Optimisation of an ash fouling model for predicting cleanliness levels in coal-fired power plant
This study develops an optimised ash fouling model for coal-fired power plants by integrating feature selection with an advanced regression model. The aim is to accurately predict cleanliness levels, providing a reliable indication for soot-blowing operations to maintain boiler performance. Comprehe...
| Main Authors: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/119224/ http://psasir.upm.edu.my/id/eprint/119224/1/119224.pdf |
| _version_ | 1848867908152721408 |
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| author | Achmad Nazeem, Nabil Mohd Ibrahim, Maslina Che Soh, Azura Ishak, Asnor Juraiza Raja Ahmad, Raja Mohd Kamil Norsahperi, Nor Mohd Haziq Mohd Radzi, Mohd Amran |
| author_facet | Achmad Nazeem, Nabil Mohd Ibrahim, Maslina Che Soh, Azura Ishak, Asnor Juraiza Raja Ahmad, Raja Mohd Kamil Norsahperi, Nor Mohd Haziq Mohd Radzi, Mohd Amran |
| author_sort | Achmad Nazeem, Nabil |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This study develops an optimised ash fouling model for coal-fired power plants by integrating feature selection with an advanced regression model. The aim is to accurately predict cleanliness levels, providing a reliable indication for soot-blowing operations to maintain boiler performance. Comprehensive feature selection techniques are used to identify key variables influencing soot accumulation, while Gaussian Process Regression (GPR) predicts cleanliness levels. Three feature selection methods are implemented and compared to determine the most accurate approach for guiding soot-blowing operations. Using operational data from a power plant station in Malaysia, this study aims to enhance sootblowing practices, improve efficiency, and reduce environmental impact. By optimising soot-blowing strategies, the research supports sustainable energy practices and contributes to improved power plant performance. |
| first_indexed | 2025-11-15T14:43:58Z |
| format | Conference or Workshop Item |
| id | upm-119224 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:43:58Z |
| publishDate | 2025 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1192242025-08-12T08:14:22Z http://psasir.upm.edu.my/id/eprint/119224/ Optimisation of an ash fouling model for predicting cleanliness levels in coal-fired power plant Achmad Nazeem, Nabil Mohd Ibrahim, Maslina Che Soh, Azura Ishak, Asnor Juraiza Raja Ahmad, Raja Mohd Kamil Norsahperi, Nor Mohd Haziq Mohd Radzi, Mohd Amran This study develops an optimised ash fouling model for coal-fired power plants by integrating feature selection with an advanced regression model. The aim is to accurately predict cleanliness levels, providing a reliable indication for soot-blowing operations to maintain boiler performance. Comprehensive feature selection techniques are used to identify key variables influencing soot accumulation, while Gaussian Process Regression (GPR) predicts cleanliness levels. Three feature selection methods are implemented and compared to determine the most accurate approach for guiding soot-blowing operations. Using operational data from a power plant station in Malaysia, this study aims to enhance sootblowing practices, improve efficiency, and reduce environmental impact. By optimising soot-blowing strategies, the research supports sustainable energy practices and contributes to improved power plant performance. IEEE 2025 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/119224/1/119224.pdf Achmad Nazeem, Nabil and Mohd Ibrahim, Maslina and Che Soh, Azura and Ishak, Asnor Juraiza and Raja Ahmad, Raja Mohd Kamil and Norsahperi, Nor Mohd Haziq and Mohd Radzi, Mohd Amran (2025) Optimisation of an ash fouling model for predicting cleanliness levels in coal-fired power plant. In: The 2025 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), 3-5 July 2025, Bali, Indonesia. (pp. 687-693). https://ieeexplore.ieee.org/document/11100845/ 10.1109/IAICT65714.2025.11100845 |
| spellingShingle | Achmad Nazeem, Nabil Mohd Ibrahim, Maslina Che Soh, Azura Ishak, Asnor Juraiza Raja Ahmad, Raja Mohd Kamil Norsahperi, Nor Mohd Haziq Mohd Radzi, Mohd Amran Optimisation of an ash fouling model for predicting cleanliness levels in coal-fired power plant |
| title | Optimisation of an ash fouling model for predicting cleanliness levels in coal-fired power plant |
| title_full | Optimisation of an ash fouling model for predicting cleanliness levels in coal-fired power plant |
| title_fullStr | Optimisation of an ash fouling model for predicting cleanliness levels in coal-fired power plant |
| title_full_unstemmed | Optimisation of an ash fouling model for predicting cleanliness levels in coal-fired power plant |
| title_short | Optimisation of an ash fouling model for predicting cleanliness levels in coal-fired power plant |
| title_sort | optimisation of an ash fouling model for predicting cleanliness levels in coal-fired power plant |
| url | http://psasir.upm.edu.my/id/eprint/119224/ http://psasir.upm.edu.my/id/eprint/119224/ http://psasir.upm.edu.my/id/eprint/119224/ http://psasir.upm.edu.my/id/eprint/119224/1/119224.pdf |