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<p>The Solar chimney power plant is a reassuring technology for sustainable electricity generation. The aim of this research is to improve SCPP performance through geometric optimization by the use of artificial neural network (ANN) modeling to generate accurate predictions. The validated numerical model helped researchers study how collector inlet height together with collector angle and chimney diameter affect flow characteristics (velocity and pressure and temperature) and system performance. The research shows that treating each parameter separately leads to better efficiency and higher power output results. The research used an ANN model to determine the most efficient geometric design. The research found the best design with a collector inlet height of 0.6&#xa0;m and a chimney diameter of 30&#xa0;m and a collector angle of 2°. The optimized configuration under 800&#xa0;Wm<sup>−2</sup> solar radiation improved energy production by 50% compared to the Manzanares prototype at different pressure drops according to validation simulations. The research findings resulted in practical knowledge which supported the large-scale SCPP deployment and proved that machine learning integration with thermodynamic modeling is an effective and practical solution for renewable energy system optimization.</p>

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A systematic approach for enhancing the overall efficiency of solar chimney power plants

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Emadoddin Erfani Farsi Eidgah,
  • Abolfazl Sabzi,
  • A. Ali-Wadi,
  • Amirhossein Joveini,
  • Mohammad Derayatifar,
  • Hossein Ajam,
  • Ali Kianifar

摘要

The Solar chimney power plant is a reassuring technology for sustainable electricity generation. The aim of this research is to improve SCPP performance through geometric optimization by the use of artificial neural network (ANN) modeling to generate accurate predictions. The validated numerical model helped researchers study how collector inlet height together with collector angle and chimney diameter affect flow characteristics (velocity and pressure and temperature) and system performance. The research shows that treating each parameter separately leads to better efficiency and higher power output results. The research used an ANN model to determine the most efficient geometric design. The research found the best design with a collector inlet height of 0.6 m and a chimney diameter of 30 m and a collector angle of 2°. The optimized configuration under 800 Wm−2 solar radiation improved energy production by 50% compared to the Manzanares prototype at different pressure drops according to validation simulations. The research findings resulted in practical knowledge which supported the large-scale SCPP deployment and proved that machine learning integration with thermodynamic modeling is an effective and practical solution for renewable energy system optimization.