Process flow simulation system for water treatment plant design

Abstract

This invention discloses a process simulation system designed for water treatment plants, belonging to the field of process simulation technology. It addresses the problem of accurately identifying key control moments in coagulation reactions. The system scans the raw water under test using a scatterometer to acquire scattered light intensity data, calculates the scattering fluctuation coefficient, determines whether metastable phenomena exist in the raw water, and decides whether to activate the metastable analysis mechanism. Under the metastable analysis mechanism, disturbance tests are conducted, effluent turbidity data is collected, and disturbance response coefficients are calculated to pinpoint metastable candidate moments and define metastable failure intervals. Within these intervals, real-time monitoring of the dual conductivity and hydrolysis reaction data in the coagulation reaction zone generates a conductivity differential response. Combined with reaction information, the system analyzes the raw water potential recovery trend, collects raw water particle size distribution data, calculates skewness coefficients to correct the potential recovery trend, and obtains the collision efficiency. After time integration processing, the raw water is subjected to graded control to improve process stability and treatment efficiency.

Description

Technical Field

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

[0002] In the operation of existing water treatment plants, coagulation and sedimentation is the core treatment process. Its treatment effect directly depends on the characteristics of the raw water and the dynamic matching degree of the reaction process. Generally, factors such as the stability, electrical state and particle size distribution of colloidal particles in the raw water will have a significant impact on the coagulant dosing effect, floc formation efficiency and sedimentation performance.

[0003] The existing technology has the following shortcomings: Currently, existing technologies mainly rely on static monitoring and empirical control based on a single water quality parameter, which makes it difficult to characterize the dynamic response process of the raw water system under disturbance conditions and the evolution characteristics from metastable to unstable states. They also lack a multi-parameter coupled analysis mechanism for scattering fluctuation behavior, conductivity differential response, and particle size distribution skewness. This makes it difficult to accurately identify the key control timing of the coagulation reaction, and the process adjustment has lag and instability, which in turn affects the floc formation efficiency and the stable improvement of the overall water treatment effect. Therefore, a process simulation system for water treatment plant design is proposed. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a process flow simulation system for water treatment plant design. This system employs a multi-parameter coupled simulation analysis method that utilizes a metastable state determination mechanism based on scattering fluctuation coefficients, a metastable failure interval identification mechanism based on disturbance response coefficients, a potential recovery trend analysis mechanism based on the coupling of dual conductivity differential and hydrolysis reaction, and a collision efficiency dynamic modeling and time integral hierarchical control mechanism based on particle size distribution skewness coefficient correction to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a process flow simulation system for water treatment plants, comprising a scattering evaluation module, a disturbance identification module, a conductivity analysis module, and a particle size control module, the functions of which are as follows: The scattering assessment module uses a scattering instrument to scan the raw water under test, collects the scattered light intensity data of the raw water under test, calculates the scattering fluctuation coefficient based on the scattered light intensity data and assesses whether there is metastability. Based on the assessment results, it determines whether the metastability analysis mechanism is triggered and transmits the judgment result to the disturbance identification module. In the metastable analysis mechanism, the disturbance identification module performs disturbance tests on the raw water to be tested, collects the effluent turbidity data of the raw water to be tested and calculates the disturbance response coefficient, identifies metastable candidate moments based on the disturbance response coefficient and constructs metastable failure intervals, and transmits the metastable failure intervals to the conductivity analysis module. The conductivity analysis module detects the dual conductivity data and hydrolysis reaction data of the coagulation reaction zone within the metastable failure range. It generates a conductivity differential response based on the dual conductivity data, analyzes the potential rise trend of the raw water under test in conjunction with the hydrolysis reaction data, and transmits the potential rise trend to the particle size control module. The particle size control module collects particle size distribution data of the raw water to be tested and calculates the skewness coefficient. The skewness coefficient is used to correct the potential rise trend and generate the collision efficiency of the raw water to be tested. After time integration processing of the collision efficiency, the raw water to be tested is subjected to graded control.

[0006] In a preferred embodiment, in the scattering assessment module, a scattering instrument located at the front end of the coagulation reaction zone is used to continuously scan the raw water to be tested and obtain scattered light intensity data per unit time. Scattered light intensity data represents the intensity of incident light scattered by particles in water at a specific detection angle; The mean value of scattered light intensity is obtained by averaging the time series of scattered light intensity. The standard deviation of the scattered light intensity time series is calculated to obtain the scattered light intensity fluctuation coefficient. The ratio of the scattered light intensity fluctuation coefficient to the average scattered light intensity is used as the scattering fluctuation coefficient.

