Estimation of excess air coefficient on coal combustion processes via gauss model and artificial neural network

Alexandria Engineering Journal

Muhammed Fatih Talu¹  Sedat Golgiyaz²  Mahmut Daşkın³  Cem Onat⁴ 

[1] Corresponding author. Tel.: [phone]-1928.;Department of Computer Engineering, Bingol University, 12000 Bingol, Turkey;Department of Computer Engineering, Inonu University, 44280 Malatya, Turkey;Department of Mechanical Engineering, Inonu University, 44280 Malatya, Turkey;

关键词: Excess air coefficient estimation;    Flame image;    Gauss model;    Flame stability;    Artificial neural network regression model;   

DOI  :  

来源: DOAJ

【 摘 要 】

It is no doubt that the most important contributing cause of global efficiency of coal fired thermal systems is combustion efficiency. In this study, the relationship between the flame image obtained by a CCD camera and the excess air coefficient (λ) has been modelled. The model has been obtained with a three-stage approach: 1) Data collection and synchronization: Obtaining the flame images by means of a CCD camera mounted on a 10 cm diameter observation port, λ data has been coordinately measured and recorded by the flue gas analyzer. 2) Feature extraction: Gridding the flame image, it is divided into small pieces. The uniformity of each piece to the optimal flame image has been calculated by means of modelling with single and multivariable Gaussian, calculating of color probabilities and Gauss mixture approach. 3) Matching and testing: A multilayer artificial neural network (ANN) has been used for the matching of feature−λ.

【 授权许可】

Unknown