Modeling of H<sub>2</sub>S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches.

Mousavi, Seyed-Pezhman; Nakhaei-Kohani, Reza; Atashrouz, Saeid; Hadavimoghaddam, Fahimeh; Abedi, Ali; Hemmati-Sarapardeh, Abdolhossein; Mohaddespour, Ahmad

Modeling of H₂S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches.

Mousavi, Seyed-Pezhman; Nakhaei-Kohani, Reza; Atashrouz, Saeid; Hadavimoghaddam, Fahimeh; Abedi, Ali; Hemmati-Sarapardeh, Abdolhossein; Mohaddespour, Ahmad.

Afiliação

Mousavi SP; Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Nakhaei-Kohani R; Department of Chemical and Petroleum Engineering, Shiraz University, Shiraz, Iran.
Atashrouz S; Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. s.atashrouz@gmail.com.
Hadavimoghaddam F; Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing, 163318, China.
Abedi A; Ufa State Petroleum Technological University, Ufa, 450064, Russia.
Hemmati-Sarapardeh A; College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait.
Mohaddespour A; Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. hemmati@uk.ac.ir.

Sci Rep ; 13(1): 7946, 2023 May 16.

Article em En | MEDLINE | ID: mdl-37193679

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Irã

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