Stochastic model predictive control approaches applied to drinking water networks

Journal Article (2017)


Optimal Control Applications and Methods

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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.


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, 2017, to appear.