智能废水处理的代理-反应堆集成:基于强化学习的控制的实验验证和解释性
Ziang Zhu1, Zhengxin Yu2, Yingzheng Fan1
1State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, P. R. China.
Environmental science & technology
|March 10, 2026
概括
强化学习剂在真实生物反应器中控制生物营养除去 (BNR),性能优于传统方法. 这项研究通过使人工智能成为人工智能来增强信任.
科学领域:
- 环境工程 环境工程
- 人工智能的人工智能
- 过程控制 过程控制
背景情况:
- 强化学习 (RL) 对生物营养去除 (BNR) 有希望,但缺乏现实世界的验证.
- 在BNR中现有的RL应用程序仅限于模拟,由于它们的黑子性质,阻碍了实际采用和运营商信任.
研究的目的:
- 将RL剂与物理生物反应器集成,以便实时控制BNR过程.
- 证明基于RL的控制在管理干扰方面优于传统方法的优越性.
- 提高废水处理中RL决策的解释性和透明度.
主要方法:
- 开发了用于直接物理生物反应器-RL剂相互作用的反应器-试剂集成.
- 实施基于RL的控制策略,以管理影响干扰和优化操作参数.
- 采用一种新的分析框架,结合控制器-动作可视化,替代决策树,Sobol灵敏度分析和决策轨迹分析来解释政策.
主要成果:
- 与基于知识的控制相比,基于RL的控制在管理影响干扰方面表现优越.
- 实现了大约30%的物种超标量减少和36.5%的运营成本下降.
- 分析框架成功地将RL代理人的黑子政策转化为可解释和可审计的控制策略.
结论:
- 基于代理的智能控制对现实世界的BNR流程来说是可行的,强大的和透明的.
- 开发的可解释性框架将算法智能与工艺工程透明度联系起来.
- 这种方法为可靠地部署RL在大型废水处理厂 (WWTP) 铺平了道路.
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