城市排水系统的实时控制使用神经进化
Shengwei Pei1, Lan Hoang2, Guangtao Fu1
1Centre for Water Systems, Department of Engineering, University of Exeter, EX4 4QF, United Kingdom.
Journal of environmental management
|May 19, 2024
概括
本研究介绍了城市排水系统实时控制 (RTC) 的神经进化,有效地减少综合下水道溢出 (CSO) 量. 开发的控制政策显示出卓越的表现,特别是在较小的民间社会组织活动中.
科学领域:
- 环境工程 环境工程
- 人工智能的人工智能
- 水资源管理 水资源管理
背景情况:
- 城市排水系统因气候变化和城市化而面临越来越大的压力.
- 实时控制 (RTC) 为提高系统性能和减轻升级需求提供了解决方案.
- 由于计算需求和系统不确定性,开发最佳的RTC政策是复杂的.
研究的目的:
- 介绍一种新的神经进化方法,用于控制城市排水系统中的综合下水道溢出 (CSO).
- 为了训练一个离线的控制政策,绕过在线优化挑战.
- 评估神经进化控制政策与现有策略的有效性.
主要方法:
- 利用神经进化,一种结合神经网络和进化算法的方法,用于RTC政策制定.
- 预先培训了控制政策.
- 模拟了对基准Astlingen网络的性能,并分析了典型的CSO事件.
主要成果:
- 神经进化控制政策显示,与同等填充程度策略相比,CSO的体积减少率更高.
- 该政策在坦克水位的不确定性方面表现出强性.
- 在较小的民间社会组织活动中,有效性更为明显,特别是在初始阶段.
结论:
- 神经进化为城市排水系统中的RTC提供了一种可行和有效的方法.
- 制定的政策为民间社会组织管理提供了显著的改进,特别是在频繁,较小的溢出事件中.
- 这项研究为未来城市用神经进化控制水系统的进展奠定了基础.
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