ABSTRACT

A USB4000 spectrometer was installed in the converter mouth to collect spectral information of the flame in theprocess of steelmaking in real time, Gaussian fitting algorithm and wavelet analysis algorithm are used to extract the stableand unstable eigenvalues of flame spectral information. The spectral information characteristic value and the accumulatedoxygen consumption information index data in the smelting process were input as samples. Combined with the corresponding static model, the corresponding carbon content and temperature values were calculated as sample outputs,and to establish a one-to-one correspondence sample set. A continuous intelligent prediction model of carbon content andtemperature in the later stage of steelmaking was established by using the backpropagation neural network algorithm, witha model forecast accuracy in the experimental stage of more than 95% and above 89% in practical steelmaking, andprovides new research ideas to control the endpoint of steelmaking.

摘要

在转炉口安装USB4000光谱仪，实时采集炼钢过程中火焰的光谱信息，利用高斯拟合算法和小波分析算法提取火焰光谱信息的稳定和不稳定特征值。以冶炼过程中光谱信息特征值和累计耗氧信息指标数据作为样本输入。结合对应的静态模型，计算出对应的碳含量和温度值作为样本输出，建立一一对应的样本集。利用反向传播神经网络算法建立了炼钢后期碳含量和温度的连续智能预测模型，