Hydrodesulfurization of Dibenzothiophene: A Machine Learning Approach

The hydrodesulfurization (HDS) process is widely used in the industry to eliminate sulfur compounds from fuels. However, removing dibenzothiophene (DBT) and its derivatives is a challenge. Here, the key aspects that affect the efficiency of catalysts in the HDS of DBT were investigated using machine...

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Bibliographic Details
Published in:ChemistryOpen (Weinheim) 2024-09, Vol.13 (9), p.e202400062-n/a
Main Authors: Castro, Guadalupe, Cruz‐Borbolla, Julián, Galván, Marcelo, Guevara‐García, Alfredo, Ireta, Joel, Matus, Myrna H., Meneses‐Viveros, Amilcar, Ignacio Perea‐Ramírez, Luis, Pescador‐Rojas, Miriam
Format: Article
Language:English
Subjects:
Algorithms
Catalysts
Chemical reactions
Clean technology
Computational chemistry
Dibenzothiophene
Efficiency
Heterogeneous catalysis
Hydrodesulfurization
Hydrogenation
Lasso regression
Machine learning
Nanomaterials
Pore size
Regression coefficients
Selectivity HYD/DDS
Sulfur compounds
Sulfur content
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Summary:The hydrodesulfurization (HDS) process is widely used in the industry to eliminate sulfur compounds from fuels. However, removing dibenzothiophene (DBT) and its derivatives is a challenge. Here, the key aspects that affect the efficiency of catalysts in the HDS of DBT were investigated using machine learning (ML) algorithms. The conversion of DBT and selectivity was estimated by applying Lasso, Ridge, and Random Forest regression techniques. For the estimation of conversion of DBT, Random Forest and Lasso offer adequate predictions. At the same time, regularized regressions have similar outcomes, which are suitable for selectivity estimations. According to the regression coefficient, the structural parameters are essential predictors for selectivity, highlighting the pore size, and slab length. These properties can connect with aspects like the availability of active sites. The insights gained through ML techniques about the HDS catalysts agree with the interpretations of previous experimental reports. This work analyzes the hydrodesulfurization (HDS) process by compiling data on catalysts derived from MoS2, including their composition, structure, and experimental conditions. Using supervised machine learning methods such as regularized regression and random forest, the study estimates the conversion of DBT and the reaction selectivity. The results obtained allow the identification of an appropriate method for these estimations.
ISSN:2191-1363
2191-1363
DOI:10.1002/open.202400062