关键词: 污水, 优化, 多目标, 温室气体, 运行成本, 能源消耗, 深度强化学习

Abstract:

Papermaking wastewater treatment process is susceptible to uncertain factors such as production process conditions switching and raw material heterogeneity. In the context of the coordinated development of pollution reduction and carbon reduction in the industry, how to ensure the discharge of sewage treatment in the water quality standard, and achieve synchronous reduction of treatment costs, energy consumption, and greenhouse gas emissions is an important issue restricting the development of the industry. In this paper, a multi-objective wastewater optimization method based on Kriging method and high dimensional model representation (HDMR) is proposed for the dynamic uncertainty of papermaking wastewater treatment. In this study, benchmark simulation model No. 1 (BSM1) was used to simulate the biochemical and precipitation processes of papermaking wastewater treatment process. Based on biochemical metabolism mechanism and data fusion, a Kriging-HDMR proxy model for real-time solving of greenhouse gas emissions in wastewater treatment process was established. By integrating the agent model into reinforcement learning, a multi-agent system based on“solving-decision-observation” for dynamic optimization of the wastewater treatment process was established, and a coordinated multi-objective optimization model for pollution reduction and carbon reduction was obtained. The study scenario results show that compared with the open-loop system, the dynamic optimization system can reduce operating cost by 4.10%, energy consumption by 22.10%, and greenhouse gas emissions by 10.30%, and can obtain and maintain an effective multi-objective dynamic optimization control strategy.

Key words: wastewater, optimization, multi-objective, greenhouse gases, operational cost, energy consumption, deep reinforcement learning