[0007] In a preferred embodiment, in the scattering evaluation module, when the scattering fluctuation coefficient is greater than the preset fluctuation judgment threshold, the particle is determined to be in a metastable state, and the metastable analysis mechanism is triggered. When the scattering fluctuation coefficient is less than or equal to the preset fluctuation judgment threshold, the particle system is determined to be in a relatively stable state and the subsequent metastability analysis mechanism is not triggered. The metastability determination result is standardized and encoded to form a trigger flag signal. The trigger flag signal is a binary variable: it takes the value of 1 when metastability exists, and takes the value of 0 otherwise. The trigger flag signal is then transmitted to the disturbance identification module.

[0008] In a preferred embodiment, when the trigger flag signal transmitted by the scattering evaluation module is 1, the metastability analysis mechanism is entered to perform a disturbance test on the raw water to be tested. Within the coagulation reaction zone, a disturbance is applied to the raw water to be tested using a stirring device. This disturbance is characterized by a stirring intensity gradient, and its variation is as follows: ; in, Let be the stirring intensity at disturbance time t. The preset benchmark stirring intensity, For the disturbance amplitude, The disturbance angular frequency is t, the disturbance time is t, and the disturbance amplitude is 5% of the reference stirring intensity. While applying the disturbance, the turbidity data of the effluent is continuously collected by a turbidity meter set at the back end of the coagulation reaction to form an effluent turbidity time series. The turbidity data of the effluent represents the degree of light scattering by unsettled particles in the water per unit time. Extract the maximum and minimum values ​​of the water turbidity time series, and subtract the minimum value from the maximum value to obtain the turbidity change range.

[0009] In a preferred embodiment, in the disturbance identification module, the disturbance response coefficient is constructed by dividing the turbidity change amplitude by the disturbance amplitude. When the disturbance response coefficient is less than the preset response threshold, the current time is marked as a metastable candidate time; otherwise, it is not marked. Calculate the time interval between adjacent metastable candidate moments. When the time interval is less than the preset candidate moment merging threshold, merge the corresponding metastable candidate moments to form a continuous time segment. All metastable candidate moments are merged to construct several continuous time segments, and the continuous time segment with the longest duration is taken as the metastable failure interval.

[0010] In a preferred embodiment, in the conductivity analysis module, within the metastable failure range, dual conductivity data of the coagulation reaction zone is detected by an online conductivity sensor. The dual conductivity data includes the real-time measured value of the conductivity of the raw water at the inlet of the coagulation reaction zone and the real-time measured value of the conductivity of the mixed water at the outlet of the coagulation reaction zone. The difference between the real-time measured conductivity of the mixed water and the real-time measured conductivity of the raw water is taken as the conductivity difference. After arranging each conductivity difference in chronological order, the difference between adjacent conductivity differences is taken and the absolute value is obtained to obtain the conductivity change. The average value of each change in conductivity is taken to obtain the differential conductivity response.

[0011] In a preferred embodiment, in the conductivity analysis module, the soluble hydrolysis products formed during the hydrolysis of the coagulant in the coagulation reaction zone are subjected to spectral detection by an ultraviolet-visible absorption spectral sensor to obtain the hydrolysis reaction data. The hydrolysis reaction data refers to the absorbance value of the soluble hydrolysis products at a preset characteristic wavelength. After arranging the hydrolysis reaction data in chronological order, subtract the previous hydrolysis reaction data from the current hydrolysis reaction data, and divide the resulting difference by the previous hydrolysis reaction data to obtain the absorbance change ratio. Take the absolute value of the absorbance change ratio to obtain the absorbance decay ratio. The hydrolysis attenuation factor was obtained by averaging the attenuation ratios of each absorbance. The conductivity differential response and hydrolysis reaction data were then standardized to obtain the conductivity differential response coefficient and the hydrolysis reaction coefficient. The potential recovery trend was calculated based on the differential response coefficient of conductivity and the hydrolysis reaction coefficient.

[0012] In a preferred embodiment, in the particle size control module, the particle size distribution data of the raw water to be tested is collected by an online particle size analyzer within the metastable failure range. The particle size distribution data includes the number of particles and the corresponding particle size value in each particle size range. The total number of particles is calculated by summing the number of particles across all particle size ranges. Multiply each particle size value by its corresponding number of particles and sum them to obtain a weighted sum of particle sizes. Divide the weighted sum of particle sizes by the total number of particles to obtain the average particle size. Subtract the average particle size from the particle size value in each particle size range to obtain the particle size deviation value. Calculate the standard deviation of the particle size distribution based on the particle size deviation value. The skewness coefficient is calculated based on the standard deviation of particle size distribution, and the absolute value of the skewness coefficient is used as the skewness intensity value.

