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dc.contributor.authorGrosso Pérez, Juan Manuel
dc.contributor.authorOcampo-Martínez, Carlos
dc.contributor.authorPuig Cayuela, Vicenç
dc.contributor.authorJoseph Duran, Bernat
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.date.accessioned2014-07-01T11:48:48Z
dc.date.available2014-07-01T11:48:48Z
dc.date.created2014-05-01
dc.date.issued2014-05-01
dc.identifier.citationGrosso, J.M. [et al.]. Chance-constrained model predictive control for drinking water networks. "Journal of process control", 01 Maig 2014, vol. 24, núm. 5, p. 504-516.
dc.identifier.issn0959-1524
dc.identifier.urihttp://hdl.handle.net/2117/23369
dc.description.abstractThis paper addresses a chance-constrained model predictive control (CC-MPC) strategy for the management of drinking water networks (DWNs) based on a finite horizon stochastic optimisation problem with joint probabilistic (chance) constraints. In this approach, water demands are considered additive stochastic disturbances with non-stationary uncertainty description, unbounded support and known (or approximated) quasi-concave probabilistic distribution. A deterministic equivalent of the stochastic problem is formulated using Boole's inequality to decompose joint chance constraints into single chance constraints and by considering a uniform allocation of risk to bound these later constraints. The resultant deterministic-equivalent optimisation problem is suitable to be solved with tractable quadratic programming (QP) or second order cone programming (SOCP) algorithms. The reformulation allows to explicitly and easily propagate uncertainty over the prediction horizon, and leads to a cost-efficient management of risk that consists in a dynamic back-off to avoid frequent violation of constraints. Results of applying the proposed approach to a real case study - the Barcelona DWN (Spain) - have shown that the network performance (in terms of operational costs) and the necessary back-off (to cope with stochastic disturbances) are optimised simultaneously within a single problem, keeping tractability of the solution, even in large-scale networks. The general formulation of the approach and the automatic computation of proper back-off within the MPC framework replace the need of experience-based heuristics or bi-level optimisation schemes that might compromise the trade-off between profits, reliability and computational burden. © 2014 Elsevier Ltd.
dc.format.extent13 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.lcshWater-supply -- Management -- Mathematical models
dc.subject.otherChance constraints
dc.subject.otherDrinking water networks
dc.subject.otherMPC
dc.subject.otherReliability
dc.subject.otherRobustness
dc.titleChance-constrained model predictive control for drinking water networks
dc.typeArticle
dc.subject.lemacAigua -- Abastament -- Control
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.1016/j.jprocont.2014.01.010
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0959152414000341
dc.rights.accessOpen Access
local.identifier.drac14939838
dc.description.versionPreprint
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/318556/EU/Efficient Integrated Real-time Monitoring and Control of Drinking Water Networks/EFFINET
local.citation.authorGrosso, J.M.; Ocampo-Martinez, C.A.; Puig, V.; Joseph, B.
local.citation.publicationNameJournal of process control
local.citation.volume24
local.citation.number5
local.citation.startingPage504
local.citation.endingPage516


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