非稳态给水管网的外源污染监测选址模型构建及求解
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摘要
针对供水管网潜在的突发性外源污染,结合非稳态水力工况特点,改进监测用时的算法并以此构建选址模型,通过借助自适应粒子群k-medoids聚类算法对选址模型进行求解。通过实际案例对比稳态与非稳态工况下水力流通最短时间的路径,得出:非稳态工况下水力流通路径较复杂,改进后的水力流通时间计算方法更符合实际供水管网。经过对自适应粒子群k-medoids聚类算法的调整,模型求解过程稳定性良好,输出结果理想——可直接定位监测点至管网节点。针对该案例提出了一套监测点布置方案:监测点数量为40个(占总节点数的3. 43%),平均监测用时为26. 4 min,污染入侵事件的有效监测率达到71. 67%。
To prepare for accidental exogenous contaminations in a water distribution network,based on the characteristics of dynamic hydraulic conditions,a response time algorithm was improved and utilized in an optimization model for water quality monitoring location selection,and the optimization model was subsequently solved by the adaptive particle swarm optimization combined k-medoids cluster algorithm (APSO-k-medoids). A water distribution network was used to analysze the shortest travel time along the hydraulic flow paths and the difference between static and dynamic hydraulic conditions. The results showed that hydraulic flow paths in dynamic hydraulic condition were more complex. The proposed model was more consistent with the actual network. The adjusted APSO-k-medoids algorithm provided stable and high quality solutions to the model in assigning monitoring points to the network nodes. A monitoring plan with 40 points (3. 43 percent in all nodes) was proposed,which had 26. 4 min average monitoring time and 71. 67% of the effective rate in monitoring the pollution intrusion.
To prepare for accidental exogenous contaminations in a water distribution network,based on the characteristics of dynamic hydraulic conditions,a response time algorithm was improved and utilized in an optimization model for water quality monitoring location selection,and the optimization model was subsequently solved by the adaptive particle swarm optimization combined k-medoids cluster algorithm (APSO-k-medoids). A water distribution network was used to analysze the shortest travel time along the hydraulic flow paths and the difference between static and dynamic hydraulic conditions. The results showed that hydraulic flow paths in dynamic hydraulic condition were more complex. The proposed model was more consistent with the actual network. The adjusted APSO-k-medoids algorithm provided stable and high quality solutions to the model in assigning monitoring points to the network nodes. A monitoring plan with 40 points (3. 43 percent in all nodes) was proposed,which had 26. 4 min average monitoring time and 71. 67% of the effective rate in monitoring the pollution intrusion.
引文
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[2]Rathi S,Gupta R.A simple sensor placement approach for regular monitoring and contamination detection in water distribution networks[J].KSCE J Civil Eng,2016,20(2):597-608.
[3]方海恩,吕谋,毕继胜.预警监测站优化布置方法的探讨[J].青岛理工大学学报,2006,27(3):71-73,81.Fang Haien,LüMou,Bi Jisheng.Discussion of optimal layout of early warning detection stations[J].Journal of Qingdao Technological University,2006,27(3):71-73,81(in Chinese).
[4]张土乔,黄亚东.基于污染物侵入模拟的供水管网水质监测点优化选址方法[J].系统工程理论与实践,2007,6(8):146-151.Zhang Tuqiao,Huang Yadong.A contaminant intrusion simulation based method for optimizing water quality monitoring stations in water distribution systems[J].Systems Engineering-Theory&Practice,2007,6(8):146-151(in Chinese).
[5]陶涛,吕存阵,信昆仑,等.基于突发污染事件的管网水质监测点优化布置[J].同济大学学报:自然科学版,2010,38(11):1621-1625.Tao Tao,LüCunzhen,Xin Kunlun,et al.Optimal layout of monitoring stations for detecting accidental contaminations in water distribution system[J].Journal of Tongji University:Natural Science,2010,38(11):1621-1625(in Chinese).
[6]吴小刚,张土乔,黄亚东.多目标约束下给水管网水质传感器选址优化算法的研究[J].水利学报,2008,39(4):433-439.Wu Xiaogang,Zhang Tuqiao,Huang Yadong.Optimal algorithm for determining locations of water quality sensors in water supply networks under multi-objective constraints[J].Journal of Hydraulic Engineering,2008,39(4):433-439(in Chinese).
[7]张鹏.G市某管网水力水质监测点优化选址研究[D].长沙:湖南大学,2013.Zhang Peng.The Research on Hydraulic and Water Quality Monitoring Stations in Municipal Water Network of G City[D].Changsha:Hunan University,2013(in Chinese).
[8]Behzadian K,Kapelan Z,Savic D.Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks[J].Environ Model Software,2009,24(4):530-541.
[9]沈承,俞亭超,张土乔.城市供水系统突发性污染监测[J].浙江大学学报:工学版,2010,44(8):1604-1607,1628.Shen Cheng,Yu Tingchao,Zhang Tuqiao.Detecting accidental contaminations in municipal water networks[J].Journal of Zhejiang University:Engineering Science,2010,44(8):1604-1607,1628(in Chinese).
[10]刘书明,吴雪,欧阳乐岩.基于污染源反追踪的水质监测点优化选址[J].同济大学学报:自然科学版,2013,41(5):742-745.Liu Shuming,Wu Xue,Ouyang Leyan.Optimal sensor placement based on contaminant backtracking in water distribution systems[J].Journal of Tongji University:Natural Science,2013,41(5):742-745(in Chinese).
[11]刘书明,吴雪,欧阳乐岩.不确定节点水量下水质监测点优化选址方法[J].环境科学,2013,34(8):3108-3112.Liu Shuming,Wu Xue,Ouyang Leyan.Method for optimal sensor placement in water distribution systems with nodal demand uncertainties[J].Environmental Science,2013,34(8):3108-3112(in Chinese).
[12]周虹.基于自适应粒子群的k-中心聚类算法研究[D].长沙:长沙理工大学,2012.Zhou Hong.Research on k-Medoids Clustering Algorithm Based on Adaptive Particle Swarm Optimization Algorithm[D].Changsha:Changsha University of Science&Technology,2012(in Chinese).
[13]王雪飞.粒子群算法的动态拓朴结构研究[D].重庆:西南大学,2008.Wang Xuefei.Research on Dynamic Topology of Particle Swarm Algorithms[D].Chongqing:Southwest University,2008(in Chinese).