Predictive Model of Mo-Si Alloy Smelting Energy Consumption Based on Double Wavelet Neural Network

摘要：

Unit electricity consumption is important indicator on production of Mn-Si alloy. There exists a serious nonlinear relationship among unit electricity consumption and the ferromanganeses grade and furnace average power and the amount of ferrosilicon powder and the amount of coke and daily average output etc. The predictive model of Mn-Si Alloy Smelting Energy Consumption based on Double 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 mean absolute relative forecasting error of unit electricity consumption was 0.9％, while the mean absolute relative forecasting errors of regression wavelet neural network and time-delay wavelet neural network were 2.1％ and 1.3％ respectively. It was proved that the double wavelet neural network model had preferable forecasting accuracy.

关键词： Mn-Si Alloy Smelting Predictive Model Double Wavelet Neural Network

作者: YANG Hong-tao LI Xiu-lan ZHANG Niao-na

作者单位: Institute of Electrical and Electronic Engineering Changchun University of Technology Changchun, Chi Institute of Mechanical Science and Engineering Jilin University Changchun, China

会议类型: 国际会议

会议名称: 2010 International Conference on Computer,Mechatronics,Control and Electronic Engineering(2010计算机、机电、控制与电子工程国际会议 CMCE 2010)

会议地点: 长春

会议语种:英文

页码: 267-270

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）