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Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring
Content Provider | MDPI |
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Author | Jo, že Martin Rožanec Alja, ž Košmerlj Dunja, Mladenić Trajkova, Elena Lu, Jinzhi Sarantinoudis, Nikolaos Arampatzis, George Eirinakis, Pavlos Mourtos, Ioannis Onat, Melike K. Yilmaz, Deren Ataç Kenda, Klemen Fortuna, Blaž |
Copyright Year | 2021 |
Description | 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. |
Starting Page | 11790 |
e-ISSN | 20763417 |
DOI | 10.3390/app112411790 |
Journal | Applied Sciences |
Issue Number | 24 |
Volume Number | 11 |
Language | English |
Publisher | MDPI |
Publisher Date | 2021-12-11 |
Access Restriction | Open |
Subject Keyword | Applied Sciences Industrial Engineering Information and Library Science Artificial Intelligence Explainable Artificial Intelligence Industry 4.0 Smart Manufacturing Crude Oil Distillation Debutanization Lpg Purification |
Content Type | Text |
Resource Type | Article |