Recently more and more attentions have been paid on producing biofuels and bioenergy from lignocellulosic biomass such as forest residue, agricultural residue (corn stover), and energy crops (hybrid poplar, switchgrass) etc., because of the limited amount of petroleum available and its increasing price, the increasing demand of energy, especially in the fast-developing countries such as China and India, the need for the national energy independence and safety, and the requirement of reduction in green house gas emission.

In conventional Kraft pulp mills, the residual black liquor from the pulping process containing hemicellulose, lignin, and inorganic chemicals (i.e. Na2S and NaOH used in pulping) is currently burnt in the Tomlinson recovery boiler for recovery of the inorganic chemicals and generation of energy (heat or process steam and power) by combustion. Even though this contributes to about two-thirds of the total energy demand in a pulp mill, this is not highly efficient owing to the inherent lower efficiency of the steam turbine used to generate power. Instead of direct combustion, the gasification of black liquor into syngas and then use of gas turbine to generate power can provide increased efficiency and environmental benefits. Also, syngas produced from gasification can be used for Fisher -Tropsch synthesis into platform of biochemicals and biofuels. Therefore, it is necessary to modify the conventional Kraft pulp mills into an integrated biorefineries with black liquor gasification. Due to the pre-existing infrastructure in current pulp and paper mills, this could present a very cost effective approach to future biorefineries.

In this study, the Gibbs free energy minimization approach is used to simulate the black liquor gasification, in order to better understand the gasification process and predict the gasification performance. The results are also useful for techno-economic analysis of the Kraft pulp mill based integrated biorefinery. The simulation results are shown to be in agreement with literature data.