Deep learning for pyrolysis reactor monitoring: From thermal imaging toward smart monitoring system

Abstract

Monitoring the operation of a pyrolysis reactor is always challenging due to the extremely high‐operating temperature (over 800°C) in the fired furnace. To improve current monitoring capability, a monitoring framework is proposed that builds upon thermal photography to provide a detailed view inside the fired furnace. Based on the infrared images generated from the temperature data provided by cameras, a deep learning approach is introduced to automatically identify tube regions from the raw images. The pixel‐wise tube segmentation network is named Res50‐UNet, which combines the popular ResNet‐50 and U‐Net architectures. By this approach, the precise temperature and shape on pyrolysis tubes are monitored. The control limits are eventually drawn by the adaptive k‐nearest neighbor method to raise alarms for faults. Through testing over real plant data, the framework assists process operators by providing in‐depth operating information of the reactor and fault diagnosis. © 2018 American Institute of Chemical Engineers AIChE J, 65: 582–591, 2019