Name
Abstract | ||
|---|---|---|
A new approach for forecasting silicon content in blast furnace hot metal is presented based on the principal component regression. Firstly, with the pre-processed data selected from Laiwu Iron and Steel Group Co., the eigenvalues and eigenvectors of the data correlation matrix are calculated. Then the eigenvectors are used for calculation of the principal components and four of them are selected to represent all the information about blast furnace ironmaking process. Finally, compared with the conventional autoregressive method, our approach is more accurate to predict the silicon content. The main benefit of the approach is that it can reduce the number of factors affecting silicon content and eliminate the multicollinearity between them. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICNC.2009.668 | ICNC (2) |
Keywords | Field | DocType |
principal component regression approach,eigenvalues,forecasting silicon content,silicon content,data correlation matrix,forecasting theory,steel group co.,laiwu iron and steel group co,pre-processed data,regression analysis,matrix algebra,prediction,laiwu iron,principal component,blast furnace ironmaking process,blast furnaces,silicon,iron,blast furnace hot metal,metallurgical industries,silicon content forecasting,eigenvectors,blast furnace,new approach,eigenvalues and eigenfunctions,principal component analysis,hot metal,principal component regression,eigenvalues and eigenvectors,predictive models,correlation matrix,data models | Process engineering,Data modeling,Mathematical optimization,Principal component regression,Computer science,Regression analysis,Multicollinearity,Blast furnace,Principal component analysis,Eigenvalues and eigenvectors,Silicon | Conference |
Volume | ISBN | Citations |
2 | 978-0-7695-3736-8 | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenhui Wang | 1 | 92 | 19.23 |
| Juner Ma | 2 | 0 | 0.34 |