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ML-based Method for Solving the Microkinetic Model of Fischer-Tropsch Synthesis with Varying Catalyst/Reactor Parameters by Demchuk, Taras; Nikolaienko, Tymofii; Panda, Aniruddha; Joshi, Subodh Madhav; Jaso, Stanislav; Kalyanaraman, Kaushic is a scholarly article available to read on EtoBox.

What is ML-based Method for Solving the Microkinetic Model of Fischer-Tropsch Synthesis with Varying Catalyst/Reactor Parameters about?

This study introduces a physics-informed machine learning framework to accelerate the computation of the microkinetic model of Fischer-Tropsch synthesis. A neural network, trained within the NVIDIA Modulus framework, approximates the fraction of vacant catalytic sites with high accuracy. The combination of implicit differentiation and the Newton-Raphson method enhances derivative calculations, ensuring physical consistency. Computational efficiency improves significantly, with speedups up to $ 10^4 $ times on a GPU. This versatile methodology generalizes across catalysts and reactors, offering a robust tool for chemical engineering applications, including model approximation and catalyst parameter fitting from experimental data.

Author

Demchuk, Taras; Nikolaienko, Tymofii; Panda, Aniruddha; Joshi, Subodh Madhav; Jaso, Stanislav; Kalyanaraman, Kaushic

Published

2025

Language

EN