Advertisement

Journal of Thermal Analysis and Calorimetry

, Volume 134, Issue 3, pp 2223–2237 | Cite as

Exergy analysis of direct-expansion solar-assisted heat pumps working with R22 and R433A

  • L. Paradeshi
  • M. Mohanraj
  • M. Srinivas
  • S. Jayaraj
Article
  • 244 Downloads

Abstract

In this paper, the exergy performance of direct-expansion solar-assisted heat pump systems working with R22 and R433A (mixture of R290 and R1270, 70:30 by mass) was experimentally assessed. The experiments were carried out under the metrological conditions of Calicut in India. (Longitude and latitude of location are \(75.78 ^{\circ }\hbox {E}\) and \(11.25^{\circ }\hbox {N}\), respectively.) The artificial neural network model was developed for simulating the performance of a direct-expansion solar-assisted heat pump system to have realistic performance comparison. The experimental data observed during the year 2016 were used for training and testing the performance of network. The results showed that the network predicted exergy performance of a direct-expansion solar-assisted heat pump was found to be closer to the experimental results with a maximum fraction of absolute variance, minimum root-mean-square values and coefficient of variance. The system exergy destruction of R22 and R433A was found to be 1.36 and 1.25 kW, respectively. Moreover, R433A is identified as an energy-efficient and environmental-friendly alternative to phase out R22 in solar-assisted heat pump systems.

Keywords

Direct-expansion solar-assisted heat pumps R433A Exergy performance 

List of symbols

A

Area of collector (m2)

ANN

Artificial neural network

COV

Coefficient of variance

\({\dot{\text{E}}}{\text{x}}\)

Exergy rate (kW)

Ex

Specific exergy (kJ kg−1)

GWP

Global warming potential

h

Enthalpy (kJ−1)

I

Irreversibility(kW)

\(I_{\mathrm{t}}\)

Total solar insolation (W m2)

\({\dot{m}}\)

Mass flow rate (kg s−1)

MLFFN

Multilayer feed-forward network

N

Speed of the compressor (RPM)

ODP

Ozone depletion potential

\(R^2\)

Fraction of absolute variance

RMSE

Root-mean-square error

S

Entropy (kJ kg−1 K−1)

T

Temperature (°C)

\({\dot{W}}\)

Work input rate (kW)

Subscripts

a

Air

amb

Ambient

con

Condenser

dest

Destruction

e

Evaporator

eff

Efficiency

ele

Electrical

i

Component in the system

in

Inlet

Mech

Mechanical

out

Outlet

r

Refrigerant

rad

Radiation

s

Sky

vol

Volumetric

0

Reference/dead state

Greek symbols

\(\Delta\)

Change

\(\varepsilon\)

Exergy efficiency

\(\eta\)

