A Novel Process Modeling Method for Steel Sulphur Content Soft Sensing during Ladle Furnace Steel Refining

抄録

Steel sulphur content soft sensing is of great importance for optimal control of the desulphurization process during ladle furnace (LF) steel refining. However, the soft sensing models in the literature at present are not able to capture the multi-stage characteristics. For addressing this problem and thereby obtaining satisfactory performance, stage-based modeling is proposed by virtue of sub-models ensemble. The central idea of this method is to establish several individual sub-models in order to focus on the local process property of each stage during desulphurization. Furthermore, soft partition strategy using nonparametric regression is developed for realizing soft handoff among the sub-models of successive stages, by which the close and changing process properties in the stage-to-stage transition region can be accurately described. Finally, the effectiveness of the presented method is validated by practical data. It can be concluded from experiments that the proposed stage-based modeling approach is able to significantly improve the sulphur content soft sensing performance, which makes it helpful in both process monitoring and operations optimization for LF process.