提出了通过分析转炉炉口辐射的光谱图像信息,实现转炉炼钢在线终点预测的方案。在该方案中,设计了火焰光强和视频捕获系统,通过编写相应代码,对采集数据进行分析,实时得到了吹炼过程中光强与图像特征值的变化情况,与经验炼钢的依据相符。通过统计逆向分析,选用几种特征变量,运用多元回归方法建立起不同炉次终点时刻的数学模型。模型通过了显著性检验,当误差不大于4s时,模型的预测精度为90.4%,为在线动态预报转炉炼钢终点提供了一种便捷的方法。

Based on the spectrum and image analysis,a method of predicting the converter end-point was proposed. A flame light and video capture system was designed to analyze the data with the edited codes. The result shows that the light intensity and the image characteristic information have the remarkable change law in the steelmaking blowing process,which coincides essentially with the experience steelmaking. Through the statistics reverse analysis,some characteristic parameters were selected,and then the end-point time mathematical model was established by the use of the multivariate regression analysis method. The experimental results indicate that the model passes the significance test,and the predication accuracy is 90.4% when the measurement error is less than 4 second. The model is stable and provides a convenient method to predict the converter steel-making end-point on line.