ML-based Method for Solving the Microkinetic Model of Fischer-Tropsch Synthesis with Varying Catalyst/Reactor Parameters
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909556020346880 |
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| author | Demchuk, Taras Nikolaienko, Tymofii Panda, Aniruddha Joshi, Subodh Madhav Jaso, Stanislav Kalyanaraman, Kaushic |
| author_facet | Demchuk, Taras Nikolaienko, Tymofii Panda, Aniruddha Joshi, Subodh Madhav Jaso, Stanislav Kalyanaraman, Kaushic |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22304 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ML-based Method for Solving the Microkinetic Model of Fischer-Tropsch Synthesis with Varying Catalyst/Reactor Parameters Demchuk, Taras Nikolaienko, Tymofii Panda, Aniruddha Joshi, Subodh Madhav Jaso, Stanislav Kalyanaraman, Kaushic Disordered Systems and Neural Networks Chemical Physics 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. |
| title | ML-based Method for Solving the Microkinetic Model of Fischer-Tropsch Synthesis with Varying Catalyst/Reactor Parameters |
| topic | Disordered Systems and Neural Networks Chemical Physics |
| url | https://arxiv.org/abs/2503.22304 |