Deep learning for pyrolysis reactor monitoring: From thermal imaging toward smart monitoring system
Document Type
Article
Publication Date
2-1-2019
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.
Publication Source (Journal or Book title)
AIChE Journal
First Page
582
Last Page
591
Recommended Citation
Zhu, W., Ma, Y., Benton, M., Romagnoli, J., & Zhan, Y. (2019). Deep learning for pyrolysis reactor monitoring: From thermal imaging toward smart monitoring system. AIChE Journal, 65 (2), 582-591. https://doi.org/10.1002/aic.16452