Adaptive multi-task optimization strategy for wastewater treatment process

Abstract

It is challenging to introduce an optimization strategy for enhancing the operational performance of wastewater treatment process (WWTP) on account of its multi-task characteristics. To cope with this problem, an adaptive multi-task optimization (AMTO) strategy is proposed to realize the optimal operation of WWTP. First, a multi-task optimization (MTO) framework is developed to depict the multi-task characteristics of WWTP. Then, it is conducive to achieve the optimization of nitrogen removal and phosphorus removal processes simultaneously. Second, a data-driven MTO model, based on the relevant process data, is established to describe the characteristics of nitrogen removal task and phosphorus removal task. Then, a MTO problem is modeled for WWTP. Third, a multi-task particle swarm optimization algorithm, based on the adaptive knowledge transfer method, is developed to cope with the above MTO problem. Then, the optimal set-points can be obtained in WWTP. Finally, the effectiveness of the proposed AMTO strategy is verified by comparing with other optimization strategies. The results demonstrate that the proposed AMTO strategy can achieve multiple tasks optimization in parallel and the optimal operation of WWTP.