Machine learning for the sulfur content prediction in the diesel hydrotreatment product Full article
Journal |
AIP Conference Proceedings
ISSN: 0094-243X , E-ISSN: 1551-7616 |
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Output data | Year: 2020, Number: 2301, Article number : 030001, Pages count : 5 DOI: 10.1063/5.0032742 | ||
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Abstract:
The article analyzes the data of a two-year monitoring of the operation of a diesel hydrotreating unit. The feed characteristics and the unit operation parameters which are most associated with the depth of hydrodesulfurization are selected. Based on the characteristics, the random forest method was used to construct a model for predicting the sulfur content in hydrotreated diesel fuel.
Cite:
Belopukhov E.A.
Machine learning for the sulfur content prediction in the diesel hydrotreatment product
AIP Conference Proceedings. 2020. N2301. 030001 :1-5. DOI: 10.1063/5.0032742 Scopus РИНЦ
Machine learning for the sulfur content prediction in the diesel hydrotreatment product
AIP Conference Proceedings. 2020. N2301. 030001 :1-5. DOI: 10.1063/5.0032742 Scopus РИНЦ
Dates:
Published online: | Dec 8, 2020 |
Identifiers:
Scopus | 2-s2.0-85098063216 |
Elibrary | 45065162 |
OpenAlex | W3111008763 |
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