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 factor 2. can be o...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 factor 2. can be obtained using very expensive air measurement instruments. The proposed method to predict ） 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） extrac- tion meaningful information （flame intensity and bright- ness）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 co- occurrence 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.展开更多
基金supported by The Scientific and Technological Research Council of Turkey(TUBITAK,Project number:114M116)and MIMSAN AS
文摘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 factor 2. can be obtained using very expensive air measurement instruments. The proposed method to predict ） 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） extrac- tion meaningful information （flame intensity and bright- ness）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 co- occurrence 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.
