Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy


Nguyen V. G., Sharma P., Ağbulut Ü., Le H. S., Truong T. H., Dzida M., ...More

Biofuels, Bioproducts and Biorefining, vol.18, no.2, pp.567-593, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Review
  • Volume: 18 Issue: 2
  • Publication Date: 2024
  • Doi Number: 10.1002/bbb.2596
  • Journal Name: Biofuels, Bioproducts and Biorefining
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Communication Abstracts, Compendex, Greenfile, INSPEC, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.567-593
  • Keywords: biochar yield, biomass, data-driven approach, machine learning, precise prognostics, sustainable energy
  • Yıldız Technical University Affiliated: No

Abstract

Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine-learning-based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far-reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data-driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.