Prediction and comparative analysis of emissions from gas turbines using random search optimization and different machine learning-based algorithms

Abstract

Gas turbines are widely used for power generation globally, and their greenhouse gas emissions have increasingly drawn public attention. Compliance with environmental regulations necessitates sophisticated emission measurement techniques and tools. Traditional sensors used for monitoring emission gases can provide inaccurate data due to malfunction or miscalibration. Accurate estimation of gas turbine emissions, such as particulate matter, carbon monoxide, and nitrogen oxides, is crucial for assessing the environmental impact of industrial activities and power generation. This study used five different machine learning models to predict emissions from gas turbines, including AdaBoost, XGBoost, k-nearest neighbour, and linear and random forest models. Random search optimization was used to set the regression parameters. The findings indicate that the AdaBoost regressor model provides superior prediction accuracy for emissions compared to other models, with an accuracy of 99.97% and a mean squared error of 2.17 on training data. This research offers a practical modelling approach for forecasting gas turbine emissions, contributing to the reduction of air pollution in industrial applications.