Publication

Stochastic model predictive control approaches applied to drinking water networks

Journal Article (2017)

Journal

Optimal Control Applications and Methods

Pages

541-558

Volume

38

Number

4

Doc link

http://dx.doi.org/10.1002/oca.2269

File

Download the digital copy of the doc pdf document

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.

Categories

automation, control theory, optimisation.

Author keywords

management of water systems; model predictive control; stochastic programming; system disturbances

Scientific reference

J.M. Grosso, P. Velarde, C. Ocampo-Martínez, J.M. Maestre and V. Puig. Stochastic model predictive control approaches applied to drinking water networks. Optimal Control Applications and Methods, 38(4): 541-558, 2017.