On the Cover
Considering their physical and chemical properties, biomass feedstocks are complex and diverse composite materials because of various factors such as genotype, harvesting time, planting location, maturity stage, and microclimate. On the other hand, thermochemical conversion processes often used to upgrade biomass feedstocks to biofuels and bioproducts are too complicated due to simultaneous transient heat and mass transfer involving several primary and secondary decomposition reactions. Machine learning technology has recently gained much attention in tackling the nonlinearities and complexities associated with biomass thermochemical conversion. Exploring the challenges and opportunities associated with machine learning techniques, in the March 2023 Issue of Biofuel Research Journal, a team of Chinese researchers critically reviewed the application of these techniques in the thermochemical conversion of biomass. They also put effort into shedding light on the future trends in this domain (DOI: 10.18331/BRJ2023.10.1.4). Cover art by BiofuelResJ. ©2023.
Pages 1764-1773
Filippo Fazzino; Altea Pedullà; Paolo S. Calabrò
Pages 1774-1785
Bilal Kazmi; Syed Imran Ali; Zahoor Ul Hussain Awan
Pages 1786-1809
Hailong Li; Jiefeng Chen; Weijin Zhang; Hao Zhan; Chao He; Zequn Yang; Haoyi Peng; Lijian Leng
Pages 1810-1815
Shabbir H. Gheewala