An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy production
Abstract This study aims to optimize the power generation of a conventional Manzanares solar chimney (SC) plant through strategic modifications to the collector inlet height, chimney diameter, and chimney divergence. Employing a finite volume-based solver for numerical analysis, we systematically sc...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-46505-1 |
| _version_ | 1827382304153010176 |
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| author | Dipak Kumar Mandal Nirmalendu Biswas Nirmal K. Manna Dilip Kumar Gayen Ali Cemal Benim |
| author_facet | Dipak Kumar Mandal Nirmalendu Biswas Nirmal K. Manna Dilip Kumar Gayen Ali Cemal Benim |
| author_sort | Dipak Kumar Mandal |
| collection | DOAJ |
| description | Abstract This study aims to optimize the power generation of a conventional Manzanares solar chimney (SC) plant through strategic modifications to the collector inlet height, chimney diameter, and chimney divergence. Employing a finite volume-based solver for numerical analysis, we systematically scrutinize influential geometric parameters, including collector height (h i = 1.85 to 0.1 m), chimney inlet diameter (d ch = 10.16 to 55.88 m), and chimney outlet diameter (d o = 10.16 to 30.48 m). Our findings demonstrate that reducing the collector inlet height consistently leads to increased power output. The optimal collector inlet height of h i = 0.2 m results in a significant power increase from 51 to 117.42 kW (~ 2.3 times) without additional installation costs, accompanied by an efficiency of 0.25%. Conversely, enlarging the chimney diameter decreases the chimney base velocity and suction pressure. However, as turbine-driven power generation rises, the flow becomes stagnant beyond a chimney diameter of 45.72 m. At this point, power generation reaches 209 kW, nearly four times greater than the Manzanares plant, with an efficiency of 0.44%. Nevertheless, the cost of expanding the chimney diameter is substantial. Furthermore, the impact of chimney divergence is evident, with power generation, collector efficiency, overall efficiency, and collector inlet velocity all peaking at an outer chimney diameter of 15.24 m (corresponding to an area ratio of 2.25). At this configuration, power generation increases to 75.91 kW, approximately 1.5 times more than the initial design. Remarkably, at a low collector inlet height of 0.2 m, combining it with a chimney diameter of 4.5 times the chimney inlet diameter (4.5d ch) results in an impressive power output of 635.02 kW, signifying a substantial 12.45-fold increase. To model the performance under these diverse conditions, an artificial neural network (ANN) is effectively utilized. |
| first_indexed | 2024-03-08T14:17:39Z |
| format | Article |
| id | doaj.art-dc9995eb81694b23a460ddc734d13edc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T14:17:39Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-dc9995eb81694b23a460ddc734d13edc2024-01-14T12:20:24ZengNature PortfolioScientific Reports2045-23222024-01-0114112510.1038/s41598-023-46505-1An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy productionDipak Kumar Mandal0Nirmalendu Biswas1Nirmal K. Manna2Dilip Kumar Gayen3Ali Cemal Benim4Department of Mechanical Engineering, Government Engineering CollegeDepartment of Power Engineering, Jadavpur UniversityDepartment of Mechanical Engineering, Jadavpur UniversityDepartment of Computer Science Engineering, College of Engineering and ManagementDepartment of Mechanical and Process Engineering, Düsseldorf University of Applied SciencesAbstract This study aims to optimize the power generation of a conventional Manzanares solar chimney (SC) plant through strategic modifications to the collector inlet height, chimney diameter, and chimney divergence. Employing a finite volume-based solver for numerical analysis, we systematically scrutinize influential geometric parameters, including collector height (h i = 1.85 to 0.1 m), chimney inlet diameter (d ch = 10.16 to 55.88 m), and chimney outlet diameter (d o = 10.16 to 30.48 m). Our findings demonstrate that reducing the collector inlet height consistently leads to increased power output. The optimal collector inlet height of h i = 0.2 m results in a significant power increase from 51 to 117.42 kW (~ 2.3 times) without additional installation costs, accompanied by an efficiency of 0.25%. Conversely, enlarging the chimney diameter decreases the chimney base velocity and suction pressure. However, as turbine-driven power generation rises, the flow becomes stagnant beyond a chimney diameter of 45.72 m. At this point, power generation reaches 209 kW, nearly four times greater than the Manzanares plant, with an efficiency of 0.44%. Nevertheless, the cost of expanding the chimney diameter is substantial. Furthermore, the impact of chimney divergence is evident, with power generation, collector efficiency, overall efficiency, and collector inlet velocity all peaking at an outer chimney diameter of 15.24 m (corresponding to an area ratio of 2.25). At this configuration, power generation increases to 75.91 kW, approximately 1.5 times more than the initial design. Remarkably, at a low collector inlet height of 0.2 m, combining it with a chimney diameter of 4.5 times the chimney inlet diameter (4.5d ch) results in an impressive power output of 635.02 kW, signifying a substantial 12.45-fold increase. To model the performance under these diverse conditions, an artificial neural network (ANN) is effectively utilized.https://doi.org/10.1038/s41598-023-46505-1 |
| spellingShingle | Dipak Kumar Mandal Nirmalendu Biswas Nirmal K. Manna Dilip Kumar Gayen Ali Cemal Benim An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy production Scientific Reports |
| title | An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy production |
| title_full | An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy production |
| title_fullStr | An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy production |
| title_full_unstemmed | An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy production |
| title_short | An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy production |
| title_sort | application of artificial neural network ann for comparative performance assessment of solar chimney sc plant for green energy production |
| url | https://doi.org/10.1038/s41598-023-46505-1 |
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