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

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.