WO2020133611A1 - 一种基于操作模式动态匹配的重金属废水净化控制方法 - Google Patents

一种基于操作模式动态匹配的重金属废水净化控制方法 Download PDF

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WO2020133611A1
WO2020133611A1 PCT/CN2019/072269 CN2019072269W WO2020133611A1 WO 2020133611 A1 WO2020133611 A1 WO 2020133611A1 CN 2019072269 W CN2019072269 W CN 2019072269W WO 2020133611 A1 WO2020133611 A1 WO 2020133611A1
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heavy metal
concentration
real
voltage
metal ions
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French (fr)
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阳春华
张凤雪
朱红求
李勇刚
李文婷
蒋晓云
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Central South University
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    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F9/00—Multistage treatment of water, waste water or sewage
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/008—Control or steering systems not provided for elsewhere in subclass C02F
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/46—Treatment of water, waste water, or sewage by electrochemical methods
    • C02F1/461—Treatment of water, waste water, or sewage by electrochemical methods by electrolysis
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/58—Treatment of water, waste water, or sewage by removing specified dissolved compounds
    • C02F1/62—Heavy metal compounds
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/66—Treatment of water, waste water, or sewage by neutralisation; pH adjustment
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2101/00—Nature of the contaminant
    • C02F2101/10—Inorganic compounds
    • C02F2101/20—Heavy metals or heavy metal compounds
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2201/00—Apparatus for treatment of water, waste water or sewage
    • C02F2201/46—Apparatus for electrochemical processes
    • C02F2201/461—Electrolysis apparatus
    • C02F2201/46105—Details relating to the electrolytic devices
    • C02F2201/4612—Controlling or monitoring
    • C02F2201/46125—Electrical variables
    • C02F2201/46135—Voltage
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2201/00—Apparatus for treatment of water, waste water or sewage
    • C02F2201/46—Apparatus for electrochemical processes
    • C02F2201/461—Electrolysis apparatus
    • C02F2201/46105—Details relating to the electrolytic devices
    • C02F2201/4612—Controlling or monitoring
    • C02F2201/4615—Time
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]

Definitions

  • the invention belongs to the technical field of waste water treatment process optimization and control, and in particular relates to a heavy metal waste water purification control method based on dynamic matching of operation modes.
  • Non-ferrous metal industrial wastewater is my country's main source of heavy metal pollution, which poses a major threat to environmental protection and human life safety. Therefore, my country has strict requirements for the stable discharge of heavy metal wastewater.
  • Neutralization precipitation-electrochemical wastewater treatment process is an effective method for large-scale deep treatment of heavy metal wastewater.
  • the pretreatment effect of the neutralization and precipitation process can realize large-scale purification of wastewater and meet the needs of large-volume wastewater treatment in China's non-ferrous metal companies; the electrochemical process can achieve deep purification of heavy metals through electrode reaction and ion migration, and no need to add chemicals to avoid In addition to secondary pollution, it is a green water purification technology.
  • the neutralization precipitation process and the electrochemical process usually use the addition of chemicals and the change of voltage to achieve the removal of heavy metal ions. Therefore, the amount of added chemicals and the set voltage are the most critical control parameters of the wastewater treatment process. Its control accuracy not only directly affects whether the export wastewater can be stably discharged, but also is closely related to the economic benefits of the enterprise. Too much dosing, too high voltage regulation, resulting in waste of chemicals and electrical energy, increasing the production cost of the wastewater treatment process; too little dosing, too low voltage, which will cause the heavy metal ions in the export wastewater to fail to meet the standard .
  • the purpose of the present invention is to provide a heavy metal waste water purification control method based on dynamic matching of operation modes, which is used to determine more reasonable and accurate dosing amount and voltage value in the process of heavy metal waste water treatment, to avoid high energy consumption caused by manual addition and adjustment , High material consumption, and the problem that the treated wastewater cannot reach the standard stably.
  • the invention provides a heavy metal wastewater purification control method based on dynamic matching of operation modes, including the following steps:
  • the model of the relationship between the concentration of heavy metal ions and the amount of drug added is used to represent the relationship between the concentration of heavy metal ions at the outlet of the precipitation reaction tank and the amount of drug added;
  • the model of the relationship between the concentration of heavy metal ions and the voltage of the cell is used to represent the electrolysis in the electrolytic cell The relationship between the concentration of heavy metal ions at the outlet of the tank and the voltage of the tank;
  • the purification process of the heavy metal wastewater is a precipitation reaction followed by an electrochemical reaction. If the concentration of the heavy metal ion at the outlet reaches the standard, the concentration of the heavy metal ion at the outlet of the electrolytic cell is less than or equal to the preset upper limit of the concentration of the dischargeable heavy metal ion; the power consumption Represents the electrical energy consumed by the electrochemical reaction in the electrolytic cell, which is related to the voltage of the cell;
  • S3 Define input conditions and operating parameters and build an operating mode knowledge base based on the operating parameters corresponding to each input condition in historical data;
  • the input conditions include the wastewater flow in the neutralization precipitation process, the concentration of heavy metal ions at the entrance of the precipitation reaction tank, and the operating time of the electrolytic cell;
  • the operating parameters include the coordinated value of the dosing amount and the coordinated value of the tank voltage, the coordinated value of the dosing amount, the tank
  • the voltage coordination values are respectively: the difference between the actual operating values of the dosing amount and tank voltage in the historical data under the same input condition and the optimized values of the dosing amount and tank voltage obtained by using the multi-objective coordinated optimization model;
  • An input condition and its operating parameters constitute an operating mode, and the knowledge base of the operating mode includes the optimal operating mode under various input conditions.
  • the optimal operating mode is the coordinated value of the dosing amount and the tank voltage under the same input condition.
  • step S4 Obtain on-site real-time input conditions, and substitute the real-time input conditions into the multi-objective coordinated optimization model described in step S2 to obtain the optimized values of the corresponding dosage and tank voltage under the real-time input conditions;
  • the operation parameters matched with the real-time input conditions are obtained according to the operation modes in the real-time input condition matching operation mode knowledge base, and then the optimized values of the dosage and tank voltage under the real-time input conditions are adjusted according to the obtained operation parameters to obtain the dosage 3.
