原油管式加热炉炉管腐蚀状态的声发射评估

Acoustic Emission Evaluation of the Tube Corrosion Condition of Crude Furnace

针对原油管式加热炉对流室炉管的腐蚀程度难以检测和评估的问题,采用声发射方法测试对流室炉管。应用灰色关联分析(GRA)原理,确定了能够有效反映炉管腐蚀状态的声发射表征因素,应用极限向量机(ELM)网络,建立基于声发射因素的对流室炉管腐蚀状态智能评价模型,并以1台正在大修的原油管式加热炉的对流室炉管为研究对象,进行腐蚀状态检测评估,评估结果表明该方法能够有效地对对流室炉管的腐蚀程度进行智能评价。

It is difficult to check and evaluate the corrosion degree of the tube in the convection cell of crude tube-type furnace. Therefore, the acoustic emission method was adopted to test the tube of the convection cell. The grey relational analysis （GRA） principle was applied to determine the acoustic emission representation factor that can effectively reflect the tube corrosion condition. The extreme learning machine （ELM） network was applied to establish the intelligent evaluation model of the tube corrosion condition of the convection cell on the basis of the acoustic emission factor. With the tube of the convection cell of an overhauling crude furnace as the object of study, the detection and evaluation of the corrosion condition were conducted. The evaluation result shows that the method can effectively conduct the intelligent evaluation of the corrosion condition of the convection cell tube.