A circulating water intelligent operation control method and system

Abstract

The present invention belongs to the technical field of circulating water treatment, and discloses a method and system for intelligent operation control of circulating water; the method comprises: performing standardization processing and feature extraction on multi-source water quality data to obtain a water quality feature parameter set; evaluating the side reaction risk between an oxidizing fungicide and a corrosion inhibitor in the circulating water based on the multi-source water quality data and the water quality feature parameter set to obtain a side reaction risk level; for a medium side reaction risk level, generating a side reaction activation identifier based on the collected side reaction associated parameters, and executing a circulating water adaptive control strategy based on the side reaction activation identifier; for a high side reaction risk level, performing feature extraction based on the collected side reaction product parameters to obtain a side reaction product feature set, and executing a circulating water adaptive control strategy based on the side reaction product feature set; the present application reduces the generation of side reaction products in the circulating water system, realizes synergistic optimization between reagents and improves the utilization rate of drug efficacy.

Description

Technical Field

[0001] The present invention relates to the technical field of circulating water treatment, and more specifically, to a circulating water intelligent operation control method and system. Background Art

[0002] Circulating water systems are widely used across multiple industries because they reuse water resources and reduce operating costs. To improve operational efficiency and water quality safety, the gradual development of smart water platforms is driving the integrated development of water quality perception, risk prediction, and dosing control. Traditional dosing methods, which rely on manual experience or fixed rules, are gradually shifting to data-driven, intelligent, and automated control models.

[0003] However, in existing technologies, the circulating water system contains a certain concentration of active substances such as organic pollutants, ammonia nitrogen, and residual chlorine. Under the conditions of simultaneous addition of oxidants and corrosion inhibitors, non-target side reactions are prone to occur, generating side reaction products such as adsorbable organic halides and trihalomethanes that are harmful to humans and the environment. Furthermore, the generation process is affected by multiple factors, including water quality changes, reagent concentration, and reaction conditions, and exhibits highly dynamic and nonlinear characteristics. Existing control methods often lack accurate identification of the intensity of side reactions and the changing trends of by-products, and are unable to implement graded response control based on risk levels. This leads to mutual cancellation between reagents and low utilization rates, which in turn causes water quality safety hazards and is not conducive to the construction of an intelligent circulating water management and control system that meets water quality stability and compliance requirements.

[0004] Therefore, there is an urgent need for a smart circulating water operation and management method that can identify side effect characteristics, dynamically adjust drug strategies, and precisely intervene in high-risk side effect states. This method addresses the existing issues of insufficient drug efficacy and uncontrollable byproduct risks. In light of this, the present invention proposes a smart circulating water operation and management method and system to address these issues. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides the following technical solutions: a circulating water intelligent operation control method, comprising:

[0006] Standardize and extract features of multi-source water quality data collected from the circulating water system to obtain a set of water quality characteristic parameters;

[0007] Based on multi-source water quality data and a set of water quality characteristic parameters, the side reaction risk between the oxidizing biocide and the corrosion inhibitor in the circulating water is evaluated to obtain a side reaction risk level; the side reaction risk level includes a high side reaction risk level, a medium side reaction risk level, and a low side reaction risk level;

[0008] For side reactions with a medium risk level, a side reaction activation flag is generated based on the collected side reaction associated parameters, and a circulating water adaptive control strategy is executed according to the side reaction activation flag;

[0009] For the high risk level of side reactions, feature extraction is performed based on the collected side reaction product parameters to obtain a side reaction product feature set, and a circulating water adaptive control strategy is executed based on the side reaction product feature set.

[0010] Furthermore, the method for obtaining the side reaction product feature set includes:

[0011] Constructing an adsorbable organic halide concentration set based on the adsorbable organic halide concentration collected within a preset time window length, and constructing an organic halide concentration change rate set based on the adsorbable organic halide concentration set;

[0012] Constructing a chloramine concentration set based on the chloramine concentrations collected within a preset time window length, and constructing a chloramine concentration change rate set based on the chloramine concentration set;

[0013] Constructing a trihalomethane concentration set based on the trihalomethane concentration set collected within a preset time window length, and constructing a trihalomethane concentration change rate set based on the trihalomethane concentration set;

[0014] The organic halide concentration change rate set, chloramine concentration change rate set and trihalomethane concentration change rate set are constructed into a side reaction product feature set.

[0015] Furthermore, the method for executing the circulating water adaptive control strategy based on the side reaction product feature set includes:

[0016] Inputting the side reaction product feature set into the side reaction intensity assessment model to obtain a side reaction intensity score;

[0017] The side effect intensity status was obtained based on the side effect intensity score;

[0018] Matching the side effect intensity state with a pre-built side effect intensity state value matching table to obtain a side effect intensity state value;

[0019] Inputting the side reaction intensity state value, the side reaction intensity score, and the side reaction product feature set into a high-risk parameter setting model to obtain a high-risk parameter setting set; the high-risk parameter setting set includes an emergency reduction ratio of the oxidant, a corrosion inhibitor replacement activation flag, a replacement target corrosion inhibitor, and a bypass drainage flow rate increase ratio;

[0020] The oxidant dosage concentration is adjusted according to the emergency reduction ratio of the oxidant; if the corrosion inhibitor replacement activation flag is yes, the currently used corrosion inhibitor is replaced with the replacement target corrosion inhibitor; the drainage control unit is adjusted according to the bypass drainage flow increase ratio based on the current drainage flow.

[0021] Furthermore, the method for obtaining the side effect intensity status based on the side effect intensity score classification includes:

[0022] Presetting a side effect intensity score threshold 1 and a side effect intensity score threshold 2, wherein the side effect intensity score threshold 1 is less than the side effect intensity score threshold 2;

[0023] If the side reaction intensity score is greater than or equal to the side reaction intensity score threshold 2, the side reaction intensity state is marked as a side reaction product high activation state;

[0024] If the side effect intensity score is greater than or equal to the side effect intensity score threshold 1 and less than the side effect intensity score threshold 2, the side effect intensity status is marked as a moderate activation state of the side effect product;

[0025] If the side reaction intensity score is less than the cohesion risk score threshold of one, the side reaction intensity state is marked as a side reaction product low activation state.

[0026] Furthermore, the method for generating the side reaction activation mark includes:

[0027] The organic carbon concentration, ammonia nitrogen concentration, residual chlorine concentration decay rate per unit time and the potential difference between the front and back of the membrane among the side reaction correlation parameters were normalized to form standardized side reaction correlation parameters;

[0028] The side effect activation score was calculated based on the standardized side effect association parameters and the side effect activation score formula;

[0029] If the side reaction activation score is greater than the preset side reaction activation score threshold, the side reaction activation flag is set to yes; if the side reaction activation score is less than or equal to the preset side reaction activation score threshold, the side reaction activation flag is set to no.

[0030] Furthermore, the method for executing the circulating water adaptive control strategy according to the side reaction activation flag includes:

[0031] If the side reaction activation flag is no, the side reaction-related parameters are continuously monitored and the side reaction activation flag is continuously updated;

[0032] If the side reaction activation flag is yes, the side reaction associated parameters are input into the medium risk parameter setting model to obtain a medium risk parameter setting set; the medium risk parameter setting set includes an oxidant adjustment range, a corrosion inhibitor adjustment range, a reagent adjustment priority, and a reagent switching cycle;

[0033] Based on the reagent adjustment priority, the adjustment operation of the oxidant or corrosion inhibitor is performed first. The oxidant adjustment amplitude and the corrosion inhibitor adjustment amplitude are used to control the adjustment direction and amplitude of the addition concentration of the oxidant and corrosion inhibitor respectively; when the adjustment duration of the priority-adjusted reagent reaches the reagent switching cycle, the system automatically performs the corresponding adjustment operation on the non-priority-adjusted reagent.

[0034] Furthermore, the method for obtaining the side effect risk level includes:

[0035] Input multi-source water quality data and water quality characteristic parameter sets into the adverse reaction risk assessment model to obtain the adverse reaction risk score;

[0036] Presetting a side effect risk score threshold 1 and a side effect risk score threshold 2, wherein the side effect risk score threshold 1 is less than the side effect risk score threshold 2;

[0037] If the side effect risk score is greater than or equal to the side effect risk score threshold of two, the secondary coagulation risk level is marked as a side effect high risk level;

[0038] If the side effect risk score is greater than or equal to the side effect risk score threshold of one and less than the side effect risk score threshold of two, the secondary coagulation risk level is marked as a side effect medium risk level;

[0039] If the side effect risk score is less than the coagulation risk score threshold of one, the secondary coagulation risk level is marked as a side effect low risk level.

