期刊
JOURNAL OF PROCESS CONTROL
卷 89, 期 -, 页码 74-84出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2020.03.013
关键词
Recurrent neural networks; Model predictive control; Structural process knowledge; Nonlinear systems; Chemical processes
资金
- National Science Foundation
- Department of Energy
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. (C) 2020 Elsevier Ltd. All rights reserved.
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