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Evaluating different types of artificial neural network structures for performance prediction of compact heat exchanger

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Abstract

In the present work, the performance of an air-to-refrigerant laminated type evaporator is predicted using a genetic algorithm (GA)-integrated feed-forward neural network (FFNN) and recurrent neural network (RNN). The obtained results are compared with the results of the FFNN with back-propagation learning algorithm, as the most recommended algorithm in the literature. The considered evaporator consists of single-phase and two-phase regions in the refrigerant side which makes the ANN-based methods so suitable for its modeling. To train the mentioned neural networks, the steady-state experimental data of the evaporator performance include capacity, outlet refrigerant pressure and temperature and outlet air dry- and wet-bulb temperatures is collected with varying input parameters. The results show a good agreement with experimental data, and it is observed that RNN-based method has the best average root-mean-square error (1.169 against 5.017, 4.791 and 2.286 for FFNN, GA-trained FFNN and numerical modeling, respectively). In fact, using GA to optimize FFNN structure makes better results than conventional FFNN, but the RNN method provides the best results because of using suitable intelligent configuration. Also, in contrary to numerical method, it is much faster and calculation processing load is lower. Therefore, RNN is proposed as a substitute for FFNN and the GA-trained FFNN. Finally, a sensitivity analysis determined the inlet refrigerant pressure as the most important parameter in predicting the evaporator capacity.

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Abbreviations

h ref,in :

Evaporator inlet refrigerant enthalpy (kJ kg−1)

h ref,out :

Evaporator outlet refrigerant enthalpy (kJ kg−1)

\(\dot{m}_{{{\text{ref}} .}}\) :

Refrigerant mass flow rate (kg s−1)

P ref,in :

Evaporator inlet refrigerant pressure (kPa)

P ref,out :

Evaporator outlet refrigerant pressure (kPa)

\(\dot{Q}_{{{\text{evap}} .}}\) :

Evaporator capacity (kW)

T air,in,db :

Evaporator inlet air dry-bulb temperature (K)

T air,in,wb :

Evaporator inlet air wet-bulb temperature (K)

T air,out,db :

Evaporator outlet air dry-bulb temperature (K)

T air,out,wb :

Evaporator outlet air wet-bulb temperature (K)

T ref,in :

Evaporator inlet refrigerant temperature (K)

T ref,out :

Evaporator outlet refrigerant temperature (K)

V air :

Air volumetric flow rate (m3 h−1)

db:

Dry bulb

in:

Inlet

out:

Outlet

ref:

Refrigerant

wb:

Wet bulb

evap:

Evaporator

References

  1. Corberán J, Melón MG (1998) Modelling of plate finned tube evaporators and condensers working with R134a. Int J Refrig 21(4):273–284

    Article  Google Scholar 

  2. Horuz I, Kurem E, Yamankaradeniz R (1998) Experimental and theoretical performance analysis of air-cooled plate-finned-tube evaporators. Int Commun Heat Mass Transfer 25(6):787–798

    Article  Google Scholar 

  3. Lee G, Yoo J (2000) Performance analysis and simulation of automobile air conditioning system. Int J Refrig 23(3):243–254

    Article  Google Scholar 

  4. Wellsandt S, Vamling L (2003) Heat transfer and pressure drop in a plate-type evaporator. Int J Refrig 26(2):180–188

    Article  Google Scholar 

  5. Karray FO, De Silva CW (2004) Soft computing and intelligent systems design: theory, tools, and applications. Addison Wesley, New York, NY

    Google Scholar 

  6. Tan C, Ward J, Wilcox S, Payne R (2009) Artificial neural network modelling of the thermal performance of a compact heat exchanger. Appl Therm Eng 29(17):3609–3617

    Article  Google Scholar 

  7. Şahin AŞ (2011) Performance analysis of single-stage refrigeration system with internal heat exchanger using neural network and neuro-fuzzy. Renew Energy 36(10):2747–2752

    Article  Google Scholar 

  8. Atik K, Aktaş A, Deniz E (2010) Performance parameters estimation of MAC by using artificial neural network. Expert Syst Appl 37(7):5436–5442

    Article  Google Scholar 

  9. Belman-Flores J, Ledesma S, Garcia M, Ruiz J, Rodríguez-Muñoz J (2013) Analysis of a variable speed vapor compression system using artificial neural networks. Expert Syst Appl 40(11):4362–4369

    Article  Google Scholar 

  10. Sanaye S, Dehghandokht M, Mohammadbeigi H, Bahrami S (2011) Modeling of rotary vane compressor applying artificial neural network. Int J Refrig 34(3):764–772

    Article  Google Scholar 

  11. Li N, Xia L, Shiming D, Xu X, Chan M-Y (2012) Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network. Appl Energy 91(1):290–300

    Article  Google Scholar 

  12. Kamar HM, Ahmad R, Kamsah N, Mustafa AFM (2013) Artificial neural networks for automotive air-conditioning systems performance prediction. Appl Therm Eng 50(1):63–70

    Article  Google Scholar 

  13. Hosoz M, Ertunc H (2006) Artificial neural network analysis of an automobile air conditioning system. Energy Convers Manag 47(11):1574–1587

    Article  Google Scholar 

  14. Mohanraj M, Jayaraj S, Muraleedharan C (2012) Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—a review. Renew Sustain Energy Rev 16(2):1340–1358

