Stochastic model predictive control approaches applied to drinking water networks

Summary Control of drinking water networks is an arduous task, given their size and the presence of uncertainty in water demand. It is necessary to impose different constraints for ensuring a reliable water supply in the most economic and safe ways. To cope with uncertainty in system disturbances du...

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Bibliographic Details
Published in:Optimal control applications & methods Vol. 38; no. 4; pp. 541 - 558
Main Authors: Grosso, Juan M., Velarde, Pablo, Ocampo‐Martinez, Carlos, Maestre, José M., Puig, Vicenç
Format: Journal Article Publication
Language:English
Published: Glasgow Wiley Subscription Services, Inc 01.07.2017
Subjects:
Automàtica i control
Classificació INSPEC
Control theory
Demand (economics)
Drinking water
Informàtica
management of water systems
Mathematical models
model predictive control
Predictive control
Probability theory
Stochastic models
stochastic programming
system disturbances
Uncertainty
Water supply
Water supply systems
Àrees temàtiques de la UPC
Spain
ISSN:0143-2087, 1099-1514
Online Access:Get full text
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Summary:Summary Control of drinking water networks is an arduous task, given their size and the presence of uncertainty in water demand. It is necessary to impose different constraints for ensuring a reliable water supply in the most economic and safe ways. To cope with uncertainty in system disturbances due to the stochastic water demand/consumption and optimize operational costs, this paper proposes three stochastic model predictive control (MPC) approaches, namely, chance‐constrained MPC, tree‐based MPC, and multiple‐scenario MPC. A comparative assessment of these approaches is performed when they are applied to real case studies, specifically, a sector and an aggregate version of the Barcelona drinking water network in Spain. Copyright © 2016 John Wiley & Sons, Ltd.
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ISSN:0143-2087
1099-1514
DOI:10.1002/oca.2269