A simulation evaluation method for water treatment process

Abstract

This invention discloses a simulation evaluation method for water treatment processes, belonging to the field of process simulation technology. It addresses the problems of large prediction deviations in floc formation time and insufficient precision in dosing control. The method monitors the raw water in the intake pump, collects the influent conductivity, calculates the influent mutation index, and determines whether a water quality analysis mechanism is triggered. It uses a turbidimeter to emit test light to obtain the light scattering pulse density and collects ultraviolet absorption values ​​to assess the organic structure state. Combining the light scattering pulse density, it generates coagulation response hysteresis characteristics, predicts floc formation time based on these characteristics, accesses the management database to obtain the stirring speed and current dosage, obtains the flocculation disturbance coefficient, uses this coefficient to correct the floc formation time, and determines whether the sedimentation tank has a load risk. It then selects to execute the original dosing strategy or a modified dosing strategy, thereby improving the accuracy of floc formation time prediction.

Description

Technical Field

[0001] This invention relates to the field of process simulation technology, and more specifically, to a simulation evaluation method for a water treatment process. Background Technology

[0002] In the process of urban water supply and industrial water treatment, the quality of raw water usually exhibits significant time variability and uncertainty. In particular, under the influence of rainfall runoff, upstream discharge fluctuations or seasonal changes, key parameters such as conductivity, turbidity and organic matter content of raw water can change abruptly. Existing water treatment processes mostly rely on human experience or fixed model parameters to control the coagulation, flocculation and sedimentation processes, that is, to maintain the treatment effect by preset dosage and stirring conditions.

[0003] The existing technology has the following shortcomings: Currently, existing technologies mainly control the coagulation and flocculation processes based on single water quality parameters or static empirical models. They lack the ability to dynamically perceive and integrate the sudden changes in raw water quality and the coupling characteristics of multiple parameters. This makes it difficult to accurately characterize the lag behavior of coagulation reaction and the time-varying characteristics of floc formation, resulting in large deviations in the prediction of floc formation time and insufficient precision in chemical dosing control. Consequently, it is easy to cause fluctuations in sedimentation tank load and unstable effluent quality. Therefore, a simulation evaluation method for water treatment process is proposed. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a simulation evaluation method for water treatment processes. This method utilizes a multimodal water quality analysis mechanism triggered by the influent mutation index, a coagulation response hysteresis modeling method that integrates light scattering pulse density and ultraviolet absorption characteristics, and a dynamic correction model for the flocculation disturbance coefficient constructed by combining stirring speed and dosage to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a simulation evaluation method for a water treatment process, comprising the following steps: Step S1: Monitor the raw water in the raw water intake pump, collect the influent conductivity of the raw water, calculate the influent mutation index of the raw water based on the influent conductivity, and determine whether the water quality analysis mechanism is triggered based on the influent mutation index. Step S2: In the water quality analysis mechanism, a turbidimeter is used to emit test light into the raw water and obtain the light scattering pulse density. The ultraviolet absorption value of the raw water is collected to evaluate the organic structure state of the raw water. The coagulation response hysteresis characteristics of the raw water are generated by combining the light scattering pulse density. Step S3: Predict the floc formation time of raw water based on the coagulation response hysteresis characteristics, access the management database for the stirring speed and current dosage of raw water, and analyze the flocculation disturbance coefficient of raw water by combining the stirring speed and current dosage. Step S4: Correct the floc formation time using the flocculation disturbance coefficient, determine whether there is a load risk in the sedimentation tank based on the corrected floc formation time, and select the original dosing strategy or the modified dosing strategy for the raw water according to the judgment result.

[0006] In a preferred embodiment, in step S1, a preset collection period is established, multiple collection times are divided, and the inlet conductivity of the raw water is obtained by a conductivity sensor installed in the inlet pipeline. The change in the influent conductivity of the raw water is obtained by subtracting the influent conductivity of the raw water at one sampling time from the influent conductivity of the raw water at the next adjacent sampling time and taking the absolute value. The rate of change of influent conductivity is obtained by subtracting the change in influent conductivity of raw water at one sampling time from the change in influent conductivity of raw water at the next adjacent sampling time, and then dividing the absolute value by the sampling time interval.

