Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process

2020 ◽  
pp. 1-13
Author(s):  
Jie Hu ◽  
Min Wu ◽  
Luefeng Chen ◽  
Kailong Zhou ◽  
Pan Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Iron Ore ◽  
Time Series Prediction ◽  
Sintering Process ◽  
Fuzzy C Means ◽  
Iron Ore Sintering ◽  
Carbon Efficiency ◽  
Weighted Kernel ◽  
Iron Ore Sintering Process ◽  
Ore Sintering

A novel performance assessment method of the carbon efficiency for iron ore sintering process

2021 ◽  
Vol 106 ◽  
pp. 44-53
Author(s):  
Kailong Zhou ◽  
Xin Chen ◽  
Min Wu ◽  
Yosuke Nakanishi ◽  
Weihua Cao ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Iron Ore ◽  
Assessment Method ◽  
Sintering Process ◽  
Iron Ore Sintering ◽  
Carbon Efficiency ◽  
Iron Ore Sintering Process ◽  
Ore Sintering

Discrete Wavelet Transfer Based BPNN for Calculating Carbon Efficiency of Sintering Process

2016 ◽  
Vol 20 (7) ◽  
pp. 1070-1076 ◽  
Author(s):  
Xiaoxia Chen ◽  
◽  
Jinhua She ◽  
Xin Chen ◽  
Min Wu ◽  
...  
Keyword(s):  
Predictive Model ◽  
Iron Ore ◽  
Back Propagation ◽  
Raw Material ◽  
Discrete Wavelet ◽  
Sintering Process ◽  
Iron Ore Sintering ◽  
Carbon Efficiency ◽  
Iron Ore Sintering Process ◽  
Ore Sintering

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.

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