Study on Apparent Kinetic Prediction Model of the Smelting Reduction Based on the Time-Series

Guo-feng Fan, Shan Qing, Hua Wang, Zhe Shi, Wei-Chiang Hong and Lin Dai

Mathematical Problems in Engineering, 2012, vol. 2012, 1-15

Abstract:

A series of direct smelting reduction experiment has been carried out with high phosphorous iron ore of the different bases by thermogravimetric analyzer. The derivative thermogravimetric (DTG) data have been obtained from the experiments. One-step forward local weighted linear (LWL) method , one of the most suitable ways of predicting chaotic time-series methods which focus on the errors, is used to predict DTG. In the meanwhile, empirical mode decomposition-autoregressive (EMD-AR), a data mining technique in signal processing, is also used to predict DTG. The results show that (1) EMD-AR(4) is the most appropriate and its error is smaller than the former; (2) root mean square error (RMSE) has decreased about two-thirds; (3) standardized root mean square error (NMSE) has decreased in an order of magnitude. Finally in this paper, EMD-AR method has been improved by golden section weighting; its error would be smaller than before. Therefore, the improved EMD-AR model is a promising alternative for apparent reaction rate (DTG). The analytical results have been an important reference in the field of industrial control.

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:720849

DOI: 10.1155/2012/720849

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