This study deals with artificial neural network (ANN) modelling of a fuel-staged gas turbine combustion rig to predict exhaust emissions and combustor outlet temperature. The data used for ANN training and testing was acquired from a natural gas burning test rig at various operating conditions. The ANN model uses a multi-layer feed-forward network architecture and was trained with experimental data using backpropagation. The ANN has 8 input neurons, 27 neurons in the hidden layer and 3 output neurons. The ANN model can predict the experimental results quite well with correlation coefficients in the range of 0.96 to 0.99. The ANN model was then used to create performance maps (response surfaces) and to predict two operating conditions that optimize the conflicting criteria of low emissions and high gas outlet temperature. The ANN predicted optimum operating conditions yielded NOx emissions below 20 ppm corrected to 15% O2 and a combustor outlet temperature of 1838 K. These optimum operating conditions were then experimental validated. The experimental validation showed that the first ANN predicted optimum operating condition was poorly predicted: the difference between the ANN predicted equivalence ratio and that of the experimental validation data for the optimum point was over 0.1. The second ANN predicted optimum operating condition was accurately predicted within the experimental uncertainty of the measurements. The difference in validity between the ANN predictions can be attributed to the sparsity of the data: for the first optimum operating condition there were 28 highly clustered training points, while the second optimum operating condition had 40 points that spanned a greater operating region.

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