Dynamic multi-objective intelligent optimal control toward wastewater treatment processes

Wastewater treatment plays a crucial role in alleviating water shortages and protecting the environment from pollution.Due to the strong time variabilities and complex nonlinearities within wastewater treatment systems,devising an efficient optimal controller to reduce energy consumption while ensuring effluent quality is still a bottleneck that needs to be addressed.In this paper,in order to comprehensively consider different needs of the wastewater treatment process(WTTP),a two-objective model is to consider a scope,in which minimizing energy consumption and guaranteeing effluent quality are both considered to improve wastewater treatment efficiency.To efficiently solve the model functions,a grid-based dynamic multi-objective evolutionary decomposition algorithm,namely GD-MOEA/D,is designed.A GD-MOEA/D-based intelligent optimal controller(GD-MOEA/D-IOC)is devised to achieve tracking control of the main operating variables of the WTTP.Finally,the benchmark simulation model No.1(BSM1)is applied to verify the validity of the proposed approach.The experimental results demonstrate that the constructed models can catch the dynamics of WWTP accurately.Moreover,GD-MOEA/D has better optimization ability in solving the designed models.GD-MOEA/D-IOC can achieve a significant improvement in terms of reducing energy consumption and improving effluent quality.Therefore,the designed multi-objective intelligent optimal control method for WWTP has great potential to be applied to practical engineering since it can easily achieve a highly intelligent control in WTTP.