Ind. Eng. Chem. Res. 2022, 61, 14, 4752–4762, https://doi.org/10.1021/acs.iecr.1c03995
Systematic data-driven modeling of bimetallic catalyst performance for the hydrogenation of 5-ethoxymethylfurfural with variable selection and regularization
|Author:||Uusitalo, Pekka1; Sorsa, Aki1; Abegão, Fernando Russo2;|
1Environmental and Chemical Engineering Research Unit, Control Engineering Group, Faculty of Technology, P.O. Box 4300, University of Oulu, Oulu 90014, Finland
2School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
|Online Access:||PDF Full Text (PDF, 1.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022042630433
American Chemical Society,
|Publish Date:|| 2022-04-26
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 heterogeneous catalysts with relevant descriptors. A systematic approach for selecting the most appropriate ML method is taken with focus on the variable selection. Regularization algorithms were applied to variable selection. Several different candidate model structures were compared in modeling with interpretation of results. The systematic modeling approach presented aims to highlight the necessary tools and aspects to unexperienced users of ML. Literature datasets for the hydrogenation of 5-ethoxymethylfurfural with simple bimetal catalysts, including main metals and promoters, were studied with the addition of catalyst descriptors found in the literature. Good results were obtained with the best models for estimating conversion, selectivity, and yield with correlations between 0.90 and 0.98. The best identified model structures were support vector regression, Gaussian process regression, and decision tree methods. In general, the use of variable selection procedures was found to improve the performance of models. The modeling methods applied thus seem to exhibit a strong potential in aiding catalyst development based mainly on the information content of descriptor datasets.
Industrial & engineering chemistry research
|Pages:||4752 - 4762|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
116 Chemical sciences
215 Chemical engineering
220 Industrial biotechnology
This project has received funding from the Bio-based Industries Joint Undertaking (JU) under the European Union’s Horizon 2020 research and innovation program under grant agreement no. 887226. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Bio-based Industries Consortium.
|EU Grant Number:||
(887226) BioSPRINT - Improve biorefinery operations through process intensification and new end products
© 2022 The Authors. Published by American Chemical Society. CC BY.