Trend similarity MWPCA based fault monitoring for xylenol tail gas treatment process

Abstract

In the process of preparing xylenol, a large amount of tail gas will be generated. When using industrial boilers to treat the xylenol tail gas, the high-pressure steam in the furnace may lead to an explosion accident; on the other hand, the toxic tail gas of incomplete combustion in the furnace may also leak, endangering the lives of staff. So it is necessary to monitor the fault of the industrial boiler which used to treat xylenol tail gas. However, due to the non-stationary characteristics of the tail gas treatment process, the conventional fault monitoring methods have the problem of low accuracy. In order to solve these problems, this paper proposes a fault monitoring method based on trend similarity feature. This method cuts the time series by sliding time window, and calculates the trend similarity between data in each time window. Then uses the sliding time window to update the monitoring model in real-time. So it can change the threshold value of the monitoring model with the change of samples, to improve the monitoring accuracy. Finally, the practical data collected from a xylenol producer are used for validation. The results show that the fault detection based on the trend similarity feature has higher accuracy than the conventional method, and the detection accuracy increases with the non-stationary of the process.