某城镇供水管网污染源反追踪模型构建及求解过程

Construction of a Reverse Tracing Model for Pollution Sources Detection in a Water Supply Network and Its Solution

城市供水管网中突发外源性污染物入侵事故时,可以根据供水管网的属性数据和水质监测数据来反演推求污染物侵入的地点、时间和速度。结合南方某城镇供水管网案例,构建污染源模拟-优化反向追踪的数学模型,并利用粒子群-蚁群融合算法进行求解。结果表明,粒子群-蚁群融合算法可提高求解模型的准确率和计算效率,结果表现出较好的稳定性。针对该案例提出了一套供水管网外源性突发污染事故下的污染源定位方案,在随机选取的15个模拟污染事故中,模型输出结果准确率达到92. 0%,10 min内求解模型的准确率为64. 7%。

When exogenous contaminations intrusion occurred in the water supply networks, the location, timing, and the rate of contamination intrusion could be identified based on attribute data and water quality monitoring data. Based on the water supply network of a town in south China, an optimized simulation method was proposed and solved by the combination of the particle swarm optimization （PSO） method and the ant colony optimization （ACO） method. The results showed that using PSO - ACO could improve the accuracy and the computational efficiency of the model output. The model results showed good stability. When tested with 15 randomly selected simulated contamination incidents, the model showed 92.0% accuracy in the output, and 64.7% accuracy within 10 min.