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

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dc.authorid0000-0002-0181-3658en_US
dc.contributor.authorAslan, Emrah
dc.date.accessioned2024-11-20T11:50:49Z
dc.date.available2024-11-20T11:50:49Z
dc.date.issued2024en_US
dc.departmentDicle Üniversitesi, Silvan Meslek Yüksek Okulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractGas 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.en_US
dc.identifier.citationAslan, E. (2024). Prediction and comparative analysis of emissions from gas turbines using random search optimization and different machine learning-based algorithms. Bulletin of the Polish Academy of Sciences: Technical Sciences, 72(6), 1-10.en_US
dc.identifier.endpage10en_US
dc.identifier.issn0239-7528
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85208061775
dc.identifier.scopusqualityQ2
dc.identifier.startpage1en_US
dc.identifier.urihttps://journals.pan.pl/dlibra/publication/151956/edition/132453/content
dc.identifier.urihttps://hdl.handle.net/11468/29069
dc.identifier.volume72en_US
dc.indekslendigikaynakScopus
dc.institutionauthorAslan, Emrah
dc.language.isoenen_US
dc.publisherPolska Akademia Nauken_US
dc.relation.ispartofBulletin of the Polish Academy of Sciences: Technical Sciences
dc.relation.isversionof10.24425/bpasts.2024.151956en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEfficiencyen_US
dc.subjectEmissionen_US
dc.subjectGas turbinesen_US
dc.subjectMachine learningen_US
dc.subjectRandom search optimizationen_US
dc.titlePrediction and comparative analysis of emissions from gas turbines using random search optimization and different machine learning-based algorithmsen_US
dc.titlePrediction and comparative analysis of emissions from gas turbines using random search optimization and different machine learning-based algorithms
dc.typeArticleen_US

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