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

Abstract

The application provides a kind of electroplating wastewater dosing quantity prediction system based on dynamic flow-concentration coupling model, relating to industrial wastewater treatment technical field, it passes through the real-time flow of wastewater treatment system and heavy metal ion concentration parameter acquisition, establishes dynamic flow-concentration coupling model, identifies the time lag relationship between flow change and concentration fluctuation and coupling strength, and generates flow-concentration coupling strength matrix, based on this matrix, using piecewise linear mapping method constructs dosing quantity prediction curve, matches positioning to current flow-concentration coupling state, applies gradient pulse dosing parameter prediction technology, accurately predicts reagent dosage. In this way, it can solve the problem of not being able to adjust the dosing amount and dosing timing of various reagents in a timely and accurate manner, leading to a decrease in the removal rate of some heavy metals, a low utilization rate of reagents, and even the possibility of secondary pollution or a significant increase in operating costs.

Description

Technical Field

[0001] This invention relates to the field of industrial wastewater treatment technology, and more specifically, to a chemical dosage prediction system for electroplating wastewater based on a dynamic flow-concentration coupling model. Background Technology

[0002] With the acceleration of industrialization, electroplating wastewater, due to its high content of various highly toxic heavy metal ions such as hexavalent chromium, copper, nickel, and zinc, as well as its drastic fluctuations in water quality parameters, has become one of the most pressing challenges in environmental governance. Traditional electroplating wastewater treatment processes, with chemical precipitation as the core and supplemented by reduction, chelation, and flocculation units, have achieved preliminary removal of conventional heavy metal pollutants. However, in recent years, with increasingly stringent emission standards and frequent fluctuations in water volume and quality, single or static dosing methods are no longer sufficient to meet the complex and dynamic wastewater treatment needs. Therefore, scholars and engineers both domestically and internationally have been continuously exploring intelligent and automated dosing control technologies, attempting to achieve automated adjustment of the dosing process through online monitoring, model prediction, and feedback control to improve the stability and economy of wastewater treatment. Some studies have introduced multivariate models to optimize the dosage for single heavy metal ions or specific operating conditions, but significant limitations remain in areas such as synergistic treatment of multiple metals, adaptation to dynamic operating conditions, and reagent interactions.

[0003] Existing intelligent dosing technologies generally 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 concentration changes among multiple heavy metal ions and their dynamic coupling relationship with multiple parameters such as flow rate, pH, and temperature. Second, existing technologies often neglect the reaction priorities among heavy metal ions in wastewater, the time lag effect of reagent dosing, and the potential synergistic or antagonistic relationships between reagents. This leads to delayed dosing decision-making response, low reagent utilization, and even the potential for secondary pollution under complex and fluctuating actual operating conditions. Furthermore, due to the lack of real-time dynamic discrimination of flow-concentration coupling strength and adaptive correction of the dosing prediction model, existing systems struggle to cope with extreme conditions such as high shock loads, sudden flow changes, or peak heavy metal concentrations, thus limiting both safety margin and economic efficiency. Currently, most systems rely on static or empirical segmented dosing curves, lacking a systematic intelligent decision-making mechanism that organically integrates operating condition feature vector clustering, expert knowledge base screening, chemical reaction kinetics, and multi-objective intelligent optimization algorithms. A full-process dynamic closed-loop control for the synergistic and efficient removal of multiple metal ions has not yet been established. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention is proposed. This invention provides a chemical dosage prediction system for electroplating wastewater based on a dynamic flow-concentration coupling model. This system can, to some extent, solve the problem of difficulty in timely and accurately adjusting the dosage and timing of various chemicals, which leads to a decrease in the removal rate of some heavy metals, low chemical utilization, and may even cause secondary pollution or a significant increase in operating costs.

[0005] According to one aspect of the present invention, a chemical dosage prediction system for electroplating wastewater based on a dynamic flow-concentration coupling model is provided, comprising:

[0006] The data acquisition module is used to collect real-time flow parameters and heavy metal ion concentration parameters of the electroplating wastewater treatment system.

[0007] The calculation module is 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 lag 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.

[0008] The partition mapping module is used to construct a dosage prediction curve based on the flow-concentration coupling strength matrix using a piecewise linear mapping method, and to perform partitioned quantitative mapping of the optimal dosage for different flow-concentration coupling states.

[0009] The prediction module is used to match and locate the current flow-concentration coupling state of electroplating wastewater according to the dosing prediction curve, and to predict the dosage of the reagent by using gradient pulse dosing parameter prediction technology.

[0010] Furthermore, the real-time flow parameters include influent flow rate, instantaneous flow rate change rate, and flow fluctuation frequency;

[0011] 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.

[0012] Furthermore, based on the real-time flow parameters and the heavy metal ion concentration parameters, data preprocessing is performed, and the data is divided into continuous segments in chronological order using a sliding time window technique. Within each time window, a multiple autoregressive model is constructed with influent flow rate, flow rate change rate, and fluctuation frequency as independent variables and the concentration of each heavy metal ion as dependent variable. The model parameter sequence reflecting the relationship between flow rate and concentration is dynamically fitted to obtain a dynamic flow-concentration coupling model.

[0013] Furthermore, based on the dynamic flow-concentration coupling model, cross-correlation analysis is used to identify the time lag relationship and coupling strength between flow changes and heavy metal ion concentration fluctuations. Partial correlation analysis is then 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.

[0014] Furthermore, based on the coupling strength matrix, cluster analysis is performed to divide the elements in the matrix into five coupling state categories according to the coupling strength and coupling direction characteristics: strong positive correlation region, weak positive correlation region, slight correlation region, weak negative correlation region, and strong negative correlation region.

