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Machine learning applied to predict key petroleum crude oil constituents

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dc.contributor.author Mohanta, Hare Krishna
dc.contributor.author Sande, Priya Christina
dc.date.accessioned 2025-04-09T04:41:34Z
dc.date.available 2025-04-09T04:41:34Z
dc.date.issued 2023-10
dc.identifier.uri https://onlinelibrary.wiley.com/doi/full/10.1002/ceat.202300192
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/18579
dc.description.abstract Sulfur compounds are the most important inorganic constituents of petroleum and require to be estimated beforehand because of their corrosive nature and other processing anomalies during crude oil processing. Paraffins, naphthene, and aromatics form the bulk of crude oil. Machine learning (ML) predictions of these constituents were made by training the ML model with a diverse industrial data set of 515 oils. The XGBoost model gave an excellent R2 in the range 0.88–0.99 for the bulk compounds. R2 for sulfur was in the modest range of 0.45–0.6, which improved significantly to 0.8 for additional inputs. ML applicability was thereby found to depend on the nature of the constituent. This work furthers ML-based predictions, with the incentive of reducing expensive spectroscopic analytical methods. en_US
dc.language.iso en en_US
dc.publisher Wiley en_US
dc.subject Chemical engineering en_US
dc.subject Machine learning (ML) en_US
dc.subject XGBoost en_US
dc.title Machine learning applied to predict key petroleum crude oil constituents en_US
dc.type Article en_US


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