[0013] In a preferred embodiment, in the particle size control module, the collision scale reference is obtained by accessing the parameter calibration library, the ratio of the skewness intensity value to the collision scale reference is used as the structure correction factor, and the product of the structure correction factor and the potential rise trend is used as the correction potential rise amount. After standardizing each average particle size, the average particle size factor is obtained. The ratio of each average particle size factor to the correction potential rise is used as the collision efficiency. The collision efficiency is obtained by arranging the collision efficiency in chronological order, and the cumulative collision efficiency is obtained by performing time integration on the collision efficiency sequence. Access the control strategy library to obtain the target control parameter set corresponding to the cumulative collision efficiency, input the target control parameter set into the coagulation process control unit, and perform parameter adjustment operations on the coagulation treatment process of the raw water to be tested.

[0014] The technical effects and advantages of this invention are as follows: This invention uses a scatterometer to scan the raw water under test, acquires scattered light intensity data, calculates the scattering fluctuation coefficient, determines whether metastable phenomena exist in the raw water, and decides whether to activate the metastable analysis mechanism. Under the metastable analysis mechanism, disturbance tests are carried out, effluent turbidity data is collected, and disturbance response coefficients are calculated to lock in metastable candidate moments and define metastable failure intervals. Within the metastable failure intervals, the dual conductivity and hydrolysis reaction data of the coagulation reaction zone are monitored in real time to generate a conductivity differential response. Combined with the reaction information, the potential rise trend of the raw water is judged, providing key parameters for particle size control. Raw water particle size distribution data is collected, and the skewness coefficient is calculated to correct the potential rise trend, obtaining the collision efficiency. After time integration processing, the raw water is subjected to graded control, thereby optimizing the coagulation reaction effect of water treatment and improving process stability and treatment efficiency. Attached Figure Description

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

[0016] Figure 2 This is a schematic diagram of the process simulation system designed for water treatment plants 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 uses a scatterometer to scan the raw water under test, acquires scattered light intensity data, calculates the scattering fluctuation coefficient, determines whether metastable phenomena exist in the raw water, and decides whether to activate the metastable analysis mechanism. Under the metastable analysis mechanism, disturbance tests are carried out, effluent turbidity data is collected, and disturbance response coefficients are calculated to lock in metastable candidate moments and delineate metastable failure intervals. Within the metastable failure intervals, the dual conductivity and hydrolysis reaction data of the coagulation reaction zone are monitored in real time to generate a conductivity differential response. Combined with the reaction information, the potential rise trend of the raw water is judged, providing key parameters for particle size control. Raw water particle size distribution data is collected, and the skewness coefficient is calculated to correct the potential rise trend, obtaining the collision efficiency. After time integration processing, the raw water is subjected to graded control, thereby optimizing the coagulation reaction effect of water treatment.

[0019] Example 1, as Figures 1 to 2 As shown, the process simulation system designed for water treatment plants includes a scattering assessment module, a disturbance identification module, a conductivity analysis module, and a particle size control module. The functions of each module are as follows: The scattering assessment module uses a scattering instrument to scan the raw water under test, collects the scattered light intensity data of the raw water under test, calculates the scattering fluctuation coefficient based on the scattered light intensity data and assesses whether there is metastability. Based on the assessment results, it determines whether the metastability analysis mechanism is triggered and transmits the judgment result to the disturbance identification module. In the metastable analysis mechanism, the disturbance identification module performs disturbance tests on the raw water to be tested, collects the effluent turbidity data of the raw water to be tested and calculates the disturbance response coefficient, identifies metastable candidate moments based on the disturbance response coefficient and constructs metastable failure intervals, and transmits the metastable failure intervals to the conductivity analysis module. The conductivity analysis module detects the dual conductivity data and hydrolysis reaction data of the coagulation reaction zone within the metastable failure range. It generates a conductivity differential response based on the dual conductivity data, analyzes the potential rise trend of the raw water under test in conjunction with the hydrolysis reaction data, and transmits the potential rise trend to the particle size control module. The particle size control module collects particle size distribution data of the raw water to be tested and calculates the skewness coefficient. The skewness coefficient is used to correct the potential rise trend and generate the collision efficiency of the raw water to be tested. After time integration processing of the collision efficiency, the raw water to be tested is subjected to graded control.

[0020] The specific implementation is as follows: In the scattering assessment module, a scattering instrument set at the front end of the coagulation reaction zone is used to continuously scan the raw water to be tested. The scattering instrument detects the light scattering of suspended particles in the raw water to be tested at a preset sampling frequency, and obtains the scattered light intensity data per unit time to form a time series of scattered light intensity.

