Prediction of NOx emission of an experimental swirl stabilized combustor using the flame image processing techniques and data mining methods

Document Type : Research Paper



So far, some methods have been presented for prediction of parameters of a combustion system based on the flame image processing techniques. Almost all of them use various techniques to convert the extracted geometrical and luminosity data of flame image into combustion information. Potentially, image of a flame includes many information in terms of different features which could be related to the combustion field. In this work, relations between recorded images taken from turbulent flames in a combustion chamber with measured values of NOx emission have been investigated using image processing techniques along with three methods of data mining. For this purpose geometrical, statistical and luminosity features extracted from flame images were used as input data for the LVQ, MLP and SOM neural networks. Based on these extracted features from flame images the neural networks predicted the level of NOx emission and these predicted values were validated with the measured data of combustor. Moreover by using forward feature selection technique in each of the above-mentioned algorithms, five features were selected. The related experiments were already performed by using four different types of secondary fuel injectors (with four different designs) for an overall equivalence ratio between =0.7~0.9 along with different amount of secondary fuel injection rate in the range of Qsec=0.6~4.2 L/min. The result shows that the LVQ with 97% accuracy has better capability for prediction of the level of NOx emission than SOM and MLP methods.


Main Subjects

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