Discrete Wavelet Transfer Based BPNN for Calculating Carbon Efficiency of Sintering Process
Xiaoxia Chen*, Jinhua She**, Xin Chen***,†, and Min Wu***
*School of Information Science and Engineering, Central South University
Changsha, 410083, China
**School of Engineering, Tokyo University of Technology
Hachioji, Tokyo 192-0982, Japan
***School of Automation, China University of Geosciences
Wuhan, 430074, China
Iron ore sintering process is the secondary most energy consuming procedure in steel making industry. In this study, a discrete wavelet transfer based back-propagation neural network (BPNN) model is built to predict the carbon efficiency of an iron ore sintering process. The raw-material variables and manipulated variables are chosen to be the inputs of the predictive model. First, the input variables are decomposed into 5 components. Then, BPNN models of each component are built. Finally, the prediction results are obtained by adding the output from each wave series. Actual run data are collected to verify the validity of the predictive model. The results show the validity of the proposed method with a MSE of 0.7708, a MAPE of 0.0125, and a R2 of 0.7016.
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