| Title |
Stochastic model predictive control approaches applied to drinking water networks |
| Authors |
Grosso, Juan M. , VELARDE RUEDA, PABLO ANIBAL, Ocampo-Martinez, Carlos , Maestre, Jose M. , Puig, Vicenc |
| External publication |
Si |
| Means |
OPTIMAL CONTROL APPLICATIONS & METHODS |
| Scope |
Article |
| Nature |
Científica |
| JCR Quartile |
1 |
| SJR Quartile |
1 |
| JCR Impact |
1.614 |
| SJR Impact |
0.825 |
| Publication date |
01/07/2017 |
| ISI |
000405077500005 |
| DOI |
10.1002/oca.2269 |
| Abstract |
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 (c) 2016 John Wiley & Sons, Ltd. |
| Keywords |
management of water systems; model predictive control; stochastic programming; system disturbances |
| Universidad Loyola members |
|
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