根据经济,能源和环境目标,纸制造废水处理过程的多目标优化
Zhenglei He1, Zaohao Lu1, Xu Wang2
1State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510640, China.
Environmental science & technology
|July 22, 2024
概括
这项研究引入了一种新的多代理深度强化学习框架,以优化造纸废水处理过程. 该方法有效平衡成本,能源消耗和温室气体排放,同时确保废水质量.
科学领域:
- 环境工程 环境工程
- 人工智能的人工智能
- 过程优化 过程优化
背景情况:
- 由于污染物水平的波动和严格的环境法规,造纸废水处理面临着挑战.
- 优化经济,能源和环境 (3E) 目标同时提出了一个复杂的多目标问题.
研究的目的:
- 开发和评估一个多代理深度强化学习 (MADRL) 框架,用于同时优化造纸废水处理 (PWTP) 过程中的成本,能源消耗和温室气体 (GHG) 排放.
- 确保在实现3E目标的同时保持废水质量.
主要方法:
- 一个MADRL框架被设计和实施.
- 基准模拟模型没有. 1 (BSM1) 用于模拟造纸废水的生物处理过程.
- 模型训练使用了从BSM1手册中生成的数据,与用于性能验证的造纸厂的现实数据.
主要成果:
- 与传统方法相比,拟议的MADRL框架显示出更高的性能.
- 该框架成功确定了实现多个目标的最佳控制策略,包括经济,能源和环境目标.
- 实现了有效的同时优化工艺成本,能源消耗和温室气体排放.
结论:
- 开发的MADRL框架为PWTP复杂的多目标优化提供了强大的解决方案.
- 这种方法为实现造纸废水处理中的3E目标提供了可行的策略.
- 该研究强调了人工智能在提高工业废水处理的效率和可持续性方面的潜力.
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