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.
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