A feature-centric predictive and sensitivity analysis framework for bio-oil yield assessment in biomass pyrolysis

Abstract

Accurately predicting bio-oil yield from biomass pyrolysis is a real challenge due to nonlinear interactions between feedstock physicochemical properties and operating conditions. In addition, high feature dimensionality, uncertainties in experimental measurements, and multicollinearity make prediction accuracy and interpretability even more difficult, which explains the demand for a feature-driven and explainable modeling framework. We developed an advanced regression feature-based machine learning framework trained on a very comprehensive dataset of both biomass characteristics and pyrolysis conditions. Correlation-based feature screening was performed to identify informative variables and examine potential redundancy among input parameters before predictive modeling. The model’s performance in the training, validation, and testing phases was assessed using a variety of statistical metrics. To keep the model understandable, global sensitivity analysis and feature attribution methods were used to determine the relative impacts of each input and their combined effects on yield variation. The suggested system design attained very accurate predictions, demonstrating a test-stage coefficient of determination over 0.91 and consistently low prediction errors. Sensitivity analysis identified working temperature and volatile content as the key substrates for yield, followed by fixed carbon and ash content, while elemental hydrogen and oxygen showed condition-dependent behavior. The agreement in feature scoring between sensitivity methods demonstrates the reliability of the concept-based interpretation. The primary novelty of the paper is incorporating feature selection, predictive modeling, and sensitivity analysis into one feature-oriented yield prediction framework. Unlike purely accuracy-driven studies, this approach simultaneously enhances predictive performance and interpretability, providing quantitative insight into feature importance and system sensitivity.