[0015] Furthermore, a flow-concentration coupling feature vector is constructed based on the coupling strength matrix and the time delay relationship;

[0016] Based on the similarity between the flow-concentration coupled feature vector and the standard vector in the historical operating condition database, the typical treatment mode of wastewater is determined, and the timing of the corresponding reagent addition is adjusted.

[0017] Furthermore, based on the aforementioned typical processing mode, a drug dosing model is constructed, and a piecewise linear mapping function is used to construct a drug dosing prediction curve for different drug dosing predictions.

[0018] Furthermore, for complex wastewater containing multiple heavy metal ions, a collaborative dosing decision-making mechanism should be established, including:

[0019] Extract the coupling state information between each heavy metal ion and each flow parameter to form a coupling mode feature vector;

[0020] Based on the feature vector clustering analysis, the current working condition is classified into the preset working condition template library, and the most matching basic reagent ratio scheme is selected as the initial solution.

[0021] After screening and eliminating unreasonable combinations from the initial expert knowledge base, the order of multi-metal synergistic treatment and the dosage of reagents are dynamically optimized based on the chemical reaction kinetics model combined with time delay characteristics and hierarchical priority strategy.

[0022] Meanwhile, the optimal drug dosing scheme is obtained through iterative search using an improved particle swarm optimization algorithm while satisfying the processing objective and cost constraints.

[0023] Furthermore, the improved particle swarm optimization algorithm uses the dosage of various agents as decision variables to construct an optimization model with the weighted ratio of processing efficiency to cost as the fitness metric.

[0024] By using an improved particle swarm optimization algorithm, the particle search strategy and parameters are dynamically adjusted. Combined with chemical constraints, synergistic effects, and simulated annealing mechanisms, the optimal reagent dosing scheme that adapts to the current flow-concentration coupling characteristics is found while meeting processing and cost constraints.

[0025] Furthermore, based on real-time flow and heavy metal ion monitoring data, the current operating conditions are matched with the dosage prediction curve library in multiple dimensions. The optimal prediction curve is selected or weighted fusion is used by similarity evaluation. According to the prediction results, gradient pulse dosing technology is used to optimize the timing, pulse intensity and dosing point distribution of the agent, and compensation adjustments are made for different agent reaction characteristics and operating condition time delays.

[0026] After each control cycle, the accuracy of the prediction is evaluated, the model parameters are continuously optimized, and the optimal drug dosing plan for each time period and region is output for the next two hours. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0028] Figure 1 This is a flowchart of a chemical dosing prediction system for electroplating wastewater based on a dynamic flow-concentration coupling model according to an embodiment of the present invention;

[0029] Figure 2 This is a flowchart of the coupling strength matrix according to an embodiment of the present invention. Detailed Implementation

[0030] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0031] As mentioned in the background section, existing electroplating wastewater dosing technologies mainly suffer from the following two prominent problems: First, existing systems often predict the dosage of chemicals based on only a single parameter or a static segmented model, making it difficult to accurately reflect the dynamic synergistic changes among various heavy metal ions and their real-time coupling relationship with process parameters such as flow rate and pH, resulting in delayed dosing response and decreased treatment efficiency. Second, existing technologies generally lack the ability to adaptively adjust to extreme conditions such as high impact loads and sudden increases in heavy metal concentrations, ignoring key factors such as chemical dosing lag and synergistic or antagonistic effects between chemicals, making it impossible to achieve efficient synergistic treatment of multiple metal ions, and making it difficult to optimize the dosing strategy in a timely manner when operating conditions change abruptly.

[0032] Figure 1 This is a flowchart of a chemical dosing prediction system for electroplating wastewater based on a dynamic flow-concentration coupling model, according to an embodiment of the present invention. Figure 1 As shown, the electroplating wastewater chemical dosage prediction system based on a dynamic flow-concentration coupling model includes:

[0033] S1: Collect real-time flow parameters and heavy metal ion concentration parameters of the electroplating wastewater treatment system.

[0034] Real-time flow parameters and heavy metal ion concentration parameters of the electroplating wastewater treatment system are collected. The real-time flow parameters include influent flow rate, instantaneous flow rate change rate, and flow fluctuation frequency.

[0035] The influent flow rate is recorded by a high-precision electromagnetic flow meter installed at the junction of the main influent pipe and each branch pipe. The electromagnetic flow meter uses electrodes lined with polytetrafluoroethylene to resist the corrosiveness 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 via an RS485 communication interface. The electromagnetic flow meter is installed at least 10 times the pipe diameter downstream of the elbow to ensure measurement accuracy and is calibrated regularly.

[0036] The instantaneous flow rate change rate is automatically calculated based on the flow rate change over a continuous period of time. The calculation result is recorded and stored in percentage / hour. When the flow rate change rate exceeds the preset threshold range, the system will automatically issue an alarm.

[0037] The frequency of flow fluctuations is obtained by performing spectral analysis on flow data over a long period of time. The sliding window technique is used to process the flow data for 24 consecutive hours. The analysis results include the main frequency components and the energy distribution of each frequency component. The data is recorded in units of times per day and used to identify the characteristics of the production cycle fluctuations in the electroplating workshop.

[0038] 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;

[0039] The concentration of hexavalent chromium ions was determined by spectrophotometry using a diphenylcarbazide spectrophotometer installed on the inlet water pipe and recorded in mg / L. The measurement range was 0.01-10.00 mg / L, the measurement accuracy was ±2%, and the measurement frequency was once every 10 minutes.

[0040] The concentrations of trivalent chromium ions, divalent nickel ions, divalent copper ions, and divalent zinc ions were measured using an online heavy metal multi-parameter analyzer installed on the inlet water pipe.

[0041] The concentration of trivalent chromium ions was determined by complexation colorimetric spectrophotometry, with diethylenetriaminepentaacetic acid as the colorimetric reagent. The measurement range was 0.05-50.00 mg / L, the measurement accuracy was ±3%, and the measurement frequency was once every 15 minutes.

