Electroplating wastewater dosage prediction system based on dynamic flow-concentration coupling model

Abstract

The invention provides an electroplating wastewater dosage prediction system based on a dynamic flow-concentration coupling model, and relates to the technical field of industrial wastewater treatment.The electroplating wastewater dosage prediction system is characterized in that the dynamic flow-concentration coupling model is established by collecting real-time flow and heavy metal ion concentration parameters of a wastewater treatment system; the method comprises the following steps: identifying a time-lag relationship and coupling strength between flow change and concentration fluctuation, generating a flow-concentration coupling strength matrix, constructing a dosage prediction curve by adopting a piecewise linear mapping method on the basis of the matrix, performing matching positioning on a current flow-concentration coupling state, and performing gradient pulse dosage parameter prediction by applying a gradient pulse dosage parameter prediction technology. And the dosage of the medicament can be accurately predicted. Therefore, the problems that the removal rate of partial heavy metals is reduced, the medicament utilization rate is low, and even secondary pollution is possibly caused or the operation cost is obviously increased due to the fact that the adding amount and the adding time sequence of various medicaments are difficult to adjust timely and accurately can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial wastewater treatment, and more specifically, to an electroplating wastewater dosing prediction system based on a dynamic flow-concentration coupling model. Background Art

[0002] With the acceleration of industrialization, electroplating wastewater has become a pressing challenge in environmental management due to its presence of a variety of highly toxic heavy metal ions, such as hexavalent chromium, copper, nickel, and zinc, and its volatile water quality parameters. Traditional electroplating wastewater treatment processes, centered around chemical precipitation and supplemented by reduction, chelation, and flocculation, have achieved preliminary removal of common heavy metal pollutants. However, in recent years, with increasingly stringent emission standards and frequent fluctuations in water quantity and quality, single or static dosing models have become inadequate for the complex and dynamic demands of wastewater treatment. To this end, scholars and engineers at home and abroad are continuously exploring intelligent and automated dosing control technologies, attempting to automate the dosing process through methods such as online monitoring, model prediction, and feedback control to improve the stability and cost-effectiveness of wastewater treatment. Some studies have introduced multivariable models to optimize dosing for a single heavy metal ion or under specific operating conditions. However, significant limitations remain in the synergistic treatment of multiple metals, adaptability to dynamic operating conditions, and chemical interactions.

[0003] Existing intelligent dosing technologies suffer from the following shortcomings: First, mainstream dosing prediction models often rely on single pollutant concentrations or simple flow parameters, making it difficult to accurately reflect the synergistic changes in the concentrations of multiple heavy metal ions and their dynamic coupling with multiple parameters such as flow, pH, and temperature. Second, existing technologies often overlook the reaction priorities between heavy metal ions in wastewater, the time lag effect of reagent addition, and the potential synergistic or antagonistic relationships between reagents. This leads to delayed dosing decisions and low reagent utilization when operating conditions fluctuate under complex conditions, and may even cause secondary pollution. Furthermore, due to the lack of real-time dynamic identification of the strength of flow-concentration coupling and adaptive correction of dosing prediction models, existing systems are unable to cope with extreme operating conditions such as high shock loads, sudden flow changes, or heavy metal concentration peaks, limiting both treatment safety margins and economic efficiency. Currently, static or empirical segmented dosing curves are predominant. A systematic intelligent decision-making mechanism that integrates operating condition feature vector clustering, expert knowledge base screening, chemical reaction kinetics, and multi-objective intelligent optimization algorithms is lacking. Consequently, a dynamic closed-loop control system for the efficient and synergistic removal of multiple metal ions has yet to be established. Summary of the Invention

[0004] To solve the above technical problems, the present invention is proposed. The present invention provides a dosing amount prediction system for electroplating wastewater based on a dynamic flow-concentration coupling model, which can, to a certain extent, solve the problems that it is difficult to timely and accurately adjust the dosing amounts and dosing timings of various agents, resulting in a decrease in the removal rate of some heavy metals, low utilization rate of agents, and even possible secondary pollution or a significant increase in operating costs.

[0005] According to one aspect of the present invention, there is provided a dosing amount prediction system for electroplating wastewater based on a dynamic flow-concentration coupling model, which includes: A collection module for collecting real-time flow parameters and heavy metal ion concentration parameters of an electroplating wastewater treatment system; A calculation module for establishing a dynamic flow-concentration coupling model based on the real-time flow parameters and the heavy metal ion concentration parameters, identifying the time-delay relationship and coupling strength between the flow change and the concentration fluctuation based on the dynamic flow-concentration coupling model, and calculating a flow-concentration coupling strength matrix; A partition mapping module for constructing a dosing amount prediction curve by means of a piecewise linear mapping method according to the flow-concentration coupling strength matrix, and performing partition quantitative mapping on the optimal dosing amounts under different flow-concentration coupling states; A prediction module for matching and positioning the current flow-concentration coupling state of electroplating wastewater according to the dosing amount prediction curve, and predicting the dosing amount by using a gradient pulse dosing parameter prediction technique.

[0006] Further, the real-time flow parameters include influent flow rate, instantaneous flow rate change rate, and flow fluctuation frequency; The heavy metal ion concentration parameters include hexavalent chromium ion concentration, trivalent chromium ion concentration, divalent nickel ion concentration, divalent copper ion concentration, and divalent zinc ion concentration.

[0007] Further, data preprocessing is performed based on the real-time flow parameters and the heavy metal ion concentration parameters, and the sliding time window technique is used to divide the data into continuous segments in chronological order. Within each time window, a multiple autoregressive model with the influent flow rate, flow rate change rate, and fluctuation frequency as independent variables and the heavy metal ion concentrations as dependent variables is constructed, and the model parameter sequence reflecting the relationship between flow and concentration is dynamically fitted, that is, the dynamic flow-concentration coupling model.

[0008] Further, based on the dynamic flow-concentration coupling model, the time-delay relationship and coupling strength between the flow change and the heavy metal ion concentration fluctuation are identified through cross-correlation analysis, and the partial correlation analysis method is used to quantify the coupling strength between each flow parameter and each heavy metal ion concentration, and the partial correlation analysis results are organized into a 5×3 coupling strength matrix.

