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

Xiaoxia Chen; Jinhua She; Xin Chen; Min Wu

doi:10.20965/jaciii.2016.p1070

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JACIII Vol.20 No.7 pp. 1070-1076

doi: 10.20965/jaciii.2016.p1070

(2016)

Paper:

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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

^†Corresponding author

Received:

July 8, 2016

Accepted:

September 5, 2016

Published:

December 20, 2016

Keywords:

carbon efficiency, discrete wavelet transfer (DWT), back-propagation neural network (BPNN), iron ore sintering

Abstract

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.

Cite this article as:

X. Chen, J. She, X. Chen, and M. Wu, “Discrete Wavelet Transfer Based BPNN for Calculating Carbon Efficiency of Sintering Process,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.7, pp. 1070-1076, 2016.

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References

[1] X. Chen, X. X. Chen, M. Wu, and J. H. She, “Modeling and optimization method featuring multiple operating modes for improving carbon efficiency of iron ore sintering process,” Control Engineering Practice, Vol.54, pp. 117-128, 2016.
[2] X. Chen, W. W. Wen, M. Wu, and W. H. Cao, “BP neural network model of coke consumption of sintering process based on chaotic PSO algorithm,” Computers and Applied Chemistry, Vol.30, No.10, pp. 111-114, 2013.
[3] M. Wu, X. X. Chen, W. H. Cao, J. H. She, and C. S. Wang, “An intelligent integrated optimization system for the proportioning of iron ore in a sintering process,” J. of Process Control, Vol.24, No.1, pp. 182-202, 2014.
[4] J. Z. Wang, J. J. Wang, Z. G. Zhang, and S. P. Guo, “Forecasting stock indices with back propagation neural network,” Expert Systems with Applications, Vol.38, No.11, pp. 14346-14355, 2011.
[5] X. X. Chen, J. H. She, X. Chen, and M. Wu, “Modeling method of carbon efficiency in iron ore sintering process,” IEEE Int. Conf. on Industrial Technology, pp. 1033-1038, 2016.
[6] D. C. Kiplangat, K. Asokan, and K. S. Kumar, “Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition,” Renewable Energy, Vol.93, pp. 38-44, 2016.
[7] R. Maity, M. Suman, and N. K. Verma, “Drought prediction using a wavelet based approach to model the temporal consequences of different types of droughts,” J. of Hydrology, 2016.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] X. Chen, X. X. Chen, M. Wu, and J. H. She, “Modeling and optimization method featuring multiple operating modes for improving carbon efficiency of iron ore sintering process,” Control Engineering Practice, Vol.54, pp. 117-128, 2016.

[2] [2] X. Chen, W. W. Wen, M. Wu, and W. H. Cao, “BP neural network model of coke consumption of sintering process based on chaotic PSO algorithm,” Computers and Applied Chemistry, Vol.30, No.10, pp. 111-114, 2013.

[3] [3] M. Wu, X. X. Chen, W. H. Cao, J. H. She, and C. S. Wang, “An intelligent integrated optimization system for the proportioning of iron ore in a sintering process,” J. of Process Control, Vol.24, No.1, pp. 182-202, 2014.

[4] [4] J. Z. Wang, J. J. Wang, Z. G. Zhang, and S. P. Guo, “Forecasting stock indices with back propagation neural network,” Expert Systems with Applications, Vol.38, No.11, pp. 14346-14355, 2011.

[5] [5] X. X. Chen, J. H. She, X. Chen, and M. Wu, “Modeling method of carbon efficiency in iron ore sintering process,” IEEE Int. Conf. on Industrial Technology, pp. 1033-1038, 2016.

[6] [6] D. C. Kiplangat, K. Asokan, and K. S. Kumar, “Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition,” Renewable Energy, Vol.93, pp. 38-44, 2016.

[7] [7] R. Maity, M. Suman, and N. K. Verma, “Drought prediction using a wavelet based approach to model the temporal consequences of different types of droughts,” J. of Hydrology, 2016.

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

Xiaoxia Chen*, Jinhua She**, Xin Chen***,†, and Min Wu***

Xiaoxia Chen^, Jinhua She^, Xin Chen^,†, and Min Wu^***