[0042] The concentration of divalent nickel ions was determined by the dimethyl ethylene glycol oxime spectrophotometric method, with a measurement range of 0.02-20.00 mg / L, a measurement accuracy of ±2.5%, and a measurement frequency of once every 15 minutes.

[0043] The concentration of divalent copper ions was determined by sodium diethyldithiocarbamate spectrophotometry, with a measurement range of 0.01-25.00 mg / L, a measurement accuracy of ±2%, and a measurement frequency of once every 15 minutes.

[0044] The concentration of divalent zinc ions was determined by dithizone spectrophotometry, with a measurement range of 0.01-30.00 mg / L, a measurement accuracy of ±2.5%, and a measurement frequency of once every 15 minutes.

[0045] A fully automated sampler extracts 10 ml of sample from the wastewater treatment system inlet every 30 minutes for analysis. The analysis process includes automatic addition of buffer solution, automatic volume adjustment, automatic sample injection, and automatic cleaning to ensure the accuracy and continuity of the measurement results.

[0046] S2: Based on the real-time flow parameters and the heavy metal ion concentration parameters, a dynamic flow-concentration coupling model is established. Based on the dynamic flow-concentration coupling model, the time lag relationship and coupling strength between flow changes and concentration fluctuations are identified, and the flow-concentration coupling strength matrix is ​​calculated.

[0047] Based on real-time flow parameters and heavy metal ion concentration parameters, a dynamic flow-concentration coupling model is established. The establishment process first involves data preprocessing of the collected real-time flow parameters and heavy metal ion concentration parameters, including identifying and removing outliers, interpolating and filling missing data, and smoothing the data sequence.

[0048] The sliding time window technique is used to divide the preprocessed data into a series of continuous data segments in chronological order.

[0049] The time window width parameter was determined by analyzing the typical response time of concentration change caused by flow rate change in electroplating wastewater treatment. The initial parameter was set to 2 hours. Using the sensitivity analysis method, the window width was successively set to 1 hour, 1.5 hours, 2 hours, 2.5 hours and 3 hours. The average goodness-of-fit index of the model under each window width was calculated, and the window width with the best goodness-of-fit index was selected as the final parameter.

[0050] The sliding step size parameter for the time window is initially set to 10 minutes. When the system is in a stable operating state, the sliding step size can be appropriately increased to 15-30 minutes to reduce the amount of computation. When the system is detected to be in a stage of rapid change in operating conditions, the sliding step size is reduced to 5 minutes to improve the model's sensitivity to changes.

[0051] Subsequently, a multivariate autoregressive model was applied to analyze the data within each time window. The influent flow rate, instantaneous flow rate change rate, and flow fluctuation frequency were used as independent variables, and the concentrations of hexavalent chromium ions, trivalent chromium ions, divalent nickel ions, divalent copper ions, and divalent zinc ions were used as dependent variables. The model parameter set for each time window was obtained through model fitting.

[0052] Specifically, the concentration parameters of each heavy metal ion are expressed as a function of a weighted linear combination of flow parameters and historical concentration values. The least squares method is used to determine the model parameters, that is, to solve for the parameter combination that minimizes the mean square error between the predicted concentration value and the actual measured concentration value. For each time window, five sub-models are constructed, corresponding to the prediction models for hexavalent chromium ion concentration, trivalent chromium ion concentration, divalent nickel ion concentration, divalent copper ion concentration, and divalent zinc ion concentration, respectively. Each sub-model contains three sets of weighting coefficients, corresponding to the influence of influent flow rate, instantaneous flow rate change rate, and flow fluctuation frequency on the concentration of this heavy metal ion, and a set of autoregressive coefficients representing the influence of historical concentration values ​​on the current concentration value.

[0053] If the model parameters change significantly between adjacent time windows, a parameter smoothing strategy is adopted to perform a weighted average of the model parameters of the current time window and the model parameters of the previous time window to prevent drastic fluctuations in model parameters.

[0054] If the number of data points in a certain time window is insufficient to support reliable parameter estimation, then the model parameters of the previous time window are used and marked as low confidence parameters.

[0055] The model parameters for all time windows are organized into a sequence of model parameters in chronological order. Each set of model parameters contains 15 weight coefficients and 5 sets of autoregressive coefficients. The timestamps and goodness-of-fit indices for each set of parameters are also recorded. The goodness-of-fit indices include the coefficient of determination, root mean square error, and Akaike information criterion, which are used to evaluate the model's fit quality in each time window.

[0056] The model parameter sets based on each time window form a time series parameter sequence, which together constitute a dynamic flow-concentration coupling model, characterizing the dynamic correlation between flow and concentration in the electroplating wastewater treatment system.

[0057] Further identification of the time lag relationship and coupling strength between flow rate change and concentration fluctuation in the dynamic flow-concentration coupling model was conducted. The time lag relationship identification process adopted the cross-correlation analysis method. By calculating the correlation coefficient between the flow parameters and the concentration parameters of each heavy metal ion under different lag times, the lag time corresponding to the maximum value of the correlation coefficient was determined as the time lag value. The time lag values ​​were calculated to obtain the influent flow rate-hexavalent chromium ion time lag, influent flow rate-trivalent chromium ion time lag, influent flow rate-divalent nickel ion time lag, influent flow rate-divalent copper ion time lag, influent flow rate-divalent zinc ion time lag, and the corresponding time lag values ​​between the instantaneous flow rate change rate and the concentration of each heavy metal ion.