[0009] Further, based on the coupling strength matrix, clustering analysis is performed to divide the elements in the matrix into five coupling state categories: strong positive correlation region, weak positive correlation region, micro-correlation region, weak negative correlation region, and strong negative correlation region according to the coupling strength and coupling direction characteristics.

[0010] Further, based on the coupling strength matrix and in combination with the time delay relationship, a flow-concentration coupling feature vector is constructed; Based on the flow-concentration coupling feature vector, the similarity with the standard vector in the historical working condition library is calculated to judge the typical treatment mode of the wastewater, and the dosing timing of the corresponding chemicals is adjusted.

[0011] Further, based on the typical treatment mode, a chemical dosing model is constructed, and for different chemical dosing amounts, a dosing amount prediction curve is constructed using a piecewise linear mapping function respectively.

[0012] Further, for complex wastewater containing multiple heavy metal ions simultaneously, a collaborative chemical dosing decision-making mechanism is established, including: Extract the coupling state information of each heavy metal ion with each flow parameter to form a coupling mode feature vector; According to the clustering analysis of the feature vector, the current working condition is classified into a preset working condition template library, and the most matching basic chemical dosing ratio scheme is selected as the initial solution; After screening out unreasonable combinations of the initial solution through the expert knowledge base, based on the chemical reaction kinetics model, combined with the time delay characteristics and the hierarchical priority strategy, the multi-metal collaborative treatment sequence and chemical dosing amount are dynamically optimized; Meanwhile, using the improved particle swarm optimization algorithm, under the premise of meeting the treatment target and cost constraints, an optimal chemical dosing scheme is iteratively searched.

[0013] Further, the improved particle swarm optimization algorithm takes the dosing amounts of various chemicals as decision variables and constructs an optimization model with the ratio of weighted treatment efficiency to cost as the fitness; Through the improved particle swarm optimization algorithm, the particle search strategy and parameters are dynamically adjusted, combined with chemical constraints, synergistic effects and simulated annealing mechanism, to ensure that under the premise of meeting the treatment and cost constraints, an optimal chemical dosing scheme suitable for the current flow-concentration coupling characteristics is found.

[0014] Further, based on the real-time flow and heavy metal ion monitoring data, the current working condition is multi-dimensionally matched with the chemical dosing amount prediction curve library, and the optimal prediction curve is selected or weighted and fused using similarity evaluation. According to the prediction result, the gradient pulse dosing technology is adopted to optimize the dosing timing, pulse intensity and dosing point distribution of the chemicals, and compensation adjustment is carried out for the reaction characteristics of different chemicals and the time delay of the working condition. After each control cycle, the prediction accuracy is evaluated, the model parameters are continuously optimized, and the optimal chemical dosing scheme for different time periods and regions within the next two hours is output. Brief Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings: Figure 1 It is a flowchart of an electroplating wastewater chemical dosage prediction system based on a dynamic flow-concentration coupling model according to an embodiment of the present invention; Figure 2 It is a flowchart of a coupling strength matrix in an embodiment of the present invention. Detailed Embodiments

[0016] Next, exemplary embodiments according to the present invention will be described in detail with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein.

[0017] As mentioned in the above background art, the existing electroplating wastewater chemical dosing technology mainly has the following two prominent problems: First, the existing systems often predict the chemical dosage based on only a single parameter or a static segmented model, making it difficult to accurately reflect the dynamic synergistic changes between multiple heavy metal ions and the real-time coupling relationship with process parameters such as flow rate and pH, resulting in a lag in chemical dosing response and a decrease in treatment efficiency. Second, the existing technologies generally lack the adaptive adjustment ability for extreme working conditions such as high impact load and sudden increase in heavy metal concentration, ignoring key factors such as chemical dosing time lag and synergistic or antagonistic effects between chemicals, unable to achieve efficient synergistic treatment of multiple metal ions, and difficult to optimize the chemical dosing strategy in a timely manner when the working conditions change suddenly.

[0018] Figure 1 It is a flowchart of an electroplating wastewater chemical dosage prediction system based on a dynamic flow-concentration coupling model according to an embodiment of the present invention. As Figure 1 shown, in the electroplating wastewater chemical dosage prediction system based on a dynamic flow-concentration coupling model, it includes: S1: Collect the real-time flow rate parameters and heavy metal ion concentration parameters of the electroplating wastewater treatment system.

