Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Examining 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.
Açıklama
Anahtar Kelimeler
Biochar production, explainable artificial intelligence, interpretable machine learning, precise prognostics, sustainable energy, Fast Pyrolysis, Hydrothermal Carbonization, Artificial-Intelligence, Hydrogen-Production, Process Parameters, Gasification, Waste, Torrefaction, Energy, Fuel
Kaynak
International Journal of Green Energy
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
21
Sayı
12
