Nguyen, Van GiaoSharma, PrabhakarAgbulut, UemitLe, Huu SonCao, Dao NamDzida, MarekOsman, Sameh M.2024-08-232024-08-2320241543-50751543-5083https://doi.org/10.1080/15435075.2024.2326076https://hdl.handle.net/20.500.12684/14194Examining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass sources. The study argues that white-box models, which are more open and comprehensible, are crucial for biochar prediction in light of the increasing suspicion of black-box models. Accurate forecasts are guaranteed by these explainable AI systems, which also give detailed explanations of the mechanisms generating the outcomes. For prediction models to gain confidence and for biochar production processes to enable informed decision-making, there must be an emphasis on interpretability and openness. The paper comprehensively synthesizes the most critical features of biochar prediction by a rigorous assessment of current literature and relies on the authors' own experience. Explainable machine learning techniques encourage ecologically responsible decision-making by improving forecast accuracy and transparency. Biochar is positioned as a crucial participant in solving global concerns connected to soil health and climate change, and this ultimately contributes to the wider aims of environmental sustainability and renewable energy consumption.en10.1080/15435075.2024.2326076info:eu-repo/semantics/closedAccessBiochar productionexplainable artificial intelligenceinterpretable machine learningprecise prognosticssustainable energyFast PyrolysisHydrothermal CarbonizationArtificial-IntelligenceHydrogen-ProductionProcess ParametersGasificationWasteTorrefactionEnergyFuelReview2112277127982-s2.0-85188314940WOS:001185059000001Q2N/A