贝叶斯优化增强强化学习用于废水处理的自我适应和多目标控制
Ziang Zhu1, Shaokang Dong2, Han Zhang1
1State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023 Jiangsu, PR China.
Bioresource technology
|February 11, 2025
概括
污水处理厂可以使用新的AI方法优化运营. 将强化学习 (RL) 与贝叶斯优化 (BO) 整合在一起,可以在满足标准的同时将成本降低46%和能源降低12%.
科学领域:
- 环境工程 环境工程
- 人工智能在水资源管理中的应用
- 过程控制 过程控制
背景情况:
- 污水处理厂 (WWTP) 的运营面临着具有动态影响力和多重目标的挑战.
- 优化活性污泥工艺需要先进的控制策略.
研究的目的:
- 开发和测试用于WWTP控制的综合强化学习 (RL) 和贝叶斯优化 (BO) 方法.
- 在活性污泥工艺中加强对关键操作参数的控制.
主要方法:
- 将强化学习 (RL) 算法应用于各种活性污泥配置.
- 集成RL与贝叶斯优化 (BO) 进行增强控制.
- 在A2O过程中测试了该方法.
主要成果:
- 实现了46%的运营成本降低.
- 减少了12%的能源消耗.
- 保持对废水排放标准的遵守.
结论:
- 综合RL-BO方法为WWTP提供了一个实用的解决方案.
- 这种方法提高了处理效率,并降低了运营成本.
- 它有助于可持续的废水管理实践.
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