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dc.contributor.authorCirera Balcells, Josep
dc.contributor.authorCariño Corrales, Jesús Adolfo
dc.contributor.authorZurita Millán, Daniel
dc.contributor.authorOrtega Redondo, Juan Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Electrònica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2020-07-01T17:43:47Z
dc.date.available2020-07-01T17:43:47Z
dc.date.issued2020-05-21
dc.identifier.citationCirera, J. [et al.]. A data-driven-based industrial refrigeration optimization method considering demand forecasting. "Processes", 21 Maig 2020, vol. 8, núm. 5, p. 617:1-617:16.
dc.identifier.issn2227-9717
dc.identifier.urihttp://hdl.handle.net/2117/192197
dc.description.abstractOne of the main concerns of industry is energy efficiency, in which the paradigm of Industry 4.0 opens new possibilities by facing optimization approaches using data-driven methodologies. In this regard, increasing the efficiency of industrial refrigeration systems is an important challenge, since this type of process consume a huge amount of electricity that can be reduced with an optimal compressor configuration. In this paper, a novel data-driven methodology is presented, which employs self-organizing maps (SOM) and multi-layer perceptron (MLP) to deal with the (PLR) issue of refrigeration systems. The proposed methodology takes into account the variables that influence the system performance to develop a discrete model of the operating conditions. The aforementioned model is used to find the best PLR of the compressors for each operating condition of the system. Furthermore, to overcome the limitations of the historical performance, various scenarios are artificially created to find near-optimal PLR setpoints in each operation condition. Finally, the proposed method employs a forecasting strategy to manage the compressor switching situations. Thus, undesirable starts and stops of the machine are avoided, preserving its remaining useful life and being more efficient. An experimental validation in a real industrial system is performed in order to validate the suitability and the performance of the methodology. The proposed methodology improves refrigeration system efficiency up to 8%, depending on the operating conditions. The results obtained validates the feasibility of applying data-driven techniques for the optimal control of refrigeration system compressors to increase its efficiency.
dc.description.sponsorshipThe authors would like to thank the support of Corporación Alimentaria Guissona S.A. for providing access to their refrigeration system dataset and their expert advice.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshProcess control
dc.subject.lcshArtificial intelligence
dc.subject.otherData-driven
dc.subject.otherSelf-organizing maps
dc.subject.otherMulti-layer perceptron
dc.subject.otherPartial load ratio
dc.subject.otherRefrigeration systems
dc.subject.otherCompressors
dc.subject.otherEnergy efficiency
dc.subject.otherIndustrial process modelling
dc.titleA data-driven-based industrial refrigeration optimization method considering demand forecasting
dc.typeArticle
dc.subject.lemacControl de processos
dc.subject.lemacIntel·ligència artificial
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.identifier.doi10.3390/pr8050617
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/2227-9717/8/5/617?utm_source=releaseissue&utm_medium=email&utm_campaign=releaseissue_processes&utm_term=titlelink43
dc.rights.accessOpen Access
local.identifier.drac28609208
dc.description.versionPostprint (published version)
local.citation.authorCirera, J.; Cariño , J.A.; Zurita, D.; Ortega, J.A.
local.citation.publicationNameProcesses
local.citation.volume8
local.citation.number5
local.citation.startingPage617:1
local.citation.endingPage617:16


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain