Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches


Nguyen V. G., Sharma P., Ağbulut Ü., Le H. S., Cao D. N., Dzida M., ...Daha Fazla

International Journal of Green Energy, cilt.21, sa.12, ss.2771-2798, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 21 Sayı: 12
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1080/15435075.2024.2326076
  • Dergi Adı: International Journal of Green Energy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aerospace Database, CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, Greenfile, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.2771-2798
  • Anahtar Kelimeler: Biochar production, explainable artificial intelligence, interpretable machine learning, precise prognostics, sustainable energy
  • Yıldız Teknik Üniversitesi Adresli: Hayır

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.