[0040] Furthermore, the method for obtaining the water quality characteristic parameter set includes:

[0041] The oxidation potential factor was constructed based on the coupling of redox potential and residual chlorine concentration;

[0042] The oxidant-corrosion inhibitor reaction ratio factor is constructed based on the coupling of total chlorine concentration and corrosion inhibitor concentration;

[0043] Constructing the side reaction activation index based on the fusion of pH value and water temperature;

[0044] Construct flow field disturbance factor based on flow velocity;

[0045] The conductivity change rate within the preset time window is calculated based on the conductivity, and the film stability index is constructed based on the fusion of the conductivity change rate and the total concentration of calcium and magnesium;

[0046] The oxidation potential factor, oxidant-corrosion inhibitor reaction ratio factor, side reaction activation index, flow field disturbance factor and film formation stability index are constructed into a set of water quality characteristic parameters.

[0047] Furthermore, the multi-source water quality data includes pH value, conductivity, redox potential, total chlorine concentration, residual chlorine concentration, corrosion inhibitor concentration, water temperature, flow rate and total calcium and magnesium concentration.

[0048] A circulating water intelligent operation control system, used to implement the circulating water intelligent operation control method, comprising:

[0049] The water quality feature extraction module is used to perform standardization and feature extraction on the multi-source water quality data collected by the circulating water system to obtain a set of water quality feature parameters;

[0050] The side reaction assessment module evaluates the side reaction risk between oxidizing biocides and corrosion inhibitors in circulating water based on multi-source water quality data and a set of water quality characteristic parameters to obtain a side reaction risk level.

[0051] A medium risk response module is used to generate a side reaction activation flag based on the collected side reaction associated parameters for a medium risk level of the side reaction, and to execute a circulating water adaptive control strategy according to the side reaction activation flag;

[0052] The high-risk response module is used to extract features based on the collected side reaction product parameters for the high risk level of side reactions, obtain a side reaction product feature set, and execute a circulating water adaptive control strategy based on the side reaction product feature set.

[0053] Compared with the existing technology, the technical effects and advantages of the circulating water intelligent operation control method and system of the present invention are as follows:

[0054] The present application provides a method and system for intelligent operation and control of circulating water. To address technical pain points such as uncontrollable generation of side reaction products, delayed response of reagent regulation, and inability of traditional strategies to adapt to dynamic load changes during the treatment of circulating water, a complete closed-loop control architecture for multi-dimensional water quality feature extraction, risk level identification, adjustment decision generation, and graded response execution has been constructed. By introducing a side reaction risk level determination mechanism and a side reaction product feature set construction method, the system can accurately identify the dynamic change characteristics of key byproducts such as adsorbable organic halides, trihalomethanes, and chloramines at medium and high levels of side reaction risk, realize side reaction intensity state division based on side reaction intensity scores, and combine the side reaction intensity state values, side reaction intensity scores, and side reaction product feature sets to generate differentiated high-risk parameter setting sets, and execute the circulating water adaptive control strategy based on the high-risk parameter setting set.

[0055] The implementation method of the present application effectively reduces the generation level of side reaction products in the circulating water system and significantly reduces the emission risk of adsorbable organic halides and trihalomethanes through the linkage execution of pre-emptive water quality monitoring, correlation characteristic parameter mining and adaptive control strategy; at the same time, by optimizing the configuration of the oxidant and corrosion inhibitor addition strategy, the loss of effective ingredients caused by excessive dosage of reagents or synergistic reactions is avoided, and the synergistic optimization between reagents and the improvement of drug efficacy utilization are achieved. In addition, the system can dynamically adapt to changes in different water source compositions, process loads and operating scenarios. On the basis of ensuring the stability of water quality, it further improves the system's response capability to sudden risks, which is conducive to building a highly integrated, highly secure and highly compliant smart water management and control platform, and provides technical support for the intelligent operation and maintenance of circulating water systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a schematic diagram of a circulating water intelligent operation control system according to Example 1 of the present invention;

[0057] Figure 2 This is a flow chart of a circulating water intelligent operation control method according to embodiment 2 of the present invention;

[0058] Figure 3 A flow chart of a method for executing a circulating water adaptive control strategy based on a set of side reaction product characteristics;

[0059] Figure 4 A flow chart of a method for executing a circulating water adaptive control strategy based on a side reaction activation flag;

[0060] Figure 5 This is a flow chart of the method for obtaining a set of water quality characteristic parameters. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present invention will be described in detail, clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. It should be noted that the specific embodiments described below are only used to better illustrate and describe the technical solutions of the present invention, and are intended to enable those skilled in the art to better understand and implement the present invention, and should not be construed as limiting the scope of protection of the present invention. Without departing from the spirit and essence of the present invention, those skilled in the art may modify, adjust or make equivalent replacements based on the contents disclosed in the present invention, and these should all be regarded as the scope of protection of the present invention.

[0062] Example 1

[0063] See also Figure 1 As shown, this embodiment discloses a circulating water intelligent operation and control system, including a water quality feature extraction module, a side reaction assessment module, a medium risk response module and a high risk response module. Each module realizes data transmission through wired and / or wireless connections.

[0064] In order to further clarify the technical problem to be solved by this application and its background, before the specific implementation method is launched, the relevant reaction mechanism, the limitations of the existing technology and the practical difficulties faced by technical personnel in this field in the process of solving the problem are described in detail.

[0065] Specifically, in typical industrial circulating water treatment processes, oxidizing biocides such as sodium hypochlorite, chlorine dioxide, and bromide are often added to the circulating water system to inhibit microbial growth and biofilm formation. Simultaneously, corrosion inhibitors such as triazoles and phosphine-based carboxylic acids are also added to mitigate equipment corrosion. While these two classes of agents exhibit excellent inhibitory performance in their respective fields, they are susceptible to chemical side reactions under the combined effects of high temperature, high pH, ​​and high redox potential, generating harmful byproducts such as adsorbable organic halides (AOXs) and trihalomethanes (THMs). The typical mechanism of these side reactions is that the active chlorine or bromine in the halogenated oxidant undergoes electrophilic substitution and ring-opening reactions on the nitrogen heterocyclic rings (such as benzotriazole and methyltriazole) in the corrosion inhibitor molecule, generating halogenated organic compounds and releasing metal ions. This depletes the inhibitor's original corrosion inhibition capacity, exacerbating the risk of metal corrosion, unbalancing the system's efficacy, and contributing to the accumulation of persistent toxic pollutants in the water.

[0066] In the existing technology, the indirect avoidance of side reactions is mainly carried out by "enhancing the tolerance of the formula" or "limiting the dosage". For example, attempts are made to introduce halogen-free corrosion inhibitors to improve the stability of the system in a high-chlorine environment, or to weaken the rate of interactive reactions by fixing the order and interval of reagent addition. However, the solutions of the existing technology still have limitations, and it is difficult to fundamentally solve the chain problems caused by side reactions in the circulating water dosing system. First, the existing technology cannot achieve real-time identification of whether a side reaction has occurred or is occurring, and lacks the ability to perceive the dynamic process of the reaction online, resulting in the hidden risks in the system operation process being difficult to expose in time. Secondly, in the face of non-steady-state conditions such as instantaneous load fluctuations and pH changes, the system's response capability is insufficient, the control mechanism lags behind the water quality change process, and it is easy to miss the key intervention window. In addition, the existing dosing control generally relies on preset rules or manual operations to complete the switching of reagent formulas. It is impossible to flexibly adjust the type of reagents and the order of addition according to the risk prediction results, and lacks autonomous optimization capabilities. More importantly, traditional regulatory strategies often only focus on a single goal, such as maintaining bactericidal concentrations or inhibiting corrosion rates, and fail to establish a dynamic balance mechanism between maintaining efficacy and emission compliance. This can easily lead to a repeated tug-of-war between "efficacy recovery-emissions exceeding standards", and fall into the dilemma of balancing operational stability and environmental protection indicators.

