Collaborative Multimodal Optimization Control for Coupling Subprocesses in WWTP

Abstract

The optimization control technique has been extensively applied in wastewater treatment processes (WWTPs) to improve efficiency and reduce emissions during anaerobic and aerobic subprocesses. However, coupling between anaerobic and aerobic subprocesses creates an intertwined relationship between control variables and their optimization objectives, resulting in difficulties in quantifying the impact of control variables and reducing operation feasibility. To solve this problem, a collaborative multimodal optimization control (CMOC) method is proposed for coupling subprocesses in WWTPs. First, the collaborative objective mapping strategy is proposed to construct the multimodal optimization objectives of anaerobic and aerobic subprocesses. Then, the subprocess optimization objectives are mapped to the unified multimodal functions without weighting. Second, a multimodal particle swarm optimization with a attention mechanism is designed to obtain optimal states of subprocesses. Then, the particles are attracted by learning from the neighborhood best positions to improve multimodal optimization performance. Third, a heuristic-based model predictive control (H-MPC) is proposed to realize collaborative optimization control of anaerobic and aerobic subprocesses. Then, the subprocess optimization objectives are tradeoff in unison with optimal control based on heuristic functions in H-MPC. Finally, the proposed CMOC is applied to the Benchmark Simulation Model No. 1, and the experimental results demonstrate that the proposed method can effectively improve the optimization control performance of WWTPs .