ε-SVR-Based Predictive Models of Energy Consumption and Performance for Sintering

Jun Kai Wang; Fei Qiao; Guo Chen Li; Xue Chu Zhu

doi:10.4028/www.scientific.net/AMM.548-549.1905

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 548-549ε-SVR-Based Predictive Models of Energy...

ε-SVR-Based Predictive Models of Energy Consumption and Performance for Sintering

Abstract:

For realizing energy conservation and burdening optimization of sintering process in iron and steel enterprises, as to the predictive issues of energy consumption and performance indices, the Support Vector Machine for Regression (ε-SVR) was introduced into sintering production system. A general modeling mode was proposed and the predictive model of energy consumption and several performances like chemical compositions was established by history data of sintering. Then, this model was compared with several other methods such as multiple linear regressions, ELM, BPNN and RBFN in a case study. Results show that the ε-SVR method can achieve qualified prediction results rapidly with the best accuracy and time efficiency.

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Periodical:

Applied Mechanics and Materials (Volumes 548-549)

Pages:

1905-1910

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.548-549.1905

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Online since:

April 2014

Authors:

Jun Kai Wang*, Fei Qiao, Guo Chen Li, Xue Chu Zhu

Keywords:

Energy Consumption (EC), Performance Prediction, Sintering, Support Vector Machine for Regression

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References

[1] LU Zhongwu, CAI Jiuju. Basis of Systemic Energy Conservation [M]. Beijing: Science Press, (2010).

Google Scholar

[2] Han Qinghong, Jin Yonglong, Zhang Junhong. Application of neural networks in the prediction of solid fuel consumption in sintering process [J]. Energy for Metallurgical Industry, 2005, 24(2): 9-11.

Google Scholar

[3] HE Guoqiang, SUN Ying, WANG Bi. An improved neural network algorithm and its application in sinter cost prediction [J]. Computer Engineering & Science, 2010, 32(8): 138-140.

Google Scholar

[4] MENG Hui, QIAO Fei, LI Li. Predictive model of energy consumption in sintering process based on BP neural network [J]. Mechanical Engineer, 2012, (2): 45-47.

Google Scholar

[5] FENG Zhaohui, ZHANG Hua, WANG Yanhong. Study on Prediction and Optimization of Sintering Process Energy Consumption [J]. Sintering and Pelletizing, 2012, 37(6): 13-17.

Google Scholar

[6] ZHANG Junhong, XIE Anguo, SHEN Fengman. Optimization analysis on decreasing the solid fuel consumption in the sintering process by the artificial intelligence methods [J]. Energy for Metallurgical Industry, 2003, 22(4): 16-18.

Google Scholar

[7] SHEVAD S K, KEERTHI S S, BHATTACHARYYA C, et al. Improvements to the SMO Algorithm for SVM Regression [J]. IEEE Transactions on Neural Networks, 2000, 11(5): 1188-1193.

DOI: 10.1109/72.870050

Google Scholar