[0007] In a preferred embodiment, in step S1, the change in the influent conductivity and the rate of change in the influent conductivity of the raw water at each sampling time are standardized to obtain the change factor and the rate of change factor. The influent mutation index of raw water was calculated by combining the change factor and the change rate factor using a weighted summation method. If the influent mutation index of raw water is greater than or equal to the preset influent mutation index threshold, the water quality analysis mechanism is triggered. If the influent mutation index of raw water is less than the preset influent mutation index threshold, the water quality analysis mechanism will not be triggered.

[0008] In a preferred embodiment, in step S2, in the water quality analysis mechanism, a preset detection window time is set, and test light is emitted to the raw water through the light source unit of the turbidity meter. The test light is scattered after encountering suspended particles in the water, generating scattered light. Scattered light is received by a photodetector positioned at a fixed angle and converted into an electrical signal; After the electrical signal is amplified and filtered, it is discretely sampled by the data acquisition unit to obtain the scattered signal sequence. The average value of the scattered signal sequence is calculated to obtain the baseline signal. The offset of each scattered signal is obtained by subtracting the baseline signal from each scattered signal.

[0009] In a preferred embodiment, in step S2, if the offset of each scattered signal is greater than or equal to a preset pulse determination threshold, then the scattered signal is determined to be a valid scattered pulse. If the offset of each scattered signal is less than the preset pulse determination threshold, then the scattered signal is determined to be an invalid scattered pulse; The number of effective scattered pulses is counted and divided by the preset detection window time to obtain the light scattered pulse density; The raw water is tested using an ultraviolet absorption detector to obtain its ultraviolet absorption value.

[0010] In a preferred embodiment, in step S2, if the UV absorption value of the raw water is greater than or equal to a preset UV absorption threshold, the organic structure state of the raw water is determined to be a high-complexity structure state. If the UV absorption value of the raw water is less than the preset UV absorption threshold, the organic structure state of the raw water is determined to be a low-complexity structure state. If the light scattering pulse density is less than the preset light scattering pulse density threshold, and the organic structure of the raw water is a highly complex structure, then the coagulation response hysteresis characteristic of the raw water is determined to be a high hysteresis characteristic. If the light scattering pulse density is greater than or equal to the preset light scattering pulse density threshold, and the organic structure of the raw water is a low-complexity structure, then the coagulation response hysteresis characteristic of the raw water is determined to be a low-hysteresis characteristic. Conversely, the coagulation response hysteresis characteristic of the raw water is determined to be a medium hysteresis characteristic.

[0011] In a preferred embodiment, in step S3, the coagulation response hysteresis characteristics of the raw water are matched with the hysteresis characteristic database to obtain the hysteresis level coefficient of the raw water. Multiply the preset basic floc formation time by 1 plus the product of the lag level coefficient and the preset time correction adjustment coefficient to obtain the floc formation time of the raw water. Access the management database to obtain the raw water stirring speed and current dosage; The stirring speed and current dosage of the raw water are standardized to obtain the speed factor and dosage factor; The flocculation disturbance coefficient of raw water is calculated by combining the velocity factor and the dosage factor. The calculation formula is as follows: ,in, For velocity factor, As a dose factor, This represents the flocculation disturbance coefficient of the raw water.

[0012] In a preferred embodiment, in step S4, the flocculation disturbance coefficient and the floc formation time are combined to form an input feature combination; The input features are combined and fed into a pre-trained floc generation time correction model to obtain the corrected floc generation time. The floc formation time correction model is constructed based on big data statistical analysis and regression modeling methods to characterize the mapping relationship between flocculation disturbance conditions and floc formation time.

[0013] In a preferred embodiment, in step S4, if the corrected floc formation time is greater than or equal to the preset floc formation time threshold, it is determined that the sedimentation tank has a load risk, and the corrected dosing strategy is executed. The modified dosing strategy refers to a control method that adjusts the dosing plan based on water quality analysis results and model prediction output when a significant change in influent water quality is detected and the sedimentation tank is determined to be at risk of overload. If the corrected floc formation time is less than the preset floc formation time threshold, it is determined that there is no load risk in the sedimentation tank, and the original dosing strategy is executed. The original dosing strategy refers to the conventional dosing control method implemented according to the established process parameters when no significant water quality changes are detected or no sedimentation tank load risk is assessed.

