Compartment model for a dual fluidized bed biomass gasifier

- A kinetics based compartment model for a dual fluidized bed gasifier has been proposed.
- A CFD model was used to understand the flow patterns inside a fluidized bed.
- The CFD simulation was utilized to perform a RTD analysis of the reactor.
- The RTD analysis formed the basis of a steady-state compartment model.
- The model can predict the output for different biomass feedstocks.

A variety of novel processes are being proposed in order to face global challenges such as degradation of the environment and the efficient utilization of energy. Modeling and simulation tools play a crucial role in the understanding and enhancing of the execution, design and construction of these processes. Although different computational tools are available to quantify the process at different levels, they are normally utilized independently and on a stand-alone basis. This decoupled approach may undermine the true potential of these tools. The present study highlights the advantages of interlinked process modeling at different levels. This study focuses on a promising gasification technology, namely the dual fluidized bed gasifier. A computational fluid dynamics (CFD) model was used to understand the flow patterns inside a fluidized bed. This elevated the understanding of the hydrodynamics of the gasifier freeboard, which is neglected by the conventional two-phase methodology. The CFD simulation was utilized to perform a residence time distribution (RTD) analysis of the reactor. Four tracer approaches namely the frozen velocity approach, the snapshot approach, the data sampling approach and the transient approach, were compared. The RTD analysis formed the basis of a steady-state compartment model that was developed in ASPEN Plus simulation software. The ASPEN Plus gasifier model decoupled the pyrolysis, gasification, and combustion sections of the gasifier to affect a better comprehension of the process and results. The model predicated satisfactory results upon validation. Additionally, the model could also be used to predict the output for different biomass feedstocks.

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