Abstract
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.
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Funded by NSFC (No. 60808024) and the Fundamental Research Funds for the Central Universities (Wuhan University of Technology)
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Yuan, J., Zhong, L., Du, H. et al. Prediction of free lime content in cement clinker based on RBF neural network. J. Wuhan Univ. Technol.-Mat. Sci. Edit. 27, 187–190 (2012). https://doi.org/10.1007/s11595-012-0433-3
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DOI: https://doi.org/10.1007/s11595-012-0433-3