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Development of Optimal ANN Model to Estimate the Thermal Performance of Roughened Solar Air Heater Using Two different Learning Algorithms

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Abstract

In the present study, artificial neural network (ANN) model has been developed with two different training algorithms to predict the thermal efficiency of wire rib roughened solar air heater. Total 50 sets of data have been taken from experiments with three different types of absorber plate. The experimental data and calculated values of collector efficiency were used to develop ANN model. Scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) learning algorithms were used. It has been found that TRAINLM with 6 neurons and TRAINSCG with 7 neurons is optimal model on the basis of statistical error analysis. The performance of both the models have been compared with actual data and found that TRAINLM performs better than TRAINSCG. The value of coefficient of determination \((\hbox {R}^{2})\) for LM-6 is 0.99882 which gives the satisfactory performance. Learning algorithm with LM based proposed MLP ANN model seems more reliable for predicting performance of solar air heater.

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Abbreviations

\(A_{C}\) :

Area of collector surface \((\hbox {m}^{2})\)

ANN:

Artificial neural networks

\(a_{i}\) :

Input data

\(b_{j}\) :

Bias

\(C_{p}\) :

Specific heat \((\hbox {J kg}^{-1}\hbox {K}^{-1})\)

COV :

Coefficient of variance

e :

Roughness height (mm)

e / D :

Relative roughness height

I :

Solar intensity \((\hbox {W m}^{-2})\)

LM :

Levenberg–Marquardt

m :

Mass flow rate of air \((\hbox {kg s}^{-1})\)

MSE :

Mean square error

MAE :

Mean absolute error

MRE :

Mean relative error

MLP :

Multi-layered perceptron

P / e :

Relative roughness pitch

\(Q_{c}\) :

Energy gained by collector (W)

\(Q_{u}\) :

Energy gained by air (W)

RMSE :

Root mean square error

R :

Correlation coefficient

\(R^{2}\) :

Coefficient of multiple determination

SSE :

Sum square error

SCG :

Scaled conjugate gradient

T :

Temperature (\({^\circ }\)C)

\(w_{ij}\) :

Weights

\(X_{a}\) :

Actual value

\(X_{b}\) :

Predicted value

Y :

Experimental data

\(\upeta _{th} \) :

Thermal efficiency of collector (%)

a :

Ambient air

fi :

Inlet air

fo :

Outlet air

fm :

Mean air

min :

Minimum

max :

Maximum

References

  1. Duffie JA, Beckman WA (1991) Solar engineering of thermal processes, 2nd edn. Wiley Publication, New York

    Google Scholar 

  2. Garg HP, Prakash J (2000) Solar energy fundamentals and applications, 1st edn. Tata McGraw-Hill, New Delhi

    Google Scholar 

  3. Haykin S (1994) Neural networks: a comprehensive foundation. Prentice- Hall, New Jersey

    Google Scholar 

  4. Bhushan B, Singh R (2010) A review on methodology of artificial roughness used in duct of solar air heaters. Energy 35:202–212

    Article  Google Scholar 

  5. Prasad BN (2013) Thermal performance of artificially roughened solar air heaters. Sol Energy 91:59–67

    Article  Google Scholar 

  6. Prasad BN, Behura AK, Prasad L (2014) Fluid flow and heat transfer analysis for heat transfer enhancement in three sided artificially roughened solar air heater. Sol Energy 105:27–35

    Article  Google Scholar 

  7. Sharma SK, Kalamkar VR (2015) Thermo-hydraulic performance analysis of solar air heaters having artificial roughness—a review. Renew Sustain Energy Rev 41:413–435

    Article  Google Scholar 

  8. Patil AK (2015) Heat transfer mechanism and energy efficiency of artificially roughened solar air heaters—a review. Renew Sustain Energy Rev 42:681–689

    Article  Google Scholar 

  9. Kalogirou SA (2000) Applications of artificial neural-networks for energy systems. Appl Energy 67(1–2):17–35

    Article  Google Scholar 

  10. Yang IH, Yeo MS, Kim KW (2003) Application of artificial neural network to predict the optimal start time for heating system in building. Energy Convers Manag 44:2791–2809

    Article  Google Scholar 

  11. Ertunc HM, Hosoz M (2006) Artificial neural network analysis of a refrigeration system with an evaporative condenser. Appl Therm Eng 26:627–635

    Article  Google Scholar 

  12. Kalogirou SA (2006) Prediction of flat-plate collector performance parameters using artificial neural networks. Sol Energy 80:248–259

    Article  Google Scholar 

  13. Sozen A, Menlik T, Unvar S (2008) Determination of efficiency of flat-plate solar collectors using neural network approach. Expert Syst Appl 35(4):1533–1539

    Article  Google Scholar 

  14. Kurt H, Atik K, Ozkaymak M, Recebli Z (2008) Thermal performance parameters estimation of hot box type solar cooker by using artificial neural network. Int J Therm Sci 47:192–200

    Article  Google Scholar 

  15. Caner M, Gedik E, Kecebas A (2011) Investigation on thermal performance calculation of two type solar air collectors using artificial neural network. Expert Syst Appl 38(3):1668–1674

    Article  Google Scholar 

  16. Benli H (2013) Determination of thermal performance calculation of two different types solar air collectors with the use of artificial neural networks. Int J Heat Mass Transf 60:1–7

    Article  Google Scholar 

  17. Ceylan I, Gedik E, Erkaymaz O, Gürel AE (2014) The Artificial neural network model to estimate the photovoltaic module efficiency for all regions of the Turkey. Energy Build 84:258–267

    Article  Google Scholar 

  18. Esen H, Esen M, Ozsolak O (2017) Modelling and experimental performance analysis of solar-assisted ground source heat pump system. J Exp Theor Artif Intell 29(1):1–17. https://doi.org/10.1080/0952813X.2015.1056242

    Article  Google Scholar 

  19. Jani DB, Mishra M, Sahoo PK (2016) Performance prediction of rotary solid desiccant dehumidifier in hybrid air-conditioning system using artificial neural network. Appl Therm Eng 98:1091–1103

    Article  Google Scholar 

  20. Akdaga U, Komurb MA, Akcaya S (2016) Prediction of heat transfer on a flat plate subjected to a transversely pulsating jet using artificial neural networks. Appl Therm Eng 100:412–420

    Article  Google Scholar 

  21. Ghritlahre HK, Prasad RK (2017) Prediction of thermal performance of unidirectional flow porous bed solar air heater with optimal training function using artificial neural network. Energy Proc 109:369–376

    Article  Google Scholar 

  22. Ghritlahre HK, Prasad RK (2017) Energetic and exergetic performance prediction of roughened solar air heater using artificial neural network. Ciênc Téc Vitiviníc 32(11):2–24

    Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology, Jamshedpur, Jharkhand, 831014, India

    Harish Kumar Ghritlahre & Radha Krishna Prasad

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  1. Harish Kumar Ghritlahre
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  2. Radha Krishna Prasad
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Correspondence to Radha Krishna Prasad.

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Ghritlahre, H.K., Prasad, R.K. Development of Optimal ANN Model to Estimate the Thermal Performance of Roughened Solar Air Heater Using Two different Learning Algorithms. Ann. Data. Sci. 5, 453–467 (2018). https://doi.org/10.1007/s40745-018-0146-3

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  • DOI: https://doi.org/10.1007/s40745-018-0146-3

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