Multi-objective Optimization Control Method And Its Application In Urban Wastewater Treatment Process

Urban wastewater treatment processes(WWTPs)can reuse wastewater to make fresh water resources sustainable utilization.Then,the water environment can be protected maximally by recycling wastewater.WWTPs,one strategic initiative of the comprehensive water utilization in China,are important for the economic development of modern society and the protection of water resources.However,since the national discharge standards are increased year after year,the operation conditions of urban WWTPs are becoming seriously in our country.The main problems of WWTPs are high operation cost and excessive water quality.Therefore,the studies on the optimal control theories and technologies have been considered to be great significance for ensuring the safe and efficient operation of WWTPs.In this paper,the activated sludge process,with nonlinear dynamics and strong interactions,is taken as the research object.The water flow,components of wastewater and environment consitions have been considered as the main influent factors of WWTPs.To operate this process within optimal station,first,an adaptive multi-objective differential evolution algorithm based on the information of evolutionary process(AMODE-IEP)is proposed.Second,an intelligent optimization control method,based on the AMODE-IEP algorithm,is designed.Third,this intelligent optimization control method is verified on a real pilot platform.Finally,the multi-objective optimal control of dissolved oxygen and nitrate nitrogen in WWTPs has been realized,ensuring satisfy effluent water quality standards with reasonable economic expenses.The main research works and innovation points are as follows.(1)The AMODE-IEP algorithmMulti-objective differential evolution algorithm(MODE),with simple mechanism,few parameters and strong robustness,is widely used to solve the multi-objective optimization problems.However,the fixed values of parameters in MODE are difficult to meet the requirements,especially for the practical applications.In this paper,to improve the performance of MODE,the information of evolutionary process(IEP)is extracted to describe the state of the population evolution process.And an adaptive adjustment strategy,based on IEP,is developed to select the suitable scaling factor,crossover rate and population size in evolutionary process.Then,the proposed AMODE-IEP is able to balance the local search ability and global exploration ability to obtain a global optimal solution.Moreover,the convergence of AMODE-IEP is given in detail with probability theory.The performances indicate that this proposed AMODE-IEP yields better diversity and uniformity compared with other algorithms.(2)A multi-objective optimization control for WWTPsBased on the proposed AMODE-IEP,a multi-objective optimization control(MOOC)for WWTPs is proposed to satisfy the effluent requirements,and minimize the operating costs.In this MOOC,the combination of AMODE-IEP algorithm and PI controller is used to fulfill all the control objectives simultaneously.Meanwhile,the external environmental conditions and effluent quality requirements have been considered in the control process.The optimal set-points of the dissolved oxygen concentration and the nitrogen nitrate concentration in WWTPs are obtained by the AMODE-IEP algorithm.Then,the set-points are tracked by the PI controller through operating the oxygen transfer coefficient and the return flow.The results indicate that this proposed MOOC is able to obtain better effluent qualities and lower average operation consumption.(3)An intelligent multi-objective optimization control for WWTPsSince WWTPs are complex with nonlinear dynamics and strong interactions within the multivariable system,as well as the large disturbances in flow and load,it is difficult to design a suitable controller for WWTPs.In this study,an intelligent multi-objective optimization control(IMOOC)method,based on the characteristic modeling method,is proposed.In this IMOOC,the kernel function is used to established characteristic model for WWTPs.The AMODE-IEP algorithm is used to calculate the optimal set-points of dissolved oxygen concentration and nitrogen nitrate concentration.Finally,the optimal set-points are tracked by an adaptive fuzzy neural network controller.The experimental results show that this proposed IMOOC owns better adaptive ability and control effect.(4)An intelligent optimization control system for WWTPsIn fact,it is necessary to insert the proposed IMOOC into the control system to improve the control performance of real WWTPs.Therefore,an intelligent optimization control system,including the data acquisition,data transmission,data storage and data application,is developed for WWTPs.And the software is designed to survey and show data in this system.This intelligent optimization control system is used on a real pilot platform to test the control performance.The operation results demonstrate that intelligent optimization control system can improve the efficiency of wastewater treatment and save the energy consumption.The successful application results indicate that intelligent optimization control system can be used for the real WWTPs.