Predicting Diffusion Coefficients of Binary and Ternary Supercritical Water Mixtures via Machine and Transfer Learning with Deep Neural Network-Reference-Cited by-同舟云学术

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Predicting Diffusion Coefficients of Binary and Ternary Supercritical Water Mixtures via Machine and Transfer Learning with Deep Neural Network

Published:2022-02-25 Issue:24 Volume:61 Page:8542-8550
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Zhao Xiao¹²,Luo Tengfei²^ORCID,Jin Hui¹^ORCID

Affiliation:

1. State Key Laboratory of Multiphase Flow in Power Engineering (SKLMF), Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China

2. Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States

Funder

Ministry of Science and Technology of the People's Republic of China

China Scholarship Council

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.2c00017

Reference67 articles.

1. Studies in Diffusion

3. Atomically Detailed Modeling of Metal Organic Frameworks for Adsorption, Diffusion, and Separation of Noble Gas Mixtures

4. Connecting the Ion Separation Factor to the Sorption and Diffusion Selectivity of Ion Exchange Membranes

5. Understanding the Role of Molecular Diffusion and Catalytic Selectivity in Liquid-Phase Beckmann Rearrangement

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