Predicting the Electricity Consumption and the Exergetic Efficiency of a Submerged Arc Furnace with Raw Materials using an Artificial Neural Network
- 39 Downloads
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.
KeywordsElectricity consumption Exergy efficiency Artificial neural network
Unable to display preview. Download preview PDF.
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).
- 2.Børset MT, Kolbeinsen L, Tveit H, Kjelstrup S (2015) Exergy based efficiency indicators for the silicon furnace. Energy. doi: 10.1016/j.energy.2015.07.010
- 3.Schei A, Tuset J K, Tveit H (1998) Production of high silicon alloys. Trondheim, Norway, TapirGoogle Scholar
- 6.Xiao Y, Reuter M A, Holappa L (2001) Kinetic modeling of chromite pellet reduction with CO gas under rising temperatures from 700 to 1520 ∘C. In: Proceedings of the ninth international ferroalloys congress and the manganese 2001 health issues symposium. Ferroalloy Association, Quebec City, Canada, pp 147–156Google Scholar
- 7.Andresen B, Tuset J K (1995) Dynamic model for the high-temperature part of the carbothermic silicon metal process. In: Tuset JK, Tveit H, Page IG (eds) INFACON 7, FFF. Trondheim, Norway, pp 535–544Google Scholar
- 8.Kekkonen M, Syynimaa A, Holappa L, Niemela P (1998) Kinetic study on solid state reduction of chromite pellets and lumpy ores. In: Proceedings of the eighth international ferroalloys congress, CSM, Beijing, China, pp 141–146Google Scholar
- 9.Saevarsdottir G A, Bakken J A, Sevastyanenko V G, Gu L (2001) Modeling of AC arcs in submerged-arc furnaces for production of silicon and ferrosilicon. Iron Steelmaker (USA) 28:51–57Google Scholar
- 13.Kordos M, Blachnik M Wieczorek T (2011) Temperature prediction in electric arc furnace with neural network tree, Springer, BerlinGoogle Scholar
- 15.Wang F, Jin Z, Zhu Z (2005) Modeling and prediction of electric arc furnace based on neural network and chaos theory, Springer, BerlinGoogle Scholar
- 18.Joorabian M, Saedian A, Nasiri M (2004) Design and modeling of electrical arc furnaces. J Sci Technol 15(57A):42–52Google Scholar
- 19.Zeghal M (2008) Modeling the creep compliance of asphalt concrete using the artificial neural network technique. GeoCongress 2008:910–916Google Scholar