[0014] The technical effects and advantages of this invention are as follows: This invention monitors the raw water in the raw water intake pump, collects the influent conductivity, and calculates the influent mutation index to determine whether a water quality analysis mechanism is triggered. In this mechanism, a turbidity meter emits test light to obtain the light scattering pulse density and collects ultraviolet absorption values ​​to assess the organic structure state. The light scattering pulse density is combined to generate coagulation response hysteresis characteristics, which are used to predict floc formation time. Simultaneously, the management database is accessed to obtain the stirring speed and current dosage, and the flocculation disturbance coefficient is analyzed. This coefficient is used to correct the floc formation time and determine whether the sedimentation tank faces load risk, thus allowing for the selection of either the original or modified dosing strategy. By triggering the water quality analysis mechanism through the influent mutation index, a rapid response to water quality changes is achieved. Combining light scattering pulse density and ultraviolet absorption values ​​to characterize the coagulation response hysteresis characteristics improves the accuracy of floc formation time prediction. The introduction of the flocculation disturbance coefficient for dynamic correction makes dosing decisions more targeted, effectively reducing sedimentation tank load risk and improving treatment stability. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the implementation of a simulation evaluation method for a water treatment process according to the present invention.

[0016] Figure 2 This is a schematic diagram illustrating the steps of a simulation evaluation method for a water treatment process according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention monitors the raw water in the raw water intake pump, collects the influent conductivity, and calculates the influent mutation index to determine whether a water quality analysis mechanism is triggered. In this mechanism, a turbidimeter emits test light to obtain the light scattering pulse density and collects ultraviolet absorption values ​​to assess the organic structure state. The light scattering pulse density is combined to generate coagulation response hysteresis characteristics, which are used to predict floc formation time. Simultaneously, the invention accesses the management database to obtain the stirring speed and current dosage, analyzes the flocculation disturbance coefficient, and uses this coefficient to correct the floc formation time. Based on this, it determines whether the sedimentation tank has a load risk, thus selecting to execute the original dosing strategy or a modified dosing strategy. By triggering the water quality analysis mechanism through the influent mutation index, a rapid response to water quality changes is achieved. Combining light scattering pulse density and ultraviolet absorption values ​​to characterize the coagulation response hysteresis characteristics improves the accuracy of floc formation time prediction.

[0019] Example 1: A simulation evaluation method for a water treatment process, such as... Figures 1 to 2 As shown, it includes the following steps: Step S1: Monitor the raw water in the raw water intake pump, collect the influent conductivity of the raw water, calculate the influent mutation index of the raw water based on the influent conductivity, and determine whether the water quality analysis mechanism is triggered based on the influent mutation index. Step S2: In the water quality analysis mechanism, a turbidimeter is used to emit test light into the raw water and obtain the light scattering pulse density. The ultraviolet absorption value of the raw water is collected to evaluate the organic structure state of the raw water. The coagulation response hysteresis characteristics of the raw water are generated by combining the light scattering pulse density. Step S3: Predict the floc formation time of raw water based on the coagulation response hysteresis characteristics, access the management database for the stirring speed and current dosage of raw water, and analyze the flocculation disturbance coefficient of raw water by combining the stirring speed and current dosage. Step S4: Correct the floc formation time using the flocculation disturbance coefficient, determine whether there is a load risk in the sedimentation tank based on the corrected floc formation time, and select the original dosing strategy or the modified dosing strategy for the raw water according to the judgment result.

[0020] The specific implementation is as follows: In step S1, during the operation of the water treatment process, the quality of the raw water is not constant. Affected by factors such as seasonal changes, rainfall runoff, and upstream discharge, the conductivity of the influent may fluctuate instantaneously or change in a trend. If the sudden change in water quality is not detected in time, it may lead to a lag in the response of the subsequent coagulation process and affect the treatment effect. Therefore, it is necessary to establish a real-time monitoring mechanism in the influent stage, collect the conductivity of the influent and calculate the influent sudden change index to determine whether it is necessary to initiate in-depth water quality analysis. The system presets a collection period, divides the time into multiple collection points, and obtains the influent conductivity of the raw water by using a conductivity sensor installed in the influent pipeline. The conductivity time series is then formed according to the collection time sequence. The change in the influent conductivity of the raw water at a given sampling time is obtained by subtracting the influent conductivity of the raw water at the next adjacent sampling time from the absolute value of the difference. Repeat the above steps to obtain the change in the influent conductivity of the raw water at each sampling time. The absolute value of the change in the influent conductivity of raw water at a given sampling time is obtained by subtracting the change in the influent conductivity of raw water at the next adjacent sampling time and dividing the absolute value by the sampling time interval. Repeat the above steps to obtain the rate of change of the influent conductivity of the raw water at each sampling time. It should be noted that the preset collection cycle can be set according to the frequency of raw water quality fluctuations, the characteristics of influent flow rate changes, or the process control accuracy requirements; the conductivity sensor is an industrial analytical instrument that monitors the conductivity value in water in real time online and is used to obtain the influent conductivity of raw water.