[0058] The analysis of coupling strength first clarifies that the calculation object is a specific flow rate parameter and a specific heavy metal ion concentration parameter pair within each time window. For each flow rate-concentration parameter pair, all corresponding time-series data points within that time window are collected. At the same time, considering the identified time lag relationship, the concentration data is shifted accordingly to align the flow rate change with the concentration response in time. Since the electroplating wastewater treatment system is a multivariate coupled system, there may be collinearity among the flow rate parameters, and the concentrations of heavy metal ions may also influence each other. Directly calculating the simple correlation coefficient cannot accurately reflect the true correlation strength between a specific flow rate parameter and a specific concentration parameter.

[0059] Therefore, partial correlation analysis was adopted. Specifically, a multiple linear regression model was established that included all flow parameters and heavy metal ion concentration parameters. For a specific flow parameter and a specific concentration parameter pair to be analyzed, they were used as the dependent and independent variables, respectively, while all other flow parameters and concentration parameters were used as control variables. The multiple regression residuals of the flow parameters on all other variables and the multiple regression residuals of the concentration parameters on all other variables were calculated.

[0060] Calculate the Pearson correlation coefficient between the two sets of residuals above. The result is the net correlation degree after excluding the influence of other variables. This value is defined as the coupling strength of the flow-concentration parameter pair.

[0061] 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 computational stability by adding small positive numbers to the diagonal of the covariance matrix to avoid computational errors caused by ill-conditioned matrix.

[0062] For possible chemical transformation relationships between heavy metal ions, such as the redox reaction between hexavalent chromium and trivalent chromium, interaction terms characterizing chemical reactions are added to the control variables;

[0063] When the coupling strength fluctuates drastically in a short period of time, the coupling strength is smoothed by a weighted moving average method. The smoothing window width is initially set to 5 time steps, and the smoothing weight is in the form of a trigonometric function, with the center point being given the highest weight.

[0064] The obtained partial correlation coefficients are in the range of -1 to 1, ensuring that the final coupling strength is strictly in the range of -1 to 1.

[0065] The calculated coupling strength characterizes the degree of direct influence of a specific flow rate parameter on the concentration of a specific heavy metal ion after controlling for the influence of other factors. The closer the absolute value is to 1, the stronger the coupling. The positive and negative signs indicate the direction of coupling. A positive value indicates that an increase in flow rate leads to an increase in concentration, and a negative value indicates that an increase in flow rate leads to a decrease in concentration.

[0066] The influence of other variables was eliminated by partial correlation analysis, and only the direct correlation between a single flow parameter and a single heavy metal ion concentration parameter was examined.

[0067] All partial correlation analysis results are organized into a 5×3 coupling strength matrix, which is updated periodically as the time window slides. The rows represent the five heavy metal ion concentration parameters, 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.

[0068] S3: Based on the flow-concentration coupling strength matrix, construct the dosage prediction curve through piecewise linear mapping, and perform regional quantitative mapping for the optimal dosage under different flow-concentration coupling states.

[0069] Cluster analysis was performed based on the established 5×3 flow-concentration coupling strength matrix, and the elements in the matrix were divided into five coupling state categories according to the coupling strength and coupling direction characteristics: strong positive correlation region, weak positive correlation region, slight correlation region, weak negative correlation region, and strong negative correlation region.

[0070] Among them, the strong positive correlation region corresponds to elements with a coupling strength between 0.7 and 1, the weak positive correlation region corresponds to elements with a coupling strength between 0.3 and 0.7, the micro correlation region corresponds to elements with a coupling strength between -0.3 and 0.3, the weak negative correlation region corresponds to elements with a coupling strength between -0.7 and -0.3, and the strong negative correlation region corresponds to elements with a coupling strength between -1 and -0.7.

[0071] By combining the time delay relationships identified under each time window, a flow-concentration coupling feature vector containing time delay information is constructed. 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. These elements are then weighted and aggregated according to importance weights and integrated with threshold judgment rules set by expert experience to construct a multi-dimensional vector. Its dimension is equal to the product of the heavy metal ion type and the flow parameter type. Each dimension represents the influence intensity and time delay characteristics of a specific flow parameter on a specific heavy metal ion. This feature vector comprehensively represents the influence of each flow parameter on each heavy metal ion.

[0072] Calculate the similarity between the current feature vector and the standard vector in the historical operating condition database to determine the typical treatment mode of wastewater. When an element in the vector shows a large time lag value, adjust the timing of the corresponding reagent addition. For example, if the feature vector shows a large time lag value between chromium ions and flow rate, increase the dosage of chromium treatment reagent in advance by the corresponding time. Simultaneously, calculate the adjustment coefficient for reagent addition. When the vector shows a strong positive correlation and a short time lag, increase the response speed; conversely, for a negative correlation and a long time lag, reduce the base dosage but increase the slow-release ratio.

[0073] Furthermore, five commonly used reagent addition models in electroplating wastewater treatment were established, targeting sodium hydroxide precipitant, reducing agent, chelating precipitant, flocculant, and pH adjuster respectively. The dosage prediction for each reagent was performed using a specific piecewise linear mapping function.

[0074] For sodium hydroxide precipitant, the dosing model mainly considers the total equivalent concentration of various heavy metal ions in the wastewater, and adopts a five-segment linear mapping function:

[0075] When the total heavy metal concentration is below 5 mg / L, the first segment with a low slope function is used; when the concentration is between 5 and 15 mg / L, the second segment with a medium-low slope function is used; when the concentration is between 15 and 30 mg / L, the third segment with a medium slope function is used; when the concentration is between 30 and 50 mg / L, the fourth segment with a medium-high slope function is used; and when the concentration exceeds 50 mg / L, the fifth segment with a high slope function is used. The slope of each segment of the mapping function reflects the elasticity of demand for sodium hydroxide in different concentration ranges.

