Enabling micro-kinetics based simulation of industrial packed-bed reactors by physics-enhanced neural networks

• First application of physically enhanced neural networks to heterogeneous catalysis CFD. • Multiscale microkinetic CFD simulations of industrial packed-bed reactors • Retain full mechanistic detail (accuracy > 99 %) with artificial neural networks. • Achieve 20-fold total simulation speed-up by means of 60-fold chemistry speed-up. Multiscale CFD simulations can provide insights into the coupling of the surface catalytic mechanism and reactor scale transport phenomena. To address the high computational cost of detailed micro-kinetic models, we have applied for the first time artificial neural networks (NNs) as surrogate models for micro-kinetic rate evaluations, with the aim of accelerating particle-resolved CFD simulations of a catalytic reactor. To evaluate the efficacy of the proposed strategy, a methane steam reforming packed bed reactor was selected as a benchmark case. Consequently, a global reaction neural network with embedded thermodynamic and stoichiometric information has been implemented as a surrogate for a UBI-QEP micro-kinetic model available in literature. Two distinct test cases have been employed. The first targets a lab scale reactor, enabling either the full evaluation of the micro-kinetic scheme or the novel NN accelerated approach in the CFD simulations. The comparison between the two strategies showed deviations in the computed mole fractions of less than 1 % across a wide range of operating conditions along with a 19-fold total simulation and 63-fold chemistry speed-up. Consequently, the total simulation time of the benchmark was reduced from 114 h to 6 h, with only 29.2 % or 1.75 h of the computational cost being allocated to the chemistry sub-step of the solver. Therefore, source term evaluations are no longer the bottleneck of reactive CFD. Given the obtained excellent accuracy and speed-up, we applied the NN-accelerated micro-kinetics to the CFD simulation of an industrial scale packed-bed reactor, discretized with 44 M cells (54 % solid phase). To the best of our knowledge, this represents the largest reactive simulation with micro-kinetic level of detail. Overall, these results pave the way for the scale-up of multiscale simulations to industrially relevant scales.