Document Type
Journal Article
Publication Title
International Journal of Hydrogen Energy
Volume
59
First Page
1023
Last Page
1041
Publisher
Elsevier
School
School of Engineering
Funders
Beijing Zhong Dian Hua Yuan Environment Protection Technology Co., Ltd. / National Key R&D Program of China / Ningbo Science and Technology Innovation 2025 Key Project / Ningbo NSF key project
Abstract
This review focuses on the parametric impacts upon conversion and selectivity during CO2 hydrogenation via Fischer-Tropsch (FT) synthesis using iron-based catalyst to provide quantitative evaluation. Using all collected data from reported literatures as training dataset via artificial neural networks (ANNs) in TensorFlow, three categorized parameters (namely: operational, catalyst informatic and mass transfer) were deployed to assess their impacts upon conversions (CO2) and selectivity. The lump kinetic power expressions among literature reports were compared, and the best fit model is the one that was proposed by this work without arbitrarily assuming power values of individual partial pressure (CO and H2). More than five sets of binary parameters were systematically investigated to find out corresponding evolving patterns in conversion and selectivity. Aided by machine learning, tailoring product distributions based on specific selectivity or conversion for optimization purpose is practically achievable by deploying the predictions generated from ANNs in this work.
DOI
10.1016/j.ijhydene.2024.02.055
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Hu, J., Wang, Y., Zhang, X., Wang, Y., Yang, G., Shi, L., & Sun, Y. (2024). Parametric analysis of CO2 hydrogenation via fischer-tropsch synthesis: A review based on machine learning for quantitative assessment. International Journal of Hydrogen Energy, 59, article 1023-1041. https://doi.org/10.1016/j.ijhydene.2024.02.055