An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy production

• Published in: Scientific reports Vol. 14; no. 1; p. 979
• Main Authors: Mandal, Dipak Kumar, Biswas, Nirmalendu, Manna, Nirmal K., Gayen, Dilip Kumar, Benim, Ali Cemal
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10-01-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/4077; Efficiency; Green energy; Humanities and Social Sciences; multidisciplinary; Neural networks; Numerical analysis; Performance assessment; Science; Science (multidisciplinary); Turbines; Velocity
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-46505-1

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 (4.5
d
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.