Artificial Neural Network Aided Design of a Stable Co−MgO Catalyst of High-Pressure Dry Reforming of Methane
Author:
Affiliation:
1. Department of Applied Chemistry, Graduate School of Engineering, Tohoku University, Aoba 6-6-07, Aramaki, Aoba-ku, Sendai 980-8579, Japan
Publisher
American Chemical Society (ACS)
Subject
Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/ie049302q
Reference14 articles.
1. High-Quality Transportation Fuels
2. Combinatorial Materials Science and Catalysis
3. Prediction of Effective Additives to a Ni/Active Carbon Catalyst for Vapor-Phase Carbonylation of Methanol by an Artificial Neural Network
4. Combinatorial design of semiconductor chemistry for bandgap engineering: “virtual” combinatorial experimentation
5. Development of a Co-MgO Catalyst for High-pressure Dry Reforming of Methane Based on Design of Experiment, Artificial Neural Network and Grid Search
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