A Deep Learning Approach on Industrial Pyrolysis Reactor Monitoring

Abstract

The pyrolysis process monitoring is always challenging due to the high operating temperature inside a fired furnace. To obtain better understanding of the pyrolysis reactors, we proposed a monitoring framework that builds upon thermal photography to provide a detailed view inside the fired furnace. Based on the infrared photos, the convolutional neural network is introduced into the monitoring framework to automatically recognize tube regions from the photos. In this work, a segmentation network is proposed based on the U-Net and ResNet-50 frameworks, by which the precise temperature and shape information on tube regions can be extracted from the raw photos. After extracting the important monitoring measurements, a control limit is drawn by the adaptive k-nearest neighbor method to detect abnormal conditions. The testing result indicates that the proposed monitoring framework provides in-depth information of the reactor and detailed fault diagnosis to process operators.