[0021] Among them, the scattered light intensity data represents the scattering intensity of incident light by particles in the water body at a specific detection angle. Its value is related to the number of particles and the particle size distribution. The greater the scattered light intensity, the greater the effective scattering particle number or particle size contribution per unit volume.

[0022] It should be noted that the coagulation reaction zone refers to the functional unit area in the water treatment process used to achieve destabilization, collision and floc formation of colloidal particles. It consists of a rapid mixing section and a flocculation reaction section. The rapid mixing section is used to uniformly disperse the coagulant in a short time, and the flocculation reaction section is used to promote the collision and aggregation between particles. The scattering instrument is a detection device used to measure the intensity distribution of scattering of incident light by particles in water at a specific angle.

[0023] The mean value of scattered light intensity is obtained by averaging the time series of scattered light intensity, and the specific expression is as follows: in, This represents the average intensity of the scattered light. This represents the total number of scattered light intensity data. This is data on the intensity of scattered light. This is the index value for the scattered light intensity data.

[0024] The average scattered light intensity characterizes the overall scattering level of the particle system during the sampling period; the larger the value, the stronger the overall scattering ability of the particles.

[0025] Furthermore, the standard deviation of the scattered light intensity time series is calculated to obtain the scattered light intensity fluctuation coefficient, as shown in the following expression: ; in, The scattered light intensity fluctuation coefficient is denoted by . This represents the average intensity of the scattered light. This represents the total number of scattered light intensity data. This is data on the intensity of scattered light. This is the index value for the scattered light intensity data.

[0026] The scattering intensity fluctuation coefficient characterizes the degree of fluctuation in the scattering intensity. The larger the value, the more obvious the structural changes or dynamic reorganization behavior of the particle system at the microscale.

[0027] Based on the mean scattered light intensity and the scattered light intensity fluctuation coefficient, the scattering fluctuation coefficient is constructed, and its expression is as follows: ; in, The scattering fluctuation coefficient is... The scattered light intensity fluctuation coefficient is denoted by . This represents the average intensity of the scattered light.

[0028] The scattering fluctuation coefficient is a dimensionless parameter used to eliminate the influence of the absolute value of scattering intensity. It characterizes the dynamic stability of the particle system from the perspective of relative fluctuation. The larger the value, the more violent the structural fluctuation of the particle system is under the same average scattering level, reflecting that the interaction between colloidal particles is in a metastable state.

[0029] The calculated scattering fluctuation coefficient is compared with the preset fluctuation judgment threshold: When the scattering fluctuation coefficient is greater than the preset fluctuation judgment threshold, it is determined that the colloidal particle system in the raw water to be tested has significant structural fluctuation characteristics, indicating that the particles are in a metastable state that is prone to structural reorganization. In the metastable state, although the particles can form flocs in a short time, the structural stability is insufficient and there is a risk of subsequent redispersion, triggering the metastable analysis mechanism. When the scattering fluctuation coefficient is less than or equal to the preset fluctuation judgment threshold, the particle system is determined to be in a relatively stable state and the subsequent metastability analysis mechanism is not triggered.

[0030] It should be noted that the fluctuation judgment threshold is obtained through statistical analysis of historical stable operating data. For example, the mean weighted standard deviation of the scattering fluctuation coefficient under stable operating conditions can be selected as the fluctuation judgment threshold.

[0031] After completing the above determination, the metastable state determination result is standardized and encoded to form a trigger flag signal. The trigger flag signal is a binary variable: it takes the value of 1 when metastable phenomena exist, and takes the value of 0 otherwise. The trigger flag signal is then transmitted to the disturbance identification module for further identification and analysis of subsequent metastable candidate moments.

[0032] In the disturbance identification module, when a trigger flag signal of 1 is received from the scattering evaluation module, the metastability analysis mechanism is entered, and a disturbance test is performed on the raw water to be tested. Specifically, a disturbance is applied to the raw water to be tested within the coagulation reaction zone using a stirring device. The disturbance is characterized as a stirring intensity gradient, and its variation is as follows: ; in, Let be the stirring intensity at disturbance time t. The preset benchmark stirring intensity, For the disturbance amplitude, Let ω be the angular frequency of the disturbance, and t be the time of the disturbance.

[0033] It should be noted that the disturbance amplitude is taken as 5% of the benchmark stirring intensity, which is used to introduce controllable micro-disturbances without disrupting the overall coagulation conditions; the stirring device is an actuator set in the coagulation reaction zone to provide mechanical energy input to the water body. It drives the fluid movement through impeller rotation, thereby forming a velocity gradient field in the water body.