[0067] In summary, the side reactions between oxidizing biocides and corrosion inhibitors in circulating water not only lead to imbalanced efficacy and equipment corrosion risks, but also cause serious environmental emissions. Existing technologies are unable to identify and dynamically control this problem early, creating a critical bottleneck restricting the efficiency and compliance of intelligent circulating water operations. Therefore, there is an urgent need to establish a full-process collaborative dosing control system that integrates side reaction risk identification, adaptive switching of chemical formulations, and multi-objective control optimization without interrupting stable system operation.

[0068] The water quality feature extraction module is used to standardize and extract features from multi-source water quality data collected from the circulating water system to generate a set of water quality feature parameters. This set of water quality feature parameters provides highly reliable input data for subsequent side reaction risk assessment and dosing optimization. This multi-source water quality data includes pH, conductivity, redox potential, total chlorine concentration, residual chlorine concentration, corrosion inhibitor concentration, water temperature, flow rate, and total calcium and magnesium concentrations.

[0069] It should be noted that in the embodiment of the present application, in order to realize the real-time identification and adaptive regulation of the side reaction behavior between the corrosion inhibitor and the oxidizing fungicide in the circulating water system, the system needs to continuously collect and process a series of multi-source water quality data that characterize the physical and chemical state of the water body. The multi-source water quality data is collected through a multi-functional water quality monitoring port arranged near the circulating water main pipe, the cooling tower outlet or the dosing mixing area. The collection equipment used includes an industrial-grade online electrode pH meter, a conductivity meter, an ORP electrode, a colorimetric chlorine content analyzer, an inductive or ultrasonic flow meter, a thermistor temperature sensor and a titration method or an ion-selective hardness sensor module. The corrosion inhibitor concentration is measured by an online chromatograph or a fluorescent probe. The total concentration of calcium and magnesium refers to the soluble calcium ions (Ca 2+ ) and magnesium ions (Mg 2+ ), preferably measured directly using an online electrode, titration autoanalyzer, or multi-parameter hardness sensor. These sensors are connected to a data acquisition unit via an industrial bus protocol and uploaded to a water quality feature extraction module for processing.

[0070] The collection of multi-source water quality data not only reflects the water quality stability of the circulating water system during its current operation but also serves as a key basis for assessing the potential risks of side reactions between corrosion inhibitors and oxidizing biocides. Specifically, pH significantly affects the rate of electrophilic reactions between nitrogen heterocyclic compounds in corrosion inhibitors and oxidizing biocides. Changes in conductivity are closely related to the concentration of soluble reaction products, the effective ion concentration of the biocides, and changes in the system charge distribution caused by side reactions. The redox potential reflects the redox conditions of the water and is a key criterion for determining whether chlorine- or bromine-based biocides are in a free or bound state. The difference between total chlorine concentration and residual chlorine concentration reflects the degree of reactive oxidant consumption; excessive consumption often indicates unintended reactions between the oxidant and the corrosion inhibitor. Water temperature directly determines the rate of side reactions by affecting the activation energy of the reaction. Flow rate, which is related to the mixing state, determines whether the formation of a local concentration gradient may trigger a transient side reaction peak. The total calcium and magnesium concentration is closely related to the film-forming ability of the corrosion inhibitor, the stability of the reaction pathway of the scale inhibitor, and the risk of deposits from side reaction products on equipment surfaces.

[0071] Therefore, the real-time acquisition of multi-source water quality data not only provides the necessary data basis for the water quality feature extraction module in this application, but also, through synergistic action with the side reaction risk assessment model, enables the circulating water system to accurately identify whether there is a high risk of side reactions between corrosion inhibitors and fungicides, and whether it is in the rapid generation range of by-products, and accordingly triggers the optimization of the agent formula and the adjustment of the dosing strategy, thereby effectively controlling the generation and accumulation of harmful by-products while ensuring the stability of the drug efficacy, and realizing the green, efficient and compliant operation of the circulating water system.

[0072] The standardization process includes filtering, normalization, and sliding averaging. These processes are used to remove high-frequency noise from the sensor signal, normalize dimensional differences, and smooth short-period disturbances. The standardization process can be implemented using existing signal processing algorithms and is conventionally known to those skilled in the art, so it will not be detailed in this specification.

[0073] like Figure 5 As shown, the method for obtaining the water quality characteristic parameter set includes:

[0074] The oxidation potential factor was constructed based on the coupling of redox potential and residual chlorine concentration;

[0075] The oxidant-corrosion inhibitor reaction ratio factor is constructed based on the coupling of total chlorine concentration and corrosion inhibitor concentration;

[0076] Constructing the side reaction activation index based on the fusion of pH value and water temperature;

[0077] Construct flow field disturbance factor based on flow velocity;

[0078] The conductivity change rate within a preset time window is calculated based on the conductivity, and the film stability index is constructed based on the fusion of the conductivity change rate and the total calcium and magnesium concentration; illustratively, in a preferred embodiment of the present application, the time window can be preset within 5 minutes to 10 minutes.

[0079] The oxidation potential factor, oxidant-corrosion inhibitor reaction ratio factor, side reaction activation index, flow field disturbance factor and film formation stability index are constructed into a set of water quality characteristic parameters.

[0080] The method for constructing the oxidation potential energy factor comprises:

[0081] ;

[0082] in, is the oxidation potential factor. is the redox potential, is the logarithmic function with base 2. is the residual chlorine concentration. The oxidation potential factor is used to measure the actual oxidizing capacity that the oxidant can provide in the current system. It reflects both the strength of the oxidant and its concentration level. It can better capture the possibility of oxidizing biocides producing side reactions on corrosion inhibitors.

[0083] It should be noted that the redox potential (ORP) reflects the electron transfer trend and the degree of dominance of oxidized states in water and is an important electrochemical parameter for evaluating whether a water has oxidizing properties. A higher ORP value indicates a predominance of oxidants in the water, and electrons are more likely to transfer from reducing substances to oxidants, thereby accelerating oxidation reactions such as sterilization, dechlorination, and degradation. However, the ORP value itself only reflects the oxidizing trend or tendency and cannot directly reflect the oxidant concentration level. This is especially true when the concentration of strong oxidants is low but the background reducing activity is weak, resulting in a high ORP value and insufficient actual oxidizing capacity.

[0084] In view of the fact that the redox potential is affected by a variety of redox substances in the water body, although it can reflect the overall oxidation trend, it is easy to have a "characterization offset" problem of the system oxidation capacity caused by specific redox in the circulating water system, especially when the oxidant concentration is low or the concentration of the side reactant is high. The redox potential cannot truly reflect the effective oxidation capacity of the oxidant itself. Therefore, in order to achieve a more accurate measurement of the actual oxidation capacity, the oxidant concentration factor is introduced in this application for reconciliation. Therefore, the residual chlorine concentration is selected as a representative indicator of the effective content of the oxidant in this application. The residual chlorine concentration can represent the presence intensity of hypochlorite and hypochlorite equilibrium species, and is closely related to the corrosion inhibitor side reaction.

[0085] Considering that the change of residual chlorine concentration has a nonlinear response effect in actual engineering, the residual chlorine concentration is compressed in logarithmic form to construct the residual chlorine concentration adjustment term , where 1 is a smoothing term, used to prevent the function from reaching negative infinity or abrupt gradient changes when the concentration approaches 0, ensuring that the function is differentiable and comparable in the low concentration range. The oxidation potential factor uses a product structure to couple the calculation of the electrochemical trend (redox potential) and actual oxidizing capacity (residual chlorine concentration). This allows the oxidation potential factor to respond to both the reaction potential of the oxidant in the system and the actual dosage of the oxidant, improving the ability to discriminate against oxidative attack potential in side reaction risk assessments.

[0086] The method for constructing the oxidant-corrosion inhibitor reaction ratio factor includes:

[0087] ;

[0088] in, is the oxidant-corrosion inhibitor reaction ratio factor, is the total chlorine concentration, is the corrosion inhibitor concentration.

[0089] It should be noted that in the examples of this application, to identify the potential risk of side reactions between oxidizing biocides and corrosion inhibitors in circulating water systems, an oxidant-corrosion inhibitor reaction ratio factor is proposed to quantify the proportional relationship between the two key components of the oxidizing biocide and corrosion inhibitor. The design of the oxidant-corrosion inhibitor reaction ratio factor is based on the principle of reaction stoichiometry, comprehensively considering the effective supply level of the oxidant in the water body and the real-time protective concentration of the corrosion inhibitor, reflecting the stoichiometric driving trend of the side reactions triggered in the current water body.

