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Article

Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

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Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia
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Qlector d.o.o., Rovšnikova 7, 1000 Ljubljana, Slovenia
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Jožef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia
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Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia
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EPFL SCI-STI-DK, Station 9, CH-1015 Lausanne, Switzerland
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School of Production Engineering and Management, Akrotiri Campus, Technical University of Crete, 731 00 Chania, Greece
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Department of Industrial Management and Technology, University of Piraeus, Karaoli and Dimitriou 80, 185 34 Pireas, Greece
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Department of Management Science and Technology, Athens University of Economics and Business, Patission 76, 104 34 Athens, Greece
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Tüpras, Körfez-Kocaeli 41780, Turkey
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Author to whom correspondence should be addressed.
Academic Editor: Vincent A. Cicirello
Appl. Sci. 2021, 11(24), 11790; https://doi.org/10.3390/app112411790
Received: 23 October 2021 / Revised: 24 November 2021 / Accepted: 3 December 2021 / Published: 11 December 2021
(This article belongs to the Special Issue Smart Resilient Manufacturing)
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. View Full-Text
Keywords: artificial intelligence; explainable artificial intelligence; Industry 4.0; smart manufacturing; crude oil distillation; debutanization; LPG purification artificial intelligence; explainable artificial intelligence; Industry 4.0; smart manufacturing; crude oil distillation; debutanization; LPG purification
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MDPI and ACS Style

Rožanec, J.M.; Trajkova, E.; Lu, J.; Sarantinoudis, N.; Arampatzis, G.; Eirinakis, P.; Mourtos, I.; Onat, M.K.; Yilmaz, D.A.; Košmerlj, A.; Kenda, K.; Fortuna, B.; Mladenić, D. Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring. Appl. Sci. 2021, 11, 11790. https://doi.org/10.3390/app112411790

AMA Style

Rožanec JM, Trajkova E, Lu J, Sarantinoudis N, Arampatzis G, Eirinakis P, Mourtos I, Onat MK, Yilmaz DA, Košmerlj A, Kenda K, Fortuna B, Mladenić D. Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring. Applied Sciences. 2021; 11(24):11790. https://doi.org/10.3390/app112411790

Chicago/Turabian Style

Rožanec, Jože Martin, Elena Trajkova, Jinzhi Lu, Nikolaos Sarantinoudis, George Arampatzis, Pavlos Eirinakis, Ioannis Mourtos, Melike K. Onat, Deren Ataç Yilmaz, Aljaž Košmerlj, Klemen Kenda, Blaž Fortuna, and Dunja Mladenić. 2021. "Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring" Applied Sciences 11, no. 24: 11790. https://doi.org/10.3390/app112411790

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