Temperature Measurement of Flame at Converter Mouth Based on Multi-Spectral Thermometry

Yanming, 邵艳明 Shao; Shu′an, 赵书安 Zhao; Yanru, 陈延如 Chen; Renlong, 俞仁龙 Yu; Qi, 赵琦 Zhao

doi:10.3788/aos201535.1130002

Cited by 1 publication

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“…Medium and small converters are not amenable to sublance equipment installation because of the small converter mouth and the method of flue-gas analysis dynamic control smelting process is the only suitable approach. However, the sensitivity of its flue-gas analysis elements is easily affected by the purity of converter flue gas in the detection and analysis process, which makes the endpoint hit ratio extremely unstable and greatly affects the popularisation and application of the flue-gas analysis method in converter smelting process [6][7][8][9][10]. Therefore, for small-and mediumsized converters in which sub-gun equipment cannot be installed, improvements in the endpoint hit ratio with a high efficiency and low cost is the restrictive link to achieve automatic smelting.…”

Section: Introductionmentioning

confidence: 99%

Research on terminal control model of intelligent mining of flame spectral information of converter mouth in late smelting stage

Zhang

Zeng

et al. 2021

Ironmaking & Steelmaking

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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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Section: Introductionmentioning

confidence: 99%