[0090] In a typical water treatment system, oxidizing fungicides such as hypochlorous acid and chlorine dioxide exert their bactericidal effects by providing active oxygen or halogen free radicals. However, when the concentration of the oxidizing fungicide is significantly excessive relative to the corrosion inhibitor, it is very easy to undergo electrophilic reactions with the nitrogen heterocyclic functional groups in the corrosion inhibitor molecules, generating halogenated byproducts and destroying the corrosion inhibition film structure, resulting in a significant increase in the risk of side reactions. The extent of this side reaction is not only related to the concentration of the oxidizing fungicide, but also closely related to the molar ratio or mass ratio of the oxidizing fungicide to the corrosion inhibitor. Therefore, in order to construct a simplified criterion that can be used for early side reaction risk identification, this application proposes to quantify the ratio of the oxidizing fungicide concentration to the corrosion inhibitor concentration as a basic factor.

[0091] Specifically, the total chlorine concentration in the water is used as a representative parameter for the oxidant. The total chlorine concentration includes free chlorine (hypochlorous acid, hypochlorite) and combined chlorine, which can reflect the total reactive halogen supply capacity of the system. Based on the proportional relationship between the total chlorine concentration and the corrosion inhibitor concentration, an oxidant-corrosion inhibitor reaction ratio factor is constructed. When the oxidant-corrosion inhibitor reaction ratio factor value is greater than 1, it indicates that there is more oxidant than corrosion inhibitor per unit water volume, and the potential for side reactions is high, which may cause oxidative decomposition of the corrosion inhibitor. When the oxidant-corrosion inhibitor reaction ratio factor value is close to or less than 1, it indicates that the corrosion inhibitor in the system is relatively sufficient, has strong reaction resistance and membrane stabilization capabilities, and the risk of side reactions is low. Therefore, the oxidant-corrosion inhibitor reaction ratio factor value can be used as a criterion for judging the ratio of reaction precursor concentrations, used to assess whether the circulating water is in a high-risk range for side reactions, and can serve as a trigger condition for optimizing the dosing formula.

[0092] The method for constructing the side reaction activation index includes:

[0093] ;

[0094] in, is the side reaction activation index, e is a constant, For water temperature, It is the reference value of water temperature stability; is the pH value, It is the reference value for pH stability; is the temperature sensitivity coefficient, which is preferably set at , used to control the exponential growth rate caused by temperature increase; is the acid-base deviation sensitivity coefficient, which is preferably set at , used to adjust the intensity of the effect of pH deviation on the degree of excitation; for example, when the system is running on a pharmaceutical formulation that is extremely sensitive to pH abnormalities, such as a combination of a triazole corrosion inhibitor and a strong oxidizing fungicide, Can be set in , in order to enhance the risk response capability to acid-base deviation; in a circulating water system where the pH value remains stable for a long time and the fluctuation range is less than ±0.3, it can usually be considered that the pH parameter is under control, so when setting the parameters for adjusting the response sensitivity hour, Can be set in , to avoid misjudgments caused by over-amplification of minor deviations.

[0095] The construction logic of the side reaction activation index is that when the water temperature is higher than hour, The exponential term increases significantly, which can represent the physical phenomenon that the reaction rate increases exponentially with increasing temperature; and when the pH value deviates from When the electrophilic reaction pathway is activated or the corrosion inhibitor film becomes unstable, the side reaction window is opened. By combining these two effects into a multiplicative structure, the side reaction activation index can dynamically quantify the overall driving trend of side reactions. It is differentiable and can be calculated online, making it suitable for the side reaction risk assessment in this application.

[0096] The method for constructing the flow field disturbance factor includes:

[0097] ;

[0098] in, is the flow field disturbance factor, is the current collected flow rate, It is the average flow velocity obtained by statistics during the standard stable operation stage. The larger the flow field disturbance factor is, the greater the degree of flow field disturbance currently exists in the system, which will cause the formation of high or low concentration abnormal points of the reagent in the local spatial area, thereby inducing a high risk of side reactions between the corrosion inhibitor and the oxidant.

[0099] The method for constructing the film forming stability index includes:

[0100] ;

[0101] in, is the film stability index, is the total concentration of calcium and magnesium, is the conductivity change rate, is the conductivity.

[0102] It should be noted that to accurately characterize the stability of the corrosion inhibitor's film formation process in water and its sensitivity to water quality disturbances, this application constructs a film stability index to reflect the favorable degree of formation and maintenance of the corrosion inhibitor protective film under current operating conditions. The film stability index couples conductivity and total calcium and magnesium concentrations to achieve a unified quantification of the film structure's continuity, stability, and interference sensitivity.

[0103] In the corrosion inhibition mechanism, calcium ions (Ca 2+ ) and magnesium ions (Mg 2+ ) usually acts as an auxiliary ion in the film-forming coordination reaction, collaborating with triazoles and phosphine carboxylic acids in the corrosion inhibitor to form a dense protective film on the metal surface. The higher the total concentration of calcium and magnesium, the better the coordination efficiency of the film-forming reaction, the faster the reaction rate, and the denser the film. Therefore, the total concentration of calcium and magnesium in water can be used as a representative variable of the favorable factors for film formation and used to construct the positive molecular term of film formation ability, expressed as .

[0104] Conductivity It reflects the total intensity of all dissolved ions in the water. When it is large, it indicates that the water quality of the system is in an unstable state, and the ion concentration changes dramatically over time, which may cause the film layer to form discontinuously in a local area or the film structure to be unstable, thereby increasing the frequency of corrosion inhibition film peeling, destruction or reconstruction. It is introduced as a film formation disturbance factor, forming a negative adjustment term. To ensure the positive correlation logic between the calculation stability and the physical meaning of the index, the film formation disturbance factor is placed in the denominator to form a complete film formation stability index calculation formula.

[0105] The physical logic of the film forming stability index calculation formula in this application is that, specifically, under stable working conditions where the total calcium and magnesium concentration is high and the conductivity change rate is small, the larger the film forming stability index value, the stronger the film forming ability and the more stable the film layer; conversely, if the total calcium and magnesium concentration is low and the conductivity change rate is large, the film forming stability index value decreases, indicating that the film is easily disturbed or the film forming reaction is interrupted, and the risk of side reactions increases accordingly.

[0106] The side reaction assessment module evaluates the side reaction risk between the oxidizing fungicide and the corrosion inhibitor in the circulating water based on multi-source water quality data and a set of water quality characteristic parameters to obtain a side reaction risk level; the side reaction risk level includes a high side reaction risk level, a medium side reaction risk level, and a low side reaction risk level.

[0107] The method for obtaining the side effect risk level includes:

[0108] Input multi-source water quality data and water quality characteristic parameter sets into the adverse reaction risk assessment model to obtain the adverse reaction risk score;

[0109] Presetting a side effect risk score threshold 1 and a side effect risk score threshold 2, wherein the side effect risk score threshold 1 is less than the side effect risk score threshold 2;

[0110] If the side effect risk score is greater than or equal to the side effect risk score threshold of two, the secondary coagulation risk level is marked as a side effect high risk level;

[0111] If the side effect risk score is greater than or equal to the side effect risk score threshold of one and less than the side effect risk score threshold of two, the secondary coagulation risk level is marked as a side effect medium risk level;

[0112] If the side effect risk score is less than the coagulation risk score threshold of one, the secondary coagulation risk level is marked as a side effect low risk level.

[0113] It should be noted that in the embodiment of the present application, in order to achieve dynamic identification and graded response to the side reaction behavior between the corrosion inhibitor and the oxidizing fungicide in the circulating water system, the system constructs a side reaction risk assessment model based on the water quality characteristic parameter set, and combines the set side reaction risk score threshold one and side reaction risk score threshold two, and finally outputs a side reaction risk level with a clear direction to guide the subsequent adjustment of the agent formula and optimization of the dosing strategy. The side reaction risk level is divided into three levels, namely, low side reaction risk level, medium side reaction risk level and high side reaction risk level. Different levels represent the possibility and intensity of side reaction triggering in the current water environment. The water quality status characteristics and operating trends corresponding to different side reaction risk levels are described as follows.

