Proxanal-based predictive model for estimating ultanal attributes of lignocellulosic biomass

Abstract

Lignocellulosic materials represent one of the clean alternative energy sources that have carbon in their building blocks, which can be processed into liquid biofuel and useful chemicals. Elemental compositions of biomass such as carbon (C), hydrogen (H) and oxygen (O) are key indicators for establishing calorific value, energy efficiency and carbon footprint during direct application as fuel and feedstock in thermochemical conversion. These characteristics usually require very expensive equipment, which may not always be readily available for examination of biomass feedstock. This study presents a new predictive non-linear model for ultanal characteristics of lignocellulosic biomass (C, H and O) derived from the proxanal attributes such as fixed carbon (FC), volatile matter (VM) following least square method. Four hundred and fifty (450) proximate analysis data from literature were used for model development and fifty (50) experimentally determined data points for model validation. The elemental composition {C=C[VMFC,(VM)²,(FC)], H=H[(VMFC),VM,FC] and O=O[(VM)^0.75,(1/FC)^0.33]} prediction models were developed and evaluated using indices such as average absolute percentage error (AAPE), average bias percentage error (ABPE) and coefficient of determination (R-squared). The results of analysis showed AAPE, ABEP and R-squared of 2.12%, 0.06% and 0.9993; 2.88%, 0.11% and 0.9989; 3.16%, -0.04% and 0.9982 for C, H and O model respectively. This suggests that the developed models could be used to predict the ultanal attributes of lignocellulosic biomass within 60<VM<90 and 10<FC<30 with high fidelity. The models would serve as a quick means of assessing lignocellulosic biomass prior to any bioenergy application.