Enhancement of biomass energy: thermal conversion, biogas yield, and machine learning insights

Abstract

Biomass is a promising feedstock that can be used to reduce the reliance on fossil fuel sources. This review has two main objectives: (i) to investigate the effects of temperature on the energy yield and higher heating value (HHV) of torrefied feedstocks and (ii) to provide an overview of biomass composition and its impact on conversion processes. The novelty of this review lies in highlighting that the physical and chemical properties of biomass largely depend on feedstock sources, which substantially influence conversion processes. Biomass bonds and moisture content are among the primary challenges for conversion, necessitating the selection of appropriate techniques. This review emphasizes that HHV and energy recovery from biomass raw materials are influenced by temperature when torrefaction and pyrolysis are adopted as the thermal pretreatment processes. HHV and energy recovery values range from 74.4 to 89.3%, when torrefaction is performed at temperatures between 250 and 300 °C. HHV can reach up to 35.26 MJ/kg under appropriate conditions, including the use of suitable feedstocks, temperatures, and durations. Cellulose and hemicellulose are more suitable for biochemical processes and positively impact yields, whereas lignin is better suited for thermochemical techniques but may hinder overall yields due to its recalcitrant nature. Moreover, this review introduces a novel classification system linking biomass composition to optimal conversion pathways, providing a new framework to improve biomass utilization efficiency and renewable energy production. Besides, it highlights the role of machine learning (ML) in predicting biogas yield with an accuracy of up to 1.0, which optimizes biomass conversion and improves energy recovery efficiency.