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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/0FD7E0E4-71CB-4864-82A7-4B3AE98F5320 | - |
| Identifier | https://doi.org/10.3390/s23187858 | - |
| Identifier | https://www.mdpi.com/1424-8220/23/18/7858 | - |
| Language | en | - |
| Extent | 19 pages | en |
| Title | Soft sensing of LPG processes using deep learning | en |
| Creator | Sifakis Nikolaos | en |
| Creator | Σηφακης Νικολαος | el |
| Creator | Sarantinoudis Nikolaos | en |
| Creator | Σαραντινουδης Νικολαος | el |
| Creator | Tsinarakis Georgios | en |
| Creator | Τσιναρακης Γεωργιος | el |
| Creator | Politis Christos | en |
| Creator | Πολιτης Χρηστος | el |
| Creator | Arampatzis Georgios | en |
| Creator | Αραμπατζης Γεωργιος | el |
| Publisher | MDPI | en |
| Description | This work was supported by the European Union’s Horizon 2020 program project FACTLOG867 under grant agreement number H2020–869951. | en |
| Content Summary | 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 |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2025-02-19 | - |
| Date of Publication | 2023 | - |
| Subject | Industrial monitoring | en |
| Subject | Early fault detection | en |
| Subject | Soft sensors | en |
| Subject | Deep learning | en |
| Subject | Industrial processes | en |
| Subject | Oil refinery | en |
| Bibliographic Citation | 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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