    Article  Google Scholar 

  15. Pacheco-Vega A, Dίaz G, Sen M, Yang K, McClain RL (2001) Heat rate predictions in humid air-water heat exchangers using correlations and neural networks. J Heat Transfer 123(2):348–354

    Article  Google Scholar 

  16. Pacheco-Vega A, Sen M, McClain RL (2000) Analysis of fin-tube evaporator performance with limited experimental data using artificial neural networks. ASME-PUBLICATIONS-HTD 366:95–102

    Google Scholar 

  17. Diaz G, Sen M, Yang K, McClain RL (1999) Simulation of heat exchanger performance by artificial neural networks. Hvac&R Res 5(3):195–208

    Article  Google Scholar 

  18. Tian Z, Gu B, Yang L, Liu F (2014) Performance prediction for a parallel flow condenser based on artificial neural network. Appl Therm Eng 63(1):459–467

    Article  Google Scholar 

  19. Yang L, Li Z-Y, Shao L-L, Zhang C-L (2014) Model-based dimensionless neural networks for fin-and-tube condenser performance evaluation. Int J Refrig 48:1–9

    Article  Google Scholar 

  20. Zhao L-X, Zhang C-L (2010) Fin-and-tube condenser performance evaluation using neural networks. Int J Refrig 33(3):625–634

    Article  Google Scholar 

  21. Pacheco-Vega A, Sen M, Yang K, McClain RL (2001) Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data. Int J Heat Mass Transf 44(4):763–770

    Article  MATH  Google Scholar 

  22. Li Z-Y, Shao L-L, Zhang C-L (2015) Fin-and-tube condenser performance modeling with neural network and response surface methodology. Int J Refrig 59:124–134

    Article  Google Scholar 

  23. Saeedan M, Bahiraei M (2015) Effects of geometrical parameters on hydrothermal characteristics of shell-and-tube heat exchanger with helical baffles: numerical investigation, modeling and optimization. Chem Eng Res Des 96:43–53

    Article  Google Scholar 

  24. Bahiraei M, Hangi M, Saeedan M (2015) A novel application for energy efficiency improvement using nanofluid in shell and tube heat exchanger equipped with helical baffles. Energy 93:2229–2240

    Article  Google Scholar 

  25. Bahiraei M, Hangi M (2013) Investigating the efficacy of magnetic nanofluid as a coolant in double-pipe heat exchanger in the presence of magnetic field. Energy Convers Manag 76:1125–1133

    Article  Google Scholar 

  26. Bahiraei M, Hosseinalipour SM, Saeedan M (2015) Prediction of Nusselt number and friction factor of water-Al2O3 nanofluid flow in shell-and-tube heat exchanger with helical baffles. Chem Eng Commun 202(2):260–268

    Article  Google Scholar 

  27. Mohanraj M, Jayaraj S, Muraleedharan C (2015) Applications of artificial neural networks for thermal analysis of heat exchangers—a review. Int J Therm Sci 90:150–172

    Article  Google Scholar 

  28. Tabatabaei A, Mosavi MR (2014) Rapid and precise GLONASS GDOP approximation using neural networks. Wirel Pers Commun 77(4):2675–2685

    Article  Google Scholar 

  29. Mosavi M (2010) Estimation of pseudo-range DGPS corrections using neural networks trained by evolutionary algorithms. Int Rev Electr Eng 5(6):2715–2721

    Google Scholar 

  30. Mosavi M, Tabatabaei A (2014) Wavelet and neural network-based fault location in power systems using statistical analysis of traveling wave. Arab J Sci Eng 39(8):6207–6214

    Article  Google Scholar 

  31. Tabatabaei A, Mosavi M, Farajiparvar P (2013) A traveling-wave fault location technique for three-terminal lines based on wavelet analysis and recurrent neural network using GPS timing. In: Smart Grid Conference (SGC). IEEE, pp 268–272

  32. Tafazoli S, Mosavi MR (2011) Performance improvement of GPS GDOP approximation using recurrent wavelet neural network. J Geogr Inf Syst 3(04):318

    Google Scholar 

  33. Sanaye S, Dehghandokht M (2012) Thermal modeling of mini-channel and laminated types evaporator in mobile air conditioning system. Int J Autom Eng 2(2):68–83

    Google Scholar 

  34. Klein V, Morelli EA (2006) Aircraft system identification: theory and practice. American Institute of Aeronautics and Astronautics, Reston

    Book  Google Scholar 

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Acknowledgments

This research was supported by the Sardsaz Khodro Ind. Co., and the authors gratefully acknowledge Mr. Abbasali Gorji as the chief executive officer for providing the experimental results.

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Authors and Affiliations

  1. School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran

    Mohammad Hassan Shojaeefard & Javad Zare

  2. School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

    Amir Tabatabaei

  3. R&D Division, Sardsaz Khodro Ind. Co., Tehran, Iran

    Hassan Mohammadbeigi

Authors
  1. Mohammad Hassan Shojaeefard
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  2. Javad Zare
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  3. Amir Tabatabaei
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  4. Hassan Mohammadbeigi
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Correspondence to Javad Zare.

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Shojaeefard, M.H., Zare, J., Tabatabaei, A. et al. Evaluating different types of artificial neural network structures for performance prediction of compact heat exchanger. Neural Comput & Applic 28, 3953–3965 (2017). https://doi.org/10.1007/s00521-016-2302-z

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  • DOI: https://doi.org/10.1007/s00521-016-2302-z

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