Integrated modeling and decision control method and system for industrial wastewater treatment process

Abstract

The application discloses an industrial wastewater treatment process modeling and decision control integrated method and system, and the method comprises the following steps: (1) based on the analysis of reaction mechanism under the conservation of mass and multiphase coexistence and the analysis of reactor structure, an industrial wastewater treatment process model of multi-reactor cascade is constructed; (2) a hierarchical optimization control strategy is adopted to separate and control the target components of the industrial wastewater treatment process, namely: the upper layer determines the process control target according to the requirements and economic indexes of the industrial wastewater treatment process; the lower layer adopts a model predictive control method, obtains the control input sequence of the industrial wastewater treatment process based on the determined process control target, and issues the optimal control input to act on the industrial wastewater treatment process. The application can be applied to the control of the industrial wastewater treatment process, and can significantly improve the control precision and process indexes.

Description

Technical Field

[0001] This invention belongs to the field of industrial wastewater treatment and control, specifically relating to an integrated method and system for modeling and decision control of industrial wastewater treatment processes. Background Technology

[0002] Non-ferrous metallurgical production processes generate large amounts of industrial wastewater, which typically contains high concentrations of heavy metal ions (copper, arsenic, antimony, bismuth, nickel, etc.). Therefore, it is necessary to treat this industrial wastewater to achieve the recycling of metal resources. When both copper and arsenic content are high, copper-arsenic separation is the primary goal of heavy metal wastewater treatment. This means that copper slag and arsenic slag are generated in batches during the reaction stage through chemical reactions. The higher the separation degree, the lower the mixed slag content, and the higher the utilization value of the metal slag. Among various industrial wastewater treatment methods, the gas-liquid sulfidation process has received widespread attention due to its low raw material cost, mature preparation technology, and lack of new impurity generation. Its principle is that when copper ions and arsenic ions (existing in the liquid as arsenous acid) coexist, H2S gas exhibits a phenomenon where it first reacts with copper ions to form copper sulfide, and then reacts with arsenic ions to form arsenic sulfide precipitate. Based on this phenomenon, the goal of copper-arsenic separation can be achieved by controlling the H2S introduction rate into the reactor.

[0003] Sulfidation is a typical nonlinear kinetic process. Due to the nonlinear reaction rate and the dynamic characteristics of industrial wastewater input, precise control of the process is difficult. Furthermore, the coupling between key variables and the cascading of multiple reactors further complicate the control of sulfidation. Lacking effective control methods, current industrial sites employ a strategy of introducing as much H2S as possible. This not only leads to resource waste, increased alkali consumption and burden on tail gas treatment equipment, but also prevents staged sulfidation, resulting in the mixing of different heavy metal sulfidation slags, low-grade sulfidation slags, and in severe cases, even H2S leakage. Summary of the Invention

[0004] In view of the above problems, the present invention provides an integrated method and system for modeling and decision control of industrial wastewater treatment process, which improves the control accuracy and process indicators of industrial wastewater treatment process.

[0005] To achieve the above technical objectives, the present invention adopts the following technical solution:

[0006] An integrated method for modeling and decision control of industrial wastewater treatment processes includes:

[0007] Based on the analysis of reaction mechanisms under material conservation, multiphase coexistence, and reactor structure, a model of industrial wastewater treatment process with multiple reactor cascades is constructed.

[0008] A hierarchical optimization control strategy is adopted to separate and control the target components in the industrial wastewater treatment process. Specifically, the upper layer determines the process control target based on the requirements and economic indicators of the industrial wastewater treatment process; the lower layer adopts the model predictive control method to obtain the control input sequence of the industrial wastewater treatment process based on the determined process control target, and issues the optimal control input to the industrial wastewater treatment process.

[0009] Furthermore, the industrial wastewater includes copper ions and arsenic ions. The industrial wastewater treatment includes using H2S gas to sulfide the copper ions and arsenic ions in the industrial wastewater to generate copper sulfide and arsenic sulfide precipitates respectively, thereby achieving the separation of copper ions and arsenic ions.