  • the similarity of the optimal operating mode under the real-time input condition and the various input conditions in the operating mode knowledge base is calculated in sequence, and the operating parameter of the optimal operating mode with the smallest similarity is selected as the operating parameter matching the real-time input condition;
  • the optimized value of the dosing amount and tank voltage under the real-time input condition will be used as the control value of the dosing amount and tank voltage to perform control adjustment.
  • the present invention Based on the mechanism model of neutralizing the precipitation process and the electrochemical process, the present invention builds a relationship model of heavy metal ion concentration, dosing amount, and tank voltage, derives its theoretical relationship model from the reaction mechanism, and then based on the historical data for the relationship The parameters of the model are identified to obtain a complete relationship model; then based on the actual demand, the dosage and power consumption are used as optimization goals to build a multi-objective coordinated optimization model, and based on this multi-objective coordinated optimization model, the dosing under real-time input conditions can be obtained
  • the optimized values of volume and tank voltage solve the problem of waste of medicine and power consumption caused by manual experience.
  • the present invention further builds an operating mode knowledge base based on historical data and optimized values, and proposes a method of dynamic matching of operating modes to obtain the coordinated values of the dosing amount and tank voltage under real-time input conditions and adjust the optimized values to solve the problem due to the entry conditions
  • the fluctuations in export heavy metal ions caused by fluctuations are large, and the quality of export wastewater is unstable. This is because there may be large fluctuations in the operating conditions of the historical data used to build the relationship model and parameter identification and the current real-time operating conditions, so the obtained optimization values under the current real-time input conditions need to be further adjusted to be more current operating conditions. Consistent with this, the operating mode knowledge base constructed by the historical data and the optimized value in the present invention can effectively reduce the influence caused by the fluctuation of working conditions.
  • the relationship model between the concentration of heavy metal ions and the amount of drug added in step S1 is constructed based on the principle of adsorption kinetics and material balance in the neutralization precipitation process, as follows:
  • V 1 is the volume of the precipitation reaction tank, To change the concentration of heavy metal ions in the precipitation reaction tank, It is the concentration of heavy metal ions M at the entrance of the precipitation reaction tank; It is the concentration of heavy metal ions M at the outlet of the precipitation reaction tank; Q 1 is the flow rate of the wastewater in the neutralization precipitation process; G is the dosage of the drug; k 1 and p are the identification parameters of the neutralization precipitation process.
  • the relationship model between the heavy metal ion concentration and the cell voltage is constructed according to Faraday's law and the principle of material balance, as follows:
  • V 2 is the volume of the electrolytic cell
  • Is the M concentration of heavy metal ions at the entrance of the electrolytic cell
  • Q 2 is the flow rate of the wastewater in the electrochemical process
  • i is the current density of the cell
  • q max is the adsorption capacity of 1 mole of iron hydroxide
  • K L is the Langmuir constant
  • S is the electrode plate area
  • z is the charge transfer number
  • F is the Faraday constant
  • U the cell voltage
  • d the plate spacing
  • ⁇ is the conductivity
  • k 2 , k 3 , and k 4 are all identification parameters of the electrochemical process.
  • the relationship model is identified by least squares method to obtain k 1 and p, k 2 , k 3 , k 4 , due to the least squares method It is a common technical method for parameter identification, so its implementation process is not described in detail. It uses the smallest error between the model output value and the true value to optimize the identification parameter to be solved in the present invention, that is, the outlet heavy metal ion concentration calculated using the above formula and The true ion concentration error in historical data is the smallest.
  • the multi-objective coordination optimization model is as follows:
  • J 1 and J 2 denote the objective function values respectively denote the objective function
  • E is the power consumption
  • n is the number of electrolysis cells
  • t is the electrolysis time
  • I is the cell current
  • ⁇ 1 and ⁇ 2 are the neutralization and precipitation respectively Removal efficiency of heavy metals in processes and electrochemical processes, It is the upper limit of the concentration of heavy metal ions that can be discharged.
  • the similarity calculation formula of the optimal operation mode under the real-time input condition in step S5 and a type of input condition in the operation mode knowledge base is as follows:
  • ⁇ ( ⁇ , ⁇ t ) represents the similarity between the optimal operation mode under real-time input conditions and a class of input conditions in the operation mode knowledge base
  • ⁇ j and ⁇ t, j represent a class in the operation mode knowledge base
  • j 1, 2, 3, which respectively correspond to the flow rate of the neutralization precipitation process in the input conditions, the concentration of heavy metal ions at the entrance of the precipitation reaction tank, and the running time of the electrolytic cell.
  • the criteria for judging whether operation matching is required according to the real-time input conditions in step S5 are as follows:
  • the operation mode is performed match;
  • the operation mode matching is performed
  • Whether the operation matching needs to be judged is based on the comparison between the current real-time operating conditions and the historical data conditions during the identification of the relational model parameters in step S1. If the current real-time operating conditions change significantly compared to the relational model construction conditions, the operation mode is required. match. In addition, the preset duration corresponding to the running time of the groove removal is determined according to the size and material of the electrode plate, which is an empirical value.
  • the preset threshold corresponding to the relative error is 5%.
  • the optimized values of the dosing amount and tank voltage under the real-time input conditions are adjusted to obtain the control values of the dosing amount and tank voltage as follows:
  • the control value of the dosage is equal to the sum of the coordination value of the dosage and the optimization value of the dosage in the operating parameters
  • the control value of the tank voltage is equal to the sum of the coordination value of the tank voltage and the optimized value of the tank voltage in the operating parameters.
  • control value is equal to the sum of the coordination value and the optimization value.
  • the optimized value of the dosing amount and the tank voltage is obtained by using a state transition algorithm to solve the multi-objective coordinated optimization model.
  • the invention provides a heavy metal wastewater purification control method based on dynamic matching of operation mode.
  • the method is based on the constructed mechanism model of neutralization precipitation process and electrochemical process, and quantitatively considers the concentration of heavy metal ions at the outlet and the dosage
  • a relationship model is constructed based on the correspondence between voltages; then a multi-objective coordinated optimization model is constructed for the optimization target based on the minimum dose and power consumption. Therefore, the present invention solves the multi-objective coordinated optimization model calculation based on field data and state transfer algorithm
  • the optimized value of dosing amount and tank voltage is solved, which solves the problem of waste of medicine consumption and power consumption caused by manual experience operation.
  • an operating mode knowledge base was constructed, and a method of dynamic matching of operating modes was proposed.
