Multiobjective design of fuzzy neural network controller for wastewater treatment process

In this paper, an improved multiobjective optimal control (MOOC) strategy is developed to improve the operational efficiency, satisfy the effluent quality (EQ) and reduce the energy consumption (EC) in wastewater treatment process (WWTP). First, the adaptive kernel function models of the process, which can describe the complex dynamics of EQand EC, are developed for the proposed MOOC strategy. Meanwhile, a multiobjective optimization problem is constituted to account for WWTP. Second, an improved multiobjective particle swarm optimization (MOPSO) algorithm, using a self-adaptive flight parameters mechanism and a multiobjective gradient (MOG) method, is designed to minimize the established objectives. And then the optimal set-points of dissolved oxygen (So) and nitrate (SNQ) are obtained in the treatment process. Third, an adaptive fuzzy neural network controller (FNNC) is applied for realizing the tracking control of the obtained set-points in the proposed MOOC strategy. Finally, Benchmark Simulation Model No.1 (BSM1) is introduced to evaluate the effectiveness of the proposed MOOC strategy. Experimental results show the efficacy of the proposed method. (C) 2018 Elsevier B.V. All rights reserved.