[0010] Furthermore, the constructed industrial wastewater treatment process model is represented as follows:

[0011] x k+1 =f(x) k u k )

[0012] Where: u k The control inputs representing the industrial wastewater treatment process at time t=k include the H2S gas flow rates of each stage of the sulfidation reactor; x k State variables representing the industrial wastewater treatment process include the volume of industrial wastewater in each stage of the sulfidation reactor and the concentration or content of each component; the components in each stage of the sulfidation reactor include Cu. 2+ H2S, As 3+ As2S3, CuS.

[0013] Furthermore, the analysis of reaction mechanisms and reactor structures based on material conservation and multiphase coexistence specifically includes:

[0014] (1) Analyze the chemical reactions in the industrial wastewater treatment process, analyze the reaction rates of various chemical reactions based on the chemical reaction mechanism, and determine the reaction rates of each component based on the relationship of the parallel competing reaction system: r CuS The order is Cu 2+ H2S, As 3+ The reaction rates of As2S3 and CuS;

[0015] (2) Based on the influence of influent flow rate and valve on the inflow of industrial wastewater, the volume change of industrial wastewater in the primary and secondary sulfidation reactors can be described as follows:

[0016]

[0017]

[0018] Where V1 is the wastewater volume in the primary sulfidation reactor, Q is the wastewater flow rate, and K is the wastewater volume. V1 V1 is the outlet valve constant of the primary sulfidation reactor, V2 is the wastewater volume in the secondary sulfidation reactor, and K is the K value. V2 This refers to the outlet valve constant of the secondary sulfidation reactor.

[0019] (3) Combining the reaction rates of each component, the changes in each liquid phase component in the primary sulfidation reactor can be described by the following differential equation:

[0020]

[0021]

[0022]

[0023] in, This represents the copper ion concentration in the primary sulfidation reactor. This represents the concentration of copper ions in the wastewater. V represents the H2S concentration in the primary sulfurization reactor, F1 represents the H2S flow rate into the primary sulfurization reactor, and V m For the molar volume of the gas, This represents the arsenic ion concentration in the primary sulfidation reactor. This represents the concentration of arsenic ions in the wastewater;

[0024] The changes in each liquid phase component in the secondary sulfidation reactor can be described by the following differential equation:

[0025]

[0026]

[0027]

[0028] The changes in each solid phase component in the primary vulcanization reactor can be described by the following differential equation:

[0029]

[0030]

[0031] in, It refers to the content of arsenic sulfide per unit volume in the primary sulfidation reactor. It is the content of copper sulfide per unit volume in the primary sulfidation reactor;

[0032] The changes in each solid phase component in the secondary sulfidation reactor can be described by the following differential equation:

[0033]

[0034]

[0035] The multi-reactor cascade model of the sulfidation process, composed of equations (12)-(23) above, is the industrial wastewater treatment process model. Its discrete form is obtained by using the forward difference method and is expressed as: x k+1 =f(x) k u k ), and x k Including V1, V2 and The value at time t = k.

[0036] Furthermore, when determining process control objectives, the requirements for industrial wastewater treatment processes include: obtaining copper sulfide slag with the highest possible purity using a primary sulfidation reactor and obtaining arsenic sulfide slag with the highest possible purity using a secondary sulfidation reactor.

[0037] Furthermore, the requirement to obtain copper sulfide slag with the highest possible purity using a single-stage sulfidation reactor is described using copper removal efficiency:

[0038]

[0039]

[0040] In the formula, F1 and F2 represent the contents of As2S3 and CuS in the primary sulfidation reactor, respectively, and the H2S flow rates in the primary and secondary sulfidation reactors are respectively; F min F max The amplitude limit value input by the system;

[0041] The requirement to obtain arsenic sulfide slag with the highest possible purity using a two-stage sulfidation reactor is described using arsenic removal efficiency:

[0042]

[0043]

[0044] Furthermore, the two requirements of the industrial wastewater treatment process—obtaining copper sulfide slag with the highest possible purity using a primary sulfidation reactor and obtaining arsenic sulfide slag with the highest possible purity using a secondary sulfidation reactor—are converted into objective functions based on metal ion concentrations, expressed as follows:

[0045]

[0046]

