Reinforcement learning-based particle swarm optimization for sewage treatment control

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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 the control setting in the sewage process. This algorithm tries to take advantage of the valid history information to guide the behavior of particles through a reinforcement learning strategy. First, an elite network is constructed by selecting elite particles and recording their successful search behavior. Then the network is trained and evaluated to effectively predict the particle velocity. In the periodic wastewater treatment process, the RLPSO runs repeatedly according to the optimized cycle. Finally, RLPSO was tested based on Benchmark Simulation Model 1 (BSM1) of sewage treatment, and the simulation results showed that it could effectively reduce the energy consumption on the premise of ensuring qualified water quality. Furthermore, the performance of RLPSO was analyzed using the benchmarks with higher dimension, which verifies the effectiveness of the algorithm and provides the possibility for RLPSO to be applied to a wider range of problems.
Funding Information
  • National Natural Science Foundation of China (Grant No. 62003306)
  • Natural Science Foundation of Zhejiang Province (LQ21F030009, LY19F030003)

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