosing correction value is obtained.

[0009] Furthermore, the process of determining whether the wastewater treatment of the Fenton reaction process meets the standards includes: The Fenton reaction submodule is used to acquire influent data, real-time EC value, target EC range, real-time ORP value, and target ORP range. Influent data is used as feedforward influent data, including influent flow rate and influent COD value. Real-time EC value, target EC range, real-time ORP value, and target ORP range are used as feedback monitoring data. The initial catalyst dosing rate is obtained based on the influent data and the catalyst dosing prediction model. The initial oxidant dosing rate is obtained based on the influent data and the oxidant dosing prediction model. In the Fenton reaction process, EC and ORP values ​​are continuously monitored. The real-time EC value is compared with the target EC range. When the real-time EC value is not within the target EC range, it is determined that the wastewater catalytic treatment is not up to standard, and a catalyst addition correction signal is generated. Similarly, when the real-time ORP value is not within the target ORP range, it is determined that the wastewater oxidation treatment is not up to standard, and an oxidant addition correction signal is generated.

[0010] Furthermore, in response to the catalyst addition correction signal, the deviation between the real-time EC value and the target EC range is calculated to obtain the EC deviation value. The EC deviation value is then combined with the catalyst addition correction model to obtain the catalyst addition correction value. In response to the oxidant addition correction signal, the deviation between the real-time ORP value and the target ORP range is calculated to obtain the ORP deviation value. The ORP deviation value is then combined with the oxidant addition correction model to obtain the oxidant addition correction value.

[0011] Furthermore, the process for determining whether the wastewater treatment of the neutralization reaction process meets the standards includes: The neutralization reaction submodule is used to acquire Fenton effluent data, pH neutralization value, target pH neutralization range, EC neutralization value, and target EC neutralization range. The Fenton effluent data is used as feedforward influent data, including Fenton effluent flow rate, Fenton effluent pH value, and Fenton effluent COD value. The pH neutralization value, target pH neutralization range, EC neutralization value, and target EC neutralization range are used as feedback monitoring data. The initial alkali dosage rate is obtained by combining Fenton effluent data with an alkali dosage prediction model. During the neutralization reaction, the pH neutralization value and EC neutralization value are continuously acquired. The pH neutralization value is compared with the target neutralization pH range, and the EC neutralization value is compared with the target EC neutralization range. When the pH neutralization value exceeds the target pH neutralization range, or when the pH neutralization value is within the target pH neutralization range but the EC neutralization value is not within the target EC neutralization range, it is determined that the wastewater neutralization and adjustment treatment is not up to standard, and an alkali dosage correction signal is generated.

[0012] Furthermore, in response to the alkali addition correction signal, the pH neutralization value is calculated by comparing it with the target pH neutralization range to obtain the pH neutralization deviation value. Based on the pH neutralization deviation value and the analysis of the main alkali addition correction model, the main alkali addition correction value is obtained. The EC neutralization value is compared with the target EC neutralization range to calculate the EC neutralization deviation value. Based on the EC neutralization deviation value and the analysis of the auxiliary alkali addition correction model, the auxiliary alkali addition correction value is obtained. Finally, the main alkali addition correction value and the auxiliary alkali addition correction value are summed to obtain the alkali addition correction value.

[0013] Furthermore, the process for determining whether the wastewater treatment of the flocculation and sedimentation reaction process meets the standards includes: The flocculation and sedimentation submodule is used to acquire neutralized effluent data (neutralized effluent flow rate, neutralized effluent turbidity, neutralized effluent pH value, neutralized effluent EC value), process turbidity value, standard turbidity range, and effluent turbidity value. Based on the neutralized effluent data and the flocculant addition prediction model, the initial flocculant addition rate is obtained, the process turbidity value is acquired, and the process turbidity value is compared with the standard turbidity range. When the process turbidity value is not within the standard turbidity range, a flocculant addition correction signal is generated.

[0014] Furthermore, in response to the flocculant addition correction signal, the deviation between the process turbidity value and the standard turbidity range is calculated to obtain the turbidity deviation value. Based on the turbidity deviation value and the flocculant addition correction model analysis, the main flocculant addition correction value is obtained. Obtain the turbidity value of the effluent, calculate the difference between the effluent turbidity value and the preset standard effluent turbidity value, and generate a corresponding standard turbidity range adjustment signal.