[0114] When the side reaction risk level is the low side reaction risk level, the system sets the side reaction activation flag to no and continuously monitors the side reaction-related parameters. At this time, the overall set of water quality characteristic parameters is in a relatively stable range, the pH value is close to the optimal reaction range of the corrosion inhibitor, the water temperature has not increased significantly, the redox potential is moderate, and the residual chlorine concentration is maintained at a low level, with no signs of excessive oxidant addition. At this time, the film formation stability index is in a high value range, the corrosion inhibition film structure is intact, the conductivity fluctuation rate is low, and the system does not show obvious disturbances. Overall, this level indicates that the water quality environment is in an ideal state with good drug efficacy and controlled side reaction risks, which is suitable for maintaining the current dosing strategy without strong intervention.

[0115] When the side reaction risk level reaches the medium risk level, some water quality parameters have already exhibited slight deviations, such as a slight decrease or increase in pH, a rise in water temperature beyond the set stability range, or an increase in residual chlorine concentration but not exceeding the high-risk threshold. At this point, the oxidant-inhibitor reaction ratio factor increases significantly, indicating a trend toward excess oxidant, which could lead to oxidative attack with the inhibitor and thus the risk of side reactions. Simultaneously, the conductivity fluctuation rate increases, and the film formation stability index decreases slightly. A medium risk level for side reactions indicates that the system has entered a potential activation zone for side reactions. If the current trend persists or no intervention is taken, it will evolve into a high-risk state for side reactions.

[0116] When the side reaction risk level reaches high, the system is in an active side reaction phase. Conditions exist for a strong reaction between the corrosion inhibitor and the oxidant, significantly increasing the risk of generating adsorbable organic halides (AOX) and trihalomethanes (THM) byproducts. Typical characteristics of a high side reaction risk include: abnormally high water temperature exceeding the set abnormal water temperature range; pH value outside the inhibitor stability zone; redox potential rising to the strong oxidation potential range; and high or rapidly fluctuating residual chlorine concentration. Furthermore, the oxidant-inhibitor reaction ratio factor significantly exceeds the empirical safety threshold, indicating a significant excess of oxidant and insufficient inhibitor protection, leading to membrane damage or a large number of side reactions. The film stability index continues to decline, conductivity fluctuations increase, and the system operating state has deviated from the steady-state control zone. Immediately switching the reagent formulation, reducing the oxidant strength, or implementing a bypass control strategy should be implemented to prevent excessive byproduct emissions and reagent waste.

[0117] In summary, this application, by refining the side effect risk level into three levels and combining water quality characteristic parameters to dynamically characterize the operating status of each level, can achieve early identification, graded response and strategic intervention of side effect risks, and provide quantifiable, predictable and executable technical support for the efficacy control and emission compliance of the circulating water system.

[0118] The training method of the adverse reaction risk assessment model includes:

[0119] Pre-collecting an adverse reaction risk assessment dataset, the adverse reaction risk assessment dataset comprising F groups of adverse reaction risk assessment data and adverse reaction risk scores corresponding to the F groups of adverse reaction risk assessment data, where F is a positive integer greater than 0, and the adverse reaction risk assessment data comprising multi-source water quality data and a set of water quality characteristic parameters; dividing the adverse reaction risk assessment dataset into a training set and a validation set, wherein the training set is used to train a adverse reaction risk assessment model, and the validation set is used to evaluate the generalization performance of the adverse reaction risk assessment model;

[0120] During the training process of the adverse reaction risk assessment model, minimizing the cross-entropy loss function is used as the optimization objective. An early stopping strategy is used to monitor the performance of the validation set, and the model performance is optimized by continuously adjusting the network parameters. When the prediction accuracy on the validation set reaches the expected accuracy, the adverse reaction risk assessment model is considered to have converged and training is stopped. The adverse reaction risk assessment model is trained using a deep neural network based on a multilayer perceptron.

[0121] The side effect risk assessment data is converted into feature vectors; the input layer of the side effect risk assessment model receives the feature vectors, and the nonlinear relationship in the data is extracted through the hidden layer. Finally, the output layer of the side effect risk assessment model calculates the probability distribution of the side effect risk score through the softmax activation function, and outputs the side effect risk score corresponding to the maximum probability as the final prediction result.

[0122] It should be noted that in the embodiments of the present application, the side reaction risk score can be comprehensively evaluated and calculated by a skilled artisan or an expert with relevant experience, using expert judgment rules or weighted linear scoring based on multi-source water quality data collected from the circulating water system and a set of water quality characteristic parameters formed after feature extraction. The side reaction risk score indicator is used to characterize the overall risk intensity of side reactions between corrosion inhibitors and oxidizing fungicides in the current water environment. It has continuous numerical values ​​and segmented classification properties, which facilitates comparison with the set side reaction risk score threshold to achieve risk level classification.

[0123] The medium risk response module is used to generate a side reaction activation identifier based on the collected side reaction associated parameters for the medium risk level of the side reaction, and execute the circulating water adaptive control strategy according to the side reaction activation identifier.

[0124] The side reaction-related parameters include organic carbon concentration, ammonia nitrogen concentration, residual chlorine concentration decay rate per unit time, and the potential difference between the front and back membranes. The side reaction activation flag has two states: yes and no. Yes indicates that the side reaction has been activated and requires adjustment; no indicates that the side reaction has not been activated and does not require adjustment, but requires continuous monitoring. This activation flag is used to determine whether to enter the adjustment phase, thereby achieving early identification and graded response of side reactions, providing a decision-making basis for dosing control in the circulating water system.

[0125] It should be noted that the organic carbon concentration is preferably monitored by an online total organic carbon analyzer, that is, all organic matter in the water sample is oxidized to carbon dioxide by high-temperature combustion or ultraviolet catalytic oxidation, and the CO2 concentration is then measured by a non-dispersive infrared detector to infer the organic carbon content. By connecting the online total organic carbon analyzer to the main water flow or bypass flow pipe of the circulating water system, continuous online monitoring can be achieved, accurately reflecting the current level of organic pollution load in the water body, and providing a basis for judging the accumulation trend of potential substrates for side reactions. The ammonia nitrogen concentration is obtained by an online monitor using an electrode method or a photometric colorimetric method. The photometric colorimetric method usually uses a salicylic acid method or a Nessler reagent method to perform a color reaction after automatic sampling. Finally, the absorbance value is read by a spectrophotometer and the corresponding ammonia nitrogen concentration is calculated.

[0126] The acquisition of the residual chlorine concentration decay rate per unit time is based on the continuous residual chlorine concentration monitoring results. The current residual chlorine concentration can be obtained in real time through the online residual chlorine analyzer installed in the main water flow channel. Within the set unit time window, for example 5 minutes, the system automatically records the starting concentration and the ending concentration, calculates the residual chlorine concentration change value within the time interval, and divides it by the time interval to obtain the residual chlorine concentration decay rate per unit time. The residual chlorine concentration decay rate per unit time reflects the consumption rate of oxidants in the water body, and indirectly reveals whether side reactions are ongoing. The potential difference before and after the membrane is measured by electrode potential sensors respectively installed at the water inlet and outlet of the circulating water membrane treatment unit. The potential difference before and after the membrane reflects the charge distribution and electrochemical state on both sides of the membrane layer, and indirectly reflects the film integrity and membrane surface reaction activity. When the membrane potential difference drops significantly, it means that the membrane structure is attacked by oxidation or the corrosion protection fails.

[0127] The method for generating the side reaction activation mark includes:

[0128] The organic carbon concentration, ammonia nitrogen concentration, residual chlorine concentration decay rate per unit time and the potential difference between the front and back of the membrane among the side reaction correlation parameters were normalized to form standardized side reaction correlation parameters;

[0129] The side effect activation score was calculated based on the standardized side effect association parameters and the side effect activation score formula;

[0130] If the side reaction activation score is greater than the preset side reaction activation score threshold, the side reaction activation flag is set to yes; if the side reaction activation score is less than or equal to the preset side reaction activation score threshold, the side reaction activation flag is set to no.

[0131] The calculation method of the side effect activation score is:

[0132] ;

[0133] in, Score the activation of side effects, is the normalized organic carbon concentration, is the normalized ammonia nitrogen concentration, is the residual chlorine concentration decay rate per unit time after normalization, is the normalized difference between the front and back membrane potentials. is the weight coefficient, ; For example, in a preferred embodiment of the present application, and Set to 0.2, and Set to 0.3. Activates the scoring formula for side reactions.

