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

中文翻译：

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用于热解反应器监控的深度学习：从热成像到智能监控系统

由于燃烧炉中的操作温度极高（超过800°C），因此监控热解反应器的运行始终是一项挑战。为了提高电流监视能力，提出了一种基于热摄影的监视框架，以提供燃烧炉内部的详细视图。基于从摄像机提供的温度数据生成的红外图像，引入了深度学习方法，可从原始图像中自动识别管子区域。逐像素管分段网络称为Res50‐UNet，它结合了流行的ResNet‐50和U‐Net架构。通过这种方法，可以监测热解管上的精确温度和形状。最终通过自适应k最近邻方法得出控制极限，以发出故障警报。通过测试真实的工厂数据，该框架通过提供反应堆和故障诊断的深入操作信息来协助过程操作员。©2018美国化学工程师学会AIChE J，65：582–591，2019