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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

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Front Envir Sci Eng    2013, Vol. 7 Issue (5) : 777-786    https://doi.org/10.1007/s11783-013-0537-9
RESEARCH ARTICLE
Decision Support System for emergency scheduling of raw water supply systems with multiple sources
Qi WANG1,2, Shuming LIU1(), Wenjun LIU1, Zoran KAPELAN2, Dragan SAVIC2
1. School of Environment, Tsinghua University, Beijing 100084, China; 2. Centre for Water Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, Devon, EX4 4QF, UK
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Abstract

A hydraulic model-based emergency scheduling Decision Support System (DSS) is designed to eliminate the impact of sudden contamination incidents occurring upstream in raw water supply systems with multiple sources. The DSS consists of four functional modules, including water quality prediction, system safety assessment, emergency strategy inference and scheduling optimization. The work flow of the DSS is as follows. First, the water quality variations on specific cross-sections are calculated given the pollution information. Next, a comprehensive evaluation on the safety of the current system is conducted using the outputs in the first module. This will assist in the assessment of whether the system is in danger of failure, taking both the impact of pollution and system capacity into account. If there is a severe impact of contamination on the reliability of the system, a fuzzy logic based inference module is employed to generate reasonable strategies including technical measures. Otherwise, a Genetic Algorithm (GA)-based optimization model will be used to find the least-cost scheduling plan. The proposed DSS has been applied to a coastal city in South China during a saline tide period as validation. Through scenario analysis, it is demonstrated that this DSS tool is instrumental in emergency scheduling for the water company to quickly and effectively respond to sudden contamination incidents.
Keywords decision support system      raw water supply system      contamination incident      emergency scheduling      hydraulic model      safety assessment     
Corresponding Author(s): LIU Shuming,Email:shumingliu@tsinghua.edu.cn   
Issue Date: 01 October 2013
 Cite this article:   
Qi WANG,Shuming LIU,Wenjun LIU, et al. Decision Support System for emergency scheduling of raw water supply systems with multiple sources[J]. Front Envir Sci Eng, 2013, 7(5): 777-786.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-013-0537-9
https://academic.hep.com.cn/fese/EN/Y2013/V7/I5/777
Fig.1  A generalized framework of DSS for emergency scheduling
Fig.2  Flowchart of drawing the security zoning map
factorunitrangemembership functionremark
location of contaminationkm[-30 20]gbellmfdistance measured along the flow direction using pumping station 1 as origin
period of occurrence of contaminationmonth[1 12]gaussmfdry period, wet period, and saline tide period
duration of contaminationhours>0pimfbased on the outcomes of water quality model
hazard rating of pollutantN/A1 to 10trapmfderived from the National Standard on surface water quality
concentration rating of pollutantN/A1 to 10trimfderived from the National Standard on surface water quality
effectiveness of on-site blocking techniquehours>0gaussmfestimated based on the results of lab experiment
effectiveness of on-site reduction technique%[0 1]gauss2mfestimated based on the results of lab experiment
Tab.1  Summary of factors in building the fuzzy logic inference system
Fig.3  Set of rules in the fuzzy logic inference system
Fig.4  Schematic diagram of the raw water supply system (a) and Layout of raw water supply system model of ZH city
Fig.5  Monitoring data of salinity at the intake of pumping station 1 (2007-2009)
Fig.6  Comparison of hydrographs between real and optimal scheduling during saline tide intrusion
dateNo. of pumps switched onenergy consumption/kWh
pumping station 2pumping station 3pumping station 2pumping station 3
realoptimalrealoptimalrealoptimalrealoptimal
Oct. 18234456664940202932536203
Oct. 19325583064551273771144159
Oct. 20326697896553024320248671
Oct. 21326697944561284362648652
Oct. 22336498016947104365436075
Oct. 23336297968950664356617821
Oct. 24326480688583704243336029
Oct. 25325379848588003720427184
Oct. 26325688632586673673951376
Oct. 27215437704255472184036716
Oct. 2812231135258661847527099
Oct. 2910421317601316717883
Oct. 3023221665592740938616211
Oct. 311020142950158180
Nov. 011020141150147830
total----888017803138440929444079
Tab.2  Comparison of real and optimal scheduling plans under saline tide intrusion
Fig.7  Safety assessment of the raw water supply system under predefined scenario
dayNo. of pumps switched onvalve opening/%
pumping station 2pumping station 3TCV 1TCV 2TCV 3
13625.016.716.7
22620.020.033.3
33616.720.025.0
42625.016.725.0
53620.020.025.0
61625.020.020.0
71616.720.020.0
83616.725.020.0
93620.025.020.0
103425.025.020.0
113520.020.020.0
123516.716.725.0
132516.716.720.0
143616.716.725.0
153616.716.716.7
Tab.3  Optimal scheduling plan under scenario 1
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