10.15131/shef.data.5365186.v1
Qiang Xu
Qiang
Xu
Kuo Liu
Kuo
Liu
Shunping Zhao
Shunping
Zhao
Nan Cao
Nan
Cao
Zhimin Qiang
Zhimin
Qiang
Weiwei Ben
Weiwei
Ben
CCWI2017: F63 'Optimal Control for Water Loss from Water Distribution Network: A Case Study of Beijing'
The University of Sheffield
2017
CCWI2017
Optimal water loss control
Water distribution network
Lowest minimal night flow (LMNF) model
Civil Engineering not elsewhere classified
2017-09-01 15:31:38
Journal contribution
https://orda.shef.ac.uk/articles/journal_contribution/CCWI2017_F63_Optimal_Control_for_Water_Loss_from_Water_Distribution_Network_A_Case_Study_of_Beijing_/5365186
Water loss control is a key issue for most water supply companies all over the world because of freshwater scarcity and ever-rising water demand. Beijing is one of the cities facing severe water scarcity, so water loss control has been emphasized during the past decades. However, due to the huge size of the water distribution network, there is a great need to optimize the water loss control strategies. The water distribution network of Beijing has been planned to be partitioned into over 700 DMAs. Assessment of the DMAs’ water loss conditions and prediction of the achievements of different water loss control measures are critical to manage these DMAs. The goal of this paper is to develop a mathematical model to reveal how low the water loss of a DMA could go, so as to optimize the water loss control strategies. 36 DMAs were selected as study areas and data of the lowest minimal nigh flow (LMNF) and DMA characters (including pipe material, pipe length, number of properties, pipe age, and water pressure) were collected. The relationship between LMNF and DMA characters was established using multi-variant regression method. The model fit the data with R 2 =0.8. The model was then compared to the commonly used water loss indicator UARL and its sensitivity to the input variables was analyzed. Finally, the model was applied by Beijing Waterworks Group to optimize its water loss control strategies.