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dc.contributor.authorDias, Ana Carolina Spindola Rangel
dc.contributor.authorSoares, Felipo Rojas
dc.contributor.authorJaschke, Johannes
dc.contributor.authorde Souza, Mauricio Bezerra
dc.contributor.authorPinto, Jose Carlos
dc.date.accessioned2019-09-30T09:32:33Z
dc.date.available2019-09-30T09:32:33Z
dc.date.created2019-06-27T15:53:04Z
dc.date.issued2019
dc.identifier.citationProcesses. 2019, 7 (5), 1-23.nb_NO
dc.identifier.issn2227-9717
dc.identifier.urihttp://hdl.handle.net/11250/2619315
dc.description.abstractThe present work investigated the use of an echo state network for a gas lift oil well. The main contribution is the evaluation of the network performance under conditions normally faced in a real production system: noisy measurements, unmeasurable disturbances, sluggish behavior and model mismatch. The main pursued objective was to verify if this tool is suitable to compose a predictive control scheme for the analyzed operation. A simpler model was used to train the neural network and a more accurate process model was used to generate time series for validation. The system performance was investigated with distinct sample sizes for training, test and validation procedures and prediction horizons. The performance of the designed ESN was characterized in terms of slugging, setpoint changes and unmeasurable disturbances. It was observed that the size and the dynamic content of the training set tightly affected the network performance. However, for data sets with reasonable information contents, the obtained ESN performance could be regarded as very good, even when longer prediction horizons were proposed.nb_NO
dc.language.isoengnb_NO
dc.publisherMDPInb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleExtracting Valuable Information from Big Data for Machine Learning Control: An Application for a Gas Lift Processnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber1-23nb_NO
dc.source.volume7nb_NO
dc.source.journalProcessesnb_NO
dc.source.issue5nb_NO
dc.identifier.doi10.3390/pr7050252
dc.identifier.cristin1708411
dc.description.localcode© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).nb_NO
cristin.unitcode194,66,30,0
cristin.unitnameInstitutt for kjemisk prosessteknologi
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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