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Research for Water Quality Simulation and Contamination Source Determination of Water Supply Networks

Author: WangMengLin
Tutor: LvMou
School: Qingdao Technological University
Course: Municipal Engineering
Keywords: Water supply system Pipe network water quality simulation Sudden pollution Simulation - optimization of anti- tracking model BP artificial neural network
CLC: TU991.21
Type: Master's thesis
Year: 2010
Downloads: 191
Quote: 0
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Dynamic changes of water quality within the pipe network for tracking sudden pollution of the water supply network , quickly and accurately locate pollution sources to assist in the development of rational strategies to protect the security of water supply experimental platform for the study of small-scale water supply network simulation system using Euler, Lagrange simulation pipe network Chlorine mass concentration over time , the spatial location of the change , and the measured data inversion residual chlorine attenuation coefficient ; based on forward analysis , select the chlorine concentration of pollutants concentration indicators to the effluent end multi- cast high concentration of sodium hypochlorite solution simulated urban water supply pipe network sudden pollution events , the experiment measured the pipe network the platform monitoring points pollutants concentration time-varying data , ensure reliable accuracy on monitoring data as the anti- track analog information source , through improved algorithm based on Lagrange simulation - optimization of anti- tracking the mathematical model \factors affect the accuracy and efficiency of the model ; determine the input and output of the BP artificial neural network analysis of pollution simulation event 40 monitoring data ( 28 groups of training samples , 10 samples tested ) finishing summary of the simulation results and theoretical analysis the advantage of the method and constraints. Impact factors conditions in a reasonable set of model simulation - optimization anti- tracking model simulation results in this study the accuracy rate of 93.3% ; BP artificial neural network accuracy rate of 100% . The two algorithms have their respective advantages in different areas , but the purpose of the current study , analog - to optimize the performance of anti- tracking model higher robustness and broad applicability .

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CLC: > Industrial Technology > Building Science > Municipal Engineering > Water supply project ( on the Water Works ) > Clean Water ( water treatment ) > Water quality
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