Accurate and Fast Fischer-Tropsch Reaction Microkinetics using PINNs

• URL: http://arxiv.org/abs/2311.10456v1
• Date: Fri, 17 Nov 2023 11:21:09 GMT
• Title: Accurate and Fast Fischer-Tropsch Reaction Microkinetics using PINNs
• Authors: Harshil Patel, Aniruddha Panda, Tymofii Nikolaienko, Stanislav Jaso, Alejandro Lopez, Kaushic Kalyanaraman
• Abstract summary: Microkinetics model for Fischer-Tropsch synthesis (FTS) becomes inefficient when it comes to more advanced real-time applications. We propose a computationally efficient and accurate method, enabling the ultra-fast solution of the existing microkinetics models. 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%.
• Score: 38.08566680893281
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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