, Volume 10, Issue 2, pp 603–608 | Cite as

Predicting the Electricity Consumption and the Exergetic Efficiency of a Submerged Arc Furnace with Raw Materials using an Artificial Neural Network

  • Zhengjie Chen
  • Wenhui Ma
  • Jijun Wu
  • Kuixian Wei
  • Guoqiang Lv
  • Zhanwei Liu
Original Paper


The problem of higher electricity consumption and lower exergy efficiency in the submerged arc furnace process of the silicon industry needs to be urgently solved. However, various raw materials play important roles in the electricity consumption and exergy efficiency of a submerged arc furnace during silicon production. An artificial neural network (ANN) method was used to model the final strain of the electricity consumption and exergy efficiency with varying silica, coke, coal and electrode. The measured strain versus predicted strain by the model was compared using the R 2 coefficient. The results showed that the exergy efficiency and the electricity consumption values of the testing data are R 2= 0.9918 and R 2 = 0.9896, respectively, in a very short time with low error levels. They clearly indicate the adequacy of the model proposed for prediction of the exergy efficiency and the electricity consumption with different raw materials in the mixture of carbonaceous raw materials in the furnace. Additionally, there is good agreement between the actual and predicted values. Therefore, this developed ANN model is useful to guide the decision about the use of raw materials in silicon production under the condition of lower electricity consumption and higher exergy efficiency.


Electricity consumption Exergy efficiency Artificial neural network 


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The authors are grateful for financial support from the NSFC (No.51334002, 51461027 and 21563017) and the Natural Science Foundation of Yunnan Province in China (2014FB124).


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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.State Key Laboratory of Complex Nonferrous Metal Resources Cleaning Utilization in Yunnan Province/The National Engineering Laboratory for Vacuum MetallurgyKunming University of Science and TechnologyKunmingChina
  2. 2.Faculty of Metallurgical and Energy EngineeringKunming University of Science and TechnologyKunmingChina
  3. 3.Key Laboratory of Non-Ferrous Metals Vacuum Metallurgy of Yunnan Province/ Engineering Research Center for Silicon Metallurgy and Silicon Materials of Yunnan Provincial UniversitiesKunmingChina

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