Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/0FD7E0E4-71CB-4864-82A7-4B3AE98F5320 | - |
| Αναγνωριστικό | https://doi.org/10.3390/s23187858 | - |
| Αναγνωριστικό | https://www.mdpi.com/1424-8220/23/18/7858 | - |
| Γλώσσα | en | - |
| Μέγεθος | 19 pages | en |
| Τίτλος | Soft sensing of LPG processes using deep learning | en |
| Δημιουργός | Sifakis Nikolaos | en |
| Δημιουργός | Σηφακης Νικολαος | el |
| Δημιουργός | Sarantinoudis Nikolaos | en |
| Δημιουργός | Σαραντινουδης Νικολαος | el |
| Δημιουργός | Tsinarakis Georgios | en |
| Δημιουργός | Τσιναρακης Γεωργιος | el |
| Δημιουργός | Politis Christos | en |
| Δημιουργός | Πολιτης Χρηστος | el |
| Δημιουργός | Arampatzis Georgios | en |
| Δημιουργός | Αραμπατζης Γεωργιος | el |
| Εκδότης | MDPI | en |
| Περιγραφή | This work was supported by the European Union’s Horizon 2020 program project FACTLOG867 under grant agreement number H2020–869951. | en |
| Περίληψη | This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery’s LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2025-02-19 | - |
| Ημερομηνία Δημοσίευσης | 2023 | - |
| Θεματική Κατηγορία | Industrial monitoring | en |
| Θεματική Κατηγορία | Early fault detection | en |
| Θεματική Κατηγορία | Soft sensors | en |
| Θεματική Κατηγορία | Deep learning | en |
| Θεματική Κατηγορία | Industrial processes | en |
| Θεματική Κατηγορία | Oil refinery | en |
| Βιβλιογραφική Αναφορά | N. Sifakis, N. Sarantinoudis, G. Tsinarakis, C. Politis and G. Arampatzis, “Soft sensing of LPG processes using deep learning,” Sensors, vol. 23, no. 18, Sep. 2023, doi: 10.3390/s23187858. | en |
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