A novel convolutional neural network based approach to predictions of process dynamic time delay sequences
Abstract It is practical that correlated process variables always involve dynamic time delay sequences. In this paper, a novel convolutional neural network (CNN) based approach is proposed to predict dynamic time delay sequences. Firstly, according to the calculating similarities between correlated process variables, the time delay sequence is extracted offline using a dynamic time delay analysis by elastic windows (EW DTDA) method. In addition, through an additional correlation analysis between the time delay sequence and process variables data, the process variables majorly influencing the time delay sequences can be obtained. Finally, a deep learning CNN model between the extracted time delay sequence and the obtained majorly influencing variables is constructed to predict the time delay sequence online. In order to validate the effectiveness of the proposed method, the method is applied to a real distillation column for analyzing dynamic time delay sequences, the simulation results conformed the effectiveness of the proposed approach.