Classification of NOx Emission in Marine Engines Utilizing kNN-Based Machine Learning Algorithms
Marine diesel engines are crucial for powering large vessels in the maritime sector and are known for their efficiency across various industries. However, increasing environmental concerns and stringent regulations targeting air pollutants such as nitrogen oxides (NOx) and particulate matter (PM) ha...
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| Format: | Article |
| Language: | English |
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levent
2024-12-01
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| Series: | International Journal of Pioneering Technology and Engineering |
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| Online Access: | https://ijpte.com/index.php/ijpte/article/view/97 |
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| author | Samet Memiş Ramazan Şener |
| author_facet | Samet Memiş Ramazan Şener |
| author_sort | Samet Memiş |
| collection | DOAJ |
| description | Marine diesel engines are crucial for powering large vessels in the maritime sector and are known for their efficiency across various industries. However, increasing environmental concerns and stringent regulations targeting air pollutants such as nitrogen oxides (NOx) and particulate matter (PM) have heightened the need for advanced emission control technologies. Addressing this challenge, the study focuses on developing a reliable method to predict NOx emission levels in marine engines, reducing reliance on resource-intensive experimental testing. Leveraging machine learning techniques, particularly k-nearest neighbors (kNN)-based algorithms, the research classifies NOx emissions in marine engines operating under the Reactivity-Controlled Compression Ignition (RCCI) strategy. Comparative performance analysis reveals that the FPFS-kNN algorithm achieves the highest accuracy (90.00%) alongside strong precision (84.23%), recall (82.37%), and F1 score (82.47%). These findings underscore the potential of machine learning in emission prediction and highlight directions for future exploration in this domain. |
| format | Article |
| id | doaj-art-5f9d706aa99f44808a6103d38f7535c7 |
| institution | OA Journals |
| issn | 2822-454X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | levent |
| record_format | Article |
| series | International Journal of Pioneering Technology and Engineering |
| spelling | doaj-art-5f9d706aa99f44808a6103d38f7535c72025-08-20T02:00:13ZengleventInternational Journal of Pioneering Technology and Engineering2822-454X2024-12-01302747910.56158/jpte.2024.97.3.0297Classification of NOx Emission in Marine Engines Utilizing kNN-Based Machine Learning AlgorithmsSamet Memiş0https://orcid.org/0000-0002-0958-5872Ramazan Şener1https://orcid.org/0000-0001-6108-8673Department of Marine Engineering, Maritime Faculty, Bandırma Onyedi Eylül University, Bandırma, Balıkesir, Türkiye Department of Marine Engineering, Maritime Faculty, Bandırma Onyedi Eylül University, Bandırma, Balıkesir, Türkiye Marine diesel engines are crucial for powering large vessels in the maritime sector and are known for their efficiency across various industries. However, increasing environmental concerns and stringent regulations targeting air pollutants such as nitrogen oxides (NOx) and particulate matter (PM) have heightened the need for advanced emission control technologies. Addressing this challenge, the study focuses on developing a reliable method to predict NOx emission levels in marine engines, reducing reliance on resource-intensive experimental testing. Leveraging machine learning techniques, particularly k-nearest neighbors (kNN)-based algorithms, the research classifies NOx emissions in marine engines operating under the Reactivity-Controlled Compression Ignition (RCCI) strategy. Comparative performance analysis reveals that the FPFS-kNN algorithm achieves the highest accuracy (90.00%) alongside strong precision (84.23%), recall (82.37%), and F1 score (82.47%). These findings underscore the potential of machine learning in emission prediction and highlight directions for future exploration in this domain.https://ijpte.com/index.php/ijpte/article/view/97marine enginesnox emissionclassificationmachine learning |
| spellingShingle | Samet Memiş Ramazan Şener Classification of NOx Emission in Marine Engines Utilizing kNN-Based Machine Learning Algorithms International Journal of Pioneering Technology and Engineering marine engines nox emission classification machine learning |
| title | Classification of NOx Emission in Marine Engines Utilizing kNN-Based Machine Learning Algorithms |
| title_full | Classification of NOx Emission in Marine Engines Utilizing kNN-Based Machine Learning Algorithms |
| title_fullStr | Classification of NOx Emission in Marine Engines Utilizing kNN-Based Machine Learning Algorithms |
| title_full_unstemmed | Classification of NOx Emission in Marine Engines Utilizing kNN-Based Machine Learning Algorithms |
| title_short | Classification of NOx Emission in Marine Engines Utilizing kNN-Based Machine Learning Algorithms |
| title_sort | classification of nox emission in marine engines utilizing knn based machine learning algorithms |
| topic | marine engines nox emission classification machine learning |
| url | https://ijpte.com/index.php/ijpte/article/view/97 |
| work_keys_str_mv | AT sametmemis classificationofnoxemissioninmarineenginesutilizingknnbasedmachinelearningalgorithms AT ramazansener classificationofnoxemissioninmarineenginesutilizingknnbasedmachinelearningalgorithms |