[0134] It should be noted that the side reaction activation score threshold is based on the historical operating data of the circulating water system, and multiple groups of operating conditions where side reactions occurred or did not occur are classified and sorted. The organic carbon concentration, ammonia nitrogen concentration, residual chlorine concentration decay rate per unit time, and the potential difference before and after the membrane are extracted respectively, and normalized to obtain a standardized side reaction associated parameter sample set; a preset side reaction activation score formula is used to score each operating condition data sample, and a side reaction activation score distribution corresponding to the activated state sample (side reaction has occurred) and the inactivated state sample (side reaction has not yet occurred) is constructed; on this basis, technical personnel or experts in this field select a score value with higher classification boundary stability and lower misjudgment rate as the side reaction activation score threshold by analyzing the overlapping intervals of the side reaction activation score distribution.

[0135] like Figure 4 As shown, the method for executing the circulating water adaptive control strategy according to the side reaction activation mark includes:

[0136] If the side reaction activation flag is no, the side reaction-related parameters are continuously monitored and the side reaction activation flag is continuously updated;

[0137] If the side reaction activation flag is yes, it indicates that there is an actual excitation phenomenon of non-target reaction between active oxidants and side reaction substrates in the current circulating water system, and the system enters the side reaction intervention state. The side reaction associated parameters are input into the medium risk parameter setting model to obtain a medium risk parameter setting set; the medium risk parameter setting set includes the oxidant adjustment range, corrosion inhibitor adjustment range, reagent adjustment priority and reagent switching cycle;

[0138] Based on the reagent adjustment priority, the adjustment operation of the oxidant or corrosion inhibitor is performed first. The oxidant adjustment amplitude and the corrosion inhibitor adjustment amplitude are used to control the adjustment direction and amplitude of the addition concentration of the oxidant and corrosion inhibitor respectively; when the adjustment duration of the priority-adjusted reagent reaches the reagent switching cycle, the system automatically performs the corresponding adjustment operation on the non-priority-adjusted reagent.

[0139] It should be noted that the oxidant adjustment amplitude and the corrosion inhibitor adjustment amplitude are used to control the direction and amplitude of the oxidant and corrosion inhibitor concentration adjustment respectively, allowing adaptive optimization within the range of addition and subtraction; the reagent adjustment priority is used to determine the priority adjustment of the oxidant or corrosion inhibitor under the medium risk level of the side reaction, so as to avoid the interference caused by the synergistic reaction between different reagents; and the reagent switching cycle is used to limit the duration of the current adjustment strategy within the system execution cycle to prevent system instability caused by frequent switching. When the first adjusted reagent reaches the switching cycle, the system will execute the strategy adjustment for another type of reagent to achieve a phased rhythm intervention in the side reaction risk. Through the dynamic generation and control execution of the medium risk parameter setting set, the graded intervention and adjustment response optimization for the medium risk level of the side reaction can be effectively achieved.

[0140] The training method of the medium risk parameter setting model includes:

[0141] Pre-collecting a moderate-risk parameter setting data set, the moderate-risk parameter setting data set including ZD group moderate-risk parameter setting data and a moderate-risk parameter setting set corresponding to the ZD group moderate-risk parameter setting data, where ZD is a positive integer greater than 0, and the moderate-risk parameter setting data includes adverse reaction-related parameters; dividing the moderate-risk parameter setting data set into a training set and a validation set, wherein the training set is used to train a moderate-risk parameter setting model, and the validation set is used to evaluate the generalization performance of the moderate-risk parameter setting model;

[0142] During the training process of the medium-risk parameter setting model, minimizing the cross-entropy loss function is used as the optimization goal. The performance of the validation set is monitored using an early stopping strategy, and the model performance is optimized by continuously adjusting the network parameters. When the prediction accuracy on the validation set reaches the expected accuracy, the medium-risk parameter setting model is considered to have converged and training is stopped. The medium-risk parameter setting model is trained using a deep neural network based on a multi-layer perceptron.

[0143] The medium-risk parameter setting data is converted into feature vectors; the input layer of the medium-risk parameter setting model receives the feature vectors, and the nonlinear relationship in the data is extracted through the hidden layer. Finally, the output layer of the medium-risk parameter setting model calculates the probability distribution of the medium-risk parameter setting set through the softmax activation function, and outputs the medium-risk parameter setting set corresponding to the maximum probability as the final prediction result.

[0144] It should be noted that, in a further embodiment of the present invention, in order to achieve accurate response and adaptive regulation of side reactions under medium risk level conditions, the system generates a medium risk parameter setting set based on the side reaction associated parameters. The setting process of the medium risk parameter setting set is based on reaction mechanism analysis, and a medium risk parameter setting model is constructed in combination with historical operation data and expert experience rules. The dynamic assignment of the adjustment parameters of the medium risk of the side reaction is completed through the medium risk parameter setting model. Specifically, the system first pre-processes and standardizes the side reaction associated parameters. The side reaction associated parameters can reflect the main inducements and reaction activity environment that trigger side reactions in the water body. Among them, the organic carbon concentration and the ammonia nitrogen concentration are used to identify the activity level of potential side reaction substrates, the residual chlorine concentration decay rate can quantify the consumption dynamics of the oxidant, and the potential difference before and after the membrane reflects the changes in the electrochemical environment and the stability of the membrane flux. According to the weight contribution, interaction relationship and influence of each parameter in the side reaction associated parameters on the evolution trend of the side reaction in the historical samples, the corresponding medium risk parameter setting set under the current side reaction associated parameters is obtained.

[0145] The high-risk response module is used to extract features based on collected side reaction product parameters for high-risk side reactions, generate a side reaction product feature set, and implement a circulating water adaptive control strategy based on the side reaction product feature set. The side reaction product parameters include the concentration of adsorbable organic halides, chloramines, and trihalomethanes. The adsorbable organic halide concentration reflects the accumulation of organic halides in the water, the chloramine concentration reflects the reaction between chlorine and ammonia nitrogen, and the trihalomethane concentration reflects the formation of halogenated methane byproducts.

[0146] The method for obtaining the side reaction product feature set includes:

[0147] Constructing an adsorbable organic halide concentration set based on the adsorbable organic halide concentration collected within a preset time window length, and constructing an organic halide concentration change rate set based on the adsorbable organic halide concentration set;

[0148] Constructing a chloramine concentration set based on the chloramine concentrations collected within a preset time window length, and constructing a chloramine concentration change rate set based on the chloramine concentration set;

[0149] Constructing a trihalomethane concentration set based on the trihalomethane concentration set collected within a preset time window length, and constructing a trihalomethane concentration change rate set based on the trihalomethane concentration set;

[0150] The organic halide concentration change rate set, chloramine concentration change rate set and trihalomethane concentration change rate set are constructed into a side reaction product feature set.

[0151] It should be noted that when the side reaction risk level is a high side reaction risk level, the system has confirmed that there are side reaction processes in the circulating water system and that they may cause potential risks such as drug efficacy deviation, water quality deterioration, or membrane structure damage. At this stage, it is difficult to accurately judge the development trend of side reactions and the effectiveness of current intervention measures based only on the historical dosage parameters of oxidants or corrosion inhibitors or indirect water quality indicators. Therefore, this application proposes to construct a side reaction product feature set to dynamically quantify the side reaction product generation rate and use it to guide the circulating water adaptive control strategy.

[0152] The side reaction product feature set can reflect the dynamic processes of reactions between oxidants and organic matter, ammonia nitrogen, and halogenated substrates in the water. By using the rate of change feature in the side reaction product feature set as a continuous quantitative indicator of the degree of side reaction activation, the system can identify whether side reaction products are still accumulating rapidly, have entered a plateau phase, or have experienced reverse decay, thereby assisting in determining current intervention measures and subsequent regulatory pathways.

