Deterministic Models for Performance Analysis of Lignocellulosic Biomass Torrefaction

Abstract

Energy plays a key role in the socioeconomic development of society, and most of its global demand is provided by conventional resources (e.g., fossil fuels). Utilizing renewable energy is significantly growing since it can meet global energy demand while minimizing the adverse impacts of carbon emissions on climate change. Biomass is an appealing option among the emerging alternatives (e.g., wind and solar). Torrefaction is a mild pyrolysis process, and this research aims to analyze the torrefaction process of lignocellulosic biomass. The methodology proposed involves employing hybrid models of artificial neural network-particle swarm optimization (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), and coupled simulated annealing-least-squares support vector machine (CSA-LSSVM). In addition to the machine learning algorithms, a correlation is developed using gene expression programming (GEP) to interrelate the biomass properties, including moisture content, volatile matter, fixed carbon, ash, sample size, and the contents of oxygen, carbon, hydrogen, and nitrogen along with the process operating condition encompassing residence time, temperature, and the concentration of CO2, O2, and N2 to the solid yield as the target variable. The results reveal that the CSA-LSSVM model has the highest accuracy, and the statistical metrics of the coefficient of determination (R2), mean square error (MSE), and average absolute relative error percentage (AARE%) are 0.98, 0.00082, and 2.61%, respectively. The parametric sensitivity analysis demonstrates the residence time, temperature, and moisture content as the most influential variables, with temperature playing the most crucial role in the torrefaction process of lignocellulosic biomass. The findings and the developed models can be used to assess similar biomass torrefaction, providing the required knowledge for the modeling and optimization of the process. Hence, the bioenergy industry can be developed with optimal operating conditions, including cost and energy, and lessen the negative impacts of CO2 emission.