Interpretable Feedforward Neural Network and XGBoost-Based Algorithms to Predict CO<sub>2</sub> Solubility in Ionic Liquids-Reference-Cited by-同舟云学术

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Interpretable Feedforward Neural Network and XGBoost-Based Algorithms to Predict CO₂ Solubility in Ionic Liquids

Published:2024-04-23 Issue:18 Volume:63 Page:8293-8305
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Yang Ao¹^ORCID,Sun Shirui²,Mi Hongfu¹,Wang Wenhe¹^ORCID,Liu Jin¹,Kong Zong Yang³^ORCID

Affiliation:

1. College of Safety Engineering, Chongqing University of Science and Technology, Chongqing 401331, P. R. China

2. College of Chemistry and Chemical Engineering, Yangtze Normal University, Fuling, Chongqing 408100, P. R. China

3. Department of Engineering, School of Engineering and Technology, Sunway University, Bandar Sunway, Selangor 47500, Malaysia

Funder

Chongqing Municipal Education Commission

Natural Science Foundation of Chongqing Municipality

National Natural Science Foundation of China

Yangtze Normal University

Chongqing Special Support Fund for Post Doctor

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.4c00397

Reference40 articles.

1. Efficient Separation of Methyl tert-Butyl Ether Using Ionic Liquids from Computational Thermodynamics to Process Intensification

2. Review of pre-combustion capture and ionic liquid in carbon capture and storage

3. Ionic-Liquid-Based CO2 Capture Systems: Structure, Interaction and Process

4. Ionic liquids for advanced materials

5. Applications of ionic liquids in the chemical industry

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