Recurrent neural networks (RNN) have demonstrated their ability in providing a remarkably accurate modeling approximation to describe the dynamic evolution of complex, nonlinear chemical processes in several applications. Although conventional fully-connected RNN models have been successfully utilized in model predictive control (MPC) to regulate chemical processes with desired approximation accuracy, the development of RNN models in terms of model structure can be further improved by incorporating physical knowledge to achieve better accuracy and computational efficiency. This work investigates the performance of MPC based on two different RNN structures. Specifically, a fully-connected RNN model, and a partially-connected RNN model developed using a prior physical knowledge, are considered. This study uses an example of a large-scale complex chemical process simulated by Aspen Plus Dynamics to demonstrate improvements in the RNN model and an RNN-based MPC performance, when the prior knowledge of the process is taken into account.

循环神经网络 (RNN) 已经证明了它们能够提供非常准确的建模近似值来描述复杂、非线性化学过程在多种应用中的动态演化。尽管传统的全连接 RNN 模型已成功用于模型预测控制 (MPC) 以调节具有所需近似精度的化学过程，但通过结合物理知识可以进一步改进 RNN 模型在模型结构方面的开发，以实现更好的精度和计算效率。这项工作基于两种不同的 RNN 结构研究了 MPC 的性能。具体来说，考虑使用先验物理知识开发的全连接 RNN 模型和部分连接 RNN 模型。