[0021] The changes in the influent conductivity and the rate of change of the influent conductivity at each sampling time were standardized to obtain the change factor and the rate of change factor. The influent mutation index of raw water is calculated by combining the change quantity factor and the change rate factor. The calculation formula is as follows: ,in, This represents the total number of data collection moments. For the first The change factor at each acquisition time. For the first The rate of change factor at each acquisition time and To preset the weighting coefficients, The influent mutation index of raw water; It should be noted that the preset weighting coefficient can be set based on the differences in the contribution of the change quantity factor and the change rate factor to water quality mutations, the historical sensitivity analysis results of different water quality indicators, or the response priority of the process to different mutation characteristics.

[0022] The influent mutation index reflects the degree of fluctuation in raw water quality within a preset collection period and its relative level of deviation from the normal baseline. The larger the influent mutation index, the more significant the instantaneous change and rate of change in the influent conductivity, and the faster the water quality changes. The smaller the influent mutation index, the more gradual the change in influent conductivity, and the more stable the water quality. The influent abrupt change index of the raw water is compared with the preset influent abrupt change index threshold for judgment: If the influent mutation index of raw water is greater than or equal to the preset influent mutation index threshold, the water quality analysis mechanism is triggered. If the influent mutation index of raw water is less than the preset influent mutation index threshold, the water quality analysis mechanism will not be triggered.

[0023] It should be explained that the standardization methods include, but are not limited to, standard linear transformation based on interval scaling, Z-Score standardization based on statistics, or normalization based on nonlinear mapping functions. The application methods of standardization will not be elaborated here. The preset influent mutation index threshold can be set according to the historical raw water quality fluctuation baseline, seasonal variation pattern, or process shock load resistance. The water quality analysis mechanism refers to a comprehensive detection and feature extraction program that is activated when the influent mutation index triggers the preset threshold. The test light is emitted into the raw water by a turbidity meter to obtain the light scattering pulse density, and the organic structure state of the raw water is evaluated by combining the ultraviolet absorption value collected by the ultraviolet absorption detector. Finally, a coagulation response hysteresis feature is generated to characterize the ease of coagulation reaction.

[0024] By monitoring the raw water in the raw water intake pump, collecting the influent conductivity and calculating the influent mutation index, real-time quantitative assessment of raw water quality fluctuations can be achieved. Based on the mutation index, it can intelligently determine whether to trigger the subsequent water quality analysis mechanism, thus achieving effective separation of routine monitoring and fine analysis, avoiding waste of system resources, and improving overall response efficiency.

[0025] In step S2, when the influent mutation index triggers the water quality analysis mechanism, conductivity alone cannot fully describe the coagulation characteristics of the raw water. The number and activity of suspended particles in the water, the content and structural complexity of dissolved organic matter, jointly determine the ease or difficulty of the coagulation reaction. If these microscopic characteristics are not accurately assessed, it may lead to a mismatch between the coagulant dosage and the actual water quality requirements, affecting the floc formation efficiency. Therefore, in the water quality analysis mechanism, the coagulation response hysteresis characteristics of the raw water need to be comprehensively characterized by light scattering pulse density and ultraviolet absorption value. In the water quality analysis mechanism, a preset detection window time is used. Test light is emitted into the raw water through the light source unit of the turbidity meter. The test light is scattered when it encounters suspended particles in the water, producing scattered light. Scattered light is received by a photodetector positioned at a fixed angle and converted into an electrical signal; After the electrical signal is amplified and filtered, it is discretely sampled by the data acquisition unit to obtain the scattered signal sequence; The baseline signal is obtained by averaging the scattered signal sequence. Subtract the baseline signal from each scattered signal to obtain the offset of each scattered signal; The offset of each scattered signal is compared with the preset pulse judgment threshold for determination: If the offset of each scattered signal is greater than or equal to the preset pulse determination threshold, then the scattered signal is determined to be a valid scattered pulse. If the offset of each scattered signal is less than the preset pulse determination threshold, then the scattered signal is determined to be an invalid scattered pulse; The number of effective scattered pulses is counted and divided by the preset detection window time to obtain the light scattered pulse density; It should be noted that the preset detection window time can be set according to the sedimentation characteristics of suspended particles in the raw water, the stability period of the light scattering signal, or the process response speed requirements; the light source unit of the turbidity meter refers to the core optical component in the equipment used to emit test light, and its function is to provide a stable, specific wavelength beam of light to illuminate the water sample being tested, for the raw water to emit test light; the photodetector is a sensor device that converts light signals into electrical signals, used to receive scattered light and convert it into electrical signals; amplification and filtering refers to the process of signal conditioning of the raw electrical signal output by the photodetector, the purpose of which is to convert the weak, noisy raw signal into a clean, stable electrical signal that can be accurately identified by the data acquisition unit; discrete sampling refers to the process of taking values ​​of a continuously changing analog signal at intervals on the time axis, thereby converting it into a time-discrete, numerically quantized digital signal sequence; the preset pulse judgment threshold can be set according to the noise level of the baseline signal, the fluctuation range of the raw water background turbidity, or the statistical characteristics of the effective scattered pulse.

