2024
DOI: 10.1155/2024/6087208
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Exploring Insights in Biomass and Waste Gasification via Ensemble Machine Learning Models and Interpretability Techniques

Ocident Bongomin,
Charles Nzila,
Josphat Igadwa Mwasiagi
et al.

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 addi… Show more

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