[0015] Compared with the prior art, the advantages of this invention are: This solution integrates multiple processes, including pH adjustment, Fenton reaction, neutralization reaction, and flocculation sedimentation reaction, into a series for monitoring. It obtains the initial dosing rate of chemicals based on feedforward influent data before each reaction, and analyzes the process treatment effect based on feedback monitoring data during the reaction process to determine whether the wastewater treatment of each process meets the standards. If not, it obtains a chemical dosing correction value based on deviation analysis of the feedback monitoring data. Based on a composite control mechanism of "feedforward setting benchmark + real-time feedback fine-tuning," it can not only cope with the complex and varied influent conditions in different wastewater treatment processes, but also dynamically optimize the chemical dosing according to water quality fluctuations and reaction conditions in each treatment process.

[0016] By utilizing effluent data from preceding stages as feedforward signals for subsequent stages, and through correlation analysis of influent and effluent data from each process stage, precise pre-dosing of chemicals in each stage is achieved. Furthermore, based on multi-parameter linkage feedback monitoring of pH, ORP, and EC, real-time dynamic allocation of chemical dosing is realized, achieving a leap from "constant value control" to "optimal adaptive control." This adapts to water quality fluctuations, resulting in more stable effluent quality and ensuring consistent compliance with standards. Attached Figure Description

[0017] Figure 1 This is a system principle block diagram of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] Example 1: This invention discloses a wastewater treatment process optimization and control system based on multivariate analysis. Please refer to [link / reference]. Figure 1 , Figure 2It includes a segmented wastewater treatment module, a multi-source data acquisition module, a multi-objective analysis and decision-making module, and a terminal control module. The segmented wastewater treatment module includes a pH adjustment submodule, a Fenton reaction submodule, a neutralization reaction submodule, and a flocculation and sedimentation submodule. Each submodule corresponds to the pH adjustment process, Fenton reaction process, neutralization reaction process, and flocculation and sedimentation process in the wastewater treatment process.

[0020] The multi-source data acquisition module is used to collect feedforward water data before each process reaction and feedback monitoring data during the reaction process, and sends the various feedforward water data and feedback monitoring data to the multi-objective analysis and decision module; The multi-objective analysis and decision-making module performs wastewater pollution analysis based on feedforward influent data to obtain the initial dosing rate of the reagents. Based on feedback monitoring data, it analyzes the process treatment effect to determine whether the wastewater treatment of each process meets the standards. If it does not meet the standards, it generates a reagent dosing correction signal for the corresponding process and calculates the deviation based on the feedback monitoring data to obtain the reagent dosing correction value for the corresponding process. The module also sends the initial dosing rate of the reagents and the reagent dosing correction value to the terminal control module. Different feedforward influent data are collected for different processes, and the core controlled variables and optimization objectives are changed based on different feedback monitoring data. Multiple processes such as pH adjustment, Fenton reaction, neutralization reaction and flocculation sedimentation reaction are dynamically connected and monitored.

