基于模拟化-颗粒群群优化算法的水分系统污染源识别
Zhenliang Liao1,2,3, Xingyang Shi4,5, Yangting Liao6
1Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, Shanghai, 200092, China. zl_liao@tongji.edu.cn.
一个新的SA-PSO算法改善了城市水系统中污染源的识别. 这种增强的方法比传统的公用事业组织更快地准确地确定污染事件,确保更安全的供水.
科学领域:
- 环境工程 环境工程
- 水资源管理 水资源管理
- 计算智能是一种计算智能.
背景情况:
- 城市水安全依赖于快速检测和应对管道网络中的水质异常.
- 准确的污染源识别 (CSI) 对于公共卫生和预防广泛的污染至关重要.
- 现有的粒子群优化 (PSO) 方法由于局部优化而与CSI作斗争.
研究的目的:
- 开发一种改进的算法,用于在水分系统 (WDS) 中识别污染源.
- 为了克服CSI中标准粒子群集优化 (PSO) 算法的局限性.
- 提高城市供水中的污染源识别的速度和准确性.
主要方法:
- 将模拟回火 (SA) 的Metropolis标准集成到PSO算法中,创建了一个新的SA-PSO算法.
- 在NET-3管道网络上进行污染局部化实验,使用模拟的突然污染事件.
- 开发一个模拟优化逆位置模型,以适应随时间的监测点的污染物度.
主要成果:
- 与标准PSO相比,SA-PSO算法在污染源识别方面表现优越.
- 在WDS中,SA-PSO在定位污染事件方面取得了更高的准确性和速度.
- 基于模拟的逆位置模型有效地利用了污染物度数据来识别污染源.
结论:
- 开发的SA-PSO算法为城市供水管理中的污染局部化提供了高效和有效的工具.
- 这项研究在解决CSI在供水系统中的挑战方面取得了重大进展.
- 这些发现支持改进的战略,以保护城市供水免受污染事件的影响.
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