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Abstract: This paper presents a novel method of identifying coal type based on mechanistic methods. The ratio of the resonance line spectrum of a luminous flame and the continuous spectrum at the same wavelength eliminates the influence of temperature on spectral intensity. The atomic line spectra of Na and K are typical and significant over continuous flame spectra. The concentrations of elemental Na and K in the flame are exclusively relative to coal type and composition. Using an experimental furnace and charge-coupled device (CCD) optical spectrometer apparatus, the continuous spectra and atomic line spectra of Na and K elements were sampled from coal flames in real time. An empirical fitting method was used to simplify the formulas of absorption strength and flame temperature calculation, and rational solutions were obtained by using an iterative algorithm. Due to the change in reaction rate and absorption by soot particles, the relative contents of Na and K in a flame vary with the temperature and absorption strength. Arrhenius’s equation for temperature compensation was adopted. Compensation for soot density in the furnace was also satisfied by an exponential expression. At any one sampling position, the compensation parameters were identical for all coal types. After compensation for temperature and density of soot particles, the relative strength of the Na and K signals and the ratio between them uniquely matched the coal type burnt in various conditions. The results were replicated and verified in various conditions, and the response time of the system was of the order of seconds.

This paper proposed a novel online coal type identification method based on the flame glowing mechanism, it is found that the resonance spectrum strength has a definite relationship with the corresponding content of the element of Na, k alkali metal species in a coal-fired flame. By means of a series of algorithm transformation around the strength relationship of the spectra, the characteristic parameter relevant to the specified coal type is achieved. Collected by the fiber spectrometer, the flame spectrum data in the specific wavelength range is employed to identify the various kinds of coal online. Such a method has been used in the industrial field and the results showed good work.

基于炉膛火焰发射光谱的煤种识别

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