[0019] Collect the real-time flow rate parameters and heavy metal ion concentration parameters of the electroplating wastewater treatment system, where the real-time flow rate parameters include influent flow rate, instantaneous flow rate change rate, and flow rate fluctuation frequency; The influent flow rate is recorded by a high-precision electromagnetic flowmeter installed at the intersection of the main influent pipeline and each branch pipeline; the electromagnetic flowmeter uses electrodes made of polytetrafluoroethylene-lined material to resist the corrosion of electroplating wastewater, and is equipped with an anti-bubble interference compensation function. The real-time acquisition frequency is once per minute, and the data is transmitted to the central monitoring system in cubic meters per hour through the RS485 communication interface; the installation position of the electromagnetic flowmeter is not less than 10 times the pipe diameter downstream of the elbow to ensure measurement accuracy, and it is calibrated regularly. The instantaneous flow rate change rate is automatically calculated based on the flow rate changes within a continuous time period. The calculation results are recorded and stored in units of percentage / hour. When the flow rate change rate exceeds the preset threshold range, the system automatically issues an alarm prompt. The flow rate fluctuation frequency is obtained by performing spectral analysis on the flow rate data over a long time period. The sliding window technique is used to process the flow rate data for 24 consecutive hours. The analysis results include the main frequency component and the energy distribution of each frequency component, which are recorded in units of times per day and used to identify the production cycle fluctuation characteristics of the electroplating workshop. The heavy metal ion concentration parameters include hexavalent chromium ion concentration, trivalent chromium ion concentration, divalent nickel ion concentration, divalent copper ion concentration, and divalent zinc ion concentration. The hexavalent chromium ion concentration is measured by an online hexavalent chromium analyzer installed on the influent pipeline using the diphenylcarbazide spectrophotometric method and recorded in milligrams per liter. The measurement range is 0.01 - 10.00 mg / L, the measurement accuracy is ±2%, and the measurement frequency is once every 10 minutes. The trivalent chromium ion concentration, divalent nickel ion concentration, divalent copper ion concentration, and divalent zinc ion concentration are measured by an online multi-parameter heavy metal analyzer installed on the influent pipeline. Among them, the trivalent chromium ion concentration is measured by the complexometric chromogenic spectrophotometric method, and the chromogenic agent is diethylenetriaminepentaacetic acid. The measurement range is 0.05 - 50.00 mg / L, the measurement accuracy is ±3%, and the measurement frequency is once every 15 minutes. The divalent nickel ion concentration is measured by the dimethylglyoxime spectrophotometric method. The measurement range is 0.02 - 20.00 mg / L, the measurement accuracy is ±2.5%, and the measurement frequency is once every 15 minutes. The divalent copper ion concentration is measured by the sodium diethyldithiocarbamate spectrophotometric method. The measurement range is 0.01 - 25.00 mg / L, the measurement accuracy is ±2%, and the measurement frequency is once every 15 minutes. The divalent zinc ion concentration is measured by the dithizone spectrophotometric method. The measurement range is 0.01 - 30.00 mg / L, the measurement accuracy is ±2.5%, and the measurement frequency is once every 15 minutes. A full-automatic sampler is used to extract 10 mL of samples from the inlet of the wastewater treatment system every 30 minutes for analysis. The analysis process includes automatic addition of buffer solution, automatic volume fixation, automatic sample injection, and automatic cleaning process to ensure the accuracy and continuity of the measurement results.

[0020] S2: According to the real-time flow rate parameters and the heavy metal ion concentration parameters, establish a dynamic flow-concentration coupling model. Based on the dynamic flow-concentration coupling model, identify the time-delay relationship and coupling strength between the flow rate change and the concentration fluctuation, and calculate the flow-concentration coupling strength matrix. According to the real-time flow rate parameters and the heavy metal ion concentration parameters, establish a dynamic flow-concentration coupling model. The establishment process first performs data preprocessing on the collected real-time flow rate parameters and heavy metal ion concentration parameters, including identifying and removing outliers, interpolating and filling in missing data, and smoothing the data sequence. Adopt the sliding time window technique to divide the preprocessed data into a series of continuous data segments according to the time sequence. Among them, for the time window width parameter, it is determined by analyzing the typical response time of the concentration change caused by the flow rate change in electroplating wastewater treatment. The initial parameter is set to 2 hours. Using the sensitivity analysis method, the window width is set to 1 hour, 1.5 hours, 2 hours, 2.5 hours, and 3 hours in turn, and the average goodness-of-fit index of the model under each window width is calculated. Select the window width with the best goodness-of-fit index as the final parameter. For the time window sliding step parameter, the initial setting is 10 minutes. When in a stable operation state, the sliding step can be appropriately increased to 15 - 30 minutes to reduce the calculation amount; while when it is monitored that the system is in a stage of rapid change of working conditions, the sliding step is reduced to 5 minutes to improve the sensitivity of the model to changes.

[0021] Subsequently, analyze the data within each time window using a multiple autoregressive model. Take the influent flow rate, instantaneous flow rate change rate, and flow rate fluctuation frequency as independent variables, and take the hexavalent chromium ion concentration, trivalent chromium ion concentration, divalent nickel ion concentration, divalent copper ion concentration, and divalent zinc ion concentration as dependent variables respectively. Obtain the model parameter set under each time window through model fitting. Specifically, represent each heavy metal ion concentration parameter as a weighted linear combination of flow parameters and a function of historical concentration values; use the least squares method to determine the model parameters, that is, solve the parameter combination that minimizes the mean square error between the predicted concentration value and the actual measured concentration value; for each time window, construct five sub-models respectively corresponding to the prediction models of hexavalent chromium ion concentration, trivalent chromium ion concentration, divalent nickel ion concentration, divalent copper ion concentration, and divalent zinc ion concentration; each sub-model contains three groups of weight coefficients, corresponding to the influence degrees of influent flow rate, instantaneous flow rate change rate, and flow rate fluctuation frequency on the concentration of this heavy metal ion respectively, and a group of autoregressive coefficients representing the influence of historical concentration values on the current concentration value; When the model parameters of adjacent time windows change significantly, adopt a parameter smoothing strategy to perform weighted averaging on the model parameters of the current time window and the previous time window to prevent drastic fluctuations in the model parameters; If the number of data points within a certain time window is insufficient to support reliable parameter estimation, then use the model parameters of the previous time window and mark them as low-confidence parameters; The model parameters of all time windows are organized into a model parameter sequence in chronological order. Each group of model parameters contains 15 weight coefficients and 5 groups of autoregressive coefficients, and at the same time record the timestamp and goodness-of-fit index of each group of parameters; The goodness-of-fit index includes the coefficient of determination, root mean square error, and Akaike information criterion, which are used to evaluate the fitting quality of the model in each time window.

[0022] Based on the model parameter sets of each time window, a time series parameter sequence is formed, which together constitutes a dynamic flow-concentration coupling model, characterizing the dynamic correlation characteristics between flow rate and concentration in the electroplating wastewater treatment system.

