基于深度强化学习的造纸废水处理过程多目标优化

作者：Lu Zaohao; Man Yi; Li Jigeng; Hong Mengna; He Zhenglei^*

来源：China Pulp and Paper, 2023, 42(3): 13-22.

DOI：10.11980/j.issn.0254-508X.2023.03.003

• deep reinforcement learning
• model
• optimization
• process control
• wastewater treatment

In this study，a dynamic optimization method based on multi-intelligent deep reinforcement learning was proposed to realize the col? laborative optimization，of the operation cost and energy consumption of papermaking wastewater treatment process. The BSM1 benchmark sim? ulation model was used to simulate the biochemical reaction and precipitation process of papermaking wastewater treatment process，the rein? forcement learning intelligences were trained，and the actual paper making wastewater data was used to verify the model system. The results showed that the wastewater treatment system based on multi-intelligent deep reinforcement learning system could guarantee the effluent quality，realized the multi-objective optimization control of cost and energy consumption，and its performance was better than the traditional methods. ? 2023 China Technical Association of Paper Industry.

• 单位
  制浆造纸工程国家重点实验室; 华南理工大学