Numerical investigation of sugarcane bagasse gasification using Aspen Plus and response surface methodology

Abstract

Efficient utilization of biomass as an alternative energy resource to fossil fuel has been considered the most promising clean energy option. Gasification technology is at the forefront of biomass conversion amidst other technologies due to its high flexibility in utilizing various kinds of biomass feedstocks. In this study, a thermodynamic model of gasification of sugarcane bagasse with air as the gasifying agent is developed to predict the composition of syngas in a downdraft gasifier using Aspen Plus software at various operating conditions. The model is validated with published experimental results from previous studies. A sensitivity analysis is performed to study the influence of the main operating parameters, namely; gasification temperature, moisture content (MC), and equivalence ratio (ER) on the syngas composition, syngas yield (Qyield), lower heating value of syngas (LHVSyngas), cold gas efficiency (CGE), and carbon conversion efficiency (CCE). Furthermore, response surface methodology is applied to study the combined effects of the main operating parameters and thus determine the optimized zone of the operating condition for maximumLHVSyngas, CGE, hydrogen production, and minimum carbon dioxide production. The regression models for lower heating value, cold gas efficiency, and the concentration of syngas (CO2, and H2) generated from the ANOVA tool are found to have a high degree of accuracy. The optimal operating condition of the gasification temperature, equivalence ratio, and moisture content for maximumLHVSyngas, CGE,H2concentration and minimum carbon dioxide concentration is found to be 877.27 °C, 0.08, and 10%, respectively with the corresponding optimal product values of 7.92 MJ/Nm3, 74.22 %, 31.24%, and 3.91%. The findings of this study show that a blend of simulation with advanced optimization tools can indeed achieve optimal operating conditions of a gasification system at a more refined precision.