[0153] like Figure 3 As shown, the method for executing the circulating water adaptive control strategy based on the side reaction product feature set includes:

[0154] Inputting the side reaction product feature set into the side reaction intensity assessment model to obtain a side reaction intensity score;

[0155] The side effect intensity status was obtained based on the side effect intensity score;

[0156] Matching the side effect intensity state with a pre-built side effect intensity state value matching table to obtain a side effect intensity state value;

[0157] Inputting the side reaction intensity state value, the side reaction intensity score, and the side reaction product feature set into a high-risk parameter setting model to obtain a high-risk parameter setting set; the high-risk parameter setting set includes an emergency reduction ratio of the oxidant, a corrosion inhibitor replacement activation flag, a replacement target corrosion inhibitor, and a bypass drainage flow rate increase ratio;

[0158] The oxidant dosage concentration is adjusted according to the emergency reduction ratio of the oxidant; if the corrosion inhibitor replacement activation flag is yes, the currently used corrosion inhibitor is replaced with the replacement target corrosion inhibitor; the drainage control unit is adjusted according to the bypass drainage flow increase ratio based on the current drainage flow.

[0159] It should be noted that the emergency oxidant reduction ratio indicates the ratio of the current oxidant dosage concentration that should be immediately reduced under the high-risk state of side reactions. For example, if it is set to 0.35, it means that the oxidant dosage will be temporarily reduced to 65% of the current concentration. The corrosion inhibitor replacement activation flag includes yes and no, which is used to decide whether to replace the corrosion inhibitor, that is, when the corrosion inhibitor replacement activation flag is yes, the corrosion inhibitor needs to be replaced. The replacement target corrosion inhibitor refers to the type of corrosion inhibitor used for replacement, such as non-halogen type and organophosphorus system. The bypass drainage flow rate increase ratio indicates that the rapid reduction of the by-product concentration in the system is accelerated by increasing the drainage flow or dilution ratio. For example, it is set to 1.5 times or 2 times to increase the bypass discharge rate for a short time.

[0160] An example of the side reaction intensity state value matching table is shown in Table 1:

[0161] Table 1 Side effect intensity state numerical matching table

[0162]

[0163] The training method of the side effect intensity assessment model includes:

[0164] Pre-collecting a side effect intensity assessment data set, wherein the side effect intensity assessment data set includes QD group side effect intensity assessment data and a side effect intensity score corresponding to the QD group side reaction intensity assessment data, where QD is a positive integer greater than 0, and the side effect intensity assessment data includes a side reaction product feature set; dividing the side reaction intensity assessment data set into a training set and a validation set, wherein the training set is used to train a side effect intensity assessment model, and the validation set is used to evaluate the generalization performance of the side reaction intensity assessment model;

[0165] During the training process of the side effect intensity assessment model, minimizing the cross-entropy loss function is used as the optimization goal. An early stopping strategy is used to monitor the performance of the validation set, and the network parameters are continuously adjusted to optimize the model performance. When the prediction accuracy on the validation set reaches the expected accuracy, the side effect intensity assessment model is considered to have converged and training is stopped. The side effect intensity assessment model is trained using a deep neural network based on a multilayer perceptron.

[0166] The side effect intensity assessment data is converted into feature vectors; the input layer of the side effect intensity assessment model receives the feature vectors, and the nonlinear relationship in the data is extracted through the hidden layer. Finally, the output layer of the side effect intensity assessment model calculates the probability distribution of the side effect intensity score through the softmax activation function, and outputs the side effect intensity score corresponding to the maximum probability as the final prediction result.

[0167] Methods for obtaining the side effect intensity status based on the side effect intensity score include:

[0168] Presetting a side effect intensity score threshold 1 and a side effect intensity score threshold 2, wherein the side effect intensity score threshold 1 is less than the side effect intensity score threshold 2;

[0169] If the side reaction intensity score is greater than or equal to the side reaction intensity score threshold 2, the side reaction intensity state is marked as a side reaction product high activation state;

[0170] If the side effect intensity score is greater than or equal to the side effect intensity score threshold 1 and less than the side effect intensity score threshold 2, the side effect intensity status is marked as a moderate activation state of the side effect product;

[0171] If the side reaction intensity score is less than the cohesion risk score threshold of one, the side reaction intensity state is marked as a side reaction product low activation state.

[0172] It should be noted that the value range of the side effect intensity score is , a larger side reaction intensity score indicates a higher rate of change in the concentration of the side reaction product and a stronger degree of activation of the side reaction. Specifically, a side reaction intensity score close to 0 indicates that the rate of change of various side reaction products is low or tends to be stable, and the system is in a side reaction mitigation state; a side reaction intensity score close to 1 indicates that at least one type of side reaction product shows a significant growth trend, indicating that the system is in a period of rapid by-product generation. Exemplarily, in a preferred embodiment of the present application, the side reaction intensity score threshold 1 can be set to 0.45, and the side reaction intensity score threshold 2 can be set to 0.75.

[0173] The high activation state of side reaction products indicates the risk of continued large-scale production of side reaction products, necessitating immediate implementation of an enhanced intervention mechanism; the moderate activation state of side reaction products indicates the need for the system to implement sustained-release intervention measures; the low activation state of side reaction products indicates that the current side reaction product production trend is stable or in a decaying state. In this low activation state, the system can maintain the current agent dosing strategy and periodically review the side reaction product feature set to prevent the risk of side reaction product reactivation.

[0174] The training method of the high-risk parameter setting model includes:

[0175] A high-risk parameter setting data set is pre-constructed, wherein the high-risk parameter setting data set includes GF group high-risk parameter setting data and a high-risk parameter setting set corresponding to the GF group high-risk parameter setting data, where GF is a positive integer; the high-risk parameter setting data includes a side effect intensity state value, a side effect intensity score, and a side effect product feature set; the high-risk parameter setting data set is divided into a training set and a validation set, the training set is used for high-risk parameter setting model parameter learning, and the validation set is used for real-time monitoring of the generalization performance and overfitting degree of the high-risk parameter setting model;

[0176] A deep neural network with a multilayer perceptron as the core is used as the high-risk parameter setting model. The high-risk parameter setting data is input into the deep neural network after standardization and vectorization processing. The deep neural network consists of an input layer, a hidden layer and an output layer; each hidden layer uses a nonlinear activation function to extract features, and the output layer uses a softmax activation function to obtain the probability distribution corresponding to each high-risk parameter setting set. Finally, the high-risk parameter setting set corresponding to the maximum probability is taken as the prediction result of the high-risk parameter setting model; during the training process, the cross-entropy loss function is used as the optimization target, and a gradient descent optimization algorithm is used to update the network weights, and an early stopping strategy is set: when the prediction accuracy on the validation set reaches or exceeds the preset threshold, the high-risk parameter setting model is judged to have converged and training is terminated.

[0177] It should be noted that in this application, the side reaction intensity state value, the side reaction intensity score and the side reaction product feature set are introduced as input parameters into the high-risk parameter setting model, aiming to achieve the generation of an adaptive adjustment strategy for the side reaction in the high activation state of the circulating water system. Specifically, the side reaction intensity state value is used as a standardized discrete representation of the current side reaction level to quickly calibrate the classification level of the system in the low activation state of the side reaction product, the moderate activation state of the side reaction product or the high activation state of the side reaction product, constituting the state identification input of the high-risk parameter setting model in the initial judgment stage; the side reaction intensity score reflects the intensity gradient of the side reaction product change in the system in the form of a continuous quantity, which is used to further refine the differential reaction trend within the same side reaction state level; and the side reaction product feature set includes three types of representative side reaction products, namely, the rate of change information of adsorbable organic halides, chloramines and trihalomethanes within a preset time window, which fully reveals the generation dynamics and response rate of the side reaction products, and constitutes the main feature carrier of the high-risk parameter setting model learning system reaction law.

[0178] Through the joint input of the side reaction intensity state value, the side reaction intensity score and the side reaction product feature set, the high-risk parameter setting model can effectively learn the nonlinear mapping relationship between the dynamic characteristics of the side reaction products and the emergency control parameters during the training process, thereby generating a set of control strategies that are highly matched with the current side reaction risk level. It has multi-dimensional responsiveness and rapid decision-making capabilities, ensuring that the system can achieve precise intervention and dynamic control in the risk situation of a sharp increase in side reaction products.