[0026] The raw water is tested using an ultraviolet absorption detector to obtain its ultraviolet absorption value; The UV absorption value of the raw water is compared with the preset UV absorption threshold for determination: If the UV absorption value of the raw water is greater than or equal to the preset UV absorption threshold, it is determined that the organic content in the raw water is high and mainly composed of high molecular weight and strong conjugated structure, and the organic structure state of the raw water is a high-complexity structure state. If the UV absorption value of the raw water is less than the preset UV absorption threshold, it is determined that the organic content in the raw water is low or mainly low molecular weight organic matter, and the organic structure state of the raw water is a low complexity structure state. The light scattering pulse density is compared with a preset light scattering pulse density threshold, and the judgment is made in conjunction with the organic structural state of the raw water: If the light scattering pulse density is less than the preset light scattering pulse density threshold, and the organic structure of the raw water is a highly complex structure, then the coagulation response hysteresis characteristic of the raw water is determined to be a high hysteresis characteristic. If the light scattering pulse density is greater than or equal to the preset light scattering pulse density threshold, and the organic structure of the raw water is a low-complexity structure, then the coagulation response hysteresis characteristic of the raw water is determined to be a low-hysteresis characteristic. Conversely, the coagulation response hysteresis characteristic of the raw water is determined to be a medium hysteresis characteristic.

[0027] It should be explained that the ultraviolet absorption detector is an analytical instrument based on the principle of ultraviolet absorption spectroscopy, used to quantitatively detect the concentration of organic matter in water. It is used to test raw water and obtain the ultraviolet absorption value of the raw water. The preset ultraviolet absorption threshold can be set according to the background concentration level of organic matter in the raw water, the removal efficiency of coagulants on organic matter, or the seasonal changes in water quality. The preset light scattering pulse density threshold can be set according to the background concentration range of suspended particles in the raw water, the critical nucleation conditions for floc formation, or the destabilization efficiency of coagulants on particles.

[0028] By emitting test light through a turbidimeter to obtain the light scattering pulse density and collecting ultraviolet absorption values ​​to assess the organic structure state, and combining the two to generate coagulation response hysteresis characteristics, a multi-dimensional and accurate characterization of the raw water coagulation properties can be achieved, providing a reliable data foundation for the accurate prediction of floc formation time.

