Parametrization of a Sectional Approach for the Entrained-Flow Gasification of Biomass Char

The thermochemical conversion of biomass, through gasification and consecutive fuel synthesis, can produce high-quality fuels. Entrained-flow gasification is regarded as the forthcoming technology providing syngas of sufficient quality, though the technology readiness level needs to be raised to allow its wide use. To this end, investigations within the bioliq project have been conducted on the gasification of fast pyrolysis products: pyrolysis oil and biochar, forming together a so-called bioslurry. This article will present the parametrization and results of numerical simulations of entrained-flow gasification. A first set of simulations on the gasification of a liquid fuel on two well-referenced experimental data sets is presented. Here, the liquid is monoethylene glycol, a surrogate for pyrolysis oil. Then, simulations of slurry fuels are considered, where the conversion of the biochar particles is numerically solved with a sectional approach. The simulations prove to retrieve the experimental results with a high accuracy, thus making CFD simulations based on the proposed approaches an adapted tool to investigate further operating conditions or for scaling-up. Particular attention is drawn on the parametrization of the approach. It will be shown that the majority of the model's parameters, such as the pseudospecies size distribution, composition, and thermodynamic properties, can be derived from regular analytical chemistry techniques. The parameter with the highest sensitivity was identified to be the gasification reaction rate of the secondary char. The proposed model is sensitive to the properties of different types of biochar as well as to the amount of solid in the slurry fuel. By presenting a methodology, which can be quickly adapted to further solid fuels, the present work will allow for tackling the variability of biomass-derived fuels and find suitable operating conditions adapted to specific biomass conditions.