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

武汉理工大学学报-材料科学版(英文版), 2012, (1): 187-190. doi: 10.1007/sl1595-012-0433-3

^1, , ^2, , ^3, , ^4,

1.School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

2.School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

3.School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

4.State Key Laboratory of Silicate Materials for Architecture(Wuhan University of Technology), Wuhan 430070, China

基金项目: Funded by NSFC(60808024) the Fundamental Research Funds for the Central Universities (Wuhan University of Technology)

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

YUAN Jingling ^1, , ZHONG Luo ^2, , DU Hongfu ^3, , TAO Haizheng ^4,

1.School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

2.School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

3.School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China

4.State Key Laboratory of Silicate Materials for Architecture(Wuhan University of Technology), Wuhan 430070, China

Keywords： RBF neural network

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.

参考文献

[1]	ZHONG Luo,Liu Lisheng,ZOU Chengming,Yuan Jingling.The Application of Neural Network in Lifetime Prediction of Concrete[J].武汉理工大学学报(材料科学版)英,2002(01):79-81.
[2]	蔡宁,颜文俊.RBF预测控制在水泥篦冷机系统中的应用[J].水泥,2010(10):58-60.
[3]	刘燕,赵胜利,易成.基于RBF神经网络的钢筋锈蚀程度预测[J].混凝土,2009(10):27-29.
[4]	徐智棋,陈邦红,徐智龙.混凝土早期弹性模量的预测RBF模型[J].混凝土,2010(08):41-42,60.
[5]	TANG Puhong,SONG Renguo,CHAI Guozhong,FENG Chongyou,LI Liping.Optimization of Laser Ablation Technology for PDPhSM Matrix Nanocomposite Thin Film by Artificial Neural Networks-particle Swarm Algorithm[J].武汉理工大学学报（材料科学版）（英文版）,2010(02):188-193.

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

参考文献