[0034] While applying disturbance, turbidity data of the effluent is continuously collected by a turbidity meter installed at the downstream end of the coagulation reaction, forming an effluent turbidity time series. The effluent turbidity data represents the degree of light scattering by unsettled particles in the water per unit time. The higher the value, the higher the concentration of suspended particles in the water, reflecting the stability of the flocs and the settling effect.

[0035] It should be noted that a turbidity meter is a detection device used to measure the degree of influence of suspended particles in water on light scattering or transmission. It converts light signals into turbidity values ​​by emitting light of a specific wavelength into the water and detecting changes in the intensity of transmitted light at a fixed angle.

[0036] Within the preset analysis time window, the maximum and minimum values ​​of the water turbidity time series are extracted respectively. The turbidity change amplitude is obtained by subtracting the minimum value from the maximum value. Based on the turbidity change amplitude and the disturbance amplitude, the disturbance response coefficient is constructed. The specific expression is as follows: ; in, The disturbance response coefficient is... The range of turbidity change. This represents the disturbance amplitude.

[0037] The disturbance response coefficient is the magnitude of turbidity change caused by a unit disturbance intensity. Its value reflects the sensitivity of the floc structure to external disturbances. The larger the disturbance response coefficient, the more significant the turbidity fluctuation can be caused by a small disturbance, indicating that the floc structure is looser, that is, in a metastable state, and has a tendency to redispermeate. The smaller the disturbance response coefficient, the more stable the floc structure is, and the smaller the effect of disturbance.

[0038] The disturbance response coefficient is then compared with a preset response threshold: When the disturbance response coefficient is less than the preset response threshold, the current time is marked as a metastable candidate time, which is used to characterize that the floc structure of the raw water to be tested is in a state that is susceptible to disturbance and destruction at that time. Conversely, no marking is performed.

[0039] It should be noted that the preset response threshold is obtained through statistical analysis of historical stable operating data. For example, the mean weighted standard deviation of the disturbance response coefficient and the scattering fluctuation coefficient under stable operating conditions is selected as the fluctuation judgment threshold.

[0040] Temporal proximity analysis is performed on multiple metastable candidate moments in a continuous time series. Specifically, the time interval between adjacent metastable candidate moments is calculated. When the time interval is less than a preset candidate moment merging threshold, the corresponding metastable candidate moments are merged to form a continuous time segment. By merging all metastable candidate moments, several continuous time segments are constructed, and the continuous time segment with the longest duration is taken as the metastable failure interval.

[0041] The metastable failure interval is used to characterize the time range of continuous instability of floc structure. The longer the interval is, the longer the metastable state lasts and the higher the risk of redispersion.

[0042] It should be noted that the preset candidate moment merging threshold is used to control the continuity of the interval. It is set according to the duration of the disturbance and the actual response delay. Several disturbance test processes are selected from the historical operation data, and the start time of each disturbance and the time when the corresponding turbidity response reaches the peak are recorded. The response delay time is calculated, and all response delay times are statistically averaged to obtain the average response delay time. The average response delay time is used as the candidate moment merging threshold.

[0043] Finally, the metastable failure interval data, along with the corresponding time markers, are transmitted to the conductivity analysis module for subsequent analysis of the potential recovery trend.

[0044] In the conductivity analysis module, within the metastable failure range, the dual conductivity data of the coagulation reaction zone is detected by an online conductivity sensor. The dual conductivity data includes the real-time measured value of the conductivity of the raw water at the inlet of the coagulation reaction zone and the real-time measured value of the conductivity of the mixed water at the outlet of the coagulation reaction zone. The coagulation reaction zone is the reaction space located after the coagulant dosing point and before the sedimentation or filtration unit in the water treatment process. The difference between the real-time measured conductivity of the mixed water and the real-time measured conductivity of the raw water is taken as the conductivity difference. After arranging each conductivity difference in chronological order, the difference between adjacent conductivity differences is taken and the absolute value is obtained to obtain the conductivity change. The average value of each conductivity change is used to obtain the conductivity differential response, which reflects the dynamic degree of electrolyte concentration change in the coagulation reaction zone. The larger the value, the more intense the ion migration and release process during the reaction. The dissolved hydrolysis products formed during the hydrolysis of coagulant in the coagulation reaction zone are detected by a UV-Vis absorption spectroscopy sensor to obtain data on each hydrolysis reaction. The hydrolysis reaction data refers to the absorbance values ​​of the dissolved hydrolysis products at a preset characteristic wavelength. After arranging the hydrolysis reaction data in chronological order, subtract the previous hydrolysis reaction data from the current hydrolysis reaction data, and divide the resulting difference by the previous hydrolysis reaction data to obtain the absorbance change ratio. Take the absolute value of the absorbance change ratio to obtain the absorbance decay ratio. The hydrolysis attenuation factor is obtained by averaging the attenuation ratios of each absorbance. The larger the value, the weaker the ability to maintain hydrolysis products, the more obvious the attenuation of the neutralization effect of particle surface charge, and the easier it is for particles to recharge. The conductivity differential response and hydrolysis reaction data were standardized to obtain the conductivity differential response coefficient and the hydrolysis reaction coefficient, respectively. Calculation of potential recovery trend based on conductivity differential response coefficient and hydrolysis reaction coefficient: , The potential is showing an upward trend. and These are the conductivity weighting coefficient and the hydrolysis weighting coefficient. The hydrolysis reaction coefficient is... The conductivity differential response coefficient; It should be explained that the conductivity weighting coefficient and the hydrolysis weighting coefficient can be calibrated based on the degree of influence of changes in conductivity on the rebound of effluent turbidity in historical experiments and the degree of influence of the decay of hydrolysis products on the restoring phenomenon.