[0076] For the reducing agent, its dosing model is mainly designed for the reduction requirements of hexavalent chromium. It adopts a four-segment linear mapping function. When the concentration of hexavalent chromium is below 10 mg / L, a low slope segment is used; when the concentration is between 10-25 mg / L, a medium-low slope segment is used; when the concentration is between 25-40 mg / L, a medium-high slope segment is used; and when the concentration exceeds 40 mg / L, a high slope segment is used. At the same time, the reducing agent model is specially set with a compensation factor for the wastewater ORP value. When the ORP value exceeds 200 mV, an additional 5% of the reducing agent dosage is added for every 20 mV increase.

[0077] For chelating precipitants, the addition model is designed to meet the chelation precipitation requirements of copper, nickel, and zinc heavy metal ions. It employs a three-segment asymmetric linear mapping function. When the concentration of heavy metal ions is in the low concentration range (total amount less than 20 mg / L), a relatively high slope segment is used to ensure the basic treatment effect. When the concentration is in the medium concentration range (20-40 mg / L), a medium slope segment is used. When the concentration is in the high concentration range (greater than 40 mg / L), a lower slope segment is used 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, the dosage is increased by 3% for every 0.5 increase in the ratio to address the preferential chelation characteristics of copper ions.

[0078] For flocculants, the dosing model is constructed based on predicted sludge production and wastewater turbidity, using a bivariate piecewise linear mapping function. When the predicted sludge production is below 200 mg / L and the turbidity is below 100 NTU, a low-dose mapping function is used; when the sludge production is between 200-500 mg / L or the turbidity is between 100-300 NTU, a medium-dose mapping function is used; and when the sludge production exceeds 500 mg / L or the turbidity exceeds 300 NTU, a high-dose mapping function is used. Water temperature is also considered; when the water temperature is below 15℃, the flocculant dosage is increased by 8% for every 5℃ decrease to compensate for the negative impact of low temperature on flocculation efficiency.

[0079] For pH adjusters, the dosing model is constructed based on the difference between the initial pH value of the wastewater and the target pH value. When the initial pH is below 6.0, a high-slope acid-base neutralization curve is used to indicate weak buffering capacity. When the pH is between 6.0 and 7.5, a medium-slope segment is used to indicate the buffer zone before entering the alkaline zone. When the pH is between 7.5 and 8.5, a low-slope segment is used to indicate the fine adjustment zone close to the target value. In addition, the acid-base dosage model specifically introduces the moving average of historical wastewater alkalinity data as the basis for buffering capacity estimation to predict the required accurate dosage.

[0080] The piecewise linear mapping functions of the above five types of agents jointly construct the dosage prediction curves for each type of agent, and all of them take into account the flow-concentration coupling characteristics:

[0081] When the flow rate-concentration coupling strength shows a strong positive correlation, it is judged that the concentration increases synchronously with the flow rate. The prediction function increases the slope of the corresponding interval and increases the dosage of the reagent to cope with possible high concentration shock loads.

[0082] When the coupling strength is in the weak positive correlation range, the slope of the dosage prediction curve decreases accordingly, reflecting the weakened sensitivity of concentration to flow rate changes.

[0083] For the micro-correlation zone, it is determined that the flow rate change has no significant effect on the concentration of specific heavy metal ions. At this time, a fixed proportion dosing strategy based on the historical average concentration is adopted, and the dosing amount prediction curve is approximately a horizontal line segment in this region.

[0084] When a weak or strong negative correlation is detected, the dosage prediction curve shows a negative slope characteristic, that is, as the flow rate increases, the dosage of the agent is appropriately reduced, which occurs under the condition that the high flow rate leads to a significant wastewater dilution effect.

[0085] At the boundaries of each interval, cubic spline interpolation is used to ensure a smooth transition of the prediction curve and avoid abrupt changes in the dosage at the critical state.

[0086] The coefficients of the piecewise linear mapping function are obtained by machine learning training using the operating parameters corresponding to the best processing effect in historical operating data. The mapping parameters are automatically optimized and updated after every 100 batches of wastewater are processed.

[0087] For complex wastewater containing multiple heavy metal ions, a collaborative dosing decision mechanism is established. Based on the overall characteristic pattern of the flow-concentration coupling strength matrix, the optimal combination dosing scheme for various reagents is calculated.

[0088] Specifically, the process of the collaborative drug administration decision-making mechanism includes:

[0089] The coupling state information between each heavy metal ion and each flow parameter is extracted from the flow-concentration coupling strength matrix to form a coupling mode feature vector;

[0090] Then, based on the feature vector clustering analysis, the current working condition is classified into the preset working condition template library, and the most matching basic reagent ratio scheme is selected as the initial solution.

[0091] The initial solution is filtered through an expert knowledge base to eliminate combinations of reagents that may produce antagonistic reactions in terms of chemical principles, such as the simultaneous use of large quantities of certain reducing agents and oxidizing precipitants.

[0092] For the candidate solutions after initial screening, a chemical reaction kinetic model was constructed, which includes four continuous sub-processes: hexavalent chromium reduction reaction, metal ion chelation reaction, hydroxide precipitation reaction, and flocculation reaction.

[0093] Each subprocess takes into account time delay characteristics. When the time delay value of a certain heavy metal ion is large, the addition sequence of the corresponding treatment agent is adjusted accordingly to ensure the optimal reaction time.

[0094] When treating multiple metal ions, a hierarchical priority strategy is adopted to first ensure the removal efficiency of highly toxic metals such as hexavalent chromium, and then optimize the treatment effect of other metals.

[0095] When different metal ion treatment requirements conflict, such as different pH optimization ranges, a segmented treatment approach is adopted, which involves setting up multiple reaction zones to gradually adjust the chemical environment of the wastewater.

[0096] When calculating the optimal dosage, four key parameters are integrated: the current concentration of heavy metal ions, the predicted concentration, the target treatment value, and the stoichiometric ratio of the reagent reaction.

