Computational fluid dynamics can have an important role in the modeling of a fluidized-bed gasifier. A CFD-based code involves a solution of conservation of mass, momentum, species, and energy over a defined domain or region. The equations can be written for an element, where the flux of the just-mentioned quantities moving in and out of the element is considered with suitable bound­ary conditions.

A CFD code for gasification typically includes a set of submodels for the sequence of operations such as the vaporization of a biomass particle, its pyrolysis (devolatilization), the secondary reaction in pyrolysis, and char oxidation (Di Blasi, 2008; Babu and Chaurasia, 2004). Further sophistications such as a subroutine for fragmentation of fuels during gasification and com­bustion are also developed (Syred et al., 2007). These subroutines can be coupled with the transport phenomenon, especially in the case of a fluidized — bed gasifier.

The hydrodynamic or transport phenomenon for a laminar flow situation is completely defined by the Navier-Stokes equation, but in the case of turbulent flow a solution becomes difficult. A complete time-dependent solution of the instantaneous Navier-Stokes equation is beyond today’s computation capabili­ties (Wang and Yan, 2008), so it is necessary to assume some models for the turbulence. The Reynolds-averaged Navier-Stokes (k-є) model or large eddy simulation filters are two means of accounting for turbulence in the flow.

For a fluidized bed, the flow is often modeled using the Eulerian-Lagrange concept. The discrete phase is applied to the particle flow; the continuous phase, to the gas. Overmann and associates (2008) used the Euler-Euler and Euler — Lagrange approaches to model wood gasification in a bubbling fluidized bed. Their preliminary results found both to have comparable agreement with exper­iments. If the flow is sufficiently dilute, the particle-particle interaction and the particle volume in the gas are neglected.

A two-fluid model is another computational fluid dynamics approach. Finite difference, finite element, and finite volume are three methods used for discreti­zation. Commercial software such as ANSYS, ASPEN, Fluent, Phoenics, and CFD2000 are available for solution (Miao et al., 2008). A review and compari­son of these codes is given in Xia and Sun (2002) and Norton et al. (2007).

Recent progress in numerical solution and modeling of complex gas-solid interactions has brought CFD much closer to real-life simulation. If successful, it will be a powerful tool for optimization and even design of thermochemical reactors like gasifiers (Wang and Yan, 2008). CFD models are most effective in modeling entrained-flow gasifiers, where the gas-solid flows are less complex than those in fluidized beds and the solid concentration is low.

Models developed by several investigators employ sophisticated reaction kinetics and complex particle-particle interaction. Most of them, however, must use some submodels, fitting parameters or major assumptions into areas where precise information is not available. Such weak links in the long array make the final result susceptible to the accuracy of those “weak links.” If the final results are known, we can use them to back-calculate the values of the unknown parameters or to refine the assumptions used.

The CFD model can thus predict the behavior of a given gasifier over a wider range of parameters using data for one situation, but this prediction might not be accurate if the code is used for a different gasifier with input parameters that are substantially different from the one for which experimental data are available.