Data Driven Determination of Reaction Conditions in Oxidative Coupling of Methane via Machine Learning. Issue 17 (30th July 2019)

• Record Type: Journal Article
• Authors: Ohyama, Junya; Nishimura, Shun; Takahashi, Keisuke
• Is Part Of: ChemCatChem. Volume 11:Issue 17(2019)
• Page Start: 4307
• Page End: 4313
• Publication Date: 2019-07-30
• Subjects: Heterogeneous catalysis -- machine learning -- oxidative coupling of methane; Catalysis -- Periodicals; 541.39505
• DOI: 10.1002/cctc.201900843
• Languages: English
• ISSNs: 1867-3880

Abstract

Abstract: The challenge in catalytic reactions lies within its complexity coming from high dimensional experimental factors. In order to solve such complexity, machine learning is implemented to treat experimental conditions in high dimensions. Oxidative coupling of methane, methane to C2 compounds (ethylene and ethane), is chosen as the prototype reaction where 156 data consisting of various experimental conditions is prepared. Machine learning reveals that the relationship between experimental conditions and C2 yield is non‐linear matter. In particular, extreme tree regression is found to accurately reproduce the experimental data. In addition, machine learning predictions can be a good indicator for designing experiments. Thus, machine learning can be a powerful approach towards understanding and determining experimental conditions in high dimension. Abstract : Data science : The challenge in catalytic reactions lies within its complexity coming from high dimensional experimental factors. In order to solve such complexity, machine learning is implemented to treat experimental conditions in high dimensions. Oxidative coupling of methane, methane to C2 compounds (ethylene and ethane), is chosen as the prototype reaction.