[0097] In particular, when the flow-concentration coupling strength is negative and the absolute value is large, the basic dosage ratio of the corresponding reagent is reduced, but its dynamic adjustment coefficient is increased to cope with possible concentration fluctuations.

[0098] The process of optimizing the treatment effect of other metals adopts an improved particle swarm optimization algorithm, sets upper and lower limits for reagent dosage and upper limit for total cost, and finds the optimal reagent combination that meets all treatment requirements through iterative search.

[0099] Specifically, the improved particle swarm optimization algorithm includes:

[0100] The dosage of various reagents is used as decision variables to form a particle position vector. The fitness function is defined as the weighted ratio of treatment efficiency to cost for the characteristics of electroplating wastewater.

[0101] During the initialization phase, some particles are generated based on the historical optimal dosing ratio, while the remaining particles are randomly generated to ensure sufficient coverage of the search space.

[0102] In each iteration, the particle position update formula integrates the global optimum, the individual historical optimum, and velocity information, but modifies the standard velocity update formula by introducing a dynamic adjustment coefficient based on the current flow-concentration coupling characteristics. When the coupling strength is strongly positively correlated, the velocity update step size of the corresponding dimension is increased, while when it is negatively correlated, the step size is decreased.

[0103] At the same time, an adaptive inertia weight mechanism is set up. In the early stage of iteration, a larger inertia weight is used to promote global search. As the iteration progresses, the inertia weight is linearly reduced to about 0.4 and dynamically fine-tuned according to the population diversity index.

[0104] The constraint handling adopts an improved penalty function method. When the dosage of the agent exceeds the upper and lower limits, the fitness value will be penalized by the square of the degree of excess. The total cost constraint is implemented through a soft constraint method. The fitness value is reduced by a penalty coefficient of 1.5 times for the part exceeding the budget. In view of the synergistic effect between agents, a chemical equilibrium constraint check step is added during the position update process. When the ratio of certain agents violates the stoichiometric ratio requirements, it is corrected to a reasonable range through expert rules.

[0105] A dynamic neighborhood search strategy is introduced. Every 10 iterations, the globally optimal particle generates multiple candidate solutions based on a small amount of perturbation to perform a local fine search.

[0106] To avoid premature convergence, a population diversity monitoring mechanism is set up, which triggers partial particle reinitialization when diversity drops below a threshold.

[0107] It also incorporates the characteristics of simulated annealing, allowing for a certain probability of accepting suboptimal solutions in order to escape local optima traps, with the acceptance probability decreasing as iterations proceed.

[0108] For time-varying operating conditions, a sliding time window evaluation strategy is adopted. When a significant change is detected in the flow-concentration coupling matrix, a partial reinitialization mechanism of the population is triggered to quickly adapt to the new operating conditions.

[0109] The convergence criterion adopts a dual standard: convergence is determined when the global optimal improvement is less than 0.1% for 30 consecutive rounds and the satisfaction of all constraints reaches 99.5% or more.

[0110] After multiple iterations, the globally optimal particle is output as the final agent dosing scheme, and the flow-concentration coupling characteristic pattern corresponding to this scheme is recorded.

[0111] The historical evolution trajectory of the globally optimal particle is preserved during the iteration process to analyze the changing trend and sensitivity of the optimal reagent ratio under different working conditions;

[0112] When the optimal solution found still cannot meet all the effluent quality requirements, the cost constraint is relaxed, a better but potentially more expensive solution is searched again, and decision-making suggestions are provided to the operators.

[0113] The final output of the synergistic dosing decision includes the precise dosage and optimal dosing sequence of five main types of agents: reducing agents, chelating agents, precipitants, flocculants, and pH adjusters.

[0114] S4: Based on the dosing prediction curve, the current flow-concentration coupling state of the electroplating wastewater is matched and located, and the dosing amount of the reagent is predicted by using gradient pulse dosing parameter prediction technology.

[0115] Based on the dosage prediction curve, the current flow-concentration coupling state of the electroplating wastewater is matched and located, and gradient pulse dosing technology is used to perform the dosing, specifically including:

[0116] Based on real-time collected multi-point flow data and online heavy metal ion monitoring data, the current wastewater characteristics are matched with the dosing prediction curve library in multiple dimensions, and the weighted cosine similarity is used to calculate the similarity between the current operating condition and each standard operating condition in the prediction curve library.

[0117] The matching process considers not only the absolute value of concentration, but also the trend of concentration change and the proportional relationship between each heavy metal ion. The best matching family of prediction curves is found by using the dual evaluation indexes of Euclidean distance of eigenvectors and correlation analysis.

[0118] When the similarity of the prediction curve with the highest matching degree exceeds 85%, the basic dosage calculation model corresponding to that curve is directly used as the basic function for this prediction.

[0119] When the highest matching degree is between 65% and 85%, the top three best matching curves are combined and a combined prediction model is constructed by weighted fusion based on similarity, with similarity as the weight coefficient.

[0120] For operating conditions with a matching degree of less than 65%, a dedicated prediction function is temporarily constructed based on the collaborative dosing decision-making mechanism;

[0121] After determining the basic prediction curve, further analyze the dynamic characteristics of the current flow-concentration coupling state, and predict the changing trend of the concentration of each heavy metal ion in the next 30-120 minutes, especially the time and magnitude of potential concentration peaks.

[0122] To predict the concentration change trend, a gradient pulse dosing parameter prediction technique is used to transform the continuous dosing curve into temporal and spatial pulse sequence parameters.

[0123] In terms of time, based on the predicted arrival time of the peak concentration of each heavy metal ion, the optimal reagent addition time sequence is calculated. For example, for the predicted peak concentration of hexavalent chromium that will appear 20 minutes later, the dosage of reducing agent is increased 5-8 minutes in advance to ensure the best contact time between the reagent and the pollutant.

