The Online Prediction of The Low Carbon Ferrochrome Terminal Composition in Smelting Process

摘要：

　　The online prediction of the low carbon ferrochrome terminal composition in electro-silicothemic smelting process plays a key role in guiding the determining the tapping time,the smelting process of the power supply system,the production quality and the energy consumption and so on.By introducing the multi-scale wavelet kernel function in the support vector machine (SVM) algorithm,and according to the Bayesian classifier to certain different smelting conditions,we chose different decomposition scales.In this way,the accuracy of the terminal composition prediction during the smelting process is improved greatly.Experiments show the effectiveness of the proposed method.

关键词： Electro-silicothermic multi-scale Support Vector Machine Bayesian

作者: Zhang niaona Wang yingying Bai yongjun

作者单位: Electronic and Electrical Engineering Institute, Changchun University of Technology, China

会议类型: 国际会议

会议名称: 2013 International Conference on Intelligent System,Applied Materials and Control Technology(GSAMCT2013)(2013年智能系统、应用材料和控制技术国际会议)

会议地点: 太原

会议语种:英文

页码: 519-522

在线出版日期: 2013-01-13（万方平台首次上网日期，不代表论文的发表时间）