[0029] In step S3, during the coagulation process, the floc formation time depends not only on the characteristics of the raw water quality, but also on the combined effects of stirring intensity and dosage. Excessive stirring speed may shear and destroy the flocs, while insufficient dosage makes it difficult to effectively destabilize them. If these disturbance factors are not comprehensively considered, the prediction of the floc formation time will deviate from the actual operating conditions, affecting the judgment of the subsequent sedimentation tank load. Therefore, it is necessary to introduce stirring speed and current dosage on the basis of the predicted floc formation time to analyze and obtain the flocculation disturbance coefficient. The coagulation response hysteresis characteristics of raw water are matched with the hysteresis characteristic database to obtain the hysteresis level coefficient of raw water. Multiply the preset basic floc formation time by 1 plus the product of the lag level coefficient and the preset time correction adjustment coefficient to obtain the floc formation time of the raw water. Access the management database to obtain the raw water stirring speed and current dosage; The stirring speed and current dosage of the raw water are standardized to obtain the speed factor and dosage factor; The flocculation disturbance coefficient of raw water is calculated by combining the velocity factor and the dosage factor. The calculation formula is as follows: ,in, For velocity factor, As a dose factor, The flocculation disturbance coefficient of the raw water; The flocculation disturbance coefficient reflects the degree of disturbance of raw water during the coagulation process under the combined influence of stirring intensity and dosage. The larger the flocculation disturbance coefficient, the higher the stirring speed and the lower the dosage, the stronger the hydraulic shearing effect may destroy the floc structure, and the greater the disturbance during the floc formation process. The smaller the flocculation disturbance coefficient, the more the stirring speed and dosage are in a relatively coordinated state, the mild hydraulic conditions and sufficient reagents, and the less the disturbance during the floc formation process.

[0030] It should be explained that the hysteresis characteristic database is a structured data set that stores the mapping relationship between coagulation response hysteresis characteristics and hysteresis grade coefficients, used to obtain the hysteresis grade coefficient of raw water; the preset basic floc formation time can be set according to the statistical baseline of floc formation in historical operating data, the design parameters of the coagulation process, or the seasonal variation of raw water temperature; the preset time correction adjustment coefficient can be set according to the influence intensity of the hysteresis grade coefficient on floc formation time, the empirical correction weight under different water quality conditions, or the statistical analysis results of model prediction errors in historical operating data; the management database is a structured data set that stores real-time parameters and historical records during the operation of the water treatment process, used to obtain the stirring speed of raw water and the current dosage.

[0031] The floc formation time is predicted based on the coagulation response hysteresis characteristics. At the same time, the stirring speed and current dosage are obtained from the management database. The flocculation disturbance coefficient is obtained through comprehensive analysis, which realizes the quantitative characterization of the disturbance factors in the coagulation process. This makes the prediction of floc formation time more consistent with the actual working conditions and improves the reliability of the prediction results.

[0032] In step S4, the length of floc formation time directly affects the operating load of the sedimentation tank. If the floc formation time is too long, the flocs will not grow sufficiently before entering the sedimentation tank, which will lead to a decrease in the settling effect and an increase in the load risk of the sedimentation tank. Conversely, if the formation time is too short, it may mean that the dosage is excessive, resulting in waste of reagents. It is necessary to use the flocculation disturbance coefficient to correct the floc formation time, and judge whether there is a load risk in the sedimentation tank based on the correction result. Based on this, the original dosing strategy or the modified dosing strategy can be selected to achieve dynamic optimization of dosing decision. The flocculation disturbance coefficient and the floc formation time are combined to form the input feature combination; The input features are combined and fed into a pre-trained floc generation time correction model to obtain the corrected floc generation time. The floc formation time correction model is constructed based on big data statistical analysis and regression modeling methods to characterize the mapping relationship between flocculation disturbance conditions and floc formation time; The model training process includes the following steps: Data preparation: Collect a large amount of historical raw water treatment operation data, including at least the initial floc formation time, flocculation disturbance coefficient, and the corresponding actual floc formation time; combine the initial floc formation time and flocculation disturbance coefficient to form an input feature combination, and use the actual floc formation time as the target output to construct a training dataset; Data preprocessing: Standardize the input feature combinations and remove or correct outlier data to ensure the stability and consistency of the data distribution; Training strategy: The big data statistical modeling method is adopted, the input feature combination is used as the model input, and the actual floc formation time is used as the target output. The floc formation time is predicted by constructing a time correction mapping relationship. The mean squared error is selected as the loss function to measure the difference between the model prediction and the true distribution. Model optimization: The model parameters are solved by error iteration optimization method to minimize the deviation between the predicted result and the actual floc formation time; Model validation: The model is evaluated on an independent validation dataset. The model's adaptability under different water quality conditions is verified by calculating the distribution of prediction errors. Model update: By continuously incorporating new operational data, the model parameters are updated in a rolling manner to improve the model's responsiveness to water quality fluctuations; The corrected floc formation time is compared with the preset floc formation time threshold for determination: If the corrected floc formation time is greater than or equal to the preset floc formation time threshold, the sedimentation tank is deemed to be at load risk, and a corrected dosing strategy is executed. The corrected dosing strategy refers to a control method that adjusts the dosing plan based on water quality analysis results and model prediction output when a significant change in influent water quality is detected and the sedimentation tank is deemed to be at load risk. By introducing an incremental dosing function, the original dosing amount is dynamically amplified or suppressed and updated on a rolling basis within a continuous control cycle, thereby improving the adaptability to complex water quality fluctuations and avoiding problems such as decreased sedimentation efficiency or waste of reagents due to dosing lag or overdose. If the corrected floc formation time is less than the preset floc formation time threshold, it is determined that there is no load risk in the sedimentation tank, and the original dosing strategy is executed. The original dosing strategy refers to the conventional dosing control method executed according to the established process parameters when no significant water quality change is detected or the sedimentation tank is assessed to be free of load risk. It adopts fixed dosing rules to keep the coagulation reaction within the normal water quality fluctuation range and maintain a predictable treatment effect, thereby reducing control complexity and unnecessary adjustment intervention.

