基于FSVM的转炉终点光辐射状态识别

State recognition of light radiation of BOF end-point based on fuzzy support vector machine

  • 摘要: 针对转炉冶炼终点传统人工肉眼看火判断存在着诸多不确定性问题,研究了一种基于模糊支持向量机的光辐射状态识别实现转炉终点判断的方法。设计了非接触式炉口光辐射采集系统,基于炉口火焰辐射规律分析,分别提取了通过高斯函数拟合表征光谱整体特征的三参数和两个发射峰离散谱参数作为支持向量机的输入,通过相关性分析选出生产过程中氧量、动枪幅度、爽枪时间、加料量参数构建子样本特征量,采用样本到类间距离的方法计算隶属度因子,建立了模糊支持向量机识别模型并进行了测试实验。实验结果表明,提出的方法对不同操作工况下的终点光辐射识别精度优于人工方法和传统SVM方法,可为转炉终点的准确判断提供依据。

     

    Abstract: In view of the end point of BOF smelting, there are many uncertain and unavoidable errors in the traditional judgment of flame by human eye. A method of BOF endpoint estimation was studied by recognition of light radiation with fuzzy support vector machine. A non-contact system was designed for light radiation acquisition of furnace mouth. Based on the analysis of the radiation, three parameters characterizing the overall spectral fitted by Gauss function and two parameters corresponding to emission peaks were extracted respectively and then used as inputs of support vector machines. Oxygen consumption, oxygen gun vibration amplitude, oxygen gun vibration time and feeding quantity of the process of production were chosen to construct subsample, the membership factors were calculated and the prediction model was built by using fuzzy support vector machine. The experimental results show that the proposed method has better recognition accuracy than the manual method and the traditional SVM method, and can provide reference for converter operator to determine the end point accurately.

     

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