Exploring Insights in Biomass and Waste Gasification via Ensemble Machine Learning Models and Interpretability Techniques

Abstract

This comprehensive review delves into the intersection of ensemble machine learning models and interpretability techniques for biomass and waste gasification, a field crucial for sustainable energy solutions. It tackles challenges like feedstock variability and temperature control, highlighting the need for deeper understanding to optimize gasification processes. The study focuses on advanced modeling techniques like random forests and gradient boosting, alongside interpretability methods like the Shapley additive explanations and partial dependence plots, emphasizing their importance for transparency and informed decision- making. Analyzing diverse case studies, the review explores successful applications while acknowledging challenges like overfitting and computational complexity, proposing strategies for practical and robust models. Notably, the review finds ensemble models consistently achieve high prediction accuracy (often exceeding R2 scores of 0.9) for gas composition, yield, and heating value. These models (34% of reviewed papers) are the most applied method, followed by artificial neural networks (26%). Heating value (12%) was the most studied performance metric. However, interpretability is often neglected during model development due to the complexity of techniques like permutation and Gini importance. The paper calls for dedicated research on utilizing and interpreting ensemble models, especially for co-gasification scenarios, to unlock new insights into process synergy. Overall, this review serves as a valuable resource for researchers, practitioners, and policymakers, offering guidance for enhancing the efficiency and sustainability of biomass and waste gasification.