[0124] Based on the reaction kinetics of different reagents, differentiated combinations of pulse parameters are predicted. For sodium hydroxide precipitants that react rapidly, short pulse parameters with a cycle of 30-60 seconds are predicted, while for chelating precipitants that react more slowly, long pulse parameters with a cycle of 3-5 minutes are predicted.

[0125] In the spatial dimension, the optimal reagent distribution ratio at 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 zone, the proportion of chelating precipitants is strengthened in the intermediate reaction zone, and the proportion of flocculants is emphasized in the final treatment zone.

[0126] To address the mutual promotion or inhibition effects detected between heavy metal ions, the phase relationship of pulsed drug administration is dynamically adjusted. For example, when copper and nickel ions coexist and their concentration peaks overlap, an staggered pulse scheme is constructed. When it is predicted that the peak concentrations of copper and nickel ions will occur at different times, the dosage of the corresponding agents is also staggered to avoid competitive reactions between the agents. When it is predicted that multiple metal ions will occur at high concentrations simultaneously, the dosage of the composite agent is increased.

[0127] During execution, the real-time change trend of water quality parameters at each measuring point is continuously monitored. When the water quality parameters are detected to deviate from the expected change trajectory by more than 10%, the pulse intensity and frequency are fine-tuned in real time.

[0128] Based on historical data analysis, three typical high-concentration scenarios are predicted for sudden high-concentration conditions: a sudden increase in hexavalent chromium, a sudden increase in mixed heavy metals, and a slow increase in total chromium.

[0129] In the event of a sudden increase in hexavalent chromium, an emergency pulse mode is activated, and a high concentration of reducing agent is added within 2-3 minutes. The predicted dosage is 2.5-3 times the normal dosage, forming a "chemical barrier".

[0130] For cases of sudden increase in mixed heavy metals, a compound drug pulse sequence is used. First, a pH adjuster is added to adjust the pH value to 9.0-9.5, and then a mixed chelating agent is added at twice the normal dose.

[0131] For cases where total chromium increases slowly, a progressively enhanced pulse sequence is used to gradually increase the dosage of TMT chelating agent to the peak value over 1 hour.

[0132] Simultaneously, the pulse frequency is automatically adjusted based on the flow rate change rate. When the flow rate change rate exceeds 15% / hour, the pulse frequency needs to be increased by 25-30%, and the intensity of a single pulse needs to be reduced by 10-15%. When the flow rate change rate is within the range of 5-15% / hour, the original pulse frequency is maintained, and the intensity is adjusted appropriately. When the flow rate change rate is less than 5% / hour, the pulse interval can be extended by 10-20%, and the intensity of a single pulse can be increased by 5-10%.

[0133] For flow-concentration coupling relationships with significant time lag characteristics, including: a 15-25 minute time lag between increased flow rate and increased chromium concentration; a 5-10 minute time lag between pH changes and heavy metal precipitation efficiency; a 30-40 minute time lag between temperature changes and reduction reaction rates; a 20-30 minute time lag between changes in influent turbidity and effluent turbidity; and a 10-15 minute time lag between reagent dosing and improvement in effluent water quality;

[0134] Based on these time-delay characteristics, a time-delay compensation strategy is adopted: for the operating condition that shows the characteristic of "zinc ion concentration increases 20 minutes after the flow rate increases", the dosage of zinc ion treatment agent is increased 15 minutes after the flow rate increase signal is detected; for the time-delay characteristic of "hexavalent chromium reduction efficiency decreases 10 minutes after pH decreases", the dosage of reducing agent is increased in the early stage of pH decrease.

[0135] Furthermore, based on the toxicity and treatment difficulty of heavy metal ions, the optimal treatment sequence was predicted, and a tandem pulse prediction sequence was constructed: hexavalent chromium was set as the highest treatment priority, followed by total chromium, and copper, nickel, and zinc were given lower priorities in that order.

[0136] For cases where multiple heavy metals exceed the standard simultaneously, the order of reagent addition should be designed according to priority: First, predict the optimal dosage of hexavalent chromium reducing agent to ensure that hexavalent chromium is reduced preferentially; then determine the dosage of chelating and precipitating agents for heavy metals such as chromium, copper, and nickel, and add the special treatment agents for different metals sequentially at intervals of 5-8 minutes; the optimal dosage and timing of flocculant addition should be 10-15 minutes after the aforementioned reagent additions are completed.

[0137] After each control cycle, the accuracy of each prediction result is evaluated: the prediction deviation is calculated by comparing the predicted optimal dosage with the actual dosage required for treatment; when the cumulative evaluation sample reaches 100 batches, the prediction algorithm parameters are optimized, especially for the working conditions with a prediction deviation greater than 15%, the corresponding prediction parameters are adjusted accordingly; for newly identified efficient prediction patterns, their feature parameters are stored in the knowledge base.

[0138] Finally, the optimal chemical dosing prediction scheme for the next two hours is output in the form of a time series table, including: the predicted dosage of various chemicals at 5-minute intervals, the predicted pulse intensity and frequency parameters, the predicted spatial distribution ratio of chemicals, the predicted trend of key water quality parameters, and the predicted treatment effect evaluation indicators.

[0139] In summary, the electroplating wastewater chemical dosage prediction system based on the dynamic flow-concentration coupling model of this invention has been clarified. Through this model, it can perceive the synergistic changes of multiple heavy metal ions and water quality parameters in electroplating wastewater in real time, accurately reflecting the treatment needs under complex operating conditions. This not only improves the synergistic removal efficiency of multiple metal ions but also significantly enhances the adaptability to extreme conditions such as sudden changes in flow and concentration. Furthermore, by integrating an expert knowledge base, chemical reaction kinetics, and multi-objective intelligent optimization algorithms, it can dynamically select the optimal reagent combination and dosage, optimize reagent utilization, and reduce operating costs. This significantly improves the reliability, economy, and intelligence level of the wastewater treatment system, providing an innovative solution for the efficient treatment of highly complex industrial wastewater.

