Artificial neural network regression model to predict flue gas temperature and emissions with the spectral norm of flame image
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper presents an experimental study on flue gas temperature (FGT) and emissions estimation in home-type nut coal-fired burner. The proposed method does not require prior knowledge of Charge-Coupled Device (CCD) camera features. Therefore, it can be applied easily without costly and complex adaptation requirement to control the combustion process. In the proposed system, the flame image was taken with a CCD camera. At the same time, reference temperature and emissions were taken with flue gas analyzer. Combustion characteristics were extracted by image processing techniques from each two-color channels of the flame image. When the features were obtained, instead of converting the flame image to grayscale and obtaining the general features, local feature extraction was preferred from each of the two-color channels that express the combustion process better. For this process, the image was divided into local windows and individual features for each two-color channel was extracted. The optimum number of windows was decided by experimental investigation. The features were obtained by using the spectral norm of the region of interest. The obtaining image features were used to train the Artificial Neural Network (ANN) regression model which predicted the FGT and emissions. Estimation accuracy (correlation coefficient (R)) of developed FGT prediction model is 0.99. The emission prediction models estimate SO2, O-2, NOx, CO2 andCO emissions with R = 0.97, R = 0.96, R = 0.77, R = 0.96, and R = 0.87 accuracies, respectively. The experimental results show that the FGT and emissions can be estimated by the flame image.
Açıklama
Anahtar Kelimeler
Flame image, Coal-fired boiler, Flue gas temperature estimation, Emission estimation, Artificial neural network regression model
Kaynak
Fuel
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
255
