Leveraging machine learning to optimize cooling tower efficiency for sustainable power generation
United Nations Sustainable Development Goal 7 is about ensuring access to clean and affordable energy, which is a key factor in the development of society. The power generation sector majorly consists of thermal power plants. Cooling towers are a significant part of any power plant to cool steam to...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1473946/full |
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| author | M. A. Mujtaba Muhammad Adeel Munir Muhammad Akhtar Bilal Mahmood Talha Ansar Zeeshan Khawar Shayan Khalid Abdul Basit Saud Jamil M. A. Kalam Fayaz Hussain Chiranjib Bhowmik |
| author_facet | M. A. Mujtaba Muhammad Adeel Munir Muhammad Akhtar Bilal Mahmood Talha Ansar Zeeshan Khawar Shayan Khalid Abdul Basit Saud Jamil M. A. Kalam Fayaz Hussain Chiranjib Bhowmik |
| author_sort | M. A. Mujtaba |
| collection | DOAJ |
| description | United Nations Sustainable Development Goal 7 is about ensuring access to clean and affordable energy, which is a key factor in the development of society. The power generation sector majorly consists of thermal power plants. Cooling towers are a significant part of any power plant to cool steam to be reused again. Hence, the efficiency of power plants can be increased by optimizing the performance of cooling towers. This research paper aims to increase the efficiency of cooling towers by investigating the effect of ambient parameters (changing with climate) on the efficiency of cooling towers for the best site selection. Ambient parameters cannot be controlled after the installation of power plants. Therefore, proper site selection, keeping ambient parameters and their expected change before the installation of power plants, effectively increases the efficiency of the cooling tower and, ultimately, the power plant. For this purpose, data is collected from the 1140 MW combined cycle power plant in Sheikhupura, Pakistan district. A machine learning (Ada boost regressor) model has been used to quantify the effect of ambient parameters on cooling tower efficiency. After tuning the hyperparameters, an R-square score of 0.983 and a root mean squared error of 0.57 are achieved. Afterwards, a sensitivity analysis of relative humidity (%), turned out to be the most important feature, with a contribution of 12%. The novelty of this research lies in its mathematical model for power plant site selection, which optimizes cooling tower efficiency, reduces pollution, and promotes environmental sustainability. |
| format | Article |
| id | doaj-art-073067853ca34abc92f99542582f66d2 |
| institution | DOAJ |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-073067853ca34abc92f99542582f66d22025-08-20T03:16:26ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-03-011310.3389/fenrg.2025.14739461473946Leveraging machine learning to optimize cooling tower efficiency for sustainable power generationM. A. Mujtaba0Muhammad Adeel Munir1Muhammad Akhtar2Bilal Mahmood3Talha Ansar4Zeeshan Khawar5Shayan Khalid6Abdul Basit7Saud Jamil8M. A. Kalam9Fayaz Hussain10Chiranjib Bhowmik11Department of Mechanical, Mechatronics and Manufacturing Engineering (New Campus), University of Engineering and Technology (UET), Lahore, PakistanDepartment of Mechanical, Mechatronics and Manufacturing Engineering (New Campus), University of Engineering and Technology (UET), Lahore, PakistanQuaid e Azam Thermal Power (Pvt) Limited, 1180MW CCPP Bhikki, Lahore, PakistanHarbin Electric Company Limited, 1180MW CCPP Bhikki, O&M, Lahore, PakistanDepartment of Mechanical, Mechatronics and Manufacturing Engineering (New Campus), University of Engineering and Technology (UET), Lahore, PakistanDepartment of Mechanical, Mechatronics and Manufacturing Engineering (New Campus), University of Engineering and Technology (UET), Lahore, PakistanDepartment of Mechanical, Mechatronics and Manufacturing Engineering (New Campus), University of Engineering and Technology (UET), Lahore, PakistanDepartment of Mechanical, Mechatronics and Manufacturing Engineering (New Campus), University of Engineering and Technology (UET), Lahore, PakistanDepartment of Mechanical, Mechatronics and Manufacturing Engineering (New Campus), University of Engineering and Technology (UET), Lahore, PakistanSchool of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, AustraliaModeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, VietnamDepartment of Mechanical Engineering, Techno India University, West Bengal, IndiaUnited Nations Sustainable Development Goal 7 is about ensuring access to clean and affordable energy, which is a key factor in the development of society. The power generation sector majorly consists of thermal power plants. Cooling towers are a significant part of any power plant to cool steam to be reused again. Hence, the efficiency of power plants can be increased by optimizing the performance of cooling towers. This research paper aims to increase the efficiency of cooling towers by investigating the effect of ambient parameters (changing with climate) on the efficiency of cooling towers for the best site selection. Ambient parameters cannot be controlled after the installation of power plants. Therefore, proper site selection, keeping ambient parameters and their expected change before the installation of power plants, effectively increases the efficiency of the cooling tower and, ultimately, the power plant. For this purpose, data is collected from the 1140 MW combined cycle power plant in Sheikhupura, Pakistan district. A machine learning (Ada boost regressor) model has been used to quantify the effect of ambient parameters on cooling tower efficiency. After tuning the hyperparameters, an R-square score of 0.983 and a root mean squared error of 0.57 are achieved. Afterwards, a sensitivity analysis of relative humidity (%), turned out to be the most important feature, with a contribution of 12%. The novelty of this research lies in its mathematical model for power plant site selection, which optimizes cooling tower efficiency, reduces pollution, and promotes environmental sustainability.https://www.frontiersin.org/articles/10.3389/fenrg.2025.1473946/fullpower plantscooling towersmachine learningambient parameterssite selecting |
| spellingShingle | M. A. Mujtaba Muhammad Adeel Munir Muhammad Akhtar Bilal Mahmood Talha Ansar Zeeshan Khawar Shayan Khalid Abdul Basit Saud Jamil M. A. Kalam Fayaz Hussain Chiranjib Bhowmik Leveraging machine learning to optimize cooling tower efficiency for sustainable power generation Frontiers in Energy Research power plants cooling towers machine learning ambient parameters site selecting |
| title | Leveraging machine learning to optimize cooling tower efficiency for sustainable power generation |
| title_full | Leveraging machine learning to optimize cooling tower efficiency for sustainable power generation |
| title_fullStr | Leveraging machine learning to optimize cooling tower efficiency for sustainable power generation |
| title_full_unstemmed | Leveraging machine learning to optimize cooling tower efficiency for sustainable power generation |
| title_short | Leveraging machine learning to optimize cooling tower efficiency for sustainable power generation |
| title_sort | leveraging machine learning to optimize cooling tower efficiency for sustainable power generation |
| topic | power plants cooling towers machine learning ambient parameters site selecting |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1473946/full |
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