Abstract In this work, physics-based recurrent neural network (RNN) modeling approaches are proposed for a general class of nonlinear dynamic process systems to improve prediction accuracy by incorporating a priori process knowledge. Specifically, a hybrid modeling method is first introduced to integrate first-principles models and RNN models. Subsequently, a partially-connected RNN modeling method that designs the RNN structure based on a priori structural process knowledge, and a weight-constrained RNN modeling method that employs weight constraints in the optimization problem of the RNN training process are developed. The proposed physics-based RNN models are utilized in model predictive controllers and applied to a chemical process network example to demonstrate their improved approximation performance compared to the fully-connected RNN model that is developed as a black box model.

中文翻译：

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基于过程结构的递归神经网络建模用于非线性过程的模型预测控制

摘要 在这项工作中，提出了基于物理的循环神经网络 (RNN) 建模方法，用于一类非线性动态过程系统，以通过结合先验过程知识来提高预测精度。具体来说，首先引入一种混合建模方法来整合第一原理模型和 RNN 模型。随后，开发了一种基于先验结构过程知识设计 RNN 结构的部分连接 RNN 建模方法，以及在 RNN 训练过程的优化问题中采用权重约束的权重约束 RNN 建模方法。