Equilibrium modeling and regression analysis of biomass gasification

Abstract

In the perspective of fossil fuel depletion, global warming, climatic changes, and atmospheric pollution, the importance of renewable fuels is remarkable. Bio-hydrogen produced from locally available biomass is an efficient source of energy. For achieving hydrogen richness in gas mixture produced by thermo-chemical gasification of biomass, gasification agent used and its feed rate, and gasifier working conditions are prominent among many other factors. In the present work, formulation and simulation of a thermodynamic equilibrium model for biomass steam gasification, incorporating the yields of unconverted char and tar (in addition to gaseous products) based on equilibrium constant method is presented. The model is based on Redlich-Kwong real gas equation of state considering the equilibrium constants of two component reactions, namely methane formation and water gas shift. A generic method for the determination of equilibrium constants from the corresponding Gibbs free energy changes, incorporating real gas residual properties is adopted. Computations including solution of simultaneous non-linear equations are performed using Gauss–Newton algorithm. Producer gas composition, heating value of gas mixture (LHV), dry gas yield, and relative yields of gasification products are studied using apricot stones as the illustrative biomass material. Parametric variations with steam to biomass ratio (SBR) and temperature are analyzed and validated with reported experimental results on steam gasification. The richest hydrogen gas mixture (58.7% at SBR of 2.2) is predicted at 1000 K. SBR is found to be favorable to hydrogen concentration and combustible gas yield. But heating value (MJ/Nm3) can be enhanced with hike in temperature of gasification only. More than 45% (77% excluding steam) is dry gas in the products of gasification above 700 K. Regression analysis is performed using the data for thirty five biomass materials simulated by the real gas model. Based on the statistically tested regression model for accuracy, parametric correlations are formulated for LHV and product gas composition for steam gasification.