  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.
