Intelligent prediction of incipient fault in vinyl chloride production process based on deep learning

Abstract

With the development of industrial information technology, deep learning (DL) has been successfully applied in chemical process fault detection. However, the features of incipient faults could not be more evident at the initial stage, which makes it difficult for deep learning to fully mine the feature probability information, resulting in poor performance of incipient fault monitoring. This paper proposes an intelligent predictive model based on mechanistic modeling to predict incipient faults and determine optimal response times for vinyl chloride production (VCP) processes. Firstly, a dynamic simulation of the VCP production process is performed to obtain datasets of early faults. Secondly, data dimensionality reduction is performed by cleverly combining spearman ranking correlation coefficient (SRCC) and slow feature analysis (SFA). Then, a long short-term memory network (LSTM) with attention mechanism (AM) is built to predict the future trends of key variables. Finally, the optimal response time for different types of incipient faults is determined by comparing the effectiveness of various control schemes. Compared with traditional methods, the R2 of the proposed prediction model corresponding to different step sizes can reach 99.7%, 99.5%, 99.2%, and 98.7%. In addition, the response times for single and parallel faults are 2 and 2.5 h, which helps to control and eliminate potential incidents in advance.