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.