Guadalupe Castro, Julián Cruz-Borbolla, Marcelo Galván, Alfredo Guevara-García, Joel Ireta, Myrna H Matus, Amilcar Meneses-Viveros, Luis Ignacio Perea-Ramírez, Miriam Pescador-Rojas
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...
April 12, 2024: ChemistryOpen