Machine learning–driven predictive modeling and process optimization of one-pot biomass conversion to FDCA via heterogeneous catalysis

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 (R2) of 95.6% for FDCA yield. The SVR model optimized reaction conditions as 165.65 °C, 5.41 h, and 0.80 g of catalyst dosage to yield 66.65% of FDCA. The proposed ML-based regressor provides new insights into effectively handling small data sets and highlights the potential of machine learning for reliable prediction and process optimization in biomass conversion to FDCA.