Research On The Optimal Control Of Wastewater Treatment Process Based On Hybrid Optimization Algorithm

Water is an extremely scarce resource in industrial production and social life,and the discharge of untreated wastewater into natural waters poses a serious threat to the ecological environment and human health.Most municipal wastewater treatment plants use biochemical methods to treat industrial and domestic wastewater,the most common of which is the degradation of pollutants through biochemical reactions in activated sludge.Activated sludge-based wastewater treatment processes(WWTPs)are typically large nonlinear systems with frequent and dramatic fluctuations in system inputs due to variations in influent flows and uncertainty in pollutant concentrations.In order to maintain the normal running of a city,WWTPs must operate continuously under stable conditions to ensure that effluent discharge meets standards.As reports of the negative effects of hazardous chemicals in wastewater on human health continue to increase and public awareness of environmental issues grows,standards and regulations related to wastewater discharge become more stringent.In this thesis,we design a metaheuristic optimization algorithm to model the wastewater treatment model and establish a multi-objective dynamic optimization control framework to optimize the wastewater treatment process.The main work of this thesis is as follows:(1)The current research status and dilemmas faced by metaheuristic optimization algorithms are described,the contributions of single-objective pity beetle algorithm(PBA)in theoretical innovation are analyzed,the shortcomings of such algorithms in terms of population diversity and rapidity of convergence are addressed.Further,a population partitioning strategy based on ranked grouping is proposed,and a new mathematical model is established to enhance the efficiency of PBA.The improved pity beetle algorithml(IPBA)enhances the optimization ability of the algorithm locally and globally through the pheromone dispersion mechanism and individual interaction relationship.(2)Considering that model parameter estimation optimization problems often contain complex objective functions,high dimensions and strict constraints,a parallel optimization strategy combining Legendre function network and dynamic partitioning strategy,called heterogeneous multi-population competition(HMC),is proposed for such high-dimensional multimodal optimization problems,and the HMC strategy is combined with IPBA to form a hybrid optimization algorithm(IPBA-HMC).(3)The ASM1 model is commonly used to simulate the wastewater treatment process in real world wastewater treatment plants to assess whether the effluent quality meets the discharge standards.In order to solve the problem that the parameter estimation of ASM1 model is difficult to be implemented in practice,this thesis first analyzes several uncertain parameters and complex biochemical reaction processes in activated sludge models 1(ASM1),conducts sensitivity tests and mechanism analysis on the chemometric and kinetic parameters in the model,and screens out the parameters with high sensitive parameters.Then the IPBA-HMC hybrid optimization algorithm was used as the optimizer to specify the parameter estimation method.(4)For the dynamic optimization control of wastewater treatment process in complex environment and to ensure the stability of wastewater treatment plant operation,a dynamic multi-objective optimization method based on static optimization method with periodic sampling is proposed.Firstly,the multi-objective metaheuristic optimization algorithm framework is used to extend the single-objective IPBA-HMC and design the multi-objective pity beetle algorithm(MOPBA).Secondly,considering environmental protection and socio-economic needs,this thesis establishes a bi-objective optimization model for wastewater treatment process based on self-organizing neural network,and designs a dynamic optimization control framework based on multi-objective optimization algorithm to track and control the key variables of wastewater treatment process.