Increasing Ethene Yield via Oxidative Coupling of Methane at Low Temperature: An Application of Machine Learning and DFT in the Design and Innovation of Effective Catalyst Compositions
Author:
Affiliation:
1. University of Regina,Clean Energy Technologies Research Institute (CETRI), Faculty of Engineering and Applied Science,Regina,Canada
2. University of Regina,Software Systems Engineering, Faculty of Engineering and Applied Science,Regina,Canada
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10288628/10288648/10288945.pdf?arnumber=10288945
Reference5 articles.
1. High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane
2. Direct Design of Catalysts in Oxidative Coupling of Methane via High‐Throughput Experiment and Deep Learning
3. ReLU-based — cvnn 0.1.0 documentation,0
4. Optimization of the Oxidative Coupling of Methane Process for Ethylene Production;alkathiri;Process,2022
5. Noncatalytic Oxidative Coupling of Methane (OCM): Gas-Phase Reactions in a Jet Stirred Reactor (JSR)
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1. A review of the application of Density Functional Theory and machine learning for oxidative coupling of methane reaction for ethylene production;Chemical Engineering Communications;2024-04-08
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