[0179] Example 2

[0180] See also Figure 2 As shown, this embodiment provides a circulating water intelligent operation control method, including:

[0181] Standardize and extract features of multi-source water quality data collected from the circulating water system to obtain a set of water quality characteristic parameters;

[0182] Based on multi-source water quality data and a set of water quality characteristic parameters, the side reaction risk between the oxidizing biocide and the corrosion inhibitor in the circulating water is evaluated to obtain a side reaction risk level; the side reaction risk level includes a high side reaction risk level, a medium side reaction risk level, and a low side reaction risk level;

[0183] For side effects with a low risk level, the side effect activation flag is set to no, and side effect-related parameters are continuously monitored;

[0184] For side reactions with a medium risk level, a side reaction activation flag is generated based on the collected side reaction associated parameters, and a circulating water adaptive control strategy is executed according to the side reaction activation flag;

[0185] For the high risk level of side reactions, feature extraction is performed based on the collected side reaction product parameters to obtain a side reaction product feature set, and a circulating water adaptive control strategy is executed based on the side reaction product feature set.

[0186] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0187] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A circulating water intelligent operation control system, characterized in that: include: The water quality feature extraction module is used to perform standardization and feature extraction on the multi-source water quality data collected by the circulating water system to obtain a set of water quality feature parameters; The method for obtaining the water quality characteristic parameter set includes: The oxidation potential factor is constructed based on the coupling of redox potential and residual chlorine concentration; the oxidant-corrosion inhibitor reaction ratio factor is constructed based on the coupling of total chlorine concentration and corrosion inhibitor concentration; the side reaction activation index is constructed based on the fusion of pH value and water temperature; the flow field disturbance factor is constructed based on flow velocity; the conductivity change rate within a preset time window is calculated based on conductivity, and the film formation stability index is constructed based on the fusion of conductivity change rate and total calcium and magnesium concentration; the oxidation potential factor, oxidant-corrosion inhibitor reaction ratio factor, side reaction activation index, flow field disturbance factor and film formation stability index are constructed into a set of water quality characteristic parameters; The side reaction assessment module evaluates the side reaction risk between oxidizing biocides and corrosion inhibitors in circulating water based on multi-source water quality data and a set of water quality characteristic parameters to obtain a side reaction risk level. The medium risk response module is used to generate a side reaction activation identifier based on the collected side reaction associated parameters for the medium risk level of the side reaction, and execute the circulating water adaptive control strategy according to the side reaction activation identifier; the method for generating the side reaction activation identifier includes: The organic carbon concentration, ammonia nitrogen concentration, residual chlorine concentration decay rate per unit time and the potential difference between the front and back of the membrane among the side reaction correlation parameters were normalized to form standardized side reaction correlation parameters; The side effect activation score was calculated based on the standardized side effect association parameters and the side effect activation score formula; If the side reaction activation score is greater than the preset side reaction activation score threshold, the side reaction activation flag is set to yes; if the side reaction activation score is less than or equal to the preset side reaction activation score threshold, the side reaction activation flag is set to no; The method for executing the circulating water adaptive control strategy according to the side reaction activation flag includes: If the side reaction activation flag is no, the side reaction-related parameters are continuously monitored and the side reaction activation flag is continuously updated; If the side reaction activation flag is yes, the side reaction associated parameters are input into the medium risk parameter setting model to obtain a medium risk parameter setting set; the medium risk parameter setting set includes an oxidant adjustment range, a corrosion inhibitor adjustment range, a reagent adjustment priority, and a reagent switching cycle; Based on the reagent adjustment priority, the oxidant or corrosion inhibitor is adjusted first. The oxidant adjustment range and the corrosion inhibitor adjustment range are used to control the adjustment direction and range of the oxidant and corrosion inhibitor dosage concentrations respectively. When the adjustment duration of the priority reagent reaches the reagent switching cycle, the system automatically performs the corresponding adjustment operation on the non-priority reagent. A high-risk response module is used to extract features based on the collected side reaction product parameters for high-risk side reactions, obtain a side reaction product feature set, and execute a circulating water adaptive control strategy based on the side reaction product feature set; The method for executing the circulating water adaptive control strategy based on the side reaction product characteristic set includes: Inputting the side reaction product feature set into the side reaction intensity assessment model to obtain a side reaction intensity score; The side effect intensity status was obtained based on the side effect intensity score; Matching the side effect intensity state with a pre-built side effect intensity state value matching table to obtain a side effect intensity state value; Inputting the side reaction intensity state value, the side reaction intensity score, and the side reaction product feature set into a high-risk parameter setting model to obtain a high-risk parameter setting set; the high-risk parameter setting set includes an emergency reduction ratio of the oxidant, a corrosion inhibitor replacement activation flag, a replacement target corrosion inhibitor, and a bypass drainage flow rate increase ratio; The oxidant dosage concentration is adjusted according to the emergency reduction ratio of the oxidant; if the corrosion inhibitor replacement activation flag is yes, the currently used corrosion inhibitor is replaced with the replacement target corrosion inhibitor; the drainage control unit is adjusted according to the bypass drainage flow increase ratio based on the current drainage flow.

2. A circulating water intelligent operation control system according to claim 1, characterized in that: The method for obtaining the side reaction product feature set includes: Constructing an adsorbable organic halide concentration set based on the adsorbable organic halide concentration collected within a preset time window length, and constructing an organic halide concentration change rate set based on the adsorbable organic halide concentration set; Constructing a chloramine concentration set based on the chloramine concentrations collected within a preset time window length, and constructing a chloramine concentration change rate set based on the chloramine concentration set; Constructing a trihalomethane concentration set based on the trihalomethane concentration set collected within a preset time window length, and constructing a trihalomethane concentration change rate set based on the trihalomethane concentration set; The organic halide concentration change rate set, chloramine concentration change rate set and trihalomethane concentration change rate set are constructed into a side reaction product feature set.

3. A circulating water intelligent operation control system according to claim 1, characterized in that: Methods for obtaining the side effect intensity status based on the side effect intensity score include: Presetting a side effect intensity score threshold 1 and a side effect intensity score threshold 2, wherein the side effect intensity score threshold 1 is less than the side effect intensity score threshold 2; If the side reaction intensity score is greater than or equal to the side reaction intensity score threshold 2, the side reaction intensity state is marked as a side reaction product high activation state; If the side effect intensity score is greater than or equal to the side effect intensity score threshold 1 and less than the side effect intensity score threshold 2, the side effect intensity status is marked as a moderate activation state of the side effect product; If the side reaction intensity score is less than the cohesion risk score threshold of one, the side reaction intensity state is marked as a side reaction product low activation state.

4. A circulating water intelligent operation control system according to claim 1, characterized in that: The method for obtaining the side effect risk level includes: Input multi-source water quality data and water quality characteristic parameter sets into the adverse reaction risk assessment model to obtain the adverse reaction risk score; Presetting a side effect risk score threshold 1 and a side effect risk score threshold 2, wherein the side effect risk score threshold 1 is less than the side effect risk score threshold 2; If the side effect risk score is greater than or equal to the side effect risk score threshold of two, the secondary coagulation risk level is marked as a side effect high risk level; If the side effect risk score is greater than or equal to the side effect risk score threshold of one and less than the side effect risk score threshold of two, the secondary coagulation risk level is marked as a side effect medium risk level; If the side effect risk score is less than the coagulation risk score threshold of one, the secondary coagulation risk level is marked as a side effect low risk level.

5. A circulating water intelligent operation control system according to claim 1, characterized in that: The multi-source water quality data includes pH value, conductivity, redox potential, total chlorine concentration, residual chlorine concentration, corrosion inhibitor concentration, water temperature, flow rate and total calcium and magnesium concentration.

6. An operation control method of a circulating water intelligent operation control system according to any one of claims 1 to 5, characterized in that: include: Standardize and extract features of multi-source water quality data collected from the circulating water system to obtain a set of water quality characteristic parameters; Based on multi-source water quality data and a set of water quality characteristic parameters, the side reaction risk between the oxidizing biocide and the corrosion inhibitor in the circulating water is evaluated to obtain a side reaction risk level; the side reaction risk level includes a high side reaction risk level, a medium side reaction risk level, and a low side reaction risk level; For side reactions with a medium risk level, a side reaction activation flag is generated based on the collected side reaction associated parameters, and a circulating water adaptive control strategy is executed according to the side reaction activation flag; For the high risk level of side reactions, feature extraction is performed based on the collected side reaction product parameters to obtain a side reaction product feature set, and a circulating water adaptive control strategy is executed based on the side reaction product feature set.

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