Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner

Abstract

This study deals with ANN (artificial neural network) modeling of a swirl burner. The model was used to predict the flame temperature and pollutant emissions (CO (carbon monoxide) and NOx (nitrogen oxide)) from combustion of LPG (liquefied petroleum gas) in the swirl burner. The data for the training and testing of the proposed ANN was obtained by combusting LPG at various equivalent ratios (LPG/air ratios) and swirler's vane angles in a low swirl burner. Vane angles of 35–60° in steps of 5° and equivalent ratios of 0.94, 0.90, 0.85, 0.80, 0.75, 0.71, 0.66 and 0.61 were considered. An ANN model based on standard back-propagation algorithms for the swirl burner was developed using some of the experimental data for training and validation. The performance of the ANN was tested by comparing the predicted outputs with the experimental values that were not used in training the network. R values of 0.94 were obtained for CO and NOx and 0.99 for flame temperature. These results show that very strong correlation exists between the ANN predicted values and the experimental results. Therefore, this study demonstrates that the performance and emissions of swirl burner can be accurately predicted using ANN approach.