[0033] It needs to be explained that big data statistical analysis and regression modeling methods refer to establishing a mapping relationship between input and output variables based on a large amount of historical operating data through statistical regularity extraction and parameter modeling; mean squared error is a commonly used error metric used to assess the difference between predicted and actual values; error iterative optimization methods are mathematical optimization techniques that gradually reduce prediction errors by repeatedly adjusting model parameters. The core idea is to calculate the error between the current model prediction and the actual value in each iteration, and correct the model parameters according to the direction and magnitude of the error, so that the error gradually decreases until the preset accuracy requirements or convergence conditions are met; rolling updates refer to continuously incorporating new actual data into the model training process as the system continues to run, periodically adjusting the model parameters so that the model can adaptively adapt to changes in water quality and operating conditions; the preset floc formation time threshold can be set according to the design hydraulic retention time of the sedimentation tank, floc settling performance requirements, or the statistical distribution of load risk in historical operating data.

[0034] The flocculation disturbance coefficient is used to dynamically correct the floc formation time. Based on the correction result, it is determined whether there is a load risk in the sedimentation tank. The original dosing strategy or the modified dosing strategy is selected according to the judgment result, so as to realize the adaptive optimization of dosing decision, reduce the load risk of sedimentation tank, and improve the operational stability of water treatment system.

[0035] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0036] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0037] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0038] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0039] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for simulation evaluation of a water treatment process flow, characterized in that: Includes the following steps: Step S1: Monitor the raw water in the raw water intake pump, collect the influent conductivity of the raw water, calculate the influent mutation index of the raw water based on the influent conductivity, and determine whether the water quality analysis mechanism is triggered based on the influent mutation index. Step S2: In the water quality analysis mechanism, a turbidimeter is used to emit test light into the raw water and obtain the light scattering pulse density. The ultraviolet absorption value of the raw water is collected to evaluate the organic structure state of the raw water. The coagulation response hysteresis characteristics of the raw water are generated by combining the light scattering pulse density. Step S3: Predict the floc formation time of raw water based on the coagulation response hysteresis characteristics, access the management database for the stirring speed and current dosage of raw water, and analyze the flocculation disturbance coefficient of raw water by combining the stirring speed and current dosage. Step S4: Correct the floc formation time using the flocculation disturbance coefficient, determine whether there is a load risk in the sedimentation tank based on the corrected floc formation time, and select the original dosing strategy or the modified dosing strategy for the raw water according to the judgment result.

2. The simulation evaluation method for a water treatment process according to claim 1, characterized in that: In step S1, a preset acquisition period is established, and multiple acquisition times are divided. The inlet conductivity of the raw water is obtained by using a conductivity sensor installed in the inlet water pipeline. The change in the influent conductivity of the raw water is obtained by subtracting the influent conductivity of the raw water at one sampling time from the influent conductivity of the raw water at the next adjacent sampling time and taking the absolute value. The rate of change of influent conductivity is obtained by subtracting the change in influent conductivity of raw water at one sampling time from the change in influent conductivity of raw water at the next adjacent sampling time, and then dividing the absolute value by the sampling time interval.

