In this study,the relationship between the visual information gathered from the flame images and the excess air factor 2 in coal burners is investigated.In conventional coal burners the excess air factorλ.can be obtained using very expensive air measurement instruments.The proposed method to predict 2 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 2.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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