An emission predictive system for CO and NOx from gas turbine based on ensemble machine learning approach

The gas turbine in a combined cyclic power plant (CCPP) produces harmful gases like carbon monoxide (CO) and nitrogen oxide (NOx) into the atmosphere. It is evident to monitor the rate at which these gases are produced during power generation to comply with the industrial standard for emission. Therefore, a system is required to continuously monitor the emission from the CCPP gas turbine. Hence, this work aims to design a stacked ensemble machine learning (SEM) based predictive model for CO and NOx emission from a CCPP gas turbine. The neural network for regression (NNR), a generalized additive model (GAM), and the bagging of regression trees (BT) act as the base learners. A generalized regression neural network (GRNN) is used as a meta-learner for SEM. The hyperparameters of SEM are optimized using a Bayesian optimization algorithm for CO and NOX prediction. In addition to this, the performance of SEM is compared with support vector regression (SVR), decision tree (DRT), and linear regression (LIR). Simulation results demonstrate that SEM can reduce the RMSE 5.7–93.8% for NOx and 1%-41.5% for CO compared to other ML techniques. Finally, comparing the results with ML techniques existing in the literature shows the higher predictive accuracy of the proposed SEM.