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Cyber-physical LPG debutanizer distillation columns: machine-learning-based soft sensors for product quality monitoring

Rožanec Jože Martin, Trajkova Elena, Lu Jinzhi, Sarantinoudis Nikolaos, Arampatzis Georgios, Eirinakis Pavlos, Mourtos Ioannis, Onat Melike K., Yilmaz Deren A., Košmerlj Aljaž, Kenda Klemen, Fortuna Blaz, Mladenic Dunja

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/0166077B-94B2-467A-9543-62A0430A7B4A-
Αναγνωριστικόhttps://doi.org/10.3390/app112411790-
Αναγνωριστικόhttps://www.mdpi.com/2076-3417/11/24/11790/htm-
Γλώσσαen-
Μέγεθος26 pagesen
ΤίτλοςCyber-physical LPG debutanizer distillation columns: machine-learning-based soft sensors for product quality monitoringen
ΔημιουργόςRožanec Jože Martinen
ΔημιουργόςTrajkova Elenaen
ΔημιουργόςLu Jinzhien
ΔημιουργόςSarantinoudis Nikolaosen
ΔημιουργόςΣαραντινουδης Νικολαοςel
ΔημιουργόςArampatzis Georgiosen
ΔημιουργόςΑραμπατζης Γεωργιοςel
ΔημιουργόςEirinakis Pavlosen
ΔημιουργόςMourtos Ioannisen
ΔημιουργόςOnat Melike K.en
ΔημιουργόςYilmaz Deren A.en
ΔημιουργόςKošmerlj Aljažen
ΔημιουργόςKenda Klemenen
ΔημιουργόςFortuna Blazen
ΔημιουργόςMladenic Dunjaen
ΕκδότηςMDPIen
ΠερίληψηRefineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models. en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2022-09-20-
Ημερομηνία Δημοσίευσης2021-
Θεματική ΚατηγορίαArtificial intelligenceen
Θεματική ΚατηγορίαExplainable artificial intelligenceen
Θεματική ΚατηγορίαIndustry 4.0en
Θεματική ΚατηγορίαSmart manufacturingen
Θεματική ΚατηγορίαCrude oil distillationen
Θεματική ΚατηγορίαDebutanizationen
Θεματική ΚατηγορίαLPG purificationen
Βιβλιογραφική ΑναφοράJ. M. Rožanec, E. Trajkova, J. Lu, N. Sarantinoudis, G. Arampatzis, P. Eirinakis, I. Mourtos, M. K. Onat, D. A. Yilmaz, A. Košmerlj, K. Kenda, B. Fortuna, and D. Mladenić, “Cyber-physical LPG debutanizer distillation columns: machine-learning-based soft sensors for product quality monitoring,” Appl. Sci., vol. 11, no. 24, Dec. 2021, doi: 10.3390/app112411790.en

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