3. The simulation evaluation method for a water treatment process according to claim 2, characterized in that: In step S1, the change in the influent conductivity and the rate of change in the influent conductivity of the raw water at each collection time are standardized to obtain the change factor and the rate of change factor. The influent mutation index of raw water was calculated by combining the change factor and the change rate factor using a weighted summation method. If the influent mutation index of raw water is greater than or equal to the preset influent mutation index threshold, the water quality analysis mechanism is triggered. If the influent mutation index of raw water is less than the preset influent mutation index threshold, the water quality analysis mechanism will not be triggered.

4. The simulation evaluation method for a water treatment process according to claim 1, characterized in that: In step S2, in the water quality analysis mechanism, a preset detection window time is set, and test light is emitted into the raw water through the light source unit of the turbidity meter. The test light is scattered after encountering suspended particles in the water, generating scattered light. Scattered light is received by a photodetector positioned at a fixed angle and converted into an electrical signal; After the electrical signal is amplified and filtered, it is discretely sampled by the data acquisition unit to obtain the scattered signal sequence. The average value of the scattered signal sequence is calculated to obtain the baseline signal. The offset of each scattered signal is obtained by subtracting the baseline signal from each scattered signal.

5. The simulation evaluation method for a water treatment process according to claim 4, characterized in that: In step S2, if the offset of each scattered signal is greater than or equal to the preset pulse determination threshold, then the scattered signal is determined to be a valid scattered pulse. If the offset of each scattered signal is less than the preset pulse determination threshold, then the scattered signal is determined to be an invalid scattered pulse; The number of effective scattered pulses is counted and divided by the preset detection window time to obtain the light scattered pulse density; The raw water is tested using an ultraviolet absorption detector to obtain its ultraviolet absorption value.

6. The simulation evaluation method for a water treatment process according to claim 5, characterized in that: In step S2, if the UV absorption value of the raw water is greater than or equal to the preset UV absorption threshold, the organic structure state of the raw water is determined to be a high-complexity structure state. If the UV absorption value of the raw water is less than the preset UV absorption threshold, the organic structure state of the raw water is determined to be a low-complexity structure state. If the light scattering pulse density is less than the preset light scattering pulse density threshold, and the organic structure of the raw water is a highly complex structure, then the coagulation response hysteresis characteristic of the raw water is determined to be a high hysteresis characteristic. If the light scattering pulse density is greater than or equal to the preset light scattering pulse density threshold, and the organic structure of the raw water is a low-complexity structure, then the coagulation response hysteresis characteristic of the raw water is determined to be a low-hysteresis characteristic. Conversely, the coagulation response hysteresis characteristic of the raw water is determined to be a medium hysteresis characteristic.

7. The simulation evaluation method for a water treatment process according to claim 6, characterized in that: In step S3, the coagulation response hysteresis characteristics of the raw water are matched with the hysteresis characteristic database to obtain the hysteresis level coefficient of the raw water. Multiply the preset basic floc formation time by 1 plus the product of the lag level coefficient and the preset time correction adjustment coefficient to obtain the floc formation time of the raw water. Access the management database to obtain the raw water stirring speed and current dosage; The stirring speed and current dosage of the raw water are standardized to obtain the speed factor and dosage factor; The flocculation disturbance coefficient of raw water is calculated by combining the velocity factor and the dosage factor. The calculation formula is as follows: ,in, For velocity factor, As a dose factor, This represents the flocculation disturbance coefficient of the raw water.

8. The simulation evaluation method for a water treatment process according to claim 7, characterized in that: In step S4, the flocculation disturbance coefficient and the floc formation time are combined to form an input feature combination; The input features are combined and fed into a pre-trained floc generation time correction model to obtain the corrected floc generation time. The floc formation time correction model is constructed based on big data statistical analysis and regression modeling methods to characterize the mapping relationship between flocculation disturbance conditions and floc formation time.

9. The simulation evaluation method for a water treatment process according to claim 8, characterized in that: In step S4, if the corrected floc formation time is greater than or equal to the preset floc formation time threshold, it is determined that the sedimentation tank has a load risk, and the corrected dosing strategy is executed. The modified dosing strategy refers to a control method that adjusts the dosing plan based on water quality analysis results and model prediction output when a significant change in influent water quality is detected and the sedimentation tank is determined to be at risk of overload. If the corrected floc formation time is less than the preset floc formation time threshold, it is determined that there is no load risk in the sedimentation tank, and the original dosing strategy is executed. The original dosing strategy refers to the conventional dosing control method implemented according to the established process parameters when no significant water quality changes are detected or no sedimentation tank load risk is assessed.

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