This paper is NOT THE PUBLISHED VERSION; but the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation below.

An extension of the random pore model has been developed that allows different pore sizes to grow at different rates, depending on the instantaneous pore-scale reactant penetration at a given location within a reacting porous particle. This is accomplished by incorporating pore-scale effectiveness factors, consistent with the random pore geometry, into equations for the growth of each individual pore size. This framework allows the evolution of the char structure with local conversion to adapt to changes in boundary conditions (reactants, temperature) and the development of intraparticle gradients, rather than being predetermined by the initial pore structure (i.e., by the value of the random pore model structural parameter, ψ). This framework also facilitates the calculation of intrinsic kinetic parameters from experimental measurements by providing an estimation of the fraction of the total surface area participating in a given reaction. Without using any fitting parameters, the model has been found to satisfactorily reproduce some coal char oxidation experiments from the literature. The model has also been applied to two cases of practical interest for char particle gasification: zone II reaction conditions and reactants that change over the course of conversion. In these cases, the results of the adaptive random pore model differ from those predicted by the original random pore model.

A multicomponent droplet vaporization model which combines the computational efficiency of continuous thermodynamic approaches with the detailed species information provided by discrete component models has been developed. The Direct Quadrature Method of Moments (DQMoM) is used to efficiently solve for the evolution of the nodes and weights of the equivalent liquid-phase mole fraction distribution without assuming any functional form. The novelty of the approach is an inexpensive delumping procedure that is used to reconstruct the time-dependent mole fractions and fluxes for all discrete species. When applied to a vaporizing kerosene droplet, agreement between the full discrete component model, which solves ODEs for every individual species, and DQMoM with delumping, which solves only a few ODEs, is excellent. This computationally inexpensive model is well-suited for implementation in CFD codes with detailed kinetic mechanisms, as it enables NOT THE PUBLISHED VERSION; this is the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation at the bottom of the page.

No abstract

In this work, we use a CFD package to model the operation of a coal gasifier with the objective of assessing the impact of devolatilization and char consumption models on the accuracy of the results. Devolatilization is modeled using the Chemical Percolation Devolitilization (CPD) model. The traditional CPD models predict the rate and the amount of volatiles released but not their species composition. We show that the knowledge of devolatilization rates is not sufficient for the accurate prediction of char consumption and a quantitative description of the devolatilization products, including the chemical composition of the tar, is needed. We incorporate experimental data on devolatilization products combined with modeling of the tar composition and reactions to improve the prediction of syngas compositions and carbon conversion. We also apply the shrinking core model and the random pore model to describe char consumption in the CFD simulations. Analysis of the results indicates distinct regimes of kinetic and diffusion control depending on the particle radius and injection conditions for both char oxidation and gasification reactions. The random pore model with Langmuir-Hinshelwood reaction kinetics are found to be better at predicting carbon conversion and exit syngas composition than the shrinking core model with Arrhenius kinetics. In addition, we gain qualitative and quantitative insights into the impact of the ash layer surrounding the char particle on the reaction rate.