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Hydrolysis of Chlorobenzene to Phenol: Dow Process

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Simple aryl halides do not react with nucleophiles under normal conditions. However, the reaction can proceed under drastic conditions involving high temperatures and high pressure to give the substituted products. For example, chlorobenzene is converted to phenol using aqueous sodium hydroxide at 350 °C under high pressure by the Dow process. The reaction follows an elimination-addition mechanism involving a benzyne intermediate. Here, the chloride ion is...

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Radical substitution reactions can be used to remove functional groups from molecules. The hydrogenolysis of alkyl halides is one such reaction, where the weak Sn–H bond in tributyltin hydride reacts with alkyl halides to form alkanes. Here, the reagent Bu3SnH yields tributyltin halide as a byproduct.
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Scale-up Chemical Synthesis of Thermally-activated Delayed Fluorescence Emitters Based on the Dibenzothiophene-S,S-Dioxide Core

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Published on: October 24, 2017

Hydrodesulfurization of Dibenzothiophene: A Machine Learning Approach.

Guadalupe Castro¹, Julián Cruz-Borbolla², Marcelo Galván¹

¹Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, Av. Ferrocarril San Rafael Atlixco 186, Col. Leyes de Reforma 1 A Sección, Iztapalapa, C.P. 09310, Ciudad de México, México.

|April 12, 2024

Summary

Machine learning models accurately predict hydrodesulfurization catalyst performance for removing challenging sulfur compounds like dibenzothiophene (DBT). Catalyst structural properties, such as pore size, are key to improving selectivity.

Keywords:

Computational chemistry Heterogeneous catalysis Hydrodesulfurization Lasso regression Selectivity HYD/DDS

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Microwave-assisted Intramolecular Dehydrogenative Diels-Alder Reactions for the Synthesis of Functionalized Naphthalenes/Solvatochromic Dyes

Microwave-assisted Intramolecular Dehydrogenative Diels-Alder Reactions for the Synthesis of Functionalized Naphthalenes/Solvatochromic Dyes

Published on: April 1, 2013

Area of Science:

Catalysis
Chemical Engineering
Materials Science

Background:

Hydrodesulfurization (HDS) is crucial for removing sulfur from fuels.
Dibenzothiophene (DBT) and its derivatives are difficult sulfur compounds to eliminate.
Developing efficient HDS catalysts remains an industrial challenge.

Purpose of the Study:

To investigate key factors influencing catalyst efficiency in DBT hydrodesulfurization.
To apply machine learning (ML) algorithms for predicting HDS performance.
To identify critical catalyst structural parameters affecting DBT conversion and selectivity.

Main Methods:

Utilized machine learning regression techniques: Lasso, Ridge, and Random Forest.
Estimated DBT conversion and selectivity based on catalyst properties.
Analyzed regression coefficients to determine the importance of structural parameters.

Main Results:

Random Forest and Lasso regression provided adequate predictions for DBT conversion.
Regularized regression models showed similar, suitable outcomes for selectivity estimation.
Catalyst pore size and slab length were identified as essential predictors for selectivity.

Conclusions:

ML models effectively predict HDS catalyst performance for DBT removal.
Catalyst structural properties significantly influence selectivity.
Findings align with existing experimental data, validating the ML approach.