[0021] Example 2: Please refer to Figure 2 The specific process for determining whether the wastewater treatment in each process meets the standards is as follows: The process for determining whether wastewater treatment in a pH adjustment process meets standards includes: The pH adjustment submodule is used to acquire influent data before the pH adjustment process, pH stability value during the pH adjustment process, and target pH range. Influent data is used as feedforward influent data, including influent flow rate, influent pH value, and influent COD value. pH stability value and target pH range are used as feedback monitoring data. The initial acid dosing rate is obtained based on the influent data and the acid dosing prediction model. For the addition of sulfuric acid, a pH change curve is established based on the continuously acquired pH value during the pH adjustment process. The pH change curve is monitored within the set reaction time window to determine whether the trend of the pH change curve has reached stability. If it has reached stability, the pH value in the stable state in the pH change curve is obtained and marked as the pH stable value. The pH stable value is compared with the target pH value range to see if the pH stable value deviates from the target pH value range. When the pH stable value is within the target pH value range, it is determined that the wastewater pH adjustment treatment in the pH adjustment process meets the standard; otherwise, it is determined that the wastewater pH adjustment treatment does not meet the standard, and a corresponding acid dosing correction signal for the pH adjustment process is generated. The process of obtaining the acid dosage correction value in the corresponding pH adjustment procedure includes: In response to the acid dosing correction signal, the pH deviation value is calculated by comparing the pH stable value with the target pH range. Based on the pH deviation value and the acid dosing correction model, the acid dosing correction value is obtained. Specifically, when the stable pH value is greater than the maximum value of the target pH range, the difference between the stable pH value and the maximum value of the target pH range is marked as a positive pH deviation value, and the amount of acid added needs to be increased. Conversely, when the stable pH value is less than the minimum value of the target pH range, the difference between the stable pH value and the minimum value of the target pH range is marked as a negative pH deviation value, and the amount of acid added needs to be reduced. The positive and negative pH deviations are summarized as pH deviation values. The positive or negative pH deviations are substituted into the acid addition correction model to obtain the acid addition correction value. The acid addition correction model is a machine learning model, which will output a correction rate based on the magnitude and trend of the deviation. This correction rate is the acid addition correction value.

[0022] The process for determining whether the wastewater treatment of the Fenton reaction process meets the standards includes: The Fenton reaction submodule is used to acquire influent data before the Fenton reaction process, real-time EC value, target EC range, real-time ORP value, and target ORP range during the Fenton reaction process. Influent data is used as feedforward influent data, including influent flow rate and influent COD value. Real-time EC value, target EC range, real-time ORP value, and target ORP range are used as feedback monitoring data. The initial catalyst dosing rate is obtained based on the influent water data and the catalyst dosing prediction model. Here, the initial oxidant dosing rate is obtained based on the influent water data and the oxidant dosing prediction model. Here, the initial oxidant dosing rate is obtained based on the hydrogen peroxide dosing. The catalytic Fenton reaction is carried out first, followed by the oxidative Fenton reaction. In the Fenton reaction process, EC and ORP values ​​are continuously monitored. The real-time EC value is compared with the target EC range. If the real-time EC value is not within the target EC range, it is determined that the wastewater catalytic treatment is not up to standard, and a corresponding catalyst addition correction signal for the Fenton reaction process is generated. Similarly, the real-time ORP value is compared with the target ORP range. If the real-time ORP value is not within the target ORP range, it is determined that the wastewater oxidation treatment of the Fenton reaction process is not up to standard, and a corresponding oxidant addition correction signal for the Fenton reaction process is generated.

[0023] In response to the catalyst addition correction signal, the deviation between the real-time EC value and the target EC range is calculated to obtain the EC deviation value. The EC deviation value is then combined with the catalyst addition correction model for analysis to obtain the catalyst addition correction value. In response to the oxidant addition correction signal, the deviation between the real-time ORP value and the target ORP range is calculated to obtain the ORP deviation value. The ORP deviation value is then combined with the oxidant addition correction model to obtain the oxidant addition correction value.

