CFD Simulation of Entrained Flow Gasification With Improved Devolatilization and Char Consumption Submodels

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.

Baseline Flowsheet Model for IGCC with Carbon Capture

“…Researchers at MIT have been collaborating on a spectrum of gasifier modeling methods, ranging from 0-D equilibrium models to multiscale models, which include homogeneous and heterogeneous kinetics, particle microstructure models, radiative and convective heat transfer, and 3-D large eddy simulation models. 12,13 The gasifier model applied in the baseline IGCC flowsheet model uses the 0-D equilibrium representation.…”

Computational Fluid Dynamic Modeling of Entrained-Flow Gasifiers with Improved Physical and Chemical Submodels

“…Some of them considered the effect of more advanced turbulence models and found that the standard k -ε model provides a simple and reasonable approach, whereas others compared the standard k -ε model with Shear Stress Transport (SST) k -ω model and found that the SST k -ω model predicted a stronger swirling flow in an entrained-flow gasifier that matched the experimental data better. In terms of gas-phase homogeneous reactions, some authors assumed local equilibrium chemistry and used mixture fractions and predefined probability density function (PDF) approach to model the coupling between turbulence and chemistry , while others solved continuity equations of individual species based on finite rate chemistry with either an eddy breakup type of model ,− or an eddy dissipation concept (EDC) model . For entrained-flow gasifiers where particle loading is not so high, almost all researchers used the Lagrangian particle-source-in-cell model to handle the coupling between the gas phase and the discrete phase (coal particles and water droplets).…”

Impact of Chemistry–Turbulence Interaction Modeling Approach on the CFD Simulations of Entrained Flow Coal Gasification

“…According to the literature review [52,[54][55][56][57][58][59][60][61], the kinetic parameters for this reaction originate from the Jones-Lindstedt approach [47]. However, some authors modified the pre-exponential factor from this reaction [18,56,[62][63][64] in order to match the experimental data. The reason lies in the fact that the rate from the Jones-Lindstedt…”

“…According to the literature review [52,[54][55][56][57][58][59][60][61], the kinetic parameters for this reaction originate from the Jones-Lindstedt approach [47]. However, some authors modified the pre-exponential factor from this reaction [18,56,[62][63][64] in order to match the experimental data. The reason lies in the fact that the rate from the Jones-Lindstedt mechanism was obtained under catalytic conditions which, in many cases, turned out to be too fast Temperature distribution along centerline for three gas phase modeling approaches-E-gas reactor.…”

“…This value was taken from the study of Lu and Wang [62]. As regards the MHI reactor, where the maximum flame temperature was below 2400 K, the pre-exponential factor was assumed to be equal to 2.75 × 10 9 and was taken from [63]. In the case of the E-gas gasifier, the flame temperature was observed to be above 3000 K and the pre-exponential factor was also assumed to be equal to 2.75 (Table 10).…”