Accurate and Fast Fischer-Tropsch Reaction Microkinetics using PINNs
This enables real-time process control and optimization for industrial chemical reactions, though it is an incremental application of an existing method to a specific domain.
The paper tackled the inefficiency of traditional microkinetics models for Fischer-Tropsch synthesis by using physics-informed neural networks (PINNs), achieving a median relative error of 0.03% for catalytic site fractions and up to 1E+06 times speed-up on GPUs.
Microkinetics allows detailed modelling of chemical transformations occurring in many industrially relevant reactions. Traditional way of solving the microkinetics model for Fischer-Tropsch synthesis (FTS) becomes inefficient when it comes to more advanced real-time applications. In this work, we address these challenges by using physics-informed neural networks(PINNs) for modelling FTS microkinetics. We propose a computationally efficient and accurate method, enabling the ultra-fast solution of the existing microkinetics models in realistic process conditions. The proposed PINN model computes the fraction of vacant catalytic sites, a key quantity in FTS microkinetics, with median relative error (MRE) of 0.03%, and the FTS product formation rates with MRE of 0.1%. Compared to conventional equation solvers, the model achieves up to 1E+06 times speed-up when running on GPUs, thus being fast enough for multi-scale and multi-physics reactor modelling and enabling its applications in real-time process control and optimization.