Short-Term Forecast of Blast Furnace Gas Production Amount Based on Grey RBF Neural Network

Abstract:

Dynamic optimization scheduling of the gas in iron and steel enterprises has great significance to reduce gas emission and the short-term forecast is the premise to realize the energy dynamic scheduling. Based on the characteristics that the influencing factors of blast furnace gas amount are complex and difficult to collect, a grey radial basis function (RBF) neural network forecast model is proposed to predict the gas amount for blast furnace in this paper. Combining grey theory, which is used to preprocess the historical data and obtain abundant information, with RBF neural network makes the effective trend forecast in the next 30 minutes come true. The model proposed in this paper is proved to be more accurate according to control experiments against the grey BP neural network.

• Periodical: Applied Mechanics and Materials (Volumes 713-715)
• Pages: 1907-1913
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.713-715.1907
• Online since: January 2015
• Authors: Zhi Min Lv, Zhao Wang*, Zi Yang Wang
• Keywords: Blast Furnace Gas, Grey Theory, RBF Neural Network, Short-Term Forecast, Trend Forecast

References

[1] Al-Ghandoor, A, Jaber, J. O., Al-Hinti, I. , Mansour, I. M. , 2009. Residential past and future energy consumption: potential savings and environmental impact [J]. Renewable and Sustainable Energy Reviews 13, 1262-1274.

DOI: 10.1016/j.rser.2008.09.008

[2] Akimoto, K. Sannomiya, N. Nishikawa. Y. &Tsuda, T. An optimal gas supply for a power plant using a mixed integer programming model [J]. Automatica, 1996, 27(3): 513-518.

DOI: 10.1016/0005-1098(91)90108-e

[3] Lady, G. M. Evaluating long term forecasts [J]. Energy Economics 2010(32): 450-457.

DOI: 10.1016/j.eneco.2009.10.006

[4] Yu Liu. Blast furnace gas recycling method in iron and steel industry [J]. 2009: Beijing, China. Techniques of Automation and Applications, 2005, 24(9): 4-6.

[5] Dong Qiu, Shuang Chen, Lihong Zhu. Blast furnace gas balance and comprehensive optimization in iron and steel enterprises [J]. Computer Technology and Development, 2009(3): 55-60.

[6] Yigong Sun. On gas balance of large iron and steel complex [J]. Iron and Steel, 2003(6): 59-61.

[7] Wenbing Li, Huade Li. Dynamic simulation of BFG system in iron and steel plant [J]. Metallurgical Industry Automation, 2009(2): 33-37.

[8] Na Liang, Jigang Zhang. China's total energy consume forecasting based on grey-RBF Neural Network [J]. Journal of Jiamusi University , 2008(3).

[9] Sung-Kwun Oh, Witold Pedrycz. Multi-layer self-organizing polynomial neural networks and their development with the use of genetic algorithms [J]. Journal of the Franklin Institute, 2006, 176(5): 475-489.

DOI: 10.1016/j.jfranklin.2005.09.005

[10] Kim JH, Yi HS, Han C. Optimal byproduct gas distribution in the iron and steel making process using mixed integer linear programming [J]. Int Symp Adv Control Ind Process 2002: 581-6.

[11] Qi Zhang. Supply-demand forecasting model of blast Furnace Gas in Iron & Steel Works and Its application[J]. Journal of Northeastern University (Natural Science), 2010(12): 1737-1740.

[12] Qiu Ping, Min Wu, Youwu Du. Gas consumption forecast model in steel corporation based on grey RBF neural network [J]. Journal of System Simulation, 2011(11): 2460-2464.