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.

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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.

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.

Reduced order models that accurately predict the operation of entrained flow gasifiers as components within integrated gasification combined cycle (IGCC) or polygeneration plants are essential for greater commercialization of gasification-based energy systems. A reduced order model, implemented in Aspen Custom Modeler, for entrained flow gasifiers that incorporates mixing and recirculation, rigorously calculated char properties, drying and devolatilization, chemical kinetics, simplified fluid dynamics, heat transfer, slag behavior and syngas cooling is presented. The model structure and submodels are described. Results are presented for the steady-state simulation of a two-metric-tonne-per-day (2 tpd) laboratory-scale Mitsubishi Heavy Industries (MHI) gasifier, fed by two different types of coal. Improvements over the state-of-the-art for reduced order modeling include the ability to incorporate realistic flow conditions and hence predict the gasifier internal and external temperature profiles, the ability to easily interface the model with plant-wide flowsheet models, and the flexibility to apply the same model to a variety of entrained flow gasifier designs. Model validation shows satisfactory agreement with measured values and computational fluid dynamics (CFD) results for syngas temperature profiles, syngas composition, carbon conversion, char flow rate, syngas heating value and cold gas efficiency. Analysis of the results shows the accuracy of the reduced order model to be similar to that of more detailed models that incorporate CFD. Next steps include the activation of pollutant chemistry and slag submodels, application of the reduced order model to other gasifier designs, parameter studies and uncertainty analysis of unknown and/or assumed physical and modeling parameters, and activation of dynamic simulation capability.

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