A hybrid just-in-time soft sensor for carbon efficiency of iron ore sintering process based on feature extraction of cross-sectional frames at discharge end

2017 ◽  
Vol 54 ◽  
pp. 14-24 ◽  
Author(s):  
Xiaoxia Chen ◽  
Xin Chen ◽  
Jinhua She ◽  
Min Wu
2021 ◽  
Vol 106 ◽  
pp. 44-53
Author(s):  
Kailong Zhou ◽  
Xin Chen ◽  
Min Wu ◽  
Yosuke Nakanishi ◽  
Weihua Cao ◽  
...  

Author(s):  
Xiaoxia Chen ◽  
◽  
Jinhua She ◽  
Xin Chen ◽  
Min Wu ◽  
...  

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 <span class="bold">R</span>2 of 0.7016.


Sign in / Sign up

Export Citation Format

Share Document