Machine learning framework to predict product distribution of lignocellulosic biomass pyrolysis

Machine learning methods have become a trend to model distinct chemical processes, as an alternative to complex first-principles models. Given the complexity of biomass pyrolysis mechanisms, these methods offer a promising approach but often face challenges regarding data scarcity and lack of interpretability. This study aims to develop an interpretable framework for modeling biomass pyrolysis using data from fixed-bed lignocellulosic biomass pyrolysis experiments. A mass change basis was proposed to construct machine learning models, including artificial neural network (ANN) and symbolic regression (SR) models. Feature importance was assessed using Shapley Additive Explanations (SHAP) and compared to Partial Least Squares (PLS) regression, with PLS consistently identifying the best features for symbolic regression. Both ANN and SR models showed similar accuracy, achieving coefficient of determination (R²) greater than 0.85 across all phase products in the testing set. Additionally, an uncertainty assessment of SR parameters was conducted to improve model robustness ensuring prediction stability. SR models exhibited superior generalization capacity during extrapolation tests, achieving R² values above 0.9 for char and gas phases. For oil values exceeding 10 grams, the SR models struggled with generalization. Overall, the proposed framework provides a valuable tool for interpreting and modeling pyrolysis process data, enabling its use in the decision-making process.