Predictive model of Mn-Si alloy Smelting Energy Consumption based on Wavelet Neural Network

Author

Yang hong-tao ; Li Hui ; Li Xiu-lan ; Zhu Ming-yi

Author_Institution

Inst. of Electr. & Electron. Eng., Changchun Univ. of Technol., Changchun, China

fYear

2010

fDate

26-28 June 2010

Firstpage

3603

Lastpage

3606

Abstract

Daily average output and unit electricity consumption are two important indicators on production of Mn-Si alloy. There exists a serious nonlinear relationship among daily average output and unit electricity consumption and the ferromanganese´s grade and furnace average power and the amount of ferrosilicon powder and the amount of coke etc. The predictive model of Mn-Si Alloy Smelting Energy Consumption based on Wavelet Neural Network was put forward, and the research of verifying the model was made by comparing the predictive value with the practical data of a Ferroalloy Company. The results show that the double hit rate for daily average output and unit electricity consumption achieves at above 90%.

Keywords

coke; energy consumption; furnaces; manganese alloys; neural nets; production engineering computing; silicon alloys; smelting; wavelet transforms; MnSi; coke; daily average output; ferromanganese grade; ferrosilicon powder; furnace average power; predictive model; smelting energy consumption; unit electricity consumption; wavelet neural network; Energy consumption; Furnaces; Iron alloys; Load forecasting; Neural networks; Power engineering and energy; Predictive models; Smelting; Wavelet analysis; Wavelet transforms; Mn-Si Alloy Smelting; Predictive Model; Wavelet Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechanic Automation and Control Engineering (MACE), 2010 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-7737-1

Type

conf

DOI

10.1109/MACE.2010.5535479

Filename

5535479