  • the coordinated values of dosing amount and tank voltage under real-time field data were obtained, and then the optimized values were adjusted based on the coordinated values to obtain
  • the control value of the working conditions is more consistent, which solves the problems of large fluctuations in the export heavy metal ions caused by fluctuations in the inlet working conditions and unstable quality of the export wastewater.
  • the fluctuation of the concentration of heavy metal ions in the outlet can be reduced, the average dosage and power consumption are saved, the quality of the export wastewater is stabilized, and the consumption of medicine is reduced And power consumption goals.
  • the invention deeply analyzes the principles of adsorption reaction and electrochemical reaction, combines on-site data, coordinates two processes to remove heavy metal ions in wastewater, is suitable for the control of the dosage and voltage of the deep purification process of heavy metal wastewater, and stabilizes the export of heavy metals in the wastewater treatment process Ion concentration, improving the quality of dischargeable wastewater and reducing costs are of great significance.
  • FIG. 1 is a flowchart of a method for dynamically purifying a heavy metal wastewater purification control method based on an operation mode provided by the present invention
  • FIG. 2 is a comparison diagram of the change in the concentration of lead ions at the outlet under the manual operation provided by the present invention and the method described in the present invention.
  • a method for purifying and controlling heavy metal wastewater based on operation mode dynamic matching includes the following steps:
  • the model of the relationship between the concentration of heavy metal ions and the amount of drug added is based on the principle of adsorption kinetics and material balance in the neutralization precipitation process, as follows:
  • V 1 is the volume of the precipitation reaction tank
  • Is the rate of change of the concentration of heavy metal ions in the precipitation reaction tank, It is the concentration of heavy metal ions M at the entrance of the precipitation reaction tank; It is the concentration of heavy metal ions M at the outlet of the precipitation reaction tank;
  • Q 1 is the flow rate of the wastewater in the neutralization precipitation process;
  • G is the dosage of the drug;
  • k 1 and p are the identification parameters of the neutralization precipitation process.
  • V 2 is the volume of the electrolytic cell
  • Is the rate of change of the concentration of heavy metal ions in the electrolytic cell Is the M concentration of heavy metal ions at the entrance of the electrolytic cell
  • Is the concentration of heavy metal ions at the outlet of the electrolytic cell Q 2 is the flow rate of the wastewater in the electrochemical process
  • i is the current density of the cell
  • q max is the adsorption capacity of 1 mole of iron hydroxide
  • K L is the Langmuir constant
  • S is the electrode plate area
  • z is the charge transfer number
  • F is the Faraday constant
  • U is the cell voltage
  • d is the plate spacing
  • ⁇ is the conductivity
  • k 2 , k 3 , and k 4 are all identification parameters of the electrochemical process.
  • the heavy metal ion M in the above formula does not specifically refer to a certain type of heavy metal ion, but is only used to represent heavy metal ions in wastewater, and can be used to represent any type of heavy metal ion.
  • the rate of change of the concentration of heavy metal ions in the precipitation reaction tank and the rate of change of the concentration of heavy metal ions in the electrolytic cell Both represent the change of ion concentration with time, which are respectively related to the concentration of heavy metal ions at the outlet of the electrolytic cell Heavy metal ion concentration at the outlet of the precipitation reaction tank Related.
  • k 1 and p, k 2 , k 3 , and k 4 all use the historical data collected on the spot to identify the parameters using the least squares method, obtain the relevant parameter values, and substitute into formula 1 and formula 2 to obtain the uniquely determined relationship model.
  • many sets of historical data collected on site are used for analysis. Therefore, for example, the concentration of heavy metal ions at the entrance of the precipitation reaction tank, and the flow rate of wastewater in the neutralization and precipitation process exist in many types, but all There will be an upper and lower limit. In the following, the upper and lower limits will be used to identify whether the real-time operating conditions are similar to the operating conditions when identifying the relationship model parameters.
  • S2 Set the minimum dosing amount and power consumption and meet the export heavy metal ion concentration as the optimization goal and build a multi-objective coordinated optimization model.
  • the multi-objective coordination optimization model is as follows:
  • J 1 and J 2 respectively represent the objective function value
  • E is the power consumption
  • n is the number of electrolysis cells
  • t is the electrolysis time
  • I is the cell current
  • ⁇ 1 and ⁇ 2 are respectively the neutralization precipitation process and electrochemistry
  • the heavy metal removal efficiency of the process It is the upper limit of the concentration of heavy metal ions that can be discharged.
  • a state transition algorithm is preferably used to solve a multi-objective coordinated optimization model to obtain the optimized values of dosing amount and tank voltage.
  • the optimization iteration process in this embodiment is briefly described as follows:
  • the iteration termination condition is a preset number of iterations; in other feasible embodiments, the iteration termination condition may also be to determine whether the current solution Gx, Ux will continue to decline, if there is no decline or the difference of the decline is less than the set value (The decline is not obvious), it is considered that the iteration termination condition is reached.
  • S3.2 Use the historical data collected on the spot to calculate the optimized values of the dosing amount and tank voltage of the multi-objective coordinated optimization model under various input conditions, and then calculate the operating parameters under various input conditions.
  • the operating parameters include the dosing amount coordination Value and coordination value of tank voltage;
  • the coordinated value of the dosing amount the actual operating value of the dosing amount-the optimized value of the dosing amount under the same input condition;
  • the optimized values of the dosing amount and the tank voltage are all calculated iteratively using the above state transition algorithm.
  • the operating mode knowledge base constructed by the present invention is based on data collected during the running process and according to the constructed multi-objective coordination optimization model.
  • step S4 Obtain on-site real-time input conditions, and substitute the real-time input conditions into the multi-objective coordinated optimization model described in step S2 to obtain the optimized values of the dosage and tank voltage under the real-time input conditions.
  • a state transition algorithm is used to calculate the optimized values of the dosage and tank voltage under the current real-time input conditions.
  • the operation mode is performed match;
  • the operation mode matching is performed
  • the operation parameters matching the real-time input conditions are obtained according to the operation modes in the real-time input condition matching operation mode knowledge base, and then the optimization of the dosage and the tank voltage under the real-time input conditions are adjusted according to the obtained operation parameters
  • the value obtains the control value of dosing amount and tank voltage and performs control adjustment.
