Due to the heterogeneity of recycled paper materials and the production conditions, pollutants in papermaking wastewater fluctuate sharply over time. Quality control of the papermaking wastewater treatment process (PWTP) is challenging and costly. As regulations are also growing about the environmental effects of the PWTP on greenhouse gas (GHG) emission, energy consumption, etc., the PWTP formulates a complex multiobjective optimization problem. This research established a multiagent deep reinforcement learning framework to simultaneously optimize process cost, energy consumption, and GHG emission in the PWTP, subjected to the effluent quality, to realize economic, energy, and environmental (3E) goals. The biological treatment process of wastewater in paper mills was simulated using benchmark simulation model no. 1 (BSM1). The data generated based on the BSM manual was utilized for model training, and real data acquired from a local papermaking factory was used to estimate the model performance. The results show that the proposed method outperforms conventional techniques in identifying the best control strategies for multiple targets.

中文翻译：

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经济、能源、环境目标下造纸废水处理工艺的多目标优化


由于再生纸原料和生产条件的异质性，造纸废水中污染物随时间波动剧烈。造纸废水处理过程 (PWTP) 的质量控制具有挑战性且成本高昂。由于关于PWTP对温室气体（GHG）排放、能源消耗等环境影响的法规也越来越多，PWTP制定了复杂的多目标优化问题。本研究建立了多智能体深度强化学习框架，根据污水水质，同时优化污水处理厂的工艺成本、能源消耗和温室气体排放，以实现经济、能源和环境（3E）目标。采用基准仿真模型No.1对造纸厂废水生物处理过程进行了模拟。 1（BSM1）。根据BSM手册生成的数据用于模型训练，并使用从当地造纸厂获取的真实数据来估计模型性能。结果表明，所提出的方法在确定多个目标的最佳控制策略方面优于传统技术。