[0024] The process for determining whether the wastewater treatment of the neutralization reaction process meets the standards includes: The neutralization reaction submodule is used to acquire Fenton effluent data before the neutralization reaction process, pH neutralization value, target pH neutralization range, EC neutralization value, and target EC neutralization range during the neutralization reaction process. Among them, the Fenton effluent data is used as feedforward influent data, including Fenton effluent flow rate, Fenton effluent pH value, and Fenton effluent COD value. The pH neutralization value, target pH neutralization range, EC neutralization value, and target EC neutralization range are used as feedback monitoring data. The initial alkali dosing rate was obtained by combining Fenton effluent data with an alkali dosing prediction model. For calcium oxide dosing, the pH neutralization value and EC neutralization value were continuously acquired during the neutralization reaction. The pH neutralization value was compared with the target neutralization pH range. When the pH neutralization value was within the target pH neutralization range, the EC neutralization value was compared with the target EC neutralization range. When the real-time EC value was within the target neutralization EC range, it was determined that the wastewater neutralization and treatment in the neutralization reaction process met the standards. Conversely, if the pH neutralization value exceeds the target pH neutralization range, or if the pH neutralization value is within the target pH neutralization range but the EC neutralization value is outside the target EC neutralization range, it is determined that the wastewater neutralization and adjustment treatment is substandard, and an alkali addition correction signal is generated. The process of obtaining the correction value for alkali addition in the neutralization reaction process includes: In response to the alkali addition correction signal, the pH neutralization value is calculated to obtain the pH neutralization deviation value by comparing the pH neutralization value with the target pH neutralization range. Based on the pH neutralization deviation value and the analysis of the main alkali addition correction model, the main alkali addition correction value is obtained. If the pH neutralization deviation value is negative, that is, the pH neutralization value is lower than the lower limit of the target pH neutralization range, a positive correction value is output to increase the oxidant addition acceleration rate; otherwise, a negative correction value is output to significantly reduce the oxidant addition acceleration rate. The EC neutralization value is compared with the target EC neutralization range to calculate the EC neutralization deviation. This deviation is then analyzed using an alkali-assisted dosing correction model to obtain the alkali-assisted dosing correction value. Finally, the main alkali dosing correction value is summed with the alkali-assisted correction value to obtain the alkali dosing correction value. Once the pH has entered the target range, the main control objective shifts from "rapid pH adjustment" to "precise control to prevent overdose." EC becomes crucial at this point because it sensitively reflects the concentration of dissolved salts in the water. For example, if EC continues to rise and... If the target range is exceeded, it indicates that the oxidant dosage is relatively excessive. At this time, the EC controller will output a negative fine-tuning value. Before the pH exceeds the standard, it will "quietly" slightly reduce the oxidant dosage rate to achieve preventive control. This is a typical cascade control structure. pH is the main controlled variable, and EC is the auxiliary controlled variable. Its core objective is to stabilize and quickly raise the pH of Fenton effluent (acidic) to the optimal range required by the subsequent flocculation and sedimentation process with the lowest alkali dosage, while avoiding excessive pH or excessive sludge production due to overdosing.

[0025] The process for determining whether wastewater treatment in a flocculation and sedimentation reaction process meets standards includes: The flocculation and sedimentation submodule is used to acquire neutralized effluent data before the reaction of the flocculation and sedimentation process, process turbidity value, standard turbidity range and effluent turbidity value during the reaction of the flocculation and sedimentation process. Neutralized effluent data is used as feedforward influent data, including neutralized effluent flow rate, neutralized effluent turbidity, neutralized effluent pH value and neutralized effluent EC value. Standard turbidity range and effluent turbidity value are used as feedback monitoring data. The initial flocculant dosing rate is obtained based on the neutralized effluent data and the flocculant dosing prediction model. During the flocculation and sedimentation reaction, the process turbidity value is obtained and compared with the standard turbidity range. When the process turbidity value is not within the standard turbidity range, a flocculant dosing correction signal is generated. The process of obtaining the flocculant dosage correction value in the corresponding flocculation and sedimentation process includes: In response to the flocculant addition correction signal, the deviation between the process turbidity value and the standard turbidity range is calculated to obtain the turbidity deviation value. Based on the turbidity deviation value and the flocculant addition correction model, the main flocculant addition correction value is obtained. If the turbidity deviation value is positive, it indicates that the floc formation is not good, and the controller will immediately increase the flocculant addition rate. If the turbidity deviation value is negative, it indicates that the addition may be excessive, and the controller will reduce the addition rate. The effluent turbidity value is obtained, and the difference between the effluent turbidity value and the preset standard effluent turbidity value is calculated to obtain the effluent turbidity deviation. When the effluent turbidity deviation is less than zero, it indicates that the effluent turbidity is consistently and stably far better than the discharge standard. Based on the effluent turbidity deviation, an upward adjustment signal and an upward adjustment index for the standard turbidity range are generated. Conversely, when the effluent turbidity deviation is greater than zero, it indicates that the effluent turbidity is lower than the discharge standard. Based on the effluent turbidity deviation, a downward adjustment signal and a downward adjustment index for the standard turbidity range are generated, and the dosage of the chemical is increased to ensure the safety of the effluent.

