[Multi-spectral measurement of Basic oxygen furnace flame temperature]
Yong-Qing Wang1, Yan-Ru Chen, Qi Zhao
1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China cxywyq@sina.cn
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|February 8, 2013
Summary
A new multi-wavelength method accurately measures basic oxygen furnace flame temperature using spectral data. This approach, employing wavelet neural networks, bypasses emissivity assumptions for precise temperature and emissivity determination.
Area of Science:
- Thermodynamics and Spectroscopy
- Computational Intelligence
Context:
- Accurate temperature measurement is crucial for optimizing basic oxygen furnace (BOF) operations.
- Traditional methods often rely on simplifying assumptions about flame emissivity.
- Developing advanced optical diagnostic techniques is essential for process control.
Purpose:
- To introduce a novel multi-wavelength analysis method for measuring BOF flame temperature.
- To validate the effectiveness of wavelet neural networks in spectral data processing for this application.
- To determine flame temperature and monochromatic emissivity without prior emissivity model assumptions.
Summary:
- A USB4000 spectrometer captured flame radiation spectra (200-1100 nm).
- Levenberg-Marquardt modeling initially derived temperature and emissivity.
- A wavelet neural network, combining sigmoidal and morlet wavelet activation functions, processed spectral data to refine temperature and emissivity measurements, removing emissivity model dependency.
Impact:
- Provides a more accurate method for determining BOF flame temperature and spectral emissivity.
- The wavelet neural network approach offers a robust alternative to traditional modeling techniques.
- Enables improved process monitoring and control in metallurgical applications.
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