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Calculating Chlorine Concentration Bounds in Water Distribution Networks: A Backtracking Uncertainty Bounding Approach
Author(s) -
Vrachimis Stelios G.,
Eliades Demetrios G.,
Polycarpou Marios M.
Publication year - 2021
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2020wr028684
Subject(s) - bounding overwatch , benchmark (surveying) , monte carlo method , chlorine , water quality , computer science , work (physics) , estimation theory , node (physics) , mathematical optimization , algorithm , mathematics , chemistry , statistics , engineering , geodesy , organic chemistry , biology , geography , mechanical engineering , ecology , structural engineering , artificial intelligence
Abstract The estimation of chlorine concentration in water distribution networks is a challenging task due to hydraulic and water‐quality parameter uncertainties. In this work, we propose a methodology for calculating chlorine concentration bounds at node locations of water distribution networks which is suitable for sensor fault and contamination detection purposes. The proposed Backtracking Uncertainty Bounding Algorithm (BUBA) considers known bounds on hydraulic states and water‐quality model parameters to calculate the chlorine concentration bounds. The validity of the calculated bounds is demonstrated using a large number of Monte Carlo simulations on benchmark networks. Moreover, the proposed methodology can be used in conjunction with a real‐time parameter estimation algorithm, as it allows the use of time‐varying bulk reaction coefficients. A parameter estimation algorithm is designed and implemented to approximate the unknown bulk reaction coefficient of water at the sources of the WDN, which are typically water tanks filled with water originating from different sources. The BUBA then uses these time‐varying parameters to improve the chlorine concentration bounds, as demonstrated in a case study with real data from a water transport network.