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arXiv · 2503.22304

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

Taras Demchuk·Tymofii Nikolaienko·Aniruddha Panda·Subodh Madhav Joshi·Stanislav Jaso·Kaushic Kalyanaraman
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Abstract

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.

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Taras Demchuk, Tymofii Nikolaienko, Aniruddha Panda, Subodh Madhav Joshi, Stanislav Jaso, Kaushic Kalyanaraman. 2025-03-28. ML-based Method for Solving the Microkinetic Model of Fischer-Tropsch Synthesis with Varying Catalyst/Reactor Parameters. https://arxiv.org/abs/2503.22304

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