[0023] Further identify the time delay relationship and coupling strength between flow rate changes and concentration fluctuations in the dynamic flow-concentration coupling model. The time delay relationship identification process uses the cross-correlation analysis method. By calculating the correlation coefficients between flow parameters and each heavy metal ion concentration parameter at different lag times, determine the lag time corresponding to the maximum correlation coefficient as the time delay value. The time delay values are calculated respectively for the influent flow rate - hexavalent chromium ion time delay, influent flow rate - trivalent chromium ion time delay, influent flow rate - divalent nickel ion time delay, influent flow rate - divalent copper ion time delay, influent flow rate - divalent zinc ion time delay, and the corresponding time delay values between the instantaneous flow rate change rate and each heavy metal ion concentration; Analysis of the coupling strength. First, clarify that the calculation object is the pair of specific flow parameters and specific heavy metal ion concentration parameters within each time window. For each pair of flow-concentration parameters, collect all corresponding time-series data points within that time window. Considering the identified time-delay relationship, perform corresponding time translation on the concentration data to align the flow changes and concentration responses in time. Since the electroplating wastewater treatment system is a multi-variable coupling system, there may be collinearity among the flow parameters, and the heavy metal ion concentrations may also affect each other. Directly calculating the simple correlation coefficient cannot accurately reflect the true correlation strength between a specific flow parameter and a specific concentration parameter. Therefore, the partial correlation analysis method is adopted. Specifically, establish a multiple linear regression model that includes all flow parameters and heavy metal ion concentration parameters. For the pair of specific flow parameter and specific concentration parameter to be analyzed, use them as the dependent variable and independent variable respectively, and use all other flow parameters and concentration parameters as control variables. Calculate the multiple regression residuals of the flow parameter with respect to all other variables, and the multiple regression residuals of the concentration parameter with respect to all other variables. Calculate the Pearson correlation coefficient between the above two sets of residuals. The resulting value is the net correlation degree after excluding the influence of other variables, and this value is defined as the coupling strength of this flow-concentration parameter pair. When the number of data points within a certain time window is insufficient to support reliable partial correlation analysis, regularization techniques are used to enhance the calculation stability by adding small positive numbers to the diagonal of the covariance matrix to avoid calculation errors caused by matrix ill-conditioning. For the possible chemical transformation relationships between heavy metal ions, such as the oxidation-reduction reaction between hexavalent chromium and trivalent chromium, add interaction terms representing chemical reactions to the control variables. When the coupling strength fluctuates violently in a short period of time, smooth the coupling strength through the weighted moving average method. The width of the smoothing window is initially set to 5 time steps, and the smoothing weights are in the form of trigonometric functions, with the highest weight assigned to the center point. The obtained partial correlation coefficient ranges from -1 to 1, ensuring that the range of the final coupling strength is strictly between -1 and 1. The calculated coupling strength characterizes the direct influence degree of a specific flow parameter on a specific heavy metal ion concentration after controlling the influence of other factors. The closer its absolute value is to 1, the stronger the coupling. The positive or negative sign represents the coupling direction, where a positive value indicates that an increase in flow leads to an increase in concentration, and a negative value indicates that an increase in flow leads to a decrease in concentration. By using the partial correlation analysis method, the influence of other variables is eliminated, and only the direct correlation degree between a single flow parameter and a single heavy metal ion concentration parameter is examined. All partial correlation analysis results are organized into a 5×3 coupling strength matrix, which is updated regularly as the time window slides. The rows represent the concentration parameters of five heavy metal ions, and the columns represent the three flow parameters. Each element in the matrix represents the coupling strength between a specific flow parameter and a specific heavy metal ion concentration parameter.

[0024] S3: According to the flow-concentration coupling strength matrix, construct a chemical dosage prediction curve through the piecewise linear mapping method, and perform zonal quantitative mapping on the optimal chemical dosage under different flow-concentration coupling states.

[0025] Perform clustering analysis based on the established 5×3 flow-concentration coupling strength matrix, and divide the elements in the matrix into five coupling state categories: strong positive correlation area, weak positive correlation area, micro-correlation area, weak negative correlation area, and strong negative correlation area according to the characteristics of coupling strength and coupling direction.

[0026] Among them, the strong positive correlation area corresponds to elements with a coupling strength between 0.7 and 1, the weak positive correlation area corresponds to elements with a coupling strength between 0.3 and 0.7, the micro-correlation area corresponds to elements with a coupling strength between -0.3 and 0.3, the weak negative correlation area corresponds to elements with a coupling strength between -0.7 and -0.3, and the strong negative correlation area corresponds to elements with a coupling strength between -1 and -0.7; Combined with the time delay relationship identified under each time window, construct a flow-concentration coupling feature vector containing time delay information. Specifically, the coupling strength between each heavy metal ion and each flow parameter is combined with the corresponding time delay value to form matrix elements, and then weighted aggregation is performed according to the importance weight, and a multi-dimensional vector is constructed by fusing the threshold judgment rules set by expert experience. Its dimension is equal to the product of the types of heavy metal ions and the types of flow parameters. Each dimension characterizes the influence strength and time delay characteristics of a specific flow parameter on a specific heavy metal ion. This feature vector comprehensively characterizes the influence of each flow parameter on each heavy metal ion.

[0027] Calculate the similarity between the current feature vector and the standard vector in the historical working condition database to judge the typical treatment mode of the wastewater. When a large time delay value is shown for an element in the vector, adjust the dosing timing of the corresponding chemical. For example, when the feature vector shows a large time delay value between chromium ions and the flow rate, increase the dosing amount of the chromium treatment chemical in advance by the corresponding time. At the same time, calculate the adjustment coefficient of the chemical dosing. When the vector shows strong positive correlation and short time delay, increase the response speed, while for the case of negative correlation and long time delay, reduce the basic dosage but increase the slow release ratio.

[0028] Furthermore, establish five chemical dosing models commonly used in electroplating wastewater treatment, namely for sodium hydroxide precipitants, reducing agents, chelating precipitants, flocculants, and pH regulators. The dosing amount prediction of each type of chemical uses a dedicated piecewise linear mapping function.

[0029] For the sodium hydroxide precipitant, its dosing model mainly considers the total equivalent concentration of various heavy metal ions in the wastewater and adopts a five-segment linear mapping function: When the total heavy metal concentration is lower than 5 mg / L, the first segment with a low slope function is adopted; when the concentration is between 5 - 15 mg / L, the second segment with a medium-low slope function is adopted; when the concentration is between 15 - 30 mg / L, the third segment with a medium slope function is adopted; when the concentration is between 30 - 50 mg / L, the fourth segment with a medium-high slope function is adopted; when the concentration exceeds 50 mg / L, the fifth segment with a high slope function is adopted. The slopes of the mapping functions in each segment reflect the demand elasticity for sodium hydroxide in different concentration ranges.

