A novel method of neural network model predictive control integrated process monitoring and applications to hot rolling process

Abstract

The stable control of product quality when abnormal working conditions occur in the industrial production process is essential to improve product quality and economic efficiency. However, the process industry suffers from multivariate, nonlinear, uncertainty, long time delays and frequent failures, making its modeling and control difficult. In this paper, a novel method of neural network model predictive control integrated process monitoring (PM-NNMPC) is proposed. First, the combination of gated recurrent unit and convolutional neural network (GRU-CNN) is used to extract the features from the time and the space separately to build prediction models for different working conditions of the process. Then, a process monitoring and fault diagnosis method of principal component analysis combined with deep neural network and XGBoost (PCA-DNN-XGBoost) is designed to monitor the working conditions in real-time and diagnose accurately when faults occur. Finally, a new operation control framework integrating process monitoring and fault diagnosis into the model prediction control is designed to solve the problem that equipment operation failure, which makes the product quality fluctuation because of the model mismatch and the system runaway. The experimental results of hot rolling process show that PM-NNMPC can effectively control the system under normal working conditions and in case of the system failure to ensure the stable product quality.