Adaptive Weighting Just-in-Time-Learning Quality Prediction Model for an Industrial Blast Furnace

The blast furnace ironmaking process is an important unit operation in the manufacturing of iron and steel. It consumes the main energy input to the integrated route of steel production and emits much carbon oxide which is the main cause of the greenhouse effect. As one of the most energy intensive and complicated industrial process, there has been a growing awareness of modeling and controlling the blast furnace ironmaking process for increasing efficiency and reducing cost. The silicon content, indicating the thermal state in blast furnace, is the most important index of pig iron quality. It must be kept at an appropriate level to facilitate the production and stable running of the ironmaking process. Therefore, accurate online prediction of the silicon content in hot metal is very critical.1–6) Extensive research on the thermodynamic and kinetic behaviors occurring inside the blast furnace ironmaking process has been investigated. However, an accurate mechanism model in industrial processes has not been constructed. Nowadays, a large amount of process data containing useful information can be obtained in industrial blast furnace ironmaking processes. To online predict the silicon content, various data-driven soft sensor modeling approaches, including various neural networks,7–14) partial least squares,14,15) fuzzy inference systems,16) nonlinear time series analysis,17–20) subspace identification,21) support vector regression (SVR) and least squares SVR (LSSVR),22–24) Adaptive Weighting Just-in-Time-Learning Quality Prediction Model for an Industrial Blast Furnace

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