[0030] For the reducing agent, its dosing model mainly targets the reduction demand of hexavalent chromium and adopts a four-segment linear mapping function. When the hexavalent chromium concentration is lower than 10 mg / L, the low slope segment is adopted; when the concentration is between 10 - 25 mg / L, the medium-low slope segment is adopted; when the concentration is between 25 - 40 mg / L, the medium-high slope segment is adopted; when the concentration exceeds 40 mg / L, the high slope segment is adopted. At the same time, a compensation factor for the ORP value of the wastewater is specially set in the reducing agent model. When the ORP value exceeds 200 mV, an additional 5% of the reducing agent dosage is increased for every 20 mV increase.

[0031] For the chelating precipitant, its dosing model targets the chelating precipitation demand of copper, nickel, and zinc heavy metal ions and adopts a three-segment asymmetric linear mapping function. When the heavy metal ion concentration is in the low concentration range, i.e., the total amount is less than 20 mg / L, a relatively high slope segment is adopted to ensure the basic treatment effect; when the concentration is in the medium concentration range, i.e., 20 - 40 mg / L, the medium slope segment is adopted; when the concentration is in the high concentration range, i.e., greater than 40 mg / L, a lower slope segment is instead adopted to utilize the self-aggregation effect at high concentrations. In addition, a correction factor for the copper-nickel ratio is introduced. When the copper-nickel ratio is higher than 2:1, an additional 3% of the dosing amount is increased for every 0.5 increase in the ratio to cope with the preferential chelating property of copper ions.

[0032] For the flocculant, its dosing model is constructed based on the predicted sludge production and wastewater turbidity and adopts a bivariate piecewise linear mapping function. When the predicted sludge production is lower than 200 mg / L and the turbidity is lower than 100 NTU, the low-dose mapping function is adopted; when the sludge production is between 200 - 500 mg / L or the turbidity is between 100 - 300 NTU, the medium-dose mapping function is adopted; when the sludge production exceeds 500 mg / L or the turbidity exceeds 300 NTU, the high-dose mapping function is adopted. At the same time, considering the water temperature factor, when the water temperature is lower than 15 °C, the flocculant dosage is increased by 8% for every 5 °C decrease to compensate for the negative impact of low temperature on the flocculation efficiency.

[0033] For the pH regulator, its dosing model is constructed based on the difference between the initial pH value and the target pH value of the wastewater. When the initial pH is lower than 6.0, a high-slope acid-base neutralization curve is used to indicate weak buffering capacity. When the pH is between 6.0 - 7.5, a medium-slope section is used to indicate the buffer zone before entering the alkaline region. When the pH is between 7.5 - 8.5, a low-slope section is used to indicate the fine-tuning zone close to the target value. Moreover, the acid-base dosage model specifically introduces the sliding average of the historical alkalinity data of the wastewater as the basis for estimating the buffering capacity to predict the required accurate dosage.

[0034] The piecewise linear mapping functions of the above five types of agents jointly construct the dosing prediction curves for various agents, and all consider the flow-concentration coupling characteristics: When the flow-concentration coupling intensity shows a strong positive correlation, it is judged as the state where the concentration increases synchronously with the flow rate. The prediction function increases the slope of the corresponding interval to increase the agent dosage to cope with possible high-concentration shock loads; When the coupling intensity is in the weak positive correlation interval, the slope of the dosing prediction curve decreases accordingly, indicating a weakened sensitivity of the concentration to flow rate changes; For the micro-correlation region, it is judged that the influence of flow rate changes on the concentration of specific heavy metal ions is not significant. At this time, a fixed-ratio dosing strategy based on the historical average concentration is adopted, and the dosing prediction curve is approximately a horizontal line segment in this region; When a weak negative correlation or strong negative correlation coupling relationship is detected, the dosing prediction curve shows a negative slope characteristic, that is, the agent dosage is appropriately reduced as the flow rate increases, which occurs in the working conditions where the dilution effect of the wastewater is significant due to high flow rates; At the boundaries of each interval, cubic spline interpolation technology is used to ensure the smooth transition of the prediction curve and avoid jumps in the agent dosage at critical states.

[0035] The coefficients of the piecewise linear mapping function are obtained through machine learning training of the operation parameters corresponding to the optimal treatment effect in the historical operation data, and the mapping parameters are automatically optimized and updated once every 100 batches of wastewater are treated.

[0036] For complex wastewater containing multiple heavy metal ions simultaneously, a collaborative dosing decision-making mechanism is established. According to the overall characteristic pattern of the flow-concentration coupling intensity matrix, the optimal combined dosing scheme for various agents is calculated.

[0037] Specifically, the process of the collaborative dosing decision-making mechanism includes: Extract the coupling state information of each heavy metal ion and each flow parameter from the flow-concentration coupling intensity matrix to form a coupling mode feature vector; Subsequently, according to the clustering analysis of the feature vector, the current working condition is classified into the preset working condition template library, and the most matching basic agent ratio scheme is selected as the initial solution; The initial solution is filtered through an expert knowledge base to eliminate reagent combinations that may produce antagonistic reactions in terms of chemical principles, such as the simultaneous large - scale use of certain reducing agents and oxidizing precipitants. For the candidate solutions after the initial screening, a chemical reaction kinetics model is constructed, which includes four consecutive sub - processes: hexavalent chromium reduction reaction, metal ion chelation reaction, hydroxide precipitation reaction, and flocculation reaction. The time - delay characteristics are considered for each sub - process. When the time - delay value of a certain heavy metal ion is large, the dosing timing of the corresponding treatment reagent for this metal is adjusted accordingly to ensure the optimal reaction timing. When dealing with multiple metal ions, a hierarchical priority strategy is adopted. First, ensure the removal efficiency of highly toxic metals such as hexavalent chromium, and then optimize the treatment effect of other metals. When there are conflicts in the treatment requirements of different metal ions, such as different optimal pH value ranges, a segmented treatment idea is adopted. The chemical environment of the wastewater is gradually adjusted by setting multiple reaction intervals.