  • the similarity between the optimal operation mode of the real-time input condition and the various input conditions in the operation mode knowledge base is calculated in sequence, and the operation parameter of the optimal operation mode with the smallest similarity is selected as the operation of the real-time input condition matching
  • the parameters are the coordinated value of the dosing amount and the coordinated value of the tank voltage.
  • the formula for calculating the similarity between the optimal operating mode under the real-time input condition and a type of input condition in the operating mode knowledge base is as follows:
  • ⁇ ( ⁇ , ⁇ t ) represents the similarity between the optimal operation mode under real-time input conditions and a class of input conditions in the operation mode knowledge base
  • ⁇ j and ⁇ t, j represent a class in the operation mode knowledge base
  • the coordinated value of the dosing amount and the optimized value of the dosing amount are summed as the control value of the dosing amount, and the coordinated value of the tank voltage and the tank voltage are optimized The sum of the values is used as the control value of the tank voltage. Then according to the control value of the dosage and tank voltage, real-time control and adjustment.
  • the optimized value of the dosing amount and tank voltage under the real-time input condition is selected as the control value of the dosing amount and tank voltage, and real-time control adjustment is performed.
  • the present invention provides the following examples to verify the effectiveness of the method of the present invention.
  • the first step is to establish the model of the relationship between the concentration of lead ions and the dosage and current density of the neutralization precipitation process and the electrochemical process, and use the data collected on site to identify the parameters of the built model;
  • the second step is to build the dosage-oriented And a multi-objective coordination optimization model for the wastewater treatment process with the smallest power consumption.
  • the third step is to constitute the operating mode of the heavy metal wastewater treatment process, collect on-site data, and build an operating mode knowledge base.
  • the fourth step is to collect one month of operating data for experiments, during which the state transfer algorithm is used to quickly solve the optimization
  • the model obtains the optimal value of the optimal dosage under real-time conditions in real time Optimized value with voltage

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Abstract

一种基于操作模式动态匹配的重金属废水净化控制方法，包括：构建中和沉淀过程、电化学过程中重金属离子浓度与加药量、槽电压的关系模型；以加药量和电耗最小为目标构建多目标协调优化模型；构建操作模式知识库；以及利用目标协调优化模型得到实时输入条件下加药量、槽电压的优化值；并判断是否需要进行操作匹配；若需要匹配，则匹配操作模式知识库中的操作模式获取加药量、槽电压协调值并调节优化值得到加药量和槽电压的控制值，若不需要匹配，将加药量、槽电压的优化值作为控制值并进行控制调节。通过该方法可以更合理和准确的加药量与电压值，避免人工添加而造成的高能耗、高物耗。

Description

一种基于操作模式动态匹配的重金属废水净化控制方法 技术领域
