Prediction of Biochar Yield and Persistent Free Radical Generation for Lignocellulose Biomass Using a Detailed Analysis of Chemical Composition

Abstract

Biochar production is essential for the development of a sustainable environment. However, it is still unknown how biochar yield and properties are dependent on biomass properties. Biochar yield and persistent free radical (PFR) characteristics (representing biochar reactivity) were estimated based on feedstock compositions in this study. Cellulose, hemicellulose, and lignin were selected as biopolymers, and rice straw, peanut hull, and pine sawdust were selected as lignocellulose biomass residues to produce biochar. A prediction model of biochar yield was proposed based on the lignocellulose compositions of the feedstock and was verified using published data. A linear model could well predict biochar yield based on these compositions (Radj2 = 0.98). The elemental and structural arrangements directly determine the PFR characteristics of biochar. The PFR intensity could be reliably predicted from the C concentration (Radj2 = 0.80). An empirical equation was established between g-Factor and H/C and verified using 40 types of lignocellulose-derived biochar. The established estimation equations will be useful to guide biochar production and application.

Keywords: Biochar yield, Persistent free radicals, prediction, Lignocellulose Biomass