Skip to main content
SpringerLink
Log in

ANN-Based Modeling and Optimization of Corrugated Solar Air Collector

  • Chapter
  • First Online:
Evolutionary Methods Based Modeling and Analysis of Solar Thermal Systems

Part of the book series: Mechanical Engineering Series ((MES))

  • 65 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kalogirou, S. A. (1999). Applications of artificial neural networks in energy systems. Energy Conversion and Management, 40(10), 1073–1087.

    Article  Google Scholar 

  2. Mekhilef, S., Saidur, R., & Safari, A. (2011). A review on solar energy use in industries. Renewable and Sustainable Energy Reviews, 15(4), 1777–1790.

    Article  Google Scholar 

  3. Kalogirou, S. A. (2000). Applications of artificial neural-networks for energy systems. Applied Energy, 67(1–2), 17–35.

    Article  Google Scholar 

  4. Kalogirou, S. A. (2001). Artificial neural networks in renewable energy systems applications: A review. Renewable and Sustainable Energy Reviews, 5(4), 373–401.

    Article  Google Scholar 

  5. Kalogirou, S. A. (2004). Optimization of solar systems using artificial neural-networks and genetic algorithms. Applied Energy, 77(4), 383–405.

    Article  Google Scholar 

  6. Kalogirou, S., & Sencan, A. (2010). Artificial intelligence techniques in solar energy applications. In Solar collectors and panels, theory and applications (Vol. 15 , pp. 315–340).

    Google Scholar 

  7. Mohanraj, M., Jayaraj, S., & Muraleedharan, C. (2012). Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems – A review. Renewable and Sustainable Energy Reviews, 16(2), 1340–1358.

    Article  Google Scholar 

  8. Kalogirou, S., Lalot, S., Florides, G., & Desmet, B. (2008). Development of a neural network-based fault diagnostic system for solar thermal applications. Solar Energy, 82(2), 164–172.

    Article  Google Scholar 

  9. Mohandes, M., Rehman, S., & Halawani, T. O. (1998). Estimation of global solar radiation using artificial neural networks. Renewable Energy, 14(1–4), 179–184.

    Article  Google Scholar 

  10. Diaz, G., Sen, M., Yang, K. T., & McClain, R. L. (1999). Simulation of heat exchanger performance by artificial neural networks. Hvac&R Research, 5(3), 195–208.

    Article  Google Scholar 

  11. Pacheco-Vega, A., Sen, M., Yang, K. T., & McClain, R. L. (2001). Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data. International Journal of Heat and Mass Transfer, 44(4), 763–770.

    Article  MATH  Google Scholar 

  12. Chow, T. T., Zhang, G. Q., Lin, Z., & Song, C. L. (2002). Global optimization of absorption chiller system by genetic algorithm and neural network. Energy and Buildings, 34(1), 103–109.

    Article  Google Scholar 

  13. Sözen, A., Arcaklioǧlu, E., & Özalp, M. (2003). A new approach to thermodynamic analysis of ejector–absorption cycle: Artificial neural networks. Applied Thermal Engineering, 23(8), 937–952.

    Article  Google Scholar 

  14. Sözen, A., & Akçayol, M. A. (2004). Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle. Applied Energy, 79(3), 309–325.

    Article  Google Scholar 

  15. Islamoglu, Y., & Kurt, A. (2004). Heat transfer analysis using ANNs with experimental data for air flowing in corrugated channels. International Journal of Heat and Mass Transfer, 47(6–7), 1361–1365.

    Article  Google Scholar 

  16. Esen, H., Inalli, M., Sengur, A., & Esen, M. (2008). Performance prediction of a ground-coupled heat pump system using artificial neural networks. Expert Systems with Applications, 35(4), 1940–1948.

    Article  Google Scholar 

  17. Akbari, S., Hemingson, H. B., Beriault, D., Simonson, C. J., & Besant, R. W. (2012). Application of neural networks to predict the steady state performance of a run-around membrane energy exchanger. International Journal of Heat and Mass Transfer, 55(5–6), 1628–1641.

    Article  MATH  Google Scholar 

  18. Palau, A., Velo, E., & Puigjaner, L. (1999). Use of neural networks and expert systems to control a gas/solid sorption chilling machine: Utilisation des réseaux neuronaux et des systèmes experts pour réguler une machine frigorifique à sorption gaz/solide. International Journal of Refrigeration, 22(1), 59–66.

