Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation

This study investigates an adaptive fuzzy neural network control system for the multiobjective operation of wastewater treatment process (WWTP) with standard effluent quality (EQ) as well as low energy consumption (EC). The control system consists of an optimization module with the adaptive multiobjective differential evolution (AMODE) algorithm and a control module with the adaptive fuzzy neural network (AFNN). First, an AMODE algorithm, using the adaptive adjustment strategies for selecting the suitable scaling factor and crossover rate, is developed to optimize all objectives simultaneously. Then, the optimal set-points of the dissolved oxygen concentration in the fifth tank (SO5) and the nitrogen nitrate concentration in the second anoxic tank (SNO2) of WWTP can be obtained by the AMODE algorithm. Second, an AFNN controller, based on an adaptive second order algorithm, is employed to trace the set-points of SO5 and SNO2 for achieving the process performance. Finally, the proposed control system is applied on the Benchmark Simulation Model 1 (BSM1). The performance comparison with other algorithms indicates that the proposed control system yields better effluent qualities and lower average operation consumption.