Abstract
The prediction of the alkalinity is difficult during the sintering process. Whether or not the level of the alkalinity of sintering process is successful is directly related to the quality of sinter. There is no very good method for predicting the alkalinity by now owing to the high complexity, high nonlinearity, strong coupling, high time delay, and etc. Therefore, a new technique, the grey squares support machine, was introduced. The grey support vector machine model of the alkalinity enabled the development of new equation and algorithm to predict the alkalinity. During modelling, the fluctuation of data sequence was weakened by the grey theory and the support vector machine was capable of processing nonlinear adaptable information, and the grey support vector machine has a combination of those advantages. The results revealed that the alkalinity of sinter could be accurately predicted using this model by reference to small sample and information. The experimental results showed that the grey support vector machine model was effective and practical owing to the advantages of high precision, less samples required, and simple calculation.
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Foundation Item: Item Sponsored by Provincial Natural Science Foundation of Henan of China (200612001)
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Song, Q., Wang, Am. Simulation and prediction of alkalinity in sintering process based on grey least squares support vector machine. J. Iron Steel Res. Int. 16, 1–6 (2009). https://doi.org/10.1016/S1006-706X(10)60001-5
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DOI: https://doi.org/10.1016/S1006-706X(10)60001-5