    Article  Google Scholar 

  19. Sharma, R., Singhal, D., Ghosh, R., & Dwivedi, A. (1999). Potential applications of artificial neural networks to thermodynamics: Vapor–liquid equilibrium predictions. Computers & Chemical Engineering, 23(3), 385–390.

    Article  Google Scholar 

  20. Bechtler, H., Browne, M. W., Bansal, P. K., & Kecman, V. (2001). New approach to dynamic modelling of vapour-compression liquid chillers: Artificial neural networks. Applied Thermal Engineering, 21(9), 941–953.

    Article  Google Scholar 

  21. Kalogirou, S. A., Panteliou, S., & Dentsoras, A. (1999). Modeling of solar domestic water heating systems using artificial neural networks. Solar Energy, 65(6), 335–342.

    Article  Google Scholar 

  22. Farkas, I., & Geczy-Vıg, P. (2003). Neural network modelling of flat-plate solar collectors. Computers and Electronics in Agriculture, 40(1–3), 87–102.

    Article  Google Scholar 

  23. Lecoeuche, S., & Lalot, S. (2005). Prediction of the daily performance of solar collectors. International Communications in Heat and Mass Transfer, 32(5), 603–611.

    Article  Google Scholar 

  24. Kalogirou, S. A. (2006). Prediction of flat-plate collector performance parameters using artificial neural networks. Solar Energy, 80(3), 248–259.

    Article  Google Scholar 

  25. Sözen, A., Menlik, T., & Ünvar, S. (2008). Determination of efficiency of flat-plate solar collectors using neural network approach. Expert Systems with Applications, 35(4), 1533–1539.

    Article  Google Scholar 

  26. Kurt, H., Atik, K., Özkaymak, M., & Recebli, Z. (2008). Thermal performance parameters estimation of hot box type solar cooker by using artificial neural network. International Journal of Thermal Sciences, 47(2), 192–200.

    Article  Google Scholar 

  27. Souliotis, M., Kalogirou, S., & Tripanagnostopoulos, Y. (2009). Modelling of an ICS solar water heater using artificial neural networks and TRNSYS. Renewable Energy, 34(5), 1333–1339.

    Article  Google Scholar 

  28. Géczy-Víg, P., & Farkas, I. (2010). Neural network modelling of thermal stratification in a solar DHW storage. Solar Energy, 84(5), 801–806.

    Article  Google Scholar 

  29. Fischer, S., Frey, P., & Drück, H. (2012). A comparison between state-of-the-art and neural network modelling of solar collectors. Sol. Energy, 86(11), 3268–3277.

    Article  Google Scholar 

  30. Benli, H. (2013). Determination of thermal performance calculation of two different types solar air collectors with the use of artificial neural networks. International Journal of Heat and Mass Transfer, 60, 1–7.

    Article  Google Scholar 

  31. Kalogirou, S. A., Mathioulakis, E., & Belessiotis, V. (2014). Artificial neural networks for the performance prediction of large solar systems. Renewable Energy, 63, 90–97.

    Article  Google Scholar 

  32. Mazloom, M. S., Rezaei, F., Hemmati-Sarapardeh, A., Husein, M. M., Zendehboudi, S., & Bemani, A. (2020). Artificial intelligence based methods for asphaltenes adsorption by nanocomposites: Application of group method of data handling, least squares support vector machine, and artificial neural networks. Nanomaterials 6, 10(5), 890.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Institute of Technology Silchar, Silchar, Assam, India

    Biplab Das

  2. SQC & OR Unit, Indian Statistical Institute, Bangalore Center, Bangalore, Karnataka, India

    Jagadish

Authors
  1. Biplab Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jagadish
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. National Institute of Technology Silchar, Silchar, Assam, India

    Biplab Das

  2. SQC & OR Unit, Indian Statistical Institute, Bangalore Center, Bangalore, Karnataka, India

    Jagadish

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Das, B., Jagadish (2023). ANN-Based Modeling and Optimization of Corrugated Solar Air Collector. In: Das, B., Jagadish (eds) Evolutionary Methods Based Modeling and Analysis of Solar Thermal Systems. Mechanical Engineering Series. Springer, Cham. https://doi.org/10.1007/978-3-031-27635-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-27635-4_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27634-7

  • Online ISBN: 978-3-031-27635-4

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation