Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/0166077B-94B2-467A-9543-62A0430A7B4A | - |
| Identifier | https://doi.org/10.3390/app112411790 | - |
| Identifier | https://www.mdpi.com/2076-3417/11/24/11790/htm | - |
| Language | en | - |
| Extent | 26 pages | en |
| Title | Cyber-physical LPG debutanizer distillation columns: machine-learning-based soft sensors for product quality monitoring | en |
| Creator | Rožanec Jože Martin | en |
| Creator | Trajkova Elena | en |
| Creator | Lu Jinzhi | en |
| Creator | Sarantinoudis Nikolaos | en |
| Creator | Σαραντινουδης Νικολαος | el |
| Creator | Arampatzis Georgios | en |
| Creator | Αραμπατζης Γεωργιος | el |
| Creator | Eirinakis Pavlos | en |
| Creator | Mourtos Ioannis | en |
| Creator | Onat Melike K. | en |
| Creator | Yilmaz Deren A. | en |
| Creator | Košmerlj Aljaž | en |
| Creator | Kenda Klemen | en |
| Creator | Fortuna Blaz | en |
| Creator | Mladenic Dunja | en |
| Publisher | MDPI | en |
| Content Summary | Refineries 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 |
| 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 | 2022-09-20 | - |
| Date of Publication | 2021 | - |
| Subject | Artificial intelligence | en |
| Subject | Explainable artificial intelligence | en |
| Subject | Industry 4.0 | en |
| Subject | Smart manufacturing | en |
| Subject | Crude oil distillation | en |
| Subject | Debutanization | en |
| Subject | LPG purification | en |
| Bibliographic Citation | J. 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 |
Copyright © DIAS 2013
