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Sensor-data-driven prognosis approach of liquefied natural gas satellite plant

dc.contributor.authorEscobet Canal, Antoni
dc.contributor.authorEscobet Canal, Teresa
dc.contributor.authorQuevedo Casín, Joseba Jokin
dc.contributor.authorMolina, Adoración
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Minera, Industrial i TIC
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2020-09-23T13:14:02Z
dc.date.available2020-09-23T13:14:02Z
dc.date.issued2020-08-11
dc.identifier.citationEscobet, A. [et al.]. Sensor-data-driven prognosis approach of liquefied natural gas satellite plant. "Applied System Innovation", 11 Agost 2020, vol. 3, núm. 34, p. 1-18.
dc.identifier.issn2571-5577
dc.identifier.urihttp://hdl.handle.net/2117/329176
dc.description.abstractThis paper proposes a sensor-data-driven prognosis approach for the predictive maintenance of a liquefied natural gas (LNG) satellite plant. By using data analytics of sensors installed in the satellite plants, it is possible to predict the remaining time to refill the tank of the remote plants. In the proposed approach, the first task of data validation and correction is presented in order to transform raw data into reliable validated data. Then, the second task presents two methods for the prognosis of gas consumption in real time and the forecast of remaining time to refill the tank of the plant. The obtained results with real satellite plants showed good performance for direct implementation in a predictive maintenance plan.
dc.description.sponsorshipThis work was funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and FEDER through projects SCAV (ref. DPI2017-88403-R), and by AGAUR ACCIO RIS3CAT UTILITIES 4.0–P1 ACTIV 4.0. ref. COMRDI-16-1-0054-03 and SMART Project (ref no EFA153/16 Interreg Cooperation Program POCTEFA 2014- 2020).
dc.format.extent18 p.
dc.language.isoslv
dc.publisherMDPI AG
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Energies::Recursos energètics no renovables
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.lcshLiquefied natural gas
dc.subject.lcshRemote sensing
dc.subject.lcshPredictive control
dc.subject.lcshEnergy consumption
dc.subject.otherEnergy management
dc.subject.otherData analytics
dc.subject.otherLNG satellite plants
dc.subject.otherPredicting LNG consumption
dc.subject.otherFault detection
dc.titleSensor-data-driven prognosis approach of liquefied natural gas satellite plant
dc.typeArticle
dc.subject.lemacGas natural liquat
dc.subject.lemacTeledetecció
dc.subject.lemacControl predictiu
dc.subject.lemacEnergia -- Consum
dc.contributor.groupUniversitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.3390/asi3030034
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/2571-5577/3/3/34
dc.rights.accessOpen Access
local.identifier.drac29419831
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-88403-R/ES/SEGURIDAD Y CONTROL EN VEHICULOS AUTONOMOS/
dc.relation.projectidinfo:eu-repo/grantAgreement/ACC10/RIS3CAT/COMRDI16-1-0054-03
dc.relation.projectidinfo:eu-repo/grantAgreement/CE/SMART/EFA153/16
local.citation.authorEscobet, A.; Escobet, T.; Quevedo, J.; Adoración Molina
local.citation.publicationNameApplied System Innovation
local.citation.volume3
local.citation.number34
local.citation.startingPage1
local.citation.endingPage18


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