Research On Intelligent Optimization And Control Method Of Wastewater Treatment Process

In recent years,with the continuous advancement of my country’s green development strategy,various types of sewage treatment and discharge standards are particularly important,and the optimal control of the sewage treatment process provides an effective method for it.Considering that the sewage treatment process is an extremely complex nonlinear system,the sewage treatment plant needs to design an efficient and intelligent control system to achieve strict effluent quality standards and reduce energy consumption as much as possible under complex working conditions.Therefore,this paper deeply studies the intelligent optimization and control methods for the sewage treatment process.The main work and innovations of the paper are as follows:(1)Analyze the process flow and mechanism of the activated sludge process,and build the benchmark simulation model BSM1 for the sewage treatment process according to its mechanism.In the process of building the simulation model,the correctness of the simulation platform is first verified by static simulation,then the sewage treatment process is analyzed according to the open-loop control simulation results,and finally the closed-loop control simulation is improved.The indicators are analyzed to verify that closed-loop control is better than open-loop control,which can effectively reduce energy consumption and improve effluent quality.However,there is still much room for improvement in terms of balancing energy consumption,effluent quality,and removal efficiency of some pollutants.The sewage treatment process can be improved in terms of intelligent optimization and control methods.(2)Aiming at the problems of high energy consumption and substandard effluent quality in the sewage treatment process,a multi-objective optimal control method of sewage treatment process based on multi-strategy adaptive differential evolution algorithm is proposed.First of all,on the basis of the conventional tracking control structure,the tracking control of the dissolved oxygen concentration of the 3rd and 4th units is added.Then,a multi-strategy adaptive differential evolution algorithm is designed,which adopts multi-strategy fusion mutation and sorting optimization method,selects appropriate mutation strategy and better random individuals to guide population mutation,and adaptively updates crossover according to evolution process information Rate.Finally,the multi-objective optimization algorithm is combined with the PID controller to realize the dynamic optimization and tracking control of the set values of dissolved oxygen and nitrate nitrogen concentrations.Based on the international benchmark simulation platform BSM1,the results show that the proposed multiobjective optimal control method can effectively reduce the energy consumption of the sewage treatment process and improve the effluent quality.(3)There is a problem that the effluent ammonia nitrogen and total nitrogen concentrations exceed the standard for a long time in the multi-objective optimal control of the conventional sewage treatment process.While tracking and controlling the set values of dissolved oxygen and nitrate nitrogen concentrations obtained by the multi-objective optimization algorithm,this method introduces an over-standard suppression decision;uses Ada Boost-LSSVM to predict the effluent ammonia nitrogen and total nitrogen concentrations in real time,and according to the two In case of exceeding the standard,select an appropriate control strategy to avoid exceeding the standard.Based on the verification on the international benchmark simulation platform BSM1,the results show that the proposed control method can ensure that the effluent ammonia nitrogen and total nitrogen concentrations do not exceed the standard while taking into account the energy consumption and effluent quality of the sewage treatment process.