本发明属于废水处理过程优化与控制技术领域，具体涉及一种基于操作模式动态匹配的重金属废水净化控制方法。
背景技术
有色金属工业废水是我国主要的重金属污染源，对环境保护与人类生命安全造成重大威胁。因此，我国对重金属废水的稳定达标排放有着严格的要求。中和沉淀-电化学废水处理过程是一种大规模深度处理重金属废水的有效方法。中和沉淀过程的预处理效果可实现废水的大规模净化，满足我国有色金属企业大流量废水的处理需求；电化学过程通过电极反应和离子迁移可实现重金属的深度净化，且无需添加药剂，避免了二次污染，是一种绿色净水技术。
中和沉淀过程和电化学过程通常利用添加药剂和改变电压来实现重金属离子的去除。因此，药剂的添加量和电压设定量是废水处理过程的最为关键的控制参数，其控制精准度不仅直接影响着出口废水能否稳定地达标排放，还与企业的经济效益息息相关。加药量过多，电压调节过高，造成药剂和电能的浪费，增大了废水处理过程的生产成本；而加药量过少，电压过低，则造成出口废水中的重金属离子无法稳定达标。
重金属废水实际处理过程中，操作人员根据入口流量和浓度凭经验调节加药量和电压。但废水来源广、水质水量波动大而造成工况波动大，且吸附反应与电化学反应机理复杂且关联耦合，造成操作人员难以及时正确地确定药剂量与电压值，无法实现两种工序协调配合完成重金属的稳定去除。不合理的药剂添加和电压设定，使得出口重金属离子波动较大，甚至无法及时排放需二次处理，造成了资源和人工的浪费。
因此，分析出口重金属离子浓度与加药量和电压的关系，研究药耗与电耗的优化方法，且在工况波动下实时确定最优加药量与电压设定值，对于废水稳定达标排放，降低企业资源浪费具有极其重要的意义。
发明内容
本发明的目的是提供一种基于操作模式动态匹配的重金属废水净化控制方法，用于确定重金属废水处理过程中更合理和准确的加药量与电压值，避免人工添加、调节而造成的高能耗、高物耗，以及处理后的废水不能稳定达标的问题。
本发明提供的一种基于操作模式动态匹配的重金属废水净化控制方法，包括如下步骤：
S1：分别构建中和沉淀过程中重金属离子浓度与加药量、电化学过程中重金属离子浓度与槽电压的关系模型，并基于历史数据进行关系模型的参数辨识；
所述重金属离子浓度与加药量的关系模型用于表示沉淀反应池中沉淀反应池出口重金属离子浓度与加药量的关系；所述重金属离子浓度与槽电压关系模型用于表示电解槽中电解槽出口重金属离子浓度与槽电压的关系；
S2：将加药量和电耗最小以及出口重金属离子浓度达标设定为优化目标并构建多目标协调优化模型；
其中，重金属废水净化过程为先进行沉淀反应后进行电化学反应，所述出口重金属离子浓度达标表示电解槽出口重金属离子浓度小于或等于预设的可排放重金属离子浓度上限值；所述电耗表示在电解槽内电化学反应消耗的电能，所述电耗与槽电压相关；
S3：定义输入条件和操作参数并基于历史数据中各个输入条件对应的操作参数构建操作模式知识库；
其中，输入条件包括中和沉淀过程废水流量，沉淀反应池入口重金属离子浓度以及电解槽运行时间；所述操作参数包括加药量协调值和槽电压协调值，所述加药量协调值、槽电压协调值分别为：同一输入条件下历史数据中加药量、槽电压的实际操作值与利用所述多目标协调优化模型得到的加药量、槽电压的优化值之差；
一个输入条件及其操作参数构成一个操作模式，所述操作模式知识库包括各类输入条件下的最优操作模式，所述最优操作模式为同一输入条件下加药量协调值和槽电压协调值最小的操作模式；
S4：获取现场实时输入条件，并将实时输入条件代入步骤S2中所述多目标协调优化模型得到实时输入条件下对应加药量、槽电压的优化值；
S5：根据实时输入条件判断是否需要进行操作匹配；
若需要，则根据实时输入条件匹配操作模式知识库中的操作模式获取实时输入条件匹配的操作参数，再依据获取的操作参数调节实时输入条件下加药量、槽电压的优化值得到加药量、槽电压的控制值并进行控制调节；
其中，依次计算实时输入条件下与操作模式知识库中各类输入条件的最优操作模式的相似度，并选取相似度最小的最优操作模式的操作参数作为实时输入条件匹配的操作参数；
若不需要，将实时输入条件下的加药量、槽电压的优化值作为加药量、槽电压的控制值并进行控制调节。
本发明以中和沉淀过程与电化学过程的机理模型为基础构建了重金属离子浓度与加药量、槽电压的关系模型，从反应机理推导了其理论关系模型，再基于运行的历史数据对关系模型的参数进行辨识得到完整的关系模型；再基于实际需求将加药量和电耗作为优化目标构建了多目标协调优化模型，而基于此多目标协调优化模型可以得到实时输入条件下的加药量、槽 电压的优化值，解决了因人工经验操作造成药耗和电耗浪费的问题。本发明进一步的基于历史数据和优化值构建了操作模式知识库，提出了操作模式动态匹配的方法得到实时输入条件下加药量、槽电压的协调值并调节优化值，解决了因入口工况波动造成的出口重金属离子波动较大，出口废水品质不稳定等问题。这是由于构建关系模型并进行参数辨识的使用的历史数据的工况与当前实时工况可能存在较大波动，因此得到的当前实时输入条件下的优化值还需要进一步调整才与当前工况更相符，本发明通过历史数据与优化值构建的操作模式知识库可以有效地降低工况波动而带来的影响。
进一步优选，步骤S1中所述重金属离子浓度与加药量的关系模型是根据中和沉淀过程吸附动力学原理与物料平衡原理构建，如下：
Figure PCTCN2019072269-appb-000001
式中，V 1为沉淀反应池体积，
Figure PCTCN2019072269-appb-000002
为沉淀反应池中重金属离子浓度的变化，
Figure PCTCN2019072269-appb-000003
为沉淀反应池入口重金属离子M浓度；
Figure PCTCN2019072269-appb-000004
为沉淀反应池出口重金属离子M浓度；Q 1为中和沉淀过程废水流量；G为加药量；k 1和p为中和沉淀过程的辨识参数。
在流程工业中物料平衡原理为：物质的变化量＝物质的流入量-物质的流出量-物质的反应量；吸附动力学原理来描述物质的反应量，譬如采用弗罗因德利希方程来进行表示，其弗罗因德利希方程即为单位量的药剂所吸附重金属的量(即反应掉的重金属量)与重金属平衡状态的浓度存在以下关系式：
Figure PCTCN2019072269-appb-000005
则添加G量的药剂，反应掉重金属离子量为
Figure PCTCN2019072269-appb-000006
基于该原理本发明构建出上述关系模型。
进一步优选，所述重金属离子浓度与槽电压的关系模型是根据法拉第定律与物料平衡原理构建，如下：
Figure PCTCN2019072269-appb-000007
式中，V 2为电解槽体积，
Figure PCTCN2019072269-appb-000008
为电解槽中重金属离子浓度的变化，
Figure PCTCN2019072269-appb-000009
为电解槽入口重金属离子M浓度，
Figure PCTCN2019072269-appb-000010
