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J4  2011, Vol. 45 Issue (1): 81-86    DOI: 10.3785/j.issn.1008-973X.2011.01.013
    
Simulation  of contamination source identification  in water
supply system and analysis of influencing factors
LI Hong-wei, WANG Meng-lin, LV Mou, LI Hong-yan
Department of Environmental and Municipal Engineering, Qingdao Technological University, Qingdao 266033, China
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Abstract  

Sodium hypochlorite solution was introduced from different intrusion points to a laboratory smallscaled and open water distribution system for the experimental study and theoretical analysis about contamination sources identification simulation in order to rapidly determine the location of pollution sources when contamination events occur in water supply system. Sets of contaminant concentrations at monitoring sites were collected to be used as data source by guaranteeing the accuracy of the measured results. The improved simulation-optimization method based on Lagrangian algorithm was applied to determine the location of contaminant intrusion points, and the effects of influencing factors such as system topologic structure, optimization time horizon, and  hydraulic condition were analyzed. Results showed that the source location was determined with 93.3% accuracy on the premise of influencing factors appropriately set.



Published: 03 March 2011
CLC:  TU 991.33  
Cite this article:

LI Hong-wei, WANG Meng-lin, LV Mou, LI Hong-yan. Simulation  of contamination source identification  in water
supply system and analysis of influencing factors. J4, 2011, 45(1): 81-86.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2011.01.013     OR     http://www.zjujournals.com/eng/Y2011/V45/I1/81


给水管网污染源定位模拟及影响因素分析

为了在供水管网突发污染发生时快速准确定位污染源,以小规模开环式供水管网模拟系统为实验平台,采用多点投加高浓度次氯酸钠溶液模拟污染物扩散的方法进行实验研究与理论分析.在实验中监测多组管网平台监测点污染物浓度时变数据,在保证可靠性、准确性的基础上将监测数据作为反追踪模拟信息源.通过改进的基于拉格朗日算法的“模拟-优化反追踪数学模型”定位污染源,分析管网拓扑结构、优化时段和水力工况等因素对模型精度和效率的影响.研究表明,在模型影响因素合理表达的条件下,模拟结果准确率达93.3%.

[1] LAMM P K. Inverse problems and illposedness [C]∥ Inverse Problems in Engineering. New York: ASME, 1993: 1-10.
[2] LIOU C P, KROON J R. Modeling the propagation of waterborne substances in distribution networks [J]. American Water Works Association, 1987, 79(11): 54-58.
[3] ROSSMAN L A, BOULOS P F, ALTMAN T. Discrete volumeelement method for network waterquality models [J]. Water Resource Planning and Management, 1993, 119(5): 505-517.
[4] ROSSMAN L A, BOULOS P F. Numerical methods for modeling water quality in distribution systems: a comparison [J]. Water Resource Planning and Management, 1996, 122(2): 137-146.
[5] RODRIGUEZ M J, SRODES J B. Assessing empirical linear and nonlinear modeling of residual chlorine in urban drinking water systems [J]. Environmental Modeling and Software, 1999,14(1): 93-102.
[6] BOWDEN G J, NIXON J B, DANDY G C, et al. Forecasting chlorine residuals in a water distribution system using a general regression neural network [J]. Mathematical and Computer Modeling, 2006, 44: 469-484.
[7] 张土乔, 王鸿翔, 郭帅. 给水管网水质模型管壁余氯衰减系数校正[J]. 浙江大学学报: 工学版,2008, 42(11):1977-1982.
ZHANG Tuqiao, WANG Hongxiang, GUO Shuai. Chlorine wall decay coefficients calibration of water distribution quality mode [J]. Journal of Zhejiang University: Engineering Science, 2008, 42(11):1977-1982.
[8] LAIRD C D, BIEGLER L T, VAN BLOEMEN WAANDERS B G, et al. Contamination source determination for water networks [J]. Journal of Water Resources Planning and Management, 2005, 131(2): 125-134.
[9] GUAN Jiabao, ARAL M M, MASLIA M L, et al. Identification of contaminant source in water distribution systems using simulationoptimization method: case study [J]. Journal of Water Resource Planning and Management, 2006, 132(4): 252-262.
[10] ZIEROLF M L, POLYCARPOU M M, UBER J G. Development and autocalibration of an inputoutput model of chlorine transport in drinking water distribution systems [J]. IEEE Transactions on Control System Technology, 1998, 6(4): 543-553.
[11] SHANG F, UBER J G, POLYCARPOU M M. Particle backtracking algorithm for water distribution system analysis [J]. Journal of Environmental Engineering, 2002, 128(5): 441-450.
[12] MINYOUNG K, CHRISTOPHER Y C, CHARLES P G. Source tracking of microbial intrusion in water systems using artificial neural networks [J]. Water Research, 2008, 42: 1308-1314.
[13] ROSSMAN L A. Epanet 2 users manual [M]. Cincinnati: USEPA, 2000.
[14] 方海恩,吕谋. 供水系统预警监测站的优化布置[J]. 中国给水排水, 2007, 23(9): 44-47.
FANG Haien, LV Mou. Optimal layout of early warning monitoring stations for water supply system [J]. China Water and Waste Water, 2007, 23(9): 44-47.
[15] 张土乔,黄亚东,吴小刚. 供水管网水质监测点优化选址研究[J]. 浙江大学学报: 工学版,2007, 40(1): 1-5.
ZHANG Tuqiao, HUANG Yadong, WU Xiaogang. Optimal locations of water quality monitoring stations in water distribution systems [J]. Journal of Zhejiang University: Engineering Science, 2007, 40(1): 1-5.
[16] BOCCELLI D L, TRYBY M E, UBER J G, et al. Optimal schedule of booster disinfection in water distribution systems [J]. Journal of Water Resource Planning and Management, 1998, 124(2): 99-111.

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