Dynamic MOPSO-Based Optimal Control for Wastewater Treatment Process

To achieve excellent treatment performance of complex and time-varying characteristics, the operation of wastewater treatment process (WWTP) has been considered as a dynamic multiobjective control problem. In this paper, an optimal controller, based on a dynamic multiobjective particle swarm optimization (DMOPSO) algorithm, is developed to deal with the dynamic multiple conflicting criteria [i.e., effluent quality (EQ), operation cost, and operation stability]. The novelties and advantages of this proposed DMOPSO-based optimal controller (DMOPSO-OC) include the following two aspects. First, an integrated optimization framework, where the multiple objectives not only conflict with each other but also change over time, is able to catch more characteristics of WWTP than the existing works. Second, a DMOPSO algorithm, with an adaptive global best selection mechanism, is designed to solve the multiobjective optimization problem (MOP) for the proposed optimal controller, thus leading to a significant improvement of optimal synthesis for performance. Finally, the proposed DMOPSO-OC is tested in the benchmark simulation model No. 1 (BSM1) and implemented in a real WWTP to evaluate its effectiveness. The experimental results demonstrate that this proposed DMOPSO-OC can achieve a significant improvement in optimal control performance and obey the requirement of multiple conflicting criteria.