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.

使用物理信息神经网络 (PINNs) 模拟微观动力学对费托合成进行了研究，提出了一种计算效率高、准确性更强的方法，能够在实际过程条件下快速解决现有的微观动力学模型问题，对多尺度和多物理反应器模拟有很好的应用潜力，并能够实现实时过程控制和优化。