Hierarchical analysis of the gas-to-particle heat and mass transfer in micro packed bed reactors

• A CFD-based hierarchical approach for lumped parameter derivation is proposed.
• This approach allows to efficiently exploit CFD simulations in reactor modeling.
• Gas-to-particle transport phenomena in micro packed bed reactors are analyzed.
• Literature correlations for gas-to-particle transport in packed beds are assessed.

In this paper we propose and apply a hierarchical approach for an efficient exploitation of fundamental multi-scale modeling for the analysis and design of the kinetic–transport interactions in chemical reactors. In essence, detailed and computationally demanding analyses – based on computational fluid dynamics simulations (CFD) of the reactor – are first used to study in detail a selected and limited number of conditions. Then, the CFD results are interpreted by means of 1D heterogeneous models for the derivation of lumped parameters to be used in classical reactor models. On one side, this approach limits the use of computationally demanding simulations. On the other side, it allows for the rational derivation of parameters, which are related to a detailed and sound description of the governing phenomena. The very good agreement between the predictions of CFD and 1D heterogeneous models at different operating conditions shows that the CFD-based correlation for transport properties fully retains all the main features of the detailed CFD simulation. Moreover, we found that the hierarchical derived correlations to be very similar to the ones experimentally obtained for typical industrial scale packed bed reactors, thus confirming that the conventional correlations may be reliably used in micro-packed bed reactors.On a more general basis, this work clearly demonstrates the potentiality of the hierarchical application of CFD simulation for the derivation of transport parameters in reactor engineering, which can be used for the efficient and fundamental analysis and design of novel reactor technologies.