[0038] When calculating the optimal reagent dosing amount, four key parameters are integrated: the current concentration, predicted concentration of heavy metal ions, treatment target value, and reagent reaction stoichiometric ratio. In particular, when the flow - concentration coupling strength is negative and has a large absolute value, reduce the basic dosing proportion of the corresponding reagent, but increase its dynamic adjustment coefficient to cope with possible concentration fluctuations.

[0039] The process of optimizing the treatment effect of other metals uses an improved particle swarm optimization algorithm. Set the upper and lower limits of the reagent dosing amount and the upper limit of the total cost constraint. Through iterative search, find the optimal reagent combination that meets all treatment requirements. Specifically, the improved particle swarm optimization algorithm includes: Take the dosing amounts of various reagents as decision variables to form the particle position vector. Define the fitness function as the ratio of weighted treatment efficiency to cost according to the characteristics of electroplating wastewater. In the initialization stage, some particles are generated based on the historical optimal dosing ratio, and the remaining particles are randomly generated to ensure full coverage of the search space. In each round of iteration, the particle position update formula integrates the global optimal, individual historical optimal, and velocity information, but modifies the standard velocity update formula. Introduce a dynamic adjustment coefficient based on the current flow - concentration coupling characteristics. When the coupling strength is strongly positively correlated, increase the velocity update step size of the corresponding dimension, and when it is negatively correlated, decrease the step size. At the same time, set an adaptive inertia weight mechanism. Use a larger inertia weight at the initial stage of iteration to promote global search. As the iteration progresses, the inertia weight linearly decreases to about 0.4 and is dynamically fine - tuned according to the population diversity index. The constraint handling adopts the improved penalty function method. When the dosage of the chemical agent exceeds the upper and lower limit constraints, the fitness value will be punished by the square of the exceeding degree. The total cost constraint is achieved through a soft constraint method. The part exceeding the budget will reduce the fitness value by a penalty coefficient of 1.5 times. Regarding the synergistic effect between chemical agents, a chemical equilibrium constraint check step is added during the position update process. When the ratio of some chemical agents violates the stoichiometric ratio requirements, it is corrected to a reasonable range through expert rules; A dynamic neighborhood search strategy is introduced. Every 10 rounds of iteration, the global optimal particle will generate multiple candidate solutions based on a small amount of perturbation for local fine search; To avoid premature convergence, a population diversity monitoring mechanism is set up. When the diversity drops below the threshold, partial particle re-initialization is triggered; At the same time, the simulated annealing characteristics are integrated, allowing the sub-optimal solution to be accepted with a certain probability to jump out of the local optimal trap, and the acceptance probability decreases with the iteration; For time-varying working conditions, a sliding time window evaluation strategy is adopted. When it is detected that the flow-concentration coupling matrix changes significantly, a partial population re-initialization mechanism is triggered to quickly adapt to the new working conditions; The convergence criterion adopts a dual standard. When the improvement amplitude of the global optimum is less than 0.1% for 30 consecutive rounds and the satisfaction degree of all constraint conditions reaches more than 99.5%, it is determined to converge; After multiple rounds of iteration, the global optimal particle is output as the final chemical agent dosage plan, and at the same time, the flow-concentration coupling characteristic pattern corresponding to this plan is recorded; The historical evolution trajectory of the global optimal particle is retained during the iteration process, which is used to analyze the change trend and sensitivity of the optimal chemical agent ratio under different working conditions; When the optimal solution found still cannot meet all the effluent index requirements, the cost constraint is relaxed, and a better but possibly more costly solution is searched again, and decision-making suggestions are provided to the operator.

[0040] The final collaborative chemical dosing decision output includes the exact dosages and the best dosing timings of five main chemical agents: reducing agent, chelating agent, precipitant, flocculant, and pH regulator.

[0041] S4: Based on the chemical agent dosage prediction curve, the current flow-concentration coupling state of the electroplating wastewater is matched and located, and the gradient pulse chemical agent dosing parameter prediction technology is used to predict the chemical agent dosage.

[0042] Based on the chemical agent dosage prediction curve, the current flow-concentration coupling state of the electroplating wastewater is matched and located, and the gradient pulse chemical agent dosing technology is used to perform the chemical agent dosing, specifically including: Relying on the multi-point flow data collected in real time and the online heavy metal ion monitoring data, the current wastewater characteristics are multi-dimensionally matched with the dosing amount prediction curve library, and the weighted cosine similarity is used to calculate the similarity between the current working condition and each standard working condition in the prediction curve library; The matching process not only considers the absolute value of the concentration, but also pays attention to the concentration change trend and the proportional relationship between various heavy metal ions. Through the dual evaluation indexes of the Euclidean distance and correlation analysis of the feature vectors, the most matching prediction curve family is found; When the similarity of the prediction curve with the highest matching degree exceeds 85%, the basic dosing amount calculation model corresponding to this curve is directly used as the basic function for this prediction; When the highest matching degree is between 65% - 85%, the top three most matching curves are comprehensively considered, and through similarity weighted fusion, a combined prediction model is constructed with the similarity as the weight coefficient; For working conditions with a matching degree lower than 65%, a dedicated prediction function is temporarily constructed based on the collaborative dosing decision-making mechanism; After determining the basic prediction curve, further analyze the dynamic characteristics of the current flow-concentration coupling state, and predict the change trend of the concentrations of various heavy metal ions within the next 30 - 120 minutes, especially the appearance time and peak value of potential concentration peaks; For the predicted concentration change trend, the gradient pulse dosing parameter prediction technology is adopted to convert the continuous dosing curve into pulse sequence parameters in time and space: In the time dimension, based on the predicted arrival time of the concentration peaks of various heavy metal ions, the optimal dosing time sequence is calculated. For example, for the predicted peak of hexavalent chromium concentration that appears 20 minutes later, the dosing amount of the reducing agent is predicted to increase 5 - 8 minutes in advance to ensure the best contact time between the reagent and the pollutant; According to the reaction kinetic characteristics of different reagents, different pulse parameter combinations are predicted. For the fast-reacting sodium hydroxide precipitant, short pulse parameters with a period of 30 - 60 seconds are predicted, while for the slow-reacting chelating precipitant, long pulse parameters with a period of 3 - 5 minutes are predicted.