[0045] The greater the potential rise trend, the more obvious the fluctuation of free ions in the coagulation system, and the weaker the stable adsorption capacity of hydrolysis products on the particle surface, and the stronger the tendency of particles to redisperse. The potential recovery trend is transmitted to the particle size control module.

[0046] It should be noted that an online conductivity sensor is an online monitoring device that continuously measures the conductivity of ions in a fluid medium based on the principle of electrodes or electromagnetic induction; an ultraviolet-visible absorption spectroscopy sensor is an optical monitoring device that quantitatively detects soluble components in a solution based on the selective absorption characteristics of substances to light of specific wavelengths; and the standardization methods include, but are not limited to, standard linear transformation based on interval scaling, statistical Z-Score standardization method, or normalization method based on nonlinear mapping function. The application methods of standardization will not be elaborated here.

[0047] In the particle size control module, the particle size distribution data of the raw water to be tested is collected by an online particle size analyzer within the metastable failure range. The particle size distribution data is the frequency statistics result of the particle size range output by the online particle size analyzer, which reflects the distribution of the number of particles in different particle size ranges. Extract the number of particles and the corresponding particle size value for each particle size range from the particle size distribution data, and sum the number of particles in all particle size ranges as the total number of particles. Multiply each particle size value by its corresponding number of particles and sum them to obtain a weighted sum of particle sizes. Divide the weighted sum of particle sizes by the total number of particles to obtain the average particle size. Subtract the average particle size from the particle size value in each particle size range to obtain the particle size deviation value. Square each particle size deviation value to obtain the deviation square value. Multiply each deviation square value by the corresponding number of particles and sum them to obtain the weighted sum of squares. Divide the weighted sum of squares by the total number of particles and take the square root of the result to obtain the standard deviation of the particle size distribution. Calculation of skewness coefficient based on particle size distribution standard deviation: ,in, The skewness coefficient, This represents the total number of particle size ranges. This represents the total number of particles. This represents the number of particles corresponding to the i-th particle size range. The average particle size is This represents the particle size value corresponding to the i-th particle size range. The standard deviation of particle size distribution; The absolute value of the skewness coefficient is used as the skewness intensity value, which reflects the overall asymmetry of the particle size distribution. The larger the value, the more unstable the particle size structure and the more uneven the distribution of particle collision paths. Access the parameter calibration library to obtain the collision scale benchmark, which is the statistical benchmark value of the skewness coefficient calculated based on the particle size distribution data under stable flocculation conditions in the historical operation database. The ratio of the skewness intensity value to the collision scale benchmark is used as the structural correction factor, and the product of the structural correction factor and the potential rise trend is used as the corrected potential rise amount. After standardizing each average particle size, we obtain each average particle size factor. The ratio of each average particle size factor to the corrected potential rise is taken as the collision efficiency. The larger the value, the higher the probability of particle collision and aggregation. The smaller the value, the stronger the influence of potential rise on the particles, and the more difficult it is for them to form a stable bond after collision. The collision efficiencies are arranged in chronological order to obtain a collision efficiency sequence. The collision efficiency sequence is then integrated over time, and the calculation formula is as follows: ,in, Let t be the time length corresponding to the t-th collision efficiency. For the t-th collision efficiency, The total number of collision efficiencies. For cumulative collision efficiency; Access the control strategy library to obtain the graded threshold group corresponding to the cumulative collision efficiency. The graded threshold group refers to the set of multiple efficiency interval boundary values ​​set according to the segmentation of the coagulation reaction state according to the cumulative collision efficiency. It comes from the mapping relationship between the cumulative collision efficiency and the compliance of the effluent turbidity in the historical operation database and is obtained by statistical regression of the operation data under different water quality conditions. The cumulative collision efficiency is sequentially matched with each graded threshold group to determine the target threshold range in which the cumulative collision efficiency lies. Furthermore, the target threshold range is used as input to retrieve the target control parameter set from the control strategy library. The target control parameter set includes the coagulant dosage adjustment coefficient, the stirring intensity adjustment coefficient, and the reaction residence time adjustment coefficient. The target control parameter set is input into the coagulation process control unit, and the parameter adjustment operation is performed on the coagulation treatment process of the raw water to be tested, so that the coagulation reaction state converges towards the direction of stable flocculation level.

