面向大规模供水管网的水质监测点布局优化

Layout optimization of water quality monitoring points for large-scale water supply networks

在供水管网的重要位置布设一定数量的水质监测点,对管网水质进行实时监测,是保障水质安全的有效途径。现有水质监测点布局优化方法的研究大多针对小规模管网,在复杂的大规模管网中迭代求解效率和解集质量低,难以实际应用。为此,针对大规模供水管网节点数量众多且邻近节点相似程度较高等特点,以最小化监测时间和最大化污染事件覆盖比例为目标,建立水质监测点布局优化求解框架。该框架使用基于复杂网络重要性指标和耦合水力特征综合评价的节点筛选模型,对大规模管网中的重要节点进行污染事件模拟,并基于节点空间相似性改进遗传算法的演化策略,提升解集质量。针对实际大规模管网的模拟实验表明,此方法能解决供水管网规模较大时迭代效率和解集质量低的问题,获得有效的水质监测点布局。

Setting up a series of water quality monitoring points at important locations of water supply networks is an effective way for monitoring water quality in real time. Most existing layout optimization methods for water quality monitoring points focus on small-scale networks and are difficult to be applied to complex large-scale networks due to inefficient iterations and inferior solutions. Considering the large number of nodes and the high similarity of neighboring nodes in a large-scale water supply network, an optimization solution framework for water quality monitoring point layouts was established to minimize the monitoring time and maximize the coverage ratio of pollution events. The solution framework adopted water quality monitoring point selection models based on node importance indexes of complex networks and comprehensive evaluations of node hydraulic characteristics to simulate pollution events on important nodes of large-scale networks. Meanwhile, an evolutionary strategy of the genetic algorithm was upgraded to improve qualities of solution sets based on node spatial similarity. Simulation experiments on a large-scale network show that the proposed method could solve the problems of low iteration efficiency and low solution quality when the scale of a water supply network was large, so as to obtain an effective layout of water quality monitoring points.