Prediction of Free Lime Content in Cement Clinker Based on RBF Neural Network

• Published in: Journal of Wuhan University of Technology. Materials science edition Vol. 27; no. 1; pp. 187 - 190
• Main Author: 袁景凌 陶海征
• Format: Journal Article
• Language: English
• Published: Heidelberg Wuhan University of Technology 01.02.2012; Springer Nature B.V
• Subjects: BP神经网络模型; Calcium oxide; Cements; Chemistry and Materials Science; Clinker; Lime; Materials Science; Mathematical models; Neural networks; Nonlinearity; RBF; Regression; 加工工艺参数; 径向基函数; 水泥熟料; 游离氧化钙; 神经网络预测; 非线性回归模型; cement clinker; free lime content; RBF neural network
• ISSN: 1000-2413; 1993-0437
• DOI: 10.1007/s11595-012-0433-3

Considering the fact that free calcium oxide content is an important parameter to evaluate the quality of cement clinker, it is very significant to predict the change of free calcium oxide content through adjusting the parameters of processing technique. In fact, the making process of cement clinker is very complex. Therefore, it is very difficult to describe this relationship using the conventional mathematical methods. Using several models, i e, linear regression model, nonlinear regression model, Back Propagation neural network model, and Radial Basis Function （RBF） neural network model, we investigated the possibility to predict the free calcium oxide content according to selected parameters of the production process. The results indicate that RBF neural network model can predict the free lime content with the highest precision （1.3%） among all the models.