Claims

1. A plating wastewater dosing amount prediction system based on a dynamic flow-concentration coupling model, characterized by, The method comprises the following steps: A collection module is used to collect real-time flow parameters and heavy metal ion concentration parameters of the electroplating wastewater treatment system; A calculation module is used to establish a dynamic flow-concentration coupling model according to the real-time flow parameters and the heavy metal ion concentration parameters, identify the time lag relationship and coupling strength between flow changes and concentration fluctuations based on the dynamic flow-concentration coupling model, and calculate a flow-concentration coupling strength matrix; A partition mapping module is used to construct a dosing amount prediction curve by using a piecewise linear mapping method according to the flow-concentration coupling strength matrix, and quantitatively map the optimal dosing amount of the medicament under different flow-concentration coupling states; A prediction module is used to match and locate the current flow-concentration coupling state of the electroplating wastewater according to the dosing amount prediction curve, and predict the dosing amount of the medicament by using a gradient pulse dosing parameter prediction technology. 2.The electroplating wastewater dosing amount prediction system based on a dynamic flow-concentration coupling model of claim 1, wherein, The real-time flow parameters include the inflow flow rate, the instantaneous flow rate change rate and the flow fluctuation frequency. The heavy metal ion concentration parameters include the concentration of hexavalent chromium ions, the concentration of trivalent chromium ions, the concentration of divalent nickel ions, the concentration of divalent copper ions and the concentration of divalent zinc ions. 3.The electroplating wastewater dosing amount prediction system based on a dynamic flow-concentration coupling model according to claim 2, characterized in that, Data preprocessing is performed based on the real-time flow parameters and the heavy metal ion concentration parameters, and a sliding time window technology is used to divide the data into continuous segments in chronological order. In each time window, a multivariate autoregressive model is constructed with the inflow flow rate, the flow rate change rate and the fluctuation frequency as independent variables and the concentrations of various heavy metal ions as dependent variables. The model parameters reflecting the relationship between flow and concentration are dynamically fitted to obtain a dynamic flow-concentration coupling model. 4.The electroplating wastewater dosing amount prediction system based on a dynamic flow-concentration coupling model according to claim 3, characterized in that, Based on the dynamic flow-concentration coupling model, the time lag relationship and the coupling strength between flow changes and heavy metal ion concentration fluctuations are identified by cross-correlation analysis, and the coupling strength of each flow parameter and each heavy metal ion concentration is quantified by using a partial correlation analysis method. The partial correlation analysis results are organized into a 5x3 coupling strength matrix. 5.The electroplating wastewater dosing amount prediction system based on a dynamic flow-concentration coupling model according to claim 4, characterized in that, Based on the coupling strength matrix, clustering analysis is performed to divide the elements in the matrix into five coupling state categories, i.e., strong positive correlation area, weak positive correlation area, weak negative correlation area and strong negative correlation area, according to the coupling strength and coupling direction characteristics. 6.The electroplating wastewater dosing amount prediction system based on a dynamic flow-concentration coupling model according to claim 5, characterized in that, Based on the coupling strength matrix and the time lag 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 determine the typical treatment mode of the wastewater and adjust the dosing time of the corresponding medicament. 7.The electroplating wastewater dosing amount prediction system based on a dynamic flow-concentration coupling model according to claim 6, characterized in that, Based on the typical treatment mode, a medicament dosing model is constructed, and a piecewise linear mapping function is used to construct a dosing amount prediction curve for different medicament dosing amounts. 8.The electroplating wastewater dosing amount prediction system based on a dynamic flow-concentration coupling model of claim 7, wherein, If there are multiple heavy metal ions in the complex wastewater, a collaborative dosing decision mechanism is established, which includes: Extracting the coupling state information of each heavy metal ion and each flow parameter to form a coupling mode feature vector; According to the feature vector clustering analysis, the current working condition is classified into a preset working condition template library, and the most matched basic medicament ratio scheme is selected as the initial solution; After the initial solution is screened out by the expert knowledge base to eliminate unreasonable combinations, the multi-metal co-processing sequence and reagent dosage are dynamically optimized based on the chemical reaction kinetics model combined with time delay characteristics and hierarchical priority strategy; At the same time, the improved particle swarm algorithm is used to search for the optimal reagent dosage scheme under the condition of meeting the treatment target and cost constraints. 9.The electroplating wastewater dosing amount prediction system based on a dynamic flow-concentration coupling model of claim 8, wherein, The improved particle swarm algorithm takes the dosage of various reagents as decision variables and constructs an optimization model with the weighted processing efficiency and cost ratio as the fitness. Through the improved particle swarm algorithm, the particle search strategy and parameters are dynamically adjusted, combined with chemical constraints, synergistic effects and simulated annealing mechanism, to ensure that the optimal reagent dosage scheme is found that meets the processing and cost constraints and adapts to the current flow-concentration coupling characteristics. 10.The electroplating wastewater dosing amount prediction system based on a dynamic flow-concentration coupling model of claim 9, wherein, Based on real-time flow and heavy metal ion monitoring data, the current working condition is matched with the reagent dosage prediction curve library in multiple dimensions, the optimal prediction curve is selected or weighted fused using similarity evaluation, and according to the prediction results, the timing, pulse strength and reagent distribution of reagent dosage are optimized using gradient pulse reagent addition technology, and adjustments are made for different reagent reaction characteristics and working condition time lag. The prediction accuracy is evaluated after each control cycle, the model parameters are continuously optimized, and the optimal reagent dosage scheme for different time periods and regions in the next two hours is output.

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