Operational trend prediction and classification for chemical processes: A novel convolutional neural network method based on symbolic hierarchical clustering

Abstract

In modern industrial chemical engineering plants, the quality of the product is closely related not only to the process design but also to the efficiency of human operation. Currently, single-step prediction models are adopted by process engineers to estimate the immediate system response. However, those single-step prediction models are limited as they don’t enable the operator to visualize the complete series of effects associated with the operation in the long run. In order to help make prescient predictions, this paper proposes a novel symbolic hierarchical clustering (SHC) based convolutional neural network (CNN) method for trend prediction and classification. Firstly, the raw historical operation data series are symbolized from numerical values to strings according to their distinct characteristics. Secondly, the hierarchical clustering method is used to eliminate the low-frequency operation trends and to determine and label the types of operational trends for the symbolized dataset. Subsequently, the categorized dataset and its respective label are fed into a specially tailored CNN for the training of the CNN model for trend classification. Finally, to demonstrate the effectiveness of the proposed SHC-CNN algorithm, the proposed method is applied to the methanol production process of Hainan Petrochemical Co., Ltd. to predict and classify its main operational trends. In addition, the superiority of SHC-CNN operational trend prediction is demonstrated through the comparison with traditional neural networks.