In this study, the relationship between the visual information gathered from the flame images and the excess air factor k in coal burners is investigated. In conventional coal burners the excess air factor k. can be obtained using very expensive air measurement instruments. The proposed method to predict k for a specific time in the coal burners consists of three distinct and consecutive stages; a) online flame images acquisition using a CCD camera, b) extraction meaningful information(flame intensity and brightness)from flame images, and c) learning these information(image features) with ANNs and estimate k. Six different feature extraction methods have been used: CDF of Blue Channel, Co-Occurrence Matrix, L∞-Frobenius Norms,Radiant Energy Signal(RES), PCA and Wavelet. When compared prediction results, it has seen that the use of cooccurrence matrix with ANNs has the best performance(RMSE = 0.07) in terms of accuracy. The results show that the proposed predicting system using flame images can be preferred instead of using expensive devices to measure excess air factor in during combustion.
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机译:Effect of inner and outer secondary air ratios on ignition, C and N conversion process of pulverized coal in swirl burner under sub- stoichiometric ratio