[0047] Where p1, p2, p3, p4, q1, q2 represent weights. F represents the steady-state concentrations of copper ions and arsenic ions in the next-stage sulfidation reactor under a given input. 1,s ,F 2,s Let x represent the steady-state input in the primary and secondary sulfidation reactors, respectively, i.e., the steady-state H2S inflow rate; x = g(u) is the steady-state model of the sulfidation process, obtained from the following equation:

[0048]

[0049] Furthermore, the lower layer employs model predictive control to transform the control problem into an online optimization problem in the prediction time domain. The objective function of model predictive control is:

[0050]

[0051]

[0052] in, This represents the optimal control input sequence to be optimized. u j+k m represents the control input at time j+k. c To control the time-domain step size; x j+k m represents the predicted process state at time j+k. p To predict the time-domain step size; x′ j+k The concentrations of copper ions in the first-stage sulfidation reactor and arsenic ions in the second-stage sulfidation reactor at time j+k are represented. This represents the process control target determined at time j+k by the upper layer, including the copper ion concentration in the first-stage sulfidation reactor. Arsenic ion concentration in the secondary sulfidation reactor Represents terminal constraints; Δu j+k Δu represents the change in control input at time j+k. k =u k -u k-1 P, Q, R u R Δu Indicates the weight of the corresponding item; u max , Δu min , Δu max These represent the minimum and maximum values ​​of the control input and the minimum and maximum values ​​of the change in the control input, respectively.

[0053] An integrated system for modeling and decision control of industrial wastewater treatment processes includes:

[0054] The process model building module is used to: construct a multi-reactor cascade industrial wastewater treatment process model based on the analysis of reaction mechanisms under material conservation, multiphase coexistence, and reactor structure.

[0055] The decision control module is used to control the industrial wastewater treatment process using a hierarchical optimization control strategy. This includes: the upper layer determining the process control objectives based on the requirements and economic indicators of the industrial wastewater treatment process; and the lower layer using model predictive control methods to obtain the control input sequence of the industrial wastewater treatment process based on the determined process control objectives, and issuing the optimal control input to the industrial wastewater treatment process.

[0056] The integrated system for modeling and decision control of industrial wastewater treatment process is used to implement the integrated method for modeling and decision control of industrial wastewater treatment process described above.

[0057] Beneficial effects

[0058] The integrated modeling and decision-making control method for industrial wastewater treatment proposed in this invention first constructs a multi-reactor cascade gas-liquid sulfidation process model to describe the process dynamics, based on material conservation, reaction mechanism analysis under multiphase coexistence, and reactor structure analysis. Then, to achieve the control objective of copper-arsenic separation, a hierarchical optimization control strategy is proposed. In this strategy, the upper layer determines the process control objective based on process requirements and economic indicators, while the lower layer is responsible for achieving the control objective. Finally, a metric reflecting the reaction depth of the sulfidation process is proposed for the copper-arsenic separation objective and integrated into the hierarchical optimization control framework to ensure the effective separation of metal sulfidation slag.

[0059] This invention can be applied to the control of industrial wastewater treatment processes, and can significantly improve control accuracy and process indicators. Specifically, compared with traditional methods, the control accuracy is improved by more than 60%, and key process indicators are also greatly improved, which can effectively achieve the control objectives of industrial wastewater treatment processes. Attached Figure Description

[0060] Figure 1 This is the overall framework of the method described in the embodiments of this application;

[0061] Figure 2 These are comparative experimental results of the method described in the embodiments of this application and the traditional method, with (a), (b), (c), and (d) respectively corresponding to...

[0062] Figure 3 These are comparative experimental results of the method described in the embodiments of this application and the traditional method, with (a), (b), (c), and (d) respectively corresponding to... Detailed Implementation

[0063] The embodiments of the present invention will be described in detail below. These embodiments are based on the technical solutions of the present invention and provide detailed implementation methods and specific operation processes to further explain the technical solutions of the present invention.

