Abstract
To study the relations between the amounts of NO x emission, noise level, and level of pressure fluctuations as the output quantities of an experimental swirl-stabilised combustor and two variables of overall equivalence ratio (ϕ) and secondary fuel injection rate (Q sec), as its input quantities, two different data mining approaches were employed in the present work (i.e., artificial neural network (ANN) and multiple polynomial regression (MPR) techniques). The related experiments were already carried out using four different types of secondary fuel injectors with an overall equivalence ratio (ϕ) in the range of 0.7~0.9. The results indicate that both the ANN and MPR methods have lower predicting capability for estimation of noise level and the level of pressure fluctuations compared with that of the emission index. Also the results show that the ANN has better predicting capability, for estimation of various combustor parameters, than the MPR method.
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