[0026] The initial dosing acceleration rates of the reagents mentioned in the text include the initial dosing acceleration rates of acid reagents, catalysts, oxidants, alkalis, and flocculants. The reagent dosing correction values ​​include the acid reagent dosing correction values, catalyst dosing correction values, oxidant dosing correction values, alkalis dosing correction values, and flocculant dosing correction values.

[0027] It should be added that the article involves comparisons of various thresholds or standard ranges. Thresholds, preset values, preset ranges, etc. are set for result comparison and analysis in order to determine good or bad. The magnitude of these values ​​is set and stored based on a combination of large-scale model analysis of sample data and human experience. They can also be appropriately adjusted based on seasonal or common-sense influencing conditions. In addition, the paper involves multiple types of prediction models and correction models. Both prediction models and correction models are machine learning models trained on historical data. During the training process, the models continuously learn different parameter features to accurately predict the initial dosing rate and dosing correction value of the reagents in different reaction processes.

[0028] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto; any equivalent substitutions or modifications made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and its improved concept, should be covered within the scope of protection of the present invention.

Claims

1. A wastewater treatment process optimization control system based on multivariate analysis, characterized in that: It includes a wastewater segmentation treatment module, a multi-source data acquisition module, a multi-objective analysis and decision-making module, and a terminal control module; The wastewater segmented treatment module includes a pH adjustment submodule, a Fenton reaction submodule, a neutralization reaction submodule, and a flocculation and sedimentation submodule. Each submodule corresponds to the pH adjustment process, Fenton reaction process, neutralization reaction process, and flocculation and sedimentation process in the wastewater treatment process, respectively. The multi-source data acquisition module is used to acquire feedforward water data before each process reaction and feedback monitoring data during the reaction, and sends the feedforward water data and feedback monitoring data to the multi-objective analysis and decision module. The multi-objective analysis and decision-making module performs wastewater pollution analysis based on feedforward influent data to obtain the initial dosing rate of the reagents. It also performs process treatment effect analysis based on feedback monitoring data to determine whether the wastewater treatment of each process meets the standards. If the standards are not met, it generates a reagent dosing correction signal for the corresponding process and calculates the deviation based on the feedback monitoring data to obtain the reagent dosing correction value for the corresponding process. Finally, it sends the initial dosing rate of the reagents and the reagent dosing correction value to the terminal control module.

2. The wastewater treatment process optimization control system based on multivariate analysis according to claim 1, characterized in that: The process for determining whether wastewater treatment in a pH adjustment process meets standards includes: The pH adjustment submodule is used to acquire influent data, pH stability value, and target pH range. Influent data serves as feedforward influent data, including influent flow rate, influent pH value, and influent COD value. pH stability value and target pH range serve as feedback monitoring data. Based on the influent data and the acid dosing prediction model, the initial acid dosing rate is obtained. During the pH adjustment process, a pH change curve is established based on the continuously acquired pH value. The pH value that is in a stable state in the pH change curve is marked as the pH stability value. When the pH stability value is not within the target pH range, it is determined that the wastewater pH adjustment treatment is not up to standard, and a corresponding acid dosing correction signal for the pH adjustment process is generated.

3. The wastewater treatment process optimization control system based on multivariate analysis according to claim 2, characterized in that: In response to the acid dosing correction signal, the pH deviation value is calculated by comparing the pH stable value with the target pH range. Based on the pH deviation value and the acid dosing correction model, the acid dosing correction value is obtained.

4. The wastewater treatment process optimization control system based on multivariate analysis according to claim 3, characterized in that: The process for determining whether the wastewater treatment of the Fenton reaction process meets the standards includes: The Fenton reaction submodule is used to acquire influent data, real-time EC value, target EC range, real-time ORP value, and target ORP range. Influent data is used as feedforward influent data, including influent flow rate and influent COD value. Real-time EC value, target EC range, real-time ORP value, and target ORP range are used as feedback monitoring data. The initial catalyst dosing rate is obtained based on the influent data and the catalyst dosing prediction model. The initial oxidant dosing rate is obtained based on the influent data and the oxidant dosing prediction model. In the Fenton reaction process, EC and ORP values ​​are continuously monitored. The real-time EC value is compared with the target EC range. When the real-time EC value is not within the target EC range, it is determined that the wastewater catalytic treatment is not up to standard, and a catalyst addition correction signal is generated. Similarly, when the real-time ORP value is not within the target ORP range, it is determined that the wastewater oxidation treatment is not up to standard, and an oxidant addition correction signal is generated.

