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.
