Accurate and Fast Fischer-Tropsch Reaction Microkinetics using PINNs

TL;DR

This paper introduces a physics-informed neural network approach to model Fischer-Tropsch synthesis microkinetics, achieving high accuracy and unprecedented computational speed, enabling real-time process control and advanced reactor simulations.

Contribution

The work presents a novel PINN-based method that significantly improves the speed and accuracy of FTS microkinetics modeling over traditional solvers.

Findings

Median relative error of 0.03% for catalytic site fraction

Achieves up to 1,000,000 times speed-up on GPUs

Enables real-time process control and multi-scale modeling

Abstract

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.