Extraction of Characteristic Parameters of Furnace Flame Based on Markov Model

The combustion of pulverized coal in furnace is a kind of complex and unstable suspension burning process. Obtaining more accurate characteristic parameters is crucial to detection of the flame, which is important to control combustion conditions, maintain economical operation and safeguard security. In this paper, first, we define the concept of the characteristic region in flame image and the characteristic parameters of the characteristic region. These characteristic parameters include the size of characteristic region; the average grey-level of characteristic region; the lessening rate of the characteristic region size and the flicker signal of the flame. Next, an algorithm of mean field approximation annealing (MFAA) based on compound Gauss-Markov random field (CGMRF) model is introduced to extract the characteristic region and these characteristic parameters. The experimentation to the sample images proves that these parameters are available to identify combustion state and this algorithm is effective to extract theses parameters.

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