5. The wastewater treatment process optimization control system based on multivariate analysis according to claim 4, characterized in that: In response to the catalyst addition correction signal, the deviation between the real-time EC value and the target EC range is calculated to obtain the EC deviation value. The EC deviation value is then analyzed in conjunction with the catalyst addition correction model to obtain the catalyst addition correction value. In response to the oxidant addition correction signal, the deviation between the real-time ORP value and the target ORP range is calculated to obtain the ORP deviation value. The ORP deviation value is then analyzed in conjunction with the oxidant addition correction model to obtain the oxidant addition correction value.

6. The wastewater treatment process optimization control system based on multivariate analysis according to claim 5, characterized in that: The process for determining whether the wastewater treatment of the neutralization reaction process meets the standards includes: The neutralization reaction submodule is used to acquire Fenton effluent data, pH neutralization value, target pH neutralization range, EC neutralization value, and target EC neutralization range. The Fenton effluent data is used as feedforward influent data, including Fenton effluent flow rate, Fenton effluent pH value, and Fenton effluent COD value. The pH neutralization value, target pH neutralization range, EC neutralization value, and target EC neutralization range are used as feedback monitoring data. The initial alkali dosage rate is obtained by combining Fenton effluent data with an alkali dosage prediction model. During the neutralization reaction, the pH neutralization value and EC neutralization value are continuously acquired. The pH neutralization value is compared with the target neutralization pH range, and the EC neutralization value is compared with the target EC neutralization range. When the pH neutralization value exceeds the target pH neutralization range, or when the pH neutralization value is within the target pH neutralization range but the EC neutralization value is not within the target EC neutralization range, it is determined that the wastewater neutralization and adjustment treatment is not up to standard, and an alkali dosage correction signal is generated.

7. The wastewater treatment process optimization control system based on multivariate analysis according to claim 6, characterized in that: In response to the alkali addition correction signal, the pH neutralization value is calculated to obtain the pH neutralization deviation value by deviating from the target pH neutralization range. Based on the pH neutralization deviation value and the analysis of the main alkali addition correction model, the main alkali addition correction value is obtained. The EC neutralization value is compared with the target EC neutralization range to calculate the EC neutralization deviation value. The EC neutralization deviation value is then combined with the alkali-assisted dosing correction model to obtain the alkali-assisted dosing correction value. Finally, the alkali-assisted dosing correction value is calculated by summing the alkali-assisted dosing correction value with the alkali-assisted dosing correction value.

8. The wastewater treatment process optimization control system based on multivariate analysis according to claim 7, characterized in that: The process for determining whether wastewater treatment in a flocculation and sedimentation reaction process meets standards includes: The flocculation and sedimentation submodule is used to acquire neutralized effluent data (neutralized effluent flow rate, neutralized effluent turbidity, neutralized effluent pH value, neutralized effluent EC value), process turbidity value, standard turbidity range, and effluent turbidity value. Based on the neutralized effluent data and the flocculant addition prediction model, the initial flocculant addition rate is obtained, the process turbidity value is acquired, and the process turbidity value is compared with the standard turbidity range. When the process turbidity value is not within the standard turbidity range, a flocculant addition correction signal is generated.

9. The wastewater treatment process optimization control system based on multivariate analysis according to claim 8, characterized in that: In response to the flocculant addition correction signal, the deviation between the process turbidity value and the standard turbidity range is calculated to obtain the turbidity deviation value. Based on the turbidity deviation value and the flocculant addition correction model, the main flocculant addition correction values ​​are obtained. Obtain the turbidity value of the effluent, calculate the difference between the effluent turbidity value and the preset standard effluent turbidity value, and generate a corresponding standard turbidity range adjustment signal.

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