[0064] The integrated method for modeling and decision control of industrial wastewater treatment processes proposed in this invention includes two aspects: first-principles modeling of the gas-liquid sulfidation process and hierarchical optimization control strategy. For example... Figure 1 As shown in the diagram. In the first part, based on the principles of material conservation, reaction mechanism analysis under multiphase coexistence, and reactor structure analysis, a multi-reactor cascade industrial wastewater treatment process model is constructed. In the second part, a hierarchical optimization control strategy is adopted to separate and control the target components of the industrial wastewater treatment process. Specifically, the upper layer determines the process control objectives based on the requirements and economic indicators of the industrial wastewater treatment process; the lower layer uses model predictive control methods to obtain the control input sequence of the industrial wastewater treatment process based on the determined process control objectives, and then issues the optimal control input to the industrial wastewater treatment process.

[0065] I. First Principles Modeling of Gas-Liquid Sulfidation Process

[0066] The reaction of copper and arsenic ions in heavy metal wastewater with H2S in the sulfidation reactor is as follows:

[0067] Cu 2+ +H₂S→CuS↓+2H + (1)

[0068] 3H2S+2H3AsO3→As2S3↓+6H2O (2)

[0069] 3Cu 2+ +As₂S₃ + ​​6H₂O → 3OuS + 2H₃AsO₃ + 6H + (3)

[0070] Based on the above reactions, it can be seen that the phenomenon of H2S gas first reacting with copper ions to form copper sulfide, and then reacting with arsenic ions to form arsenic sulfide precipitate is due to the fact that the generated arsenic sulfide precipitate reacts with the remaining copper ions in the wastewater and then re-exists in the solution as arsenic ions.

[0071] According to the chemical reaction mechanism, the reaction rate of the above reaction is:

[0072]

[0073]

[0074]

[0075] Where r1, r2, r3 represent the reaction rates of the three reactions above, and k1, k2, k3 represent the reaction rate constants of the three reactions. These represent the concentrations of copper ions, arsenic ions, and H₂S, respectively, and the amount of arsenic sulfide precipitate per unit volume. Based on the relationship of parallel competing reaction systems, the reaction rates of each component can be obtained:

[0076]

[0077]

[0078]

[0079]

[0080] r CuS =-r1-3r3 (11)

[0081] The inflow rate of industrial wastewater is affected by the influent flow rate and valves. The volume changes of industrial wastewater in the primary and secondary sulfidation reactors are described as follows:

[0082]

[0083]

[0084] Where V1 is the wastewater volume in the primary sulfidation reactor, Q is the wastewater flow rate, and K is the wastewater volume. V1 V1 is the outlet valve constant of the primary sulfidation reactor, V2 is the wastewater volume in the secondary sulfidation reactor, and K is the K value. V2 This is the outlet valve constant of the secondary sulfidation reactor.

[0085] Considering the structure of the equipment, the changes in the liquid and solid phases in the vulcanization reactor are different. The change in the liquid phase in the first-stage vulcanization reactor can be described by the following differential equation:

[0086]

[0087]

[0088]

[0089] in, This represents the copper ion concentration in the primary sulfidation reactor. This represents the concentration of copper ions in the wastewater. V represents the H2S concentration in the primary sulfurization reactor, F1 represents the H2S flow rate into the primary sulfurization reactor, and V m For the molar volume of the gas, This represents the arsenic ion concentration in the primary sulfidation reactor. This represents the concentration of arsenic ions in the wastewater.

[0090] The changes in the liquid phase in the secondary sulfidation reactor can be described by the following differential equation:

[0091]

[0092]

[0093]

[0094] For the liquid phase components, the outlet composition of the first-stage vulcanization reactor is the same as the inlet composition of the second-stage vulcanization reactor. Therefore... It is both the outlet component of the primary sulfidation reactor and the inlet component of the secondary sulfidation reactor.

[0095] The changes in the solid phase in the primary sulfidation reactor can be described by the following differential equation:

[0096]

[0097]

[0098] in, It refers to the content of arsenic sulfide per unit volume in the primary sulfidation reactor. It is the content of copper sulfide per unit volume in a primary sulfidation reactor.

[0099] The changes in the solid phase in the secondary sulfidation reactor can be described by the following differential equation:

[0100]

[0101]

[0102] For solid materials, the two reactors are not fully connected due to the presence of a thickener between the primary and secondary sulfidation reactors. Primary sulfidation slag is removed between the primary and secondary sulfidation reactors; therefore, there is no solid material input item in the description of the secondary sulfidation reactor.

