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Machine-learning-based soft sensors for energy efficient operation of crude distillation units

Rožanec, Jože Martin, Trajkova, Elena, Onat Melike K., Sarantinoudis Nikolaos, Arampatzis Georgios, Fortuna Blaž, Mladenić, Dunja

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/E5E6E326-CA91-4E67-98C4-03DF4F64D36D-
Αναγνωριστικόhttps://doi.org/10.1109/ICECET55527.2022.9872983-
Αναγνωριστικόhttps://ieeexplore.ieee.org/document/9872983-
Γλώσσαen-
Μέγεθος6 pagesen
ΤίτλοςMachine-learning-based soft sensors for energy efficient operation of crude distillation unitsen
ΔημιουργόςRožanec, Jože Martinen
ΔημιουργόςTrajkova, Elenaen
ΔημιουργόςOnat Melike K.en
ΔημιουργόςSarantinoudis Nikolaosen
ΔημιουργόςΣαραντινουδης Νικολαοςel
ΔημιουργόςArampatzis Georgiosen
ΔημιουργόςΑραμπατζης Γεωργιοςel
ΔημιουργόςFortuna Blažen
ΔημιουργόςMladenić, Dunjaen
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠεριγραφήThis work was supported by the European Union’s Horizon 2020 program project STAR under grant agreement number H2020-956573.en
ΠερίληψηThe oil refining industry is considered one of the largest energy-consuming industrial sectors worldwide and the third-largest global source of greenhouse gas emissions. In addition, increasingly restrictive environmental quality specifications for oil products worldwide require increased use of energy during the distillation process, further increasing the emissions. Therefore, energy usage reduction could help in two ways: to reduce the amount of greenhouse gas emissions and maximize the profits of the oil refining plants. The development and use of soft sensors to monitor the crude refining process in real-time enables timely insights and decision-making to ensure the products meet the required quality. Furthermore, the same approach can be used to further optimize the energy consumption over the multiple stages of the refining process. In this paper, we address the problem of predicting the energy consumption in a crude oil refinery. We do so on a real-world use case, with data obtained from a Tüpras refinery. We found the best performance was achieved with a CatBoost regressor when performing a tenfold cross-validation and assessing their significance against other models when comparing confidence intervals at a 95% level of significance.en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2024-11-25-
Ημερομηνία Δημοσίευσης2022-
Θεματική ΚατηγορίαPetroleum refineryen
Θεματική ΚατηγορίαCrude distillation uniten
Θεματική ΚατηγορίαEnergy efficiencyen
Θεματική ΚατηγορίαArtificial Intelligenceen
Θεματική ΚατηγορίαSoft Sensorsen
Βιβλιογραφική ΑναφοράJ. M. Rožanec, E. Trajkova, M. K. Onat, N. Sarantinoudis, G. Arampatzis, B. Fortuna, and D. Mladenić, "Machine-learning-based soft sensors for energy efficient operation of crude distillation units," in Proceedings of the International Conference on Electrical, Computer and Energy Technologies (ICECET 2022), Prague, Czech Republic, 2022, doi: 10.1109/ICECET55527.2022.9872983.en

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