[0048] It should be noted that the parameter calibration library is a data storage unit that stores the set of basic parameters obtained through historical operation data statistics and operating condition calibration; the control strategy library is a strategy set unit used to store the mapping relationship between graded threshold groups and corresponding process control parameters; and the coagulation process control unit is an actuator used to receive control strategies and perform control operations on the actual coagulation process.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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 process flow simulation system designed for water treatment plants, characterized in that: It includes a scattering assessment module, a perturbation identification module, a conductivity analysis module, and a particle size control module. The functions of each module are as follows: The scattering assessment module uses a scattering instrument to scan the raw water under test, collects the scattered light intensity data of the raw water under test, calculates the scattering fluctuation coefficient based on the scattered light intensity data and assesses whether there is metastability. Based on the assessment results, it determines whether the metastability analysis mechanism is triggered and transmits the judgment result to the disturbance identification module. In the metastable analysis mechanism, the disturbance identification module performs disturbance tests on the raw water to be tested, collects the effluent turbidity data of the raw water to be tested and calculates the disturbance response coefficient, identifies metastable candidate moments based on the disturbance response coefficient and constructs metastable failure intervals, and transmits the metastable failure intervals to the conductivity analysis module. The conductivity analysis module detects the dual conductivity data and hydrolysis reaction data of the coagulation reaction zone within the metastable failure range. It generates a conductivity differential response based on the dual conductivity data, analyzes the potential rise trend of the raw water under test in conjunction with the hydrolysis reaction data, and transmits the potential rise trend to the particle size control module. The particle size control module collects particle size distribution data of the raw water to be tested and calculates the skewness coefficient. The skewness coefficient is used to correct the potential rise trend and generate the collision efficiency of the raw water to be tested. After time integration processing of the collision efficiency, the raw water to be tested is subjected to graded control.

2. The process flow simulation system for water treatment plant design according to claim 1, characterized in that: In the scattering assessment module, a scattering instrument set at the front end of the coagulation reaction zone is used to continuously scan the raw water to be tested and obtain the scattered light intensity data per unit time. Scattered light intensity data represents the intensity of incident light scattered by particles in water at a specific detection angle; The mean value of scattered light intensity is obtained by averaging the time series of scattered light intensity. The standard deviation of the scattered light intensity time series is calculated to obtain the scattered light intensity fluctuation coefficient. The ratio of the scattered light intensity fluctuation coefficient to the average scattered light intensity is used as the scattering fluctuation coefficient.

3. The process flow simulation system for water treatment plant design according to claim 2, characterized in that: In the scattering evaluation module, when the scattering fluctuation coefficient is greater than the preset fluctuation judgment threshold, the particle is determined to be in a metastable state, triggering the metastable analysis mechanism. When the scattering fluctuation coefficient is less than or equal to the preset fluctuation judgment threshold, the particle system is determined to be in a relatively stable state and the subsequent metastability analysis mechanism is not triggered. The metastability determination result is standardized and encoded to form a trigger flag signal. The trigger flag signal is a binary variable: it takes the value of 1 when metastability exists, and takes the value of 0 otherwise. The trigger flag signal is then transmitted to the disturbance identification module.

4. The process flow simulation system for water treatment plant design according to claim 1, characterized in that: In the disturbance identification module, when the trigger flag signal transmitted by the scattering evaluation module is 1, the metastability analysis mechanism is entered to perform a disturbance test on the raw water to be tested. Within the coagulation reaction zone, a disturbance is applied to the raw water to be tested using a stirring device. This disturbance is characterized by a stirring intensity gradient, and its variation is as follows: ; in, Let be the stirring intensity at disturbance time t. The preset benchmark stirring intensity, For the disturbance amplitude, The disturbance angular frequency is t, the disturbance time is t, and the disturbance amplitude is 5% of the reference stirring intensity. While applying the disturbance, the turbidity data of the effluent is continuously collected by a turbidity meter set at the back end of the coagulation reaction to form an effluent turbidity time series. The turbidity data of the effluent represents the degree of light scattering by unsettled particles in the water per unit time. Extract the maximum and minimum values ​​of the water turbidity time series, and subtract the minimum value from the maximum value to obtain the turbidity change range.

