Neural Network Based Predictive Emission Monitoring Module for a GE LM2500 Gas Turbine

Abstract

A Predictive Emission Monitoring (PEM) model has been developed for a non-DLE GE LM2500 gas turbine used on a natural gas compressor station on the TransCanada Pipeline System in Alberta. The PEM model is based on an optimized Neural Network (NN) architecture which takes four fundamental engine parameters as input variables. The model predicts NOx emission in ppmv-dry-O2 corrected and in kg/hr as NO2. The NN was trained using Continuous Emission Monitoring (CEM) measurements comprising two sets of actual emission data collected over two different dates in 2009, when the ambient ambient temperatures were vastly different (∼1° C and 24 °C), respectively. These training data were supplemented by other emission data generated by GE ‘Cycle-Deck’ tool to generate emission data at different ambient temperatures ranging from −30 to +30 °C. The outcome is a total of 1872 emission data of engine emissions at different operating conditions covering the range of the engine operating parameters (402 data points from CEM and 1470 data points from GE Cycle-Deck). The PEM model comprises a simple single hidden layer perceptron type NN with only two neurons in it. The performance of the NN-based model showed a correlation coefficient greater than 0.99, and error standard deviation of 4.5 ppmv of NOx and 1.4 kg/hr as NO2. Uncertainty analysis was conducted to assess the effects of uncertainties in the engine parameters on the NOx predictions by PEM. It was shown that for uncertainty in the ambient temperature of ±1 °C, the uncertainty in the NOx prediction is ± 0.9 to ±3.5%. Uncertainties of the order of ±1% in the other three input parameters results in uncertainties in NOx predictions by ±2.5 to ±6%. Finally, the PEM model was implemented in the station CEHM (Compressor Equipment Health Monitoring) system and NOx prediction were reported online on a minutely basis. These data are presented here over the first three months since implementation.