Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 23 October 2021 / Approved: 25 October 2021 / Online: 25 October 2021 (15:43:08 CEST)

How to cite: Rožanec, J.M.; Trajkova, E.; Lu, J.; Sarantinoudis, N.; Arampatzis, G.; Eirinakis, P.; Mourtos, I.; Onat, M.K.; Ataç Yilmaz, D.; Košmerlj, A.; Kenda, K.; Fortuna, B.; Mladenić, D. Cyber-Physical LPG Debutanizer Distillation Columns: Machine Learning-Based Soft Sensors for Product Quality Monitoring. Preprints 2021, 2021100364 (doi: 10.20944/preprints202110.0364.v1). Rožanec, J.M.; Trajkova, E.; Lu, J.; Sarantinoudis, N.; Arampatzis, G.; Eirinakis, P.; Mourtos, I.; Onat, M.K.; Ataç Yilmaz, D.; Košmerlj, A.; Kenda, K.; Fortuna, B.; Mladenić, D. Cyber-Physical LPG Debutanizer Distillation Columns: Machine Learning-Based Soft Sensors for Product Quality Monitoring. Preprints 2021, 2021100364 (doi: 10.20944/preprints202110.0364.v1).

Abstract

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 to provide equipment state monitoring services and to generate 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 by 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.

Keywords

Artificial Intelligence; Machine Learning; Explainable Artificial Intelligence; Soft Sensors; Industry 4.0; Smart Manufacturing; Cyber-Physical System; Crude Oil Distillation; Debutanization; LPG Purification

Subject

ENGINEERING, Energy & Fuel Technology

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