为电解槽出口重金属离子M浓度，Q 2为电化学过程废水流量，i为槽电流密度，q max为1摩尔氢氧化铁的吸附能力，K L为朗缪尔常数，S为电极板面积，z为电荷转移数，F为法拉第常数，U是槽电压，d是极板间距，σ是电导率，k 2，k 3，k 4均为电化学过程的辨识参数。
在流程工业中物料平衡原理为：物质的变化量＝物质的流入量-物质的流出量-物质的反应 量；法拉第电解定律为：在电极界面上发生化学变化物质的质量与通入的电量成正比。因此将其两者进行结合通过数学推理得到上述关系模型。
利用现场采集的历史数据(流量、重金属离子浓度、加药量、槽电压等)对关系模型采用最小二乘法进行参数辨识得到k 1和p、k 2，k 3，k 4，由于最小二乘法是参数辨识时常用技术手段，因此对其实现过程不进行具体描述，其在本发明用模型输出值与真实值误差最小来优化待求解的辨识参数，即利用上述公式计算的出口重金属离子浓度与历史数据中真实离子浓度误差最小。
进一步优选，所述多目标协调优化模型如下所示：
minJ 1＝minG
minJ 2＝minE
Figure PCTCN2019072269-appb-000011
式中，J 1、J 2分别表示目标函数值分别表示目标函数，E是电耗；n是电解槽个数，t是电解时间，I是槽电流，θ 1、θ 2分别为中和沉淀过程、电化学过程的重金属去除效率，
Figure PCTCN2019072269-appb-000012
为可排放重金属离子浓度上限。
进一步优选，步骤S5中实时输入条件下与操作模式知识库中一类输入条件的最优操作模式的相似度计算公式如下：
Figure PCTCN2019072269-appb-000013
式中，δ(ψ,ψ t)表示实时输入条件下与操作模式知识库中一类输入条件的最优操作模式的相似度，ω j和ω t,j分别表示操作模式知识库中一类输入条件中、实时输入条件中的中和沉淀过程废水流量或沉淀反应池入口重金属离子浓度或电解槽运行时间。
j＝1、2、3，其分别对应输入条件中中和沉淀过程废水流量、沉淀反应池入口重金属离子浓度、电解槽运行时间。
进一步优选，步骤S5中根据实时输入条件判断是否需要进行操作匹配的标准如下：
若实时输入条件内中和沉淀过程废水流量大于步骤S1关系模型参数辨识时历史数据内 中和沉淀过程废水流量的上限或小于下限，且相对误差大于预设阈值，则进行操作模式匹配；
或者，若实时输入条件内沉淀反应池入口重金属离子浓度大于步骤S1关系模型参数辨识时历史数据内沉淀反应池入口重金属离子浓度的上限或小于下限，且相对误差大于预设阈值，则进行操作模式匹配；
或者，若实时输入条件内电解槽运行时间大于预设时长，则进行操作模式匹配；
若均不满足，则不需要进行操作模式匹配。
是否需要进行操作匹配的判断基于当前实时工况与步骤S1中关系模型参数辨识时历史数据的工况相比较，当前实时工况相较于关系模型构建的工况变动大，则需要进行操作模式匹配。此外，解槽运行时间对应的预设时长是根据极板大小和材料确定的，其为经验值。
进一步优选，所述相对误差对应的预设阈值为5％。
依据获取的操作参数调节实时输入条件下加药量、槽电压的优化值得到加药量、槽电压的控制值规律为：
加药量的控制值等于操作参数中加药量协调值与加药量优化值之和；
槽电压的控制值等于操作参数中槽电压协调值与槽电压优化值之和。
本发明优选若进行了操作模式匹配，控制值等于协调值与优化值之和。
进一步优选，加药量和槽电压的优化值的获取方式为：采用状态转移算法求解所述多目标协调优化模型。
有益效果
本发明提供了一种基于操作模式动态匹配的重金属废水净化控制方法，该方法以构建的中和沉淀过程与电化学过程的机理模型为基础，定量的考虑了出口重金属离子浓度与加药量和电压之间的对应关系而构建出关系模型；再基于加药量和电耗最小为优化目标构建出多目标协调优化模型，因此，本发明基于现场数据和状态转移算法求解多目标协调优化模型计算出加药量与槽电压的优化值，解决了因人工经验操作造成药耗和电耗浪费的问题。进一步又基于历史数据和优化值构建了操作模式知识库，提出了操作模式动态匹配的方法，得到了实时现场数据下加药量和槽电压协调值，再基于协调值来调整优化值得到与实时工况更加吻合的控制值，解决了因入口工况波动造成的出口重金属离子波动较大，出口废水品质不稳定等问题。
利用本发明所述方法，在同等工况条件下，相对于人工添加方法，可以使出口重金属离子浓度波动减小，节约了平均加药量和电耗，达到了稳定出口废水质量，降低药耗和电耗的目标。
本发明深入分析吸附反应和电化学反应原理，结合现场数据，协调两种工序去除废水中的重金属离子，适宜于重金属废水深度净化过程的加药量与电压的控制，对废水处理过程稳定出口重金属离子浓度，提高可排废水质量，降低成本具有重要意义。
附图说明
图1是本发明提供的一种基于操作模式动态匹配的重金属废水净化控制方法的流程图；
图2是本发明提供的人工操作下以及采用本发明所述方法下出口处铅离子浓度变化对比图。
具体实施方式
下面将结合实施例对本发明做进一步的说明。
如图1所示，本发明提供的一种基于操作模式动态匹配的重金属废水净化控制方法，包括如下步骤：
S1：分别构建中和沉淀过程中重金属离子浓度与加药量、电化学过程中重金属离子浓度与槽电压的关系模型，并基于现场历史数据进行关系模型的参数辨识。
重金属离子浓度与加药量的关系模型是根据中和沉淀过程吸附动力学原理与物料平衡原理构建，如下：
Figure PCTCN2019072269-appb-000014
式中，V 1为沉淀反应池体积，
Figure PCTCN2019072269-appb-000015
为沉淀反应池中重金属离子浓度的变化率，
Figure PCTCN2019072269-appb-000016
为沉淀反应池入口重金属离子M浓度；
Figure PCTCN2019072269-appb-000017
为沉淀反应池出口重金属离子M浓度；Q 1为中和沉淀过程废水流量；G为加药量；k 1和p为中和沉淀过程的辨识参数。
重金属离子浓度与槽电压的关系模型是根据法拉第定律与物料平衡原理构建，如下：
Figure PCTCN2019072269-appb-000018
式中，V 2为电解槽体积，
Figure PCTCN2019072269-appb-000019
为电解槽中重金属离子浓度的变化率，
Figure PCTCN2019072269-appb-000020
为电解槽入口重金属离子M浓度，
Figure PCTCN2019072269-appb-000021
为电解槽出口重金属离子M浓度，Q 2为电化学过程废水流量，i为槽电流密度，q max为1摩尔氢氧化铁的吸附能力，K L为朗缪尔常数，S为电极板面积，z为电荷转移数，F为法拉第常数，U是槽电压，d是极板间距，σ是电导率，k 2，k 3，k 4均为 电化学过程的辨识参数。
需要说明的是，上述公式中重金属离子M并非特指某一类重金属离子，而仅仅是用于表示废水中的重金属离子，可以用于表示任意一类重金属离子。
还需要说明的是，沉淀反应池中重金属离子浓度的变化率
Figure PCTCN2019072269-appb-000022
