Keypoints identification and position monitoring of the blower pipe for volatilization kiln
The position of blower pipe at the kiln head is one of the important operating parameters that affect the combustion state of the zinc oxide volatilization kiln.Currently,it is still manually adjusted through fire observation.At the same time,there is no blower state data recorded for the operation optimization of the volatilization kiln.It is also difficult to find the hidden safety hazards in time,such as the blower pipe being hit by the slag in the kiln.To tackle the above problems,a method for monitoring the position of blower pipe based on the keypoints identification is proposed.Firstly,for the flame video dataset collected from the kiln head,a data augmentation method assisted by neighborhood keypoints is designed,and the cascaded pyramid network(CPN)is constructed to predict the center position of blower pipe nozzle.Then,a cluster analysis algorithm based on the multi-frame images is proposed to eliminate the outliers caused by smoke and dust occlusion,and a quantitative index is used to realize the real-time perception and recording of blower pipe position for the volatilization kiln.Finally,some comparative experiments are carried out based on the flame video data.Experimental results show that the keypoints detection model has high accuracy and strong robustness,and the quantification accuracy proposed is as high as 92.3％.
flame videothe position of blower pipekeypoints detectionconvolutional neural networkcluster analysis
万方数据
维普
