(314e) Microwave Heating of Packed Bed Reactor | AIChE

(314e) Microwave Heating of Packed Bed Reactor


Che, F. - Presenter, University of Toronto
Goyal, H., Cornell University
Vlachos, D., University of Delaware - Catalysis Center For Ener
Microwaves (MW) can provide innovative enhancement of performance of heterogeneous catalytic reaction processes, through avoiding undesired gas-phase reactions at high temperatures and enabling unique reaction fields unattainable by conventional heating. Several mechanisms have been reported in the literature for MW-enhancement of chemical reactions, including non-equilibrium local heating (the so called ‘hot spots’), catalytic phase changes, new catalytic reaction pathways, and enhancement of dipole moments. In particular, inhomogeneous distribution of gas/solid temperature in a packed bed reactor has been considered to be one of the most important factors affecting catalytic processes. Currently, it is difficult to validate the above mechanisms experimentally. Simulation can provide a better understanding of the role of MWs.

We applied COMSOL multi-physics to solve coupled physics in our system. In this work, we develop a MW reactor setup to systematically study the key descriptors that influence the MW heating energy efficiency and inhomogeneous temperature distribution of a packed bed reactor using COMSOL multi-physics simulations. Simple machine learning methods show that the energy efficiency and hot spot generation in a packed bed rector under microwave irradiation are greatly influenced by tunable MW inputs, such as power and frequency, various properties of catalytic particles, such as the complex permittivity, and the bed porosity. For example, the microwave heating energy efficiency is increased by increasing the microwave frequency, microwave power, and loss factor. We identify materials and conditions that can generate hot spots (Figure 1). Such ‘hot spots’ can only generate at certain materials, which have high microwave adsorbility and low thermal diffusivity. These hot spots affect the catalytic performance of gas/solid reactions as compared to conventional heating. This work introduces for the first-time machine learning to optimize energy efficiency and deliver novel MW reactor designs with significant energy savings.