A Novel Deep Learning-Based Robust Dual-Rate Dynamic Data Modeling for Quality Prediction

Traditional data-driven quality prediction methods are mainly built from static models using clean data with a slow sampling rate, leaving the process dynamics unused. To make full use of dynamic process data collected at a fast sampling rate, this article proposes a novel deep learning-based robust dual-rate dynamic data modeling method for quality prediction of dynamic nonlinear processes. A new dynamic data denoising generative adversarial imputation network is first proposed for the missing value imputation among the dynamic process data. Then, a new hint convolutional neural network (HCNN) is established for dual-rate data based quality prediction. The proposed HCNN incorporates the information hint mechanism of channel expansion into the convolutional neural network to extract the dynamic features with definitive time and variable information. Finally, the proposed method is verified using the Dow distillation process dataset and Beijing multisite air quality dataset.