Dynamic Regional Viscosity Prediction Model of Blast Furnace Slag Based on the Partial Least-Squares Regression

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• 作者：Hongwei Guo ; Mengyi Zhu ; Bingji Yan ; Shichan Deng ; Xinyu Li ; Feng Liu
• 刊名：JOM
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：69
• 期：2
• 页码：395-401
• 全文大小：
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Engineering, general; Chemistry/Food Science, general; Physics, general; Environment, general; Earth Sciences, general;
• 出版者：Springer US
• ISSN：1543-1851
• 卷排序：69

文摘

Viscosity is considered to be a significant indicator of the metallurgical property of blast furnace (BF) slag. A model for viscosity prediction based on the partial least-squares regression of varietal quantity reference points is presented in this article. The present model proposes a dynamic regional algorithm for reference point selection. The study applied the partial least-squares regression to establish the dynamic regional viscosity prediction model on the basis of limited discrete points data. Then an actual prediction was carried out with a large amount of viscosity data of real and synthesized BF slags that was obtained from a certain steel plant in China. The results show that this advanced method turns out to be satisfactory in the viscosity prediction of BF slags with a low averaging error and mean value deviation.