A Neural Network Based Predictive Emission Monitoring Model for NOx Emission From a Gas Turbine Combustor

Abstract

A Predictive Emission Monitoring (PEM) model for predicting NOx emission from a gas turbine combustor has been developed by employing an optimized Neural Network (NN) architecture. The Neural Network was trained by using actual field test data and predicted results of a Computational Fluid Dynamics (CFD) model of the combustor. The field tests were performed at a natural gas compressor station driven by a General Electric (GE) LM1600 conventional gas turbine. The model takes eight fundamental parameters (operating and ambient) as input, and predicts NO and NOx as outputs. The data used for training the model covers the entire operating ranges of power and ambient temperature for the site. The CFD model employs a non-equilibrium (flamelet) combustion scheme and a set of 8 reactions including the Zeldovich mechanism for thermal NOx, and an empirical correlation for prompt NOx formation. The results predicted by the CFD model were within 15% of the measured values. Results of the field tests demonstrated that the spool speed ratio of the gas turbine remained constant throughout the tests, the power output of the engine was linearly proportional to the spool speeds, and the NOx emission was proportional to the site power output. A Multi Layer Perceptron type Neural Network with two hidden layers, each with four neurons was found to be the optimum architecture for the model. The NO levels predicted by the PEM model based on the optimized NN had a maximum absolute error of approximately 7%, mean absolute error of 2.3% and standard deviation of 1.97%. One year operating data for the site was submitted to the trained NN model with ambient temperatures varying from −29.9 °C to 35.7 °C and output powers from 5.8 MW to 17 MW. It was found that the model produced consistent contours of NO emissions. As expected, the NO levels were found to increase with increasing power and/or ambient temperature.