Reinforcement learning-based particle swarm optimization for sewage treatment control

Abstract To solve the problem of high-energy consumption in activated sludge wastewater treatment, a reinforcement learning-based particle swarm optimization (RLPSO) was proposed to optimize control setting sewage process. This algorithm tries take advantage valid history information guide behavior particles through learning strategy. First, an elite network is constructed by selecting and recording their successful search behavior. Then trained evaluated effectively predict velocity. In periodic treatment process, RLPSO runs repeatedly according optimized cycle. Finally, tested based on Benchmark Simulation Model 1 (BSM1) simulation results showed that it could reduce energy premise ensuring qualified water quality. Furthermore, performance analyzed using benchmarks with higher dimension, which verifies effectiveness provides possibility for be applied wider range problems.