Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Evolutionary Methods Based Modeling and Analysis of Solar Thermal SystemsANN-Based Modeling and Optimization of Corrugated Solar Air Collector

Evolutionary Methods Based Modeling and Analysis of Solar Thermal Systems: ANN-Based Modeling and... [This chapter describes an application of artificial neural networks (ANNs) to predict the performance of a corrugated solar air collector. Experiments were conducted under a broad range of operating conditions during different climatic conditions. These experimental data were utilized for training, validating, and testing the proposed ANN model. The model was applied to predict various performance parameters, i.e., energy, exergy, temperature rise, and pressure drop. The flow rate of inlet air is varied from 0.0039 to 0.0118 kg/s. The results predicted by ANN are compared with the values obtained from the experimental analysis. The optimal setting parameters of SAC are mass flow rate 0.0118 kg/s, tilt angle 45°, solar radiation 621 W/m2, and inlet temperature 33.8 °C, and corresponding output values are temperature rise 28.99 °C, energy efficiency 13.45%, exergy efficiency 1.022%, and pressure drop 73.75 Pa. In order to generate the ANN results, 270 data sets are analyzed, with 21 samples for training, 21 samples for testing, and 21 samples for validation. The number of neurons in the hidden layer has been optimized by the least training error. The adopted model has a root mean square error (RMSE) of 0.76 and R2 of 0.9972. An average 1.58% error is obtained for the ANN model compared to the experimental thermohydraulic efficiency. ANN can be used successfully to predict the performance of the solar air heater with a circular, perforated absorber plate.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Evolutionary Methods Based Modeling and Analysis of Solar Thermal SystemsANN-Based Modeling and Optimization of Corrugated Solar Air Collector

Download PDF
22 pages

Loading next page...
 
/lp/springer-journals/evolutionary-methods-based-modeling-and-analysis-of-solar-thermal-10mDyXxo60

References (5)

  • SA Kalogirou (2004)

    383

    Applied Energy, 77

  • SA Kalogirou (2000)

    17

    Applied Energy, 67

  • SA Kalogirou (2001)

    373

    Renewable and Sustainable Energy Reviews, 5

  • S Mekhilef (2011)

    1777

    Renewable and Sustainable Energy Reviews, 15

  • SA Kalogirou (1999)

    1073

    Energy Conversion and Management, 40

Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
ISBN
978-3-031-27634-7
Pages
41 –62
DOI
10.1007/978-3-031-27635-4_3
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter describes an application of artificial neural networks (ANNs) to predict the performance of a corrugated solar air collector. Experiments were conducted under a broad range of operating conditions during different climatic conditions. These experimental data were utilized for training, validating, and testing the proposed ANN model. The model was applied to predict various performance parameters, i.e., energy, exergy, temperature rise, and pressure drop. The flow rate of inlet air is varied from 0.0039 to 0.0118 kg/s. The results predicted by ANN are compared with the values obtained from the experimental analysis. The optimal setting parameters of SAC are mass flow rate 0.0118 kg/s, tilt angle 45°, solar radiation 621 W/m2, and inlet temperature 33.8 °C, and corresponding output values are temperature rise 28.99 °C, energy efficiency 13.45%, exergy efficiency 1.022%, and pressure drop 73.75 Pa. In order to generate the ANN results, 270 data sets are analyzed, with 21 samples for training, 21 samples for testing, and 21 samples for validation. The number of neurons in the hidden layer has been optimized by the least training error. The adopted model has a root mean square error (RMSE) of 0.76 and R2 of 0.9972. An average 1.58% error is obtained for the ANN model compared to the experimental thermohydraulic efficiency. ANN can be used successfully to predict the performance of the solar air heater with a circular, perforated absorber plate.]

Published: Apr 30, 2023

Keywords: Solar air collector; ANN; Optimization; Thermal energy; Exergy efficiency

There are no references for this article.