ArticleScientific reports2026
Machine learning-driven predictive modeling and process optimization of one-pot biomass conversion to FDCA via heterogeneous catalysis.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The modelling of cascade reactions is currently playing a major role in the scale-up of industrial processes, particularly in biomass valorization. This study investigates machine learning (ML) techniques to optimize the reaction conditions of one-pot synthesis of 2,5-furandicarboxylic acid (FDCA), a biobased platform chemical, from sugarcane bagasse via Fe-Mn zeolite catalyst. The objective of the work is to evaluate various ML regressor models for predicting FDCA yield and selectivity, particularly in the context of limited data sets generated through box-Behnken design of experiments. Three different ML models, such as ridge regression, support vector regressor (SVR), and gradient boosting regression (GBR), were compared to identifying the most suitable model for accurate prediction. Among the models, the ridge regression approach demonstrated superior performance to the lowest mean absolute error (MAE) of 0.595 and the highest coefficient of determination (R
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