Application of Machine Learning Models for Carbon Monoxide and Nitrogen Oxides Emission Prediction in Gas Turbines
Neural Networks: Overview of Current Theories and Applications, Smart Innovation, Systems and Technologies, Springer, pages 247-260 (DOI 10.1007/978-981-95-4072-3_21)
Published online on , 6 citations on Google Scholar
In short
The paper compares machine learning models (linear regression, SVM, decision trees, XGBoost, MLP, LSTM, GRU and KNN) for predicting carbon monoxide and nitrogen oxides emissions from gas turbines as part of a predictive emission monitoring system. It gives a side-by-side view of which methods estimate these emissions most accurately.
Cite
@inbook{Zeinalipour_2026,
title={Application of Machine Learning Models for Carbon Monoxide and Nitrogen Oxides Emission Prediction in Gas Turbines},
ISBN={9789819540723},
ISSN={2190-3026},
url={http://dx.doi.org/10.1007/978-981-95-4072-3_21},
DOI={10.1007/978-981-95-4072-3_21},
booktitle={Neural Networks: Overview of Current Theories and Applications},
publisher={Springer Nature Singapore},
author={Zeinalipour, Kamyar and Barriere, Laure and Ghelardi, David and Gori, Marco},
year={2026},
pages={247–260}
}