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dc.contributor.authorMendia, Izaskun
dc.contributor.authorGil-López, Sergio
dc.contributor.authorLanda-Torres, Itziar
dc.contributor.authorOrbe, Lucía
dc.contributor.authorMaqueda, Erik
dc.date.accessioned2022-03-07T10:45:06Z
dc.date.available2022-03-07T10:45:06Z
dc.date.issued2022-03
dc.identifier.citationMendia, Izaskun, Sergio Gil-López, Itziar Landa-Torres, Lucía Orbe, and Erik Maqueda. “Machine Learning Based Adaptive Soft Sensor for Flash Point Inference in a Refinery Realtime Process.” Results in Engineering 13 (March 2022): 100362. doi:10.1016/j.rineng.2022.100362.en
dc.identifier.issn2590-1230en
dc.identifier.urihttp://hdl.handle.net/11556/1276
dc.description.abstractIn industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.en
dc.description.sponsorshipThis work has received funding support from the SPRI-Basque Gov- ernment through the ELKARTEK program (OILTWIN project, ref. KK- 2020/00052).en
dc.language.isoengen
dc.publisherElsevier B.V.en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleMachine learning based adaptive soft sensor for flash point inference in a refinery realtime processen
dc.typearticleen
dc.identifier.doi10.1016/j.rineng.2022.100362en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsFlash-point temperatureen
dc.subject.keywordsControl industry processen
dc.subject.keywordsAdaptive soft sensoren
dc.subject.keywordsVirtual sensingen
dc.subject.keywordsInferential sensingen
dc.subject.keywordsData-driven techniquesen
dc.journal.titleResults in Engineeringen
dc.page.initial100362en
dc.volume.number13en


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