Online Analysis of Stirring Processes in Ladle Metallurgy

Gas stirring is commonly used in pyrometallurgical vessels to enhance mass and heat transfer and promote impurity removal. These processes can be difficult to monitor because of the severe operating conditions associated with these processes. However, experienced operators can make judgment of the process by observing the disturbance of the top surface and listening to the sound of the bubbling. In this article, we proposed a new approach of analyzing the stirring process by all the signals that could be reliably measured simultaneously, i.e., the image of the disturbed top surface, the sound of the bubbling process, and the vibrations on the wall of the vessel. The principal component analysis (PCA) results on the cold model data showed that the three types of signals are highly correlated, and they can be reduced to just one signal, which picks up most of the variation of the process and could be used to monitor the bubbling phenomena. The PCA results also showed that the combination of sound and vibration signals can still pick up most of the variations without combining the image signal from the disturbed top surface. This signal could be used as online monitoring of the stirring process and provide feedback signals for process control.