Thermal performance analysis of gas turbine power plant using soft computing techniques: a review
Gas turbines are pivotal in electricity generation and industry, prized for their efficiency and flexibility in meeting diverse power needs. Optimizing their thermal efficiency is essential for improving energy output and sustainability. Soft computing methods such as neural networks, genetic algori...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2024.2374317 |
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| author | Ravindra S. Surase Ramakrishna Konijeti Ramchandra P. Chopade |
| author_facet | Ravindra S. Surase Ramakrishna Konijeti Ramchandra P. Chopade |
| author_sort | Ravindra S. Surase |
| collection | DOAJ |
| description | Gas turbines are pivotal in electricity generation and industry, prized for their efficiency and flexibility in meeting diverse power needs. Optimizing their thermal efficiency is essential for improving energy output and sustainability. Soft computing methods such as neural networks, genetic algorithms, and fuzzy logic offer potent tools for this optimization due to their ability to handle the turbines' nonlinear and dynamic characteristics. These techniques facilitate a deeper understanding of the intricate interplay among various parameters affecting thermal performance, thereby enabling the development of intelligent and adaptive turbine systems. By leveraging soft computing, researchers can enhance gas turbine designs to align with modern energy and environmental objectives. This review emphasizes the application of soft computing approaches in analysing and improving gas turbine thermal performance. Such advancements are instrumental in achieving higher energy efficiency, reducing greenhouse gas emissions, and promoting a sustainable energy landscape. Ultimately, integrating soft computing into gas turbine operations promises to advance both technical capabilities and environmental stewardship in the detection of a more resilient and efficient energy infrastructure. |
| format | Article |
| id | doaj-art-d45589752fb04a09a34328ce634ebb4a |
| institution | Kabale University |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-d45589752fb04a09a34328ce634ebb4a2024-12-09T09:43:45ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2024-12-0118110.1080/19942060.2024.2374317Thermal performance analysis of gas turbine power plant using soft computing techniques: a reviewRavindra S. Surase0Ramakrishna Konijeti1Ramchandra P. Chopade2Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, AP, IndiaDepartment of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, AP, IndiaDepartment of Mechanical Engineering, CSMSS Chh. Shahu College of Engineering, Kanchanwadi, Chhatrapati Sambhaji Nagar, MH, IndiaGas turbines are pivotal in electricity generation and industry, prized for their efficiency and flexibility in meeting diverse power needs. Optimizing their thermal efficiency is essential for improving energy output and sustainability. Soft computing methods such as neural networks, genetic algorithms, and fuzzy logic offer potent tools for this optimization due to their ability to handle the turbines' nonlinear and dynamic characteristics. These techniques facilitate a deeper understanding of the intricate interplay among various parameters affecting thermal performance, thereby enabling the development of intelligent and adaptive turbine systems. By leveraging soft computing, researchers can enhance gas turbine designs to align with modern energy and environmental objectives. This review emphasizes the application of soft computing approaches in analysing and improving gas turbine thermal performance. Such advancements are instrumental in achieving higher energy efficiency, reducing greenhouse gas emissions, and promoting a sustainable energy landscape. Ultimately, integrating soft computing into gas turbine operations promises to advance both technical capabilities and environmental stewardship in the detection of a more resilient and efficient energy infrastructure.https://www.tandfonline.com/doi/10.1080/19942060.2024.2374317Gas turbine optimisationthermal performance analysisfuel and emission controlwaste heat recoveryturbine fault diagnosissoft computing techniques |
| spellingShingle | Ravindra S. Surase Ramakrishna Konijeti Ramchandra P. Chopade Thermal performance analysis of gas turbine power plant using soft computing techniques: a review Engineering Applications of Computational Fluid Mechanics Gas turbine optimisation thermal performance analysis fuel and emission control waste heat recovery turbine fault diagnosis soft computing techniques |
| title | Thermal performance analysis of gas turbine power plant using soft computing techniques: a review |
| title_full | Thermal performance analysis of gas turbine power plant using soft computing techniques: a review |
| title_fullStr | Thermal performance analysis of gas turbine power plant using soft computing techniques: a review |
| title_full_unstemmed | Thermal performance analysis of gas turbine power plant using soft computing techniques: a review |
| title_short | Thermal performance analysis of gas turbine power plant using soft computing techniques: a review |
| title_sort | thermal performance analysis of gas turbine power plant using soft computing techniques a review |
| topic | Gas turbine optimisation thermal performance analysis fuel and emission control waste heat recovery turbine fault diagnosis soft computing techniques |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2024.2374317 |
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