[0103] The multi-reactor cascade model of the vulcanization process is composed of equations (12)-(23). The discrete form of the vulcanization process model x can be obtained by using the forward difference method. k+1 =f(x) k u k ), where x k Represents V1, V2, The value of u at time t = k kThis represents the values ​​of the system control inputs F1 and F2 at time t = k.

[0104] II. Layered Optimization Control Strategy for the Vulcanization Process

[0105] In this section, the proposed hierarchical optimization control strategy comprises two parts: real-time optimization at the upper level and model predictive control at the lower level. At the upper level, the optimal setpoints for key parameters are obtained by solving an objective function that includes the objectives of the copper-arsenic separation process during sulfidation and economic indicators. At the lower level, considering system constraints, the control objective is achieved through a rolling optimization strategy.

[0106] ① Real-time optimization layer

[0107] The goal of copper-arsenic separation is to obtain pure heavy metal sulfide slag at the thickener. For a single-stage sulfide reactor, the control objective is to obtain copper sulfide slag with the highest possible purity, which is described by the copper removal efficiency.

[0108]

[0109] For a two-stage sulfidation reactor, the control objective is to obtain arsenic sulfide slag with the highest possible purity, which is described by the arsenic removal efficiency:

[0110]

[0111] Among them, F min F max The amplitude limit value is input by the system.

[0112] Since the sampling and testing of sulfidation slag in industrial processes are carried out in batches, and the concentration of metal ions can be measured online, it is necessary to convert the purity of heavy metal sulfidation slag into the concentration of metal ions. The purity of heavy metal sulfidation slag can be reflected by the concentration of metal ions in the solution. The objective of the primary sulfidation reactor is to maximize the proportion of copper sulfide slag, which is equivalent to maximizing the concentration of arsenic ions and minimizing the concentration of copper ions in the primary sulfidation reactor. The objective of the secondary sulfidation reactor can be obtained similarly. Considering that the amount of reactants added is related to the process cost, the following objective function is designed to determine the setpoint of the ion concentration in the sulfidation reactor:

[0113]

[0114]

[0115] Where p1, p2, p3, p4, q1, and q2 represent weights. These represent the steady-state concentrations of copper and arsenic ions in the next two stages of the sulfidation reactor, respectively, given the input; F 1,s ,F 2,sLet x and g(u) represent the steady-state inputs, i.e., the steady-state H2S inflow rates, in the primary and secondary sulfurization reactors, respectively. x = g(u) is the steady-state model of the sulfurization process, obtained from the following equation:

[0116]

[0117] ② Model Predictive Control Layer

[0118] Model predictive control (MDI) transforms the control problem into a model-based online optimization problem in the prediction time domain. It mainly consists of three parts: model-based prediction, the objective function, and a rolling optimization scheme. Compared to model-free control methods such as PID control, MDI can consider more system states and handle constraints. By predicting the system's future multi-step states, it determines the next control input by considering the cost function and system constraints. (Control time domain T) c =m c Optimal control sequence within Δt By minimizing the prediction time domain T p =m p The optimization problem within Δt is obtained, and the first value u in the resulting control sequence is obtained. j+1 In the system, the control law is obtained by performing the above operations at each control step:

[0119]

[0120] in, Represents the optimal control sequence The first control input u in j+1 .

[0121] The objective function of model predictive control is:

[0122]

[0123] in, This represents the optimal control input sequence to be optimized. u j+k m represents the control input at time j+k. c To control the time-domain step size; x′ j+k The concentrations of copper ions in the first-stage sulfidation reactor and arsenic ions in the second-stage sulfidation reactor at time j+k; m p To predict the time-domain step size, each term in the objective function is calculated as a weighted vector. x T It is the transpose of x. This represents the deviation between the process state and the reference trajectory. The result obtained from equation (26) and The reference trajectory vector formed. This represents the terminal constraint. Considering that control inputs are often related to economic costs and frequent changes in input can lead to a decrease in mechanical structure performance, in practical applications, the input quantity and its variation will be considered. Δu k =u k -u k-1 Add it to the target function.

