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Energy primarily comes from fossil fuels, which leads to environmental deterioration through increased carbon dioxide load and other greenhouse gases in the atmosphere. Renewable energy is a cheap alternative, and biomass, like municipal solid wastes (MSWs), can be suitably used for energy production. This paper reviews the impact of variations in MSW composition on its physical, chemical, and lignocellulosic properties. It further illustrates how these properties affect torrefaction products. It was observed that MSW can refer to either a combination of different waste types or independent wastes; hence, there is no standard composition of MSW. The variations in composition are responsible for fluctuating physical, chemical, and lignocellulosic properties. These properties, along with torrefaction process parameters, simultaneously affect the torrefied product, whereas lignocellulosic properties influence the biochar yield, and physical and chemical properties impact calorific value and ash content. Torrefying MSW containing low moisture content yields biochar with a high calorific value. Methods to improve the lignocellulosic properties of MSW have not been studied. Research is needed to assess the possibility of improving biochar yields in MSW by enhancing lignin percentages, possibly through blending MSW. A guide on the best blend combinations and ratios is required. Also, it is crucial to study optimal torrefaction process parameters. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]