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

作     者：YUAN Jingling ZHONG Luo DU nongfu TAO Haizheng 袁景凌;ZHONG Luo;DU nongfu;陶海征

作者机构：School of Computer Science and TechnologyWuhan University of Technology State Key Laboratory of Silicate Materials for Architecture(Wuhan University of Technology) 

出 版 物：《Journal of Wuhan University of Technology(Materials Science)》 (武汉理工大学学报（材料科学英文版）)

年 卷 期：2012年第27卷第1期

页      面：187-190页

核心收录：

学科分类：080706[工学-化工过程机械] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：NSFC (No. 60808024) the Fundamental Research Funds for the Central Universities (Wuhan University of Technology) 

主　　题：RBF neural network cement clinker free lime content 

摘      要：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.