和电解槽中重金属离子浓度的变化率
Figure PCTCN2019072269-appb-000023
均是表示离子浓度随时间的变化，其分别与电解槽出口重金属离子浓度
Figure PCTCN2019072269-appb-000024
沉淀反应池出口重金属离子浓度
Figure PCTCN2019072269-appb-000025
相关。
本实施例中，k 1和p、k 2，k 3，k 4均是利用现场采集的历史数据采用最小二乘法进行参数辨识，得到相关参数值后代入公式1和公式2得到唯一确定的关系模型。需要说明的是，参数辨识时，是利用现场采集的诸多组历史数据进行分析得到，因此譬如沉淀反应池入口重金属离子浓度、中和沉淀过程废水流量等均是存在多类取值的，但是均会存在一个上限和下限值。下文将利用上限、下限值来鉴别实时工况与关系模型参数辨识时的工况是否相近。
S2：将加药量和电耗最小以及出口重金属离子浓度达标设定为优化目标并构建多目标协调优化模型。其多目标协调优化模型如下所示：
Figure PCTCN2019072269-appb-000026
式中，J 1、J 2分别表示目标函数值，E是电耗；n是电解槽个数，t是电解时间，I是槽电流，θ 1、θ 2分别为中和沉淀过程、电化学过程的重金属去除效率，
Figure PCTCN2019072269-appb-000027
为可排放重金属离子浓度上限。
本发明优选采用状态转移算法来求解多目标协调优化模型得到加药量、槽电压的优化值。本实施例中优化迭代过程简述如下：
随机生成加药量G与槽电压U的初始值G0，U0，将G0，U0作为输入值进行状态转移算法中算子的迭代运算得到新的G1，U1，比较G0与G1(即目标函数J1的比较)，E0与E1的大小(即目标函数J2的比较)，较小的G和较小的E对应的U保留作为当前解Gx，Ux， 如果还没有达到迭代终止条件，则以Gx，Ux，作为输入值进行操作算子的运算得到新的G2，U2，然后比较G2与Gx，E2与Ex，保留较小的G和较小的E对应的U保留作为当前解Gx，Ux，以此类推直到达到迭代终止条件。本实施例中迭代终止条件为预先设置的迭代次数；其他可行的实施例中，迭代终止条件还可以是判断当前解Gx，Ux是否会继续下降，若不下降了或者下降差值小于设定值(下降不明显)，则视为达到了迭代终止条件。
S3：构建操作模式知识库；具体过程如下：
S3.1：将中和沉淀过程废水流量Q 1，沉淀反应池入口重金属离子浓度C 0以及电解槽运行时间T定义为输入条件ω，即存在ω＝[Q 1,C 0,T]；
S3.2：利用现场采集的历史数据计算各类输入条件下多目标协调优化模型的加药量、槽电压的优化值，再计算各类输入条件下的操作参数，操作参数包括加药量协调值和槽电压协调值；
其中，加药量协调值＝同一输入条件下加药量实际操作值-加药量优化值；
槽电压协调值＝同一输入条件下槽电压实际操作值-槽电压优化值。
本实施例中加药量、槽电压的优化值均采用上述状态转移算法进行迭代计算得到。
S3.3：定义一个输入条件及其操作参数构成一个操作模式；
S3.4：将相同输入条件下所对应的不同操作参数进行综合评价，将加药量协调值和槽电压协调值均最小的操作模式作为该输入条件下的最优操作模式，并将各类输入条件下的最优操作模式集合构建操作模式知识库。
从上述可知，本发明的构建的操作模式知识库是基于运行过程采集的数据以及根据构建的多目标协调优化模型构建的。
S4：获取现场实时输入条件，并将实时输入条件代入步骤S2中所述多目标协调优化模型得到实时输入条件下加药量、槽电压的优化值。
同理，本实施例中采用状态转移算法计算出当前实时输入条件下加药量、槽电压的优化值。
S5：根据实时输入条件判断是否需要进行操作匹配；本实施例中，具体判断标准如下：
若实时输入条件内中和沉淀过程废水流量大于步骤S1关系模型参数辨识时历史数据内中和沉淀过程废水流量的上限或小于下限，且相对误差大于预设阈值，则进行操作模式匹配；
或者，若实时输入条件内沉淀反应池入口重金属离子浓度大于步骤S1关系模型参数辨识时历史数据内沉淀反应池入口重金属离子浓度的上限或小于下限，且相对误差大于预设阈值，则进行操作模式匹配；
或者，若实时输入条件内电解槽运行时间大于预设时长，则进行操作模式匹配；
若均不满足上述条件，则不需要进行操作模式匹配。
其中，若需要进行操作匹配，则根据实时输入条件匹配操作模式知识库中的操作模式获取实时输入条件匹配的操作参数，再依据获取的操作参数调节实时输入条件下加药量、槽电压的优化值得到加药量、槽电压的控制值并进行控制调节。
其中，按照如下公式依次计算实时输入条件下与操作模式知识库中各类输入条件的最优操作模式的相似度，并选取相似度最小的最优操作模式的操作参数作为实时输入条件匹配的操作参数，即得到加药量协调值和槽电压协调值。
实时输入条件下与操作模式知识库中一类输入条件的最优操作模式的相似度计算公式如下：
Figure PCTCN2019072269-appb-000028
式中，δ(ψ,ψ t)表示实时输入条件下与操作模式知识库中一类输入条件的最优操作模式的相似度，ω j和ω t,j分别表示操作模式知识库中一类输入条件中、实时输入条件中的中和沉淀过程废水流量或沉淀反应池入口重金属离子浓度或电解槽运行时间。
本实施例中，优选得到加药量协调值和槽电压协调值后，将加药量协调值与加药量优化值求和作为加药量的控制值，将槽电压协调值与槽电压优化值求和作为槽电压的控制值。然后再根据加药量和槽电压的控制值进行实时控制调节。
若不需要进行操作匹配，择将实时输入条件下的加药量、槽电压的优化值作为加药量、槽电压的控制值并进行实时控制调节。
基于上述方案，本发明提供如下实例来验证本发明所述方法的有效性。
某废水处理厂中和沉淀-电化学过程去除重金属铅为例说明本发明的优越性。第一步，建立中和沉淀过程、电化学过程铅离子浓度与加药量和电流密度的关系模型，并利用现场采集的数据对所建模型进行参数辨识；第二步，构建面向加药量和电耗最小的废水处理过程多目标协调优化模型。第三步，构成的重金属废水处理过程操作模式，采集现场数据，构建操作模式知识库。采集现场一个月实际过程人工添加药剂量和电耗，并采用状态转移算法快速求解优化模型获取到对应优化值；第四步，采集一个月的运行数据进行实验，期间采用状态转移算法快速求解优化模型实时获取实时工况条件下最优的加药量优化值
Figure PCTCN2019072269-appb-000029
与电压优化值
Figure PCTCN2019072269-appb-000030
而当现场工况变化剧烈时，即废水流量和入口重金属离子浓度有一种数据相对误差大于5％或电解槽运行时间大于7天，进行操作模式匹配，工况波动下人工经验操作和多目标协调操作模式匹配的对比结果如表1和图2所示。
其结果表明，本发明所提出的方法充分考虑了重金属废水处理过程加药量与电压值的优 化对工况波动下出口重金属离子稳定达标的重要性，为进一步的控制精度的提高有着重要意义。
表1
Figure PCTCN2019072269-appb-000031
