A Bayesian inferential sensor for predicting the reactant concentration in an exothermic chemical process

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorIkonen, Teemuen_US
dc.contributor.authorBergman, Samulien_US
dc.contributor.authorCorona, Francescoen_US
dc.contributor.departmentDepartment of Chemical and Metallurgical Engineeringen
dc.contributor.groupauthorProcess Control and Automationen
dc.contributor.organizationNeste Corporationen_US
dc.date.accessioned2023-10-11T09:35:42Z
dc.date.available2023-10-11T09:35:42Z
dc.date.issued2023-10-15en_US
dc.descriptionWe wish to thank Akshaya Athwale, Kristian Bergman, Cesar de Araujo Filho, Muhammad Emzir, Katsiaryna Haitsiukevich, Sakira Hassan, Alexander Ilin, Viljami Iso-Markku, James Kabugo, Sanna Laitinen, Amir Shirdel, Simo Särkkä, Stefan Tötterman for fruitful discussions and insight on the process of catalytic hydrogenation. We acknowledge the financial support from the Academy of Finland through project RELOOP (decision number 330388).
dc.description.abstractIn many chemical reactors, concentration measurements are conducted off-line in a laboratory, which involve manual work and can therefore be conducted only infrequently. We propose a Bayesian inferential sensor to predict the reactant concentration in the inlet stream of an exothermic chemical process. The inferential sensor is based on the Bayesian inverse approach and the autoregressive integrated moving average (ARIMA) model. It enables the prediction of the reactant concentration at the frequency of automated on-line measurements, which is typically much higher than that of laboratory measurements. We demonstrate the method on real industrial process data from catalytic hydrogenation of aromatic compounds. The predicted aromatics concentration in the inlet stream, generated based on the latest on-line measurements and two-week-old laboratory data, has a coefficient of determination of 0.936 and a root mean square error of 0.654 mass-%.en
dc.description.versionPeer revieweden
dc.format.extent10
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationIkonen, T, Bergman, S & Corona, F 2023, 'A Bayesian inferential sensor for predicting the reactant concentration in an exothermic chemical process', Chemometrics and Intelligent Laboratory Systems, vol. 241, 104942. https://doi.org/10.1016/j.chemolab.2023.104942en
dc.identifier.doi10.1016/j.chemolab.2023.104942en_US
dc.identifier.issn0169-7439
dc.identifier.issn1873-3239
dc.identifier.otherPURE UUID: 8af433d1-6744-4106-bd8c-c4cb8ca0fcd3en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/8af433d1-6744-4106-bd8c-c4cb8ca0fcd3en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85170432704&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/123819174/CHEM_Ikonen_et_al_A_Bayesian_inferential_2023_Chemometrics_and_Intelligent_Laboratory_Systems.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/123919
dc.identifier.urnURN:NBN:fi:aalto-202310116266
dc.language.isoenen
dc.publisherElsevier
dc.relation.ispartofseriesChemometrics and Intelligent Laboratory Systemsen
dc.relation.ispartofseriesVolume 241en
dc.rightsopenAccessen
dc.subject.keywordinferential sensoren_US
dc.subject.keywordBayesian analysisen_US
dc.subject.keywordARIMAen_US
dc.subject.keywordexothermic processen_US
dc.subject.keywordconcentrationen_US
dc.titleA Bayesian inferential sensor for predicting the reactant concentration in an exothermic chemical processen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion

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