5. The process flow simulation system for water treatment plant design according to claim 4, characterized in that: In the disturbance identification module, the disturbance response coefficient is constructed by dividing the turbidity change amplitude by the disturbance amplitude; When the disturbance response coefficient is less than the preset response threshold, the current time is marked as a metastable candidate time; otherwise, it is not marked. Calculate the time interval between adjacent metastable candidate moments. When the time interval is less than the preset candidate moment merging threshold, merge the corresponding metastable candidate moments to form a continuous time segment. All metastable candidate moments are merged to construct several continuous time segments, and the continuous time segment with the longest duration is taken as the metastable failure interval.

6. The process flow simulation system for water treatment plant design according to claim 1, characterized in that: In the conductivity analysis module, within the metastable failure range, the dual conductivity data of the coagulation reaction zone is detected by an online conductivity sensor. The dual conductivity data includes the real-time measured value of the conductivity of the raw water at the inlet of the coagulation reaction zone and the real-time measured value of the conductivity of the mixed water at the outlet of the coagulation reaction zone. The difference between the real-time measured conductivity of the mixed water and the real-time measured conductivity of the raw water is taken as the conductivity difference. After arranging each conductivity difference in chronological order, the difference between adjacent conductivity differences is taken and the absolute value is obtained to obtain the conductivity change. The average value of each change in conductivity is taken to obtain the differential conductivity response.

7. The process flow simulation system for water treatment plant design according to claim 1, characterized in that: In the conductivity analysis module, the soluble hydrolysis products formed during the hydrolysis of coagulant in the coagulation reaction zone are detected by a UV-Vis absorption spectroscopy sensor to obtain the hydrolysis reaction data. The hydrolysis reaction data refers to the absorbance value of the soluble hydrolysis products at a preset characteristic wavelength. After arranging the hydrolysis reaction data in chronological order, subtract the previous hydrolysis reaction data from the current hydrolysis reaction data, and divide the resulting difference by the previous hydrolysis reaction data to obtain the absorbance change ratio. Take the absolute value of the absorbance change ratio to obtain the absorbance decay ratio. The hydrolysis attenuation factor was obtained by averaging the attenuation ratios of each absorbance. The conductivity differential response and hydrolysis reaction data were then standardized to obtain the conductivity differential response coefficient and the hydrolysis reaction coefficient. The potential recovery trend was calculated based on the differential response coefficient of conductivity and the hydrolysis reaction coefficient.

8. The process flow simulation system for water treatment plant design according to claim 1, characterized in that: In the particle size control module, the particle size distribution data of the raw water to be tested is collected by an online particle size analyzer within the metastable failure range. The particle size distribution data includes the number of particles and the corresponding particle size value in each particle size range. The total number of particles is calculated by summing the number of particles across all particle size ranges. Multiply each particle size value by its corresponding number of particles and sum them to obtain a weighted sum of particle sizes. Divide the weighted sum of particle sizes by the total number of particles to obtain the average particle size. Subtract the average particle size from the particle size value in each particle size range to obtain the particle size deviation value. Calculate the standard deviation of the particle size distribution based on the particle size deviation value. The skewness coefficient is calculated based on the standard deviation of particle size distribution, and the absolute value of the skewness coefficient is used as the skewness intensity value.

9. The process flow simulation system for water treatment plant design according to claim 8, characterized in that: In the particle size control module, the collision scale benchmark is obtained by accessing the parameter calibration library. The ratio of the skewness intensity value to the collision scale benchmark is used as the structure correction factor, and the product of the structure correction factor and the potential rise trend is used as the correction potential rise amount. After standardizing each average particle size, the average particle size factor is obtained. The ratio of each average particle size factor to the correction potential rise is used as the collision efficiency. The collision efficiency is obtained by arranging the collision efficiency in chronological order, and the cumulative collision efficiency is obtained by performing time integration on the collision efficiency sequence. Access the control strategy library to obtain the target control parameter set corresponding to the cumulative collision efficiency, input the target control parameter set into the coagulation process control unit, and perform parameter adjustment operations on the coagulation treatment process of the raw water to be tested.

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