Machine learning optimization for catalytic desulfurization of petroleum: Multi-layered perceptron, Multi Task Lasso, and Gaussian process regression models-Reference-Cited by-同舟云学术

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Machine learning optimization for catalytic desulfurization of petroleum: Multi-layered perceptron, Multi Task Lasso, and Gaussian process regression models

Published:2024-04 Issue: Volume:400 Page:124508
ISSN:0167-7322
Container-title:Journal of Molecular Liquids
language:en
Short-container-title:Journal of Molecular Liquids

Author:

Xu Rongjing,Yang Xianjun

Publisher

Elsevier BV

Reference29 articles.

1. Evaluation of potassium glycinate, potassium lysinate, potassium sarcosinate and potassium threonate solutions in CO2 capture using membranes;Marjani;Arab. J. Chem.,2021

2. Separation and purification of light hydrocarbon isomers in metal-organic frameworks;Guo;J. Environ. Chem. Eng.,2023

3. Single-step ethylene purification from ternary C2 hydrocarbon mixtures in a scalable metal–organic framework;Kim;Chem. Eng. J.,2023

4. Photocatalytic air purification of polycyclic aromatic hydrocarbons: application of a flow-through reactor, kinetic studies and degradation pathways;Schnabel;J. Photochem. Photobiol. A Chem.,2022

5. Multiobjectives optimization in petroleum refinery catalytic desulfurization using machine learning approach;Al-Jamimi;Fuel,2022

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