[0043] In the space dimension, the optimal reagent distribution ratio of each dosing point is calculated, and a gradient dosing strategy is implemented for different reaction sections. For example, the proportion of redox reagents is increased in the pretreatment area, the proportion of chelating precipitants is strengthened in the intermediate reaction area, and the proportion of flocculants is emphasized in the terminal treatment area; In response to the detected mutual promotion or inhibition effects between heavy metal ions, the phase relationship of pulse dosing is dynamically adjusted. For example, for the coexistence of copper ions and nickel ions with staggered concentration peaks, an interleaved pulse scheme is constructed. When it is predicted that the concentration peaks of copper ions and nickel ions will appear staggered, the dosing times of the corresponding reagents are also arranged to be staggered to avoid competitive reactions between the reagents; for the prediction of the simultaneous appearance of high concentrations of multiple metal ions, the dosing amount of the composite reagent is increased; During the execution process, continuously monitor the real-time change trends of water quality parameters at each measuring point. When it is detected that the water quality parameters deviate from the expected change trajectory by more than 10%, trigger the real-time fine-tuning of the pulse intensity and frequency; Based on historical data analysis, for sudden high-concentration working conditions, predict three typical high-concentration scenarios that may occur: sudden increase in hexavalent chromium, sudden increase in heavy metal mixture, and slow increase in total chromium; For the case of sudden increase in hexavalent chromium, start the emergency pulse mode, add high-concentration reducing agent within 2 - 3 minutes, and the predicted dosage is 2.5 - 3 times of the conventional dosage to form a "chemical interception wall"; For the case of sudden increase in heavy metal mixture, adopt a composite reagent pulse sequence. First, add a pH regulator to adjust the pH value to 9.0 - 9.5, and then add a mixed chelating agent with a dosage of 2 times the conventional dosage; For the case of slow increase in total chromium, adopt a gradually enhanced pulse sequence, and gradually increase the dosage of TMT chelating agent to the peak value within 1 hour.

[0044] At the same time, automatically adjust the pulse frequency according to the flow rate change rate. When the flow rate change rate exceeds 15% / hour, it is necessary to increase the pulse frequency by 25 - 30% and reduce the single pulse intensity by 10 - 15%; when the flow rate change rate is within the range of 5 - 15% / hour, keep the original pulse frequency and appropriately adjust the intensity; when the flow rate change rate is lower than 5% / hour, the pulse interval can be extended by 10 - 20% and the single pulse intensity can be increased by 5 - 10%; For the flow-concentration coupling relationship with obvious time-delay characteristics, including: there is a 15 - 25-minute time delay between the increase in flow rate and the increase in chromium concentration; there is a 5 - 10-minute time delay between the change in pH value and the heavy metal precipitation efficiency; there is a 30 - 40-minute time delay between the change in temperature and the reduction reaction rate; there is a 20 - 30-minute time delay between the change in influent turbidity and the effluent turbidity; there is a 10 - 15-minute time delay between the addition of reagent and the improvement of effluent water quality; Based on these time-delay characteristics, adopt a time-delay compensation strategy: for the working condition showing the characteristic of "the zinc ion concentration increases 20 minutes after the flow rate increases", increase the dosage of zinc ion treatment reagent 15 minutes after detecting the flow rate increase signal; for the time-delay characteristic of "the reduction efficiency of hexavalent chromium decreases 10 minutes after the pH drops", increase the dosage of reducing agent at the initial stage of pH drop.

[0045] Furthermore, based on the toxicity and treatment difficulty of heavy metal ions, predict the optimal treatment sequence and construct a series pulse prediction sequence: set hexavalent chromium as the highest treatment priority, followed by total chromium, and the priorities of copper, nickel, and zinc decrease in turn.

[0046] For the situation where multiple heavy metals exceed the standard simultaneously, design the dosing sequence of chemicals according to the priority order: First, predict the optimal dosing amount of hexavalent chromium reduction chemicals to ensure that hexavalent chromium is preferentially reduced; then determine the dosing amounts of chelating precipitation chemicals for heavy metals such as chromium, copper, and nickel, and sequentially dose the special treatment chemicals for different metals at intervals of 5 - 8 minutes; the optimal dosing amount and dosing timing of the flocculant are to dose the flocculant 10 - 15 minutes after the dosing of the aforementioned chemicals is completed.

[0047] After each control cycle ends, evaluate the accuracy of each prediction result: Calculate the prediction deviation by comparing the predicted optimal chemical dosage with the actual dosage required for treatment; when the cumulative evaluation samples reach 100 batches, optimize the parameters of the prediction algorithm, especially for the working conditions with a prediction deviation greater than 15%, and focus on adjusting the corresponding prediction parameters; for the newly identified efficient prediction patterns, store their characteristic parameters in the knowledge base; Finally, output the optimal chemical dosing prediction plan for the next two hours in the form of a time series table, including: the predicted dosing amounts of various chemicals at 5 - minute intervals, the predicted pulse intensity and frequency parameters, the predicted proportion of chemical spatial distribution, the predicted change trend of key water quality parameters, and the predicted treatment effect evaluation indicators.

[0048] In summary, the chemical dosing prediction system for electroplating wastewater based on the dynamic flow - concentration coupling model according to the embodiments of the present invention is elucidated. Through the dynamic flow - concentration coupling model, it can real - time sense the coordinated changes of multiple heavy metal ions and water quality parameters in electroplating wastewater, and accurately reflect the treatment requirements under complex working conditions. It not only improves the coordinated removal efficiency of multiple metal ions, but also significantly enhances the adaptability to extreme working conditions such as sudden changes in flow and concentration. At the same time, it integrates an expert knowledge base, chemical reaction kinetics, and multi - objective intelligent optimization algorithms, can dynamically select the optimal chemical combination and dosing amount, optimize the chemical utilization rate, and reduce the operating cost. It significantly improves the reliability, economy, and intelligent level of the wastewater treatment system, and provides an innovative solution for the efficient treatment of highly complex industrial wastewater.