需要强调的是，本发明所述的实例是说明性的，而不是限定性的，因此本发明不限于具体实施方式中所述的实例，凡是由本领域技术人员根据本发明的技术方案得出的其他实施方式，不脱离本发明宗旨和范围的，不论是修改还是替换，同样属于本发明的保护范围。

Claims (9)

  1. 一种基于操作模式动态匹配的重金属废水净化控制方法，其特征在于：包括如下步骤：
    S1：分别构建中和沉淀过程中重金属离子浓度与加药量、电化学过程中重金属离子浓度与槽电压的关系模型，并基于历史数据进行关系模型的参数辨识；
    所述重金属离子浓度与加药量的关系模型用于表示沉淀反应池中沉淀反应池出口重金属离子浓度与加药量的关系；所述重金属离子浓度与槽电压关系模型用于表示电解槽中电解槽出口重金属离子浓度与槽电压的关系；
    S2：将加药量和电耗最小以及出口重金属离子浓度达标设定为优化目标并构建多目标协调优化模型；
    其中，重金属废水净化过程为先进行沉淀反应后进行电化学反应，所述出口重金属离子浓度达标表示电解槽出口重金属离子浓度小于或等于预设的可排放重金属离子浓度上限值；所述电耗表示在电解槽内电化学反应消耗的电能，所述电耗与槽电压相关；
    S3：定义输入条件和操作参数并基于历史数据中各个输入条件对应的操作参数构建操作模式知识库；
    其中，输入条件包括中和沉淀过程废水流量，沉淀反应池入口重金属离子浓度以及电解槽运行时间；所述操作参数包括加药量协调值和槽电压协调值，所述加药量协调值、槽电压协调值分别为：同一输入条件下历史数据中加药量、槽电压的实际操作值与利用所述多目标协调优化模型得到的对应加药量、槽电压的优化值之差；
    一个输入条件及其操作参数构成一个操作模式，所述操作模式知识库包括各类输入条件下的最优操作模式，所述最优操作模式为同一输入条件下加药量协调值和槽电压协调值最小的操作模式；
    S4：获取现场实时输入条件，并将实时输入条件代入步骤S2中所述多目标协调优化模型得到实时输入条件下加药量、槽电压的优化值；
    S5：根据实时输入条件判断是否需要进行操作匹配；
    若需要，则根据实时输入条件匹配操作模式知识库中的操作模式获取实时输入条件匹配的操作参数，再依据获取的操作参数调节实时输入条件下加药量、槽电压的优化值得到加药量、槽电压的控制值并进行控制调节；
    其中，依次计算实时输入条件下与操作模式知识库中各类输入条件的最优操作模式的相似度，并选取相似度最小的最优操作模式的操作参数作为实时输入条件匹配的操作参数；
    若不需要，将实时输入条件下的加药量、槽电压的优化值作为加药量、槽电压的控制值并进行控制调节。
  2. 根据权利要求1所述的方法，其特征在于：步骤S1中所述重金属离子浓度与加药量的 关系模型是根据中和沉淀过程吸附动力学原理与物料平衡原理构建，如下：
    Figure PCTCN2019072269-appb-100001
    式中，V 1为沉淀反应池体积，
    Figure PCTCN2019072269-appb-100002
    为沉淀反应池中重金属离子浓度的变化，
    Figure PCTCN2019072269-appb-100003
    为沉淀反应池入口重金属离子M浓度；
    Figure PCTCN2019072269-appb-100004
    为沉淀反应池出口重金属离子M浓度；Q 1为中和沉淀过程废水流量；G为加药量；k 1和p为中和沉淀过程的辨识参数。
  3. 根据权利要求1所述的方法，其特征在于：所述重金属离子浓度与槽电压的关系模型是根据法拉第定律与物料平衡原理构建，如下：
    Figure PCTCN2019072269-appb-100005
    式中，V 2为电解槽体积，
    Figure PCTCN2019072269-appb-100006
    为电解槽中重金属离子浓度的变化，
    Figure PCTCN2019072269-appb-100007
    为电解槽入口重金属离子M浓度，
    Figure PCTCN2019072269-appb-100008
    为电解槽出口重金属离子M浓度，Q 2为电化学过程废水流量，i为槽电流密度，q max为1摩尔氢氧化铁的吸附能力，K L为朗缪尔常数，S为电极板面积，z为电荷转移数，F为法拉第常数，U是槽电压，d是极板间距，σ是电导率，k 2，k 3，k 4均为电化学过程的辨识参数。
  4. 根据权利要求1所述的方法，其特征在于：所述多目标协调优化模型如下所示：
    min J 1＝min G
    min J 2＝min E
    Figure PCTCN2019072269-appb-100009
    式中，J 1、J 2分别表示目标函数值，E是电耗；n是电解槽个数，t是电解时间，I是槽电流，θ 1、θ 2分别为中和沉淀过程、电化学过程的重金属去除效率，
    Figure PCTCN2019072269-appb-100010
    为可排放重金属离子浓度上限。
  5. 根据权利要求1所述的方法，其特征在于：步骤S5中实时输入条件下与操作模式知识库中一类输入条件的最优操作模式的相似度计算公式如下：
    Figure PCTCN2019072269-appb-100011
    式中，δ(ψ,ψ t)表示实时输入条件下与操作模式知识库中一类输入条件的最优操作模式的相似度，ω j和ω t,j分别表示操作模式知识库中一类输入条件中、实时输入条件中的中和沉淀过程废水流量或沉淀反应池入口重金属离子浓度或电解槽运行时间。
  6. 根据权利要求1所述的方法，其特征在于：步骤S5中根据实时输入条件判断是否需要进行操作匹配的标准如下：
    若实时输入条件内中和沉淀过程废水流量大于步骤S1关系模型参数辨识时历史数据内中和沉淀过程废水流量的上限或小于下限，且相对误差大于预设阈值，则进行操作模式匹配；
    或者，若实时输入条件内沉淀反应池入口重金属离子浓度大于步骤S1关系模型参数辨识时历史数据内沉淀反应池入口重金属离子浓度的上限或小于下限，且相对误差大于预设阈值，则进行操作模式匹配；
    或者，若实时输入条件内电解槽运行时间大于预设时长，则进行操作模式匹配；
    若均不满足，则不需要进行操作模式匹配。
  7. 根据权利要求6所述的方法，其特征在于：所述相对误差对应的预设阈值为5％。
  8. 根据权利要求1所述的方法，其特征在于：依据获取的操作参数调节实时输入条件下加药量、槽电压的优化值得到加药量、槽电压的控制值规律为：
    加药量的控制值等于操作参数中加药量协调值与加药量优化值之和；
    槽电压的控制值等于操作参数中槽电压协调值与槽电压优化值之和。
  9. 根据权利要求1所述的方法，其特征在于：加药量和槽电压的优化值的获取方式为：采用状态转移算法求解所述多目标协调优化模型。
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