Systematic Data-Driven Modeling of Bimetallic Catalyst Performance for the Hydrogenation of 5-Ethoxymethylfurfural with Variable Selection and Regularization

[Image: see text] Catalyst development for biorefining applications involves many challenges. Mathematical modeling can be seen as an essential tool in assisting to explain catalyst performance. This paper presents studies on several machine learning (ML) methods that can model the performance of he...

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Detalles Bibliográficos
Autores principales: Uusitalo, Pekka, Sorsa, Aki, Russo Abegão, Fernando, Ohenoja, Markku, Ruusunen, Mika
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9014324/
https://www.ncbi.nlm.nih.gov/pubmed/35450012
http://dx.doi.org/10.1021/acs.iecr.1c03995

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9014324/
https://www.ncbi.nlm.nih.gov/pubmed/35450012
http://dx.doi.org/10.1021/acs.iecr.1c03995

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