Claims

1. A dosing amount prediction system for electroplating wastewater based on a dynamic flow-concentration coupling model, characterized in that, Comprising: A collection module, used to collect real-time flow parameters and heavy metal ion concentration parameters of the electroplating wastewater treatment system; A calculation module, used to establish a dynamic flow-concentration coupling model based on the real-time flow parameters and the heavy metal ion concentration parameters, identify the time-delay relationship and coupling strength between flow changes and concentration fluctuations based on the dynamic flow-concentration coupling model, and calculate the flow-concentration coupling strength matrix; A partition mapping module, used to construct a chemical dosage prediction curve by the piecewise linear mapping method according to the flow-concentration coupling strength matrix, and perform partition quantitative mapping on the optimal chemical dosage under different flow-concentration coupling states; A prediction module, used to match and locate the current flow-concentration coupling state of the electroplating wastewater according to the chemical dosage prediction curve, and use the gradient pulse chemical dosing parameter prediction technology to predict the chemical dosage.

2. The dosing amount prediction system for electroplating wastewater based on the dynamic flow-concentration coupling model according to claim 1, wherein The real-time flow parameters include influent flow rate, instantaneous flow rate change rate, and flow fluctuation frequency; The heavy metal ion concentration parameters include hexavalent chromium ion concentration, trivalent chromium ion concentration, divalent nickel ion concentration, divalent copper ion concentration, and divalent zinc ion concentration.

3. The electroplating wastewater chemical dosage prediction system based on the dynamic flow-concentration coupling model according to claim 2, characterized in that Perform data preprocessing based on the real-time flow parameters and the heavy metal ion concentration parameters, and use the sliding time window technology to divide the data into continuous segments in chronological order. In each time window, construct a multiple autoregressive model with influent flow rate, flow rate change rate, and fluctuation frequency as independent variables and the heavy metal ion concentrations as dependent variables, and dynamically fit to obtain a model parameter sequence reflecting the relationship between flow and concentration, that is, the dynamic flow-concentration coupling model.

4. The dosing amount prediction system for electroplating wastewater based on the dynamic flow-concentration coupling model according to claim 3, wherein Based on the dynamic flow-concentration coupling model, identify the time-delay relationship and coupling strength between flow changes and heavy metal ion concentration fluctuations through cross-correlation analysis, and use the partial correlation analysis method to quantify the coupling strength between each flow parameter and each heavy metal ion concentration, and organize the partial correlation analysis results into a 5×3 coupling strength matrix.

5. The dosing amount prediction system for electroplating wastewater based on the dynamic flow-concentration coupling model according to claim 4, characterized in that Perform cluster analysis based on the coupling strength matrix, and divide the elements in the matrix into five coupling state categories: strong positive correlation area, weak positive correlation area, micro-correlation area, weak negative correlation area, and strong negative correlation area according to the coupling strength and coupling direction characteristics.

6. The dosing amount prediction system for electroplating wastewater based on the dynamic flow-concentration coupling model according to claim 5, wherein Construct a flow-concentration coupling feature vector based on the coupling strength matrix combined with the time-delay relationship; Calculate the similarity with the standard vector in the historical working condition library based on the flow-concentration coupling feature vector, judge the typical treatment mode of the wastewater, and adjust the dosing timing of the corresponding chemical.

7. The dosing amount prediction system for electroplating wastewater based on the dynamic flow-concentration coupling model according to claim 6, wherein Based on the typical treatment mode, construct a chemical dosing model, and use the piecewise linear mapping function to construct a chemical dosage prediction curve for different chemical dosage predictions.

8. The dosing amount prediction system for electroplating wastewater based on the dynamic flow-concentration coupling model according to claim 7, wherein If there is complex wastewater with multiple heavy metal ions at the same time, establish a collaborative chemical dosing decision-making mechanism, including: Extract the coupling state information of each heavy metal ion and each flow parameter to form a coupling mode feature vector; Classify the current working condition into the preset working condition template library according to the feature vector cluster analysis, and select the most matching basic chemical ratio scheme as the initial solution; After screening and removing unreasonable combinations from the initial expert knowledge base for solution analysis, based on the chemical reaction kinetics model, combined with time-delay characteristics and hierarchical priority strategies, dynamically optimize the multi-metal collaborative treatment sequence and chemical dosage; At the same time, use the improved particle swarm optimization algorithm to iteratively search for the optimal chemical dosage plan under the satisfaction of treatment objectives and cost constraints.

9. The dosing amount prediction system for electroplating wastewater based on the dynamic flow-concentration coupling model according to claim 8, wherein The improved particle swarm optimization algorithm takes the chemical dosage of various types as decision variables and constructs an optimization model with the ratio of weighted treatment efficiency to cost as the fitness; Through the improved particle swarm optimization algorithm, dynamically adjust the particle search strategy and parameters, combined with chemical constraints, synergistic effects and simulated annealing mechanism, to ensure that under the premise of meeting treatment and cost constraints, find the optimal chemical dosage plan suitable for the current flow-concentration coupling characteristics.

10. The dosing amount prediction system for electroplating wastewater based on the dynamic flow-concentration coupling model according to claim 9, wherein, Based on the real-time flow rate and heavy metal ion monitoring data, perform multi-dimensional matching of the current working conditions with the chemical dosage prediction curve library, select or weighted fuse the optimal prediction curve using similarity evaluation. According to the prediction results, use the gradient pulse dosing technology to optimize the timing, pulse intensity and dosing point distribution of chemical dosing, and compensate and adjust for the reaction characteristics of different chemicals and the time delay of working conditions; Evaluate the prediction accuracy after each control cycle, continuously optimize the model parameters, and output the optimal chemical dosage plan for different time periods and regions within the next two hours.

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