[0124] III. Testing and Experimental Verification

[0125] To verify the effectiveness of the proposed method, a comparative experiment was designed, and the experimental results are as follows: Figure 2 , Figure 3 As shown in Tables 1 and 2:

[0126] Table 1

[0127] method Rule control PID control Model predictive control Level 1 copper removal control error 2.12 1.11 0.16 Secondary arsenic removal error control 21.87 9.99 3.03

[0128] Table 2

[0129] method Rule control PID control Model predictive control First-level copper removal rate 64.46％ 94.61％ 87.62％ Primary arsenic removal rate 1.59％ 34.52％ 4.49％ Secondary copper removal rate 98.47％ 98.13％ 99.4％ Secondary arsenic removal rate 65.28％ 87.74％ 91.35％

[0130] The higher primary copper removal rate and secondary arsenic removal rate, as well as the lower primary arsenic removal rate, meet the process target of copper and arsenic separation. It can be seen that, under the same control target, the method proposed in this invention has a significant improvement in both control accuracy and process indicators.

[0131] The above embodiments are preferred embodiments of this application. Those skilled in the art can make various changes or improvements based on them. Without departing from the overall concept of this application, these changes or improvements should fall within the scope of protection claimed in this application.

Claims

1. An integrated method for modeling and decision control of industrial wastewater treatment processes, characterized in that, include: Based on the analysis of reaction mechanisms under material conservation, multiphase coexistence, and reactor structure, a model of industrial wastewater treatment process with multiple reactor cascades is constructed. A hierarchical optimization control strategy is adopted to separate and control the target components in the industrial wastewater treatment process. Specifically, the upper layer determines the process control target based on the requirements and economic indicators of the industrial wastewater treatment process; the lower layer adopts the model predictive control method to obtain the control input sequence of the industrial wastewater treatment process based on the determined process control target, and issues the optimal control input to the industrial wastewater treatment process. The two requirements of the industrial wastewater treatment process—obtaining copper sulfide slag with the highest possible purity using a primary sulfidation reactor and arsenic sulfide slag with the highest possible purity using a secondary sulfidation reactor—are converted into objective functions based on metal ion concentrations, expressed as follows: ； in, , , , , , Represents weight, , , , These represent the steady-state concentrations of copper ions and arsenic ions in the next- or second-stage sulfidation reactor, respectively, given the input. , These represent the steady-state inputs in the primary and secondary vulcanization reactors, respectively. Incoming flow; For the steady-state model of the vulcanization process, the following equation is obtained: ； The lower layer employs model predictive control to transform the control problem into an online optimization problem in the prediction time domain. The objective function of model predictive control is: ； in, This represents the optimal control input sequence to be optimized. , represent Time-based control input, To control the time-domain step size; represent Predicted process state value at time 10:

00. To predict the time-domain step size; represent The copper ion concentration in the primary sulfidation reactor and the arsenic ion concentration in the secondary sulfidation reactor at specific times; Representing the decision made by the upper management The process control targets at any given time include the steady-state value of copper ion concentration in the primary sulfidation reactor. Steady-state values ​​of arsenic ion concentration in the secondary sulfidation reactor ; Represents terminal constraints; represent Changes in control input at any given time - P, Q, R u R Δu Indicates the weight of the corresponding item; , , , These represent the minimum and maximum values ​​of the control input and the minimum and maximum values ​​of the change in the control input, respectively.

2. The integrated method for modeling and decision control of industrial wastewater treatment process according to claim 1, characterized in that, The industrial wastewater contains copper ions and arsenic ions, and industrial wastewater treatment includes using... The gas is used to sulfide copper ions and arsenic ions in industrial wastewater to generate copper sulfide and arsenic sulfide precipitates respectively, thereby achieving the separation of copper ions and arsenic ions.

3. The integrated method for modeling and decision control of industrial wastewater treatment process according to claim 2, characterized in that, The constructed industrial wastewater treatment process model is represented as follows: ； in: Represents the industrial wastewater treatment process in The control input at all times, including the vulcanization reactors at each stage. Gas flow rate; State variables representing the industrial wastewater treatment process include the volume of industrial wastewater in each stage of the sulfidation reactor and the concentration or content of each component; the components in each stage of the sulfidation reactor include... , , , , .

