Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs
This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. The innovative design aims to maximize the concurrent...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X24012413 |
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| author | Hatem Gasmi Azher M. Abed Ashit Kumar Dutta Fahad M. Alhomayani Ibrahim Mahariq Fahad Alturise Salem Alkhalaf Tamim Alkhalifah Yasser Elmasry Baseem Khan |
| author_facet | Hatem Gasmi Azher M. Abed Ashit Kumar Dutta Fahad M. Alhomayani Ibrahim Mahariq Fahad Alturise Salem Alkhalaf Tamim Alkhalifah Yasser Elmasry Baseem Khan |
| author_sort | Hatem Gasmi |
| collection | DOAJ |
| description | This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. The innovative design aims to maximize the concurrent generation of heat and electricity, ultimately benefiting environmental sustainability and energy security. By employing regression machine learning algorithms, the research evaluates and enhances system performance, achieving remarkable R-squared accuracy levels of 98.86 % for heating output and 99.89 % for power output predictions. The thermodynamic modeling, which has been validated against recognized benchmarks, confirms the accuracy of the system's design. Optimization findings indicate that operating pressures between 840 and 870 kPa and pressure ratios of 1.56–1.60 deliver optimal outputs, with power production between 2582 and 2585 kW and heating output ranging from 12260 to 12280 kW. The system reaches its maximum performance at a pressure of 850 kPa and a pressure ratio of 1.57, resulting in a power output of 2583.97 kW and a heating output of 12279.3 kW. These results highlight the potential of combining advanced thermodynamic systems with machine learning methodologies to improve the efficiency and effectiveness of renewable energy sources. |
| format | Article |
| id | doaj-art-2e74d8c5fd8d411b86503eed73daa063 |
| institution | Kabale University |
| issn | 2214-157X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-2e74d8c5fd8d411b86503eed73daa0632024-11-14T04:31:39ZengElsevierCase Studies in Thermal Engineering2214-157X2024-11-0163105210Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputsHatem Gasmi0Azher M. Abed1Ashit Kumar Dutta2Fahad M. Alhomayani3Ibrahim Mahariq4Fahad Alturise5Salem Alkhalaf6Tamim Alkhalifah7Yasser Elmasry8Baseem Khan9Department of Civil Engineering, College of Engineering, University of Hail, Hail, Saudi ArabiaAir Conditioning and Refrigeration Techniques Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University, Babylon, 51001, Iraq; Al - Mustaqbal Center for Energy Research, Al-Mustaqbal University, Babylon, 51001, Iraq; Corresponding author. Air Conditioning and Refrigeration Techniques Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University, Babylon, 51001, Iraq.Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, 13713, Kingdom of Saudi Arabia; Corresponding author.College of Computers and Information Technology, Taif University, Saudi Arabia; Applied College, Taif University, Saudi ArabiaGUST Engineering and Applied Innovation Research Center (GEAR), Gulf University for Science and Technology, Mishref, Kuwait; Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan; Corresponding author. GUST Engineering and Applied Innovation Research Center (GEAR), Gulf University for Science and Technology, Mishref, Kuwait.Department of Cybersecurity, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Computer Engineering, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Computer Engineering, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Mathematics - College of Science - King Khalid University, P.O. Box 9004, Abha, 61466, Saudi ArabiaDepartment of Electrical and Computer Engineering, Hawassa University, Hawassa, Ethiopia; Center for Renewable Energy and Microgrids, Huanjiang Laboratory, Zhejiang University, Zhejiang, 311816, China; Department of Technical Sciences, Western Caspian University, Baku, Azerbaijan; Corresponding author. Department of Electrical and Computer Engineering, Hawassa University, Hawassa, Ethiopia.This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. The innovative design aims to maximize the concurrent generation of heat and electricity, ultimately benefiting environmental sustainability and energy security. By employing regression machine learning algorithms, the research evaluates and enhances system performance, achieving remarkable R-squared accuracy levels of 98.86 % for heating output and 99.89 % for power output predictions. The thermodynamic modeling, which has been validated against recognized benchmarks, confirms the accuracy of the system's design. Optimization findings indicate that operating pressures between 840 and 870 kPa and pressure ratios of 1.56–1.60 deliver optimal outputs, with power production between 2582 and 2585 kW and heating output ranging from 12260 to 12280 kW. The system reaches its maximum performance at a pressure of 850 kPa and a pressure ratio of 1.57, resulting in a power output of 2583.97 kW and a heating output of 12279.3 kW. These results highlight the potential of combining advanced thermodynamic systems with machine learning methodologies to improve the efficiency and effectiveness of renewable energy sources.http://www.sciencedirect.com/science/article/pii/S2214157X24012413Geothermal energyIntegrated renewable systemArtificial intelligenceMachine learningOptimization |
| spellingShingle | Hatem Gasmi Azher M. Abed Ashit Kumar Dutta Fahad M. Alhomayani Ibrahim Mahariq Fahad Alturise Salem Alkhalaf Tamim Alkhalifah Yasser Elmasry Baseem Khan Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs Case Studies in Thermal Engineering Geothermal energy Integrated renewable system Artificial intelligence Machine learning Optimization |
| title | Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs |
| title_full | Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs |
| title_fullStr | Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs |
| title_full_unstemmed | Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs |
| title_short | Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs |
| title_sort | heat recovery integration in a hybrid geothermal based system producing power and heating using machine learning approach to maximize outputs |
| topic | Geothermal energy Integrated renewable system Artificial intelligence Machine learning Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X24012413 |
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