4. The integrated method for modeling and decision control of industrial wastewater treatment process according to claim 3, characterized in that, The analysis of reaction mechanisms and reactor structures based on material conservation and multiphase coexistence specifically includes: (1) Analyze the chemical reactions in the industrial wastewater treatment process, analyze the reaction rates of various chemical reactions based on the chemical reaction mechanism, and determine the reaction rates of each component based on the relationship of the parallel competing reaction system: , , , , , in order , , , , The reaction rate; (2) Based on the influence of influent flow rate and valve on the inflow of industrial wastewater, the volume change of industrial wastewater in the primary and secondary sulfidation reactors can be described as follows: (12) (13) in, This refers to the volume of wastewater in the primary sulfidation reactor. Wastewater flow rate This is the outlet valve constant of the primary sulfidation reactor. This refers to the volume of wastewater in the secondary sulfidation reactor. This refers to the outlet valve constant of the secondary sulfidation reactor. (3) Combining the reaction rates of each component, the changes in each liquid phase component in the primary sulfidation reactor can be described by the following differential equation: (14) (15) (16) in, This represents the copper ion concentration in the primary sulfidation reactor. This represents the concentration of copper ions in the wastewater. Represents the primary sulfidation reactor concentration, Representing the primary sulfidation reactor Incoming traffic, For the molar volume of the gas, This represents the arsenic ion concentration in the primary sulfidation reactor. This represents the concentration of arsenic ions in the wastewater; The changes in each liquid phase component in the secondary sulfidation reactor can be described by the following differential equation: (17) (18) (19) In the formula, For the secondary sulfidation reactor Incoming flow rate; The changes in each solid phase component in the primary vulcanization reactor can be described by the following differential equation: (20) (21) in, It refers to the content of arsenic sulfide per unit volume in the primary sulfidation reactor. It is the content of copper sulfide per unit volume in the primary sulfidation reactor; The changes in each solid phase component in the secondary sulfidation reactor can be described by the following differential equation: (22) (23) The multi-reactor cascade model of the sulfidation process, composed of equations (12)-(23) above, is the industrial wastewater treatment process model. Its discrete form is obtained by using the forward difference method and is expressed as: ,and include , , , , , , , , , , and exist The value at any given moment.

5. The integrated method for modeling and decision control of industrial wastewater treatment process according to claim 3, characterized in that, When determining process control objectives, the requirements for industrial wastewater treatment processes include: obtaining copper sulfide slag with the highest possible purity using a primary sulfidation reactor and obtaining arsenic sulfide slag with the highest possible purity using a secondary sulfidation reactor.

6. The integrated method for modeling and decision control of industrial wastewater treatment process according to claim 5, characterized in that, The requirement to obtain copper sulfide slag with the highest possible purity using a single-stage sulfidation reactor is described using copper removal efficiency: ； In the formula, , These represent the components in the primary sulfidation reactor. and The content, , In the primary and secondary sulfidation reactors respectively Incoming flow rate; , The amplitude limit value input by the system; The requirement to obtain arsenic sulfide slag with the highest possible purity using a two-stage sulfidation reactor is described using arsenic removal efficiency: 。 7. An integrated system for modeling and decision control of industrial wastewater treatment processes, characterized in that, include: The process model building module is used to: construct a multi-reactor cascade industrial wastewater treatment process model based on the analysis of reaction mechanisms under material conservation, multiphase coexistence, and reactor structure. The decision control module is used to control the industrial wastewater treatment process using a hierarchical optimization control strategy, including: the upper level determining the process control objectives based on the requirements and economic indicators of the industrial wastewater treatment process; The lower layer adopts the model predictive control method, which obtains the control input sequence of the industrial wastewater treatment process based on the determined process control objectives, and issues the optimal control input to the industrial wastewater treatment process. The integrated system for modeling and decision control of industrial wastewater treatment process is used to implement the integrated method for modeling and decision control of industrial wastewater treatment process as described in any one of claims 1-6.

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