Mechanical efficiency

References

  1. 1.
    Mohanraj M, Belyayev Y, Jayaraj S, Kaltayev A. Research and developments on solar assisted compression heat pump systems—a comprehensive review (Part A: modeling and modifications). Renew Sustain Energy Rev. 2018;83:90–123.CrossRefGoogle Scholar
  2. 2.
    Hawlader M, Jahangeer K. Solar heat pump drying and water heating in the tropics. Sol Energy. 2006;80(5):492–9.CrossRefGoogle Scholar
  3. 3.
    Li Y, Wang R, Wu J, Xu Y. Experimental performance analysis on a direct expansion solar-assisted heat pump water heater. Appl Therm Eng. 2007;27:2858–68.CrossRefGoogle Scholar
  4. 4.
    Mohanraj M, Belyayev Y, Jayaraj S, Kaltayev A. Research and developments on solar assisted compression heat pump systems—a comprehensive review (Part A: modeling and modifications). Renew Sustain Energy Rev. 2018;83:124–55.CrossRefGoogle Scholar
  5. 5.
    Ozgener O, Hepbasli A. Modeling and performance evaluation of ground source (geothermal) heat pump systems. Energy Build. 2007;39(1):66–75.CrossRefGoogle Scholar
  6. 6.
    Ozgener O, Hepbasli A. Performance analysis of a solar-assisted ground-source heat pump system for greenhouse heating: an experimental study. Build Environ. 2005;40(8):1040–50.CrossRefGoogle Scholar
  7. 7.
    Ozgener O, Hepbasli A. Experimental investigation of the performance of a solar-assisted ground-source heat pump system for greenhouse heating. Int J Energy Res. 2005;29(3):217–31.CrossRefGoogle Scholar
  8. 8.
    Mohanraj M, Muraleedharan C, Jayaraj S. A review on recent developments in new refrigerant mixtures for vapour compression-based refrigeration, air-conditioning and heat pump units. Int J Energy Res. 2011;35(8):647–69.CrossRefGoogle Scholar
  9. 9.
    Mohanraj M, Muraleedharan C, Jayaraj S. Environment friendly alternatives to halogenated refrigerants—a review. Int J Greenhouse Gas Control. 2009;3(1):108–19.CrossRefGoogle Scholar
  10. 10.
    Chata FG, Chaturvedi S, Almogbel A. Analysis of a direct expansion solar assisted heat pump using different refrigerants. Energy Convers Manag. 2005;46:2614–24.CrossRefGoogle Scholar
  11. 11.
    Chaichana C, Aye L, Charters W. Natural working fluids for solar-boosted heat pumps. Int J Refrig. 2003;26(6):637–43.CrossRefGoogle Scholar
  12. 12.
    Mohanraj M, Muraleedharan C, Jayaraj S. A comparison of the performance of a direct expansion solar-assisted heat pump working with R22 and R407C/liquid petroleum gas. Proc Inst Mech Eng Part A J Power Energy. 2009;223:821.CrossRefGoogle Scholar
  13. 13.
    Mohanraj M, Jayaraj S, Muraleedharan C. Exergy assessment of a direct expansion solar-assisted heat pump working with R22 and R407C/LPG mixture. Int J Green Energy. 2010;65(83):65–83.CrossRefGoogle Scholar
  14. 14.
    Hepbasli A. Exergetic modeling and assessment of solar assisted domestic hot water tank integrated ground-source heat pump systems for residences. Energy Build. 2007;39(12):1211–7.CrossRefGoogle Scholar
  15. 15.
    Dikici A, Akbulut A. Performance characteristics and energy-exergy analysis of solar-assisted heat pump system. Build Environ. 2008;43(11):1961–72.CrossRefGoogle Scholar
  16. 16.
    Dikici A, Akbulut A. Exergetic performance evaluation of heat pump systems having various heat sources. Int J Energy Res. 2008;32(14):1279–96.CrossRefGoogle Scholar
  17. 17.
    Ozgener O, Hepbasli A. A parametrical study on the energetic and exergetic assessment of a solar-assisted vertical ground-source heat pump system used for heating a greenhouse. Build Environ. 2007;42(1):11–24.CrossRefGoogle Scholar
  18. 18.
    Ozgener O, Hepbasli A. Experimental performance analysis of a solar assisted ground-source heat pump greenhouse heating system. Energy Build. 2005;37(1):101–10.CrossRefGoogle Scholar
  19. 19.
    Torres-Reyes E, Cervantes de Gortari J. Optimal performance of an irreversible solar-assisted heat pump. Exergy Int J. 2001;1(2):107–11.CrossRefGoogle Scholar
  20. 20.
    Mohanraj M, Muraleedharan C, Jayaraj S. Exergy analysis of direct expansion solar-assisted heat pumps using artificial neural networks. Int J Energy Res. 2009;33(11):1005–20.CrossRefGoogle Scholar
  21. 21.
    Kara O, Ulgen K, Hepbasli A. Exergetic assessment of direct-expansion solar-assisted heat pump systems: review and modeling. Renew Sustain Energy Rev. 2008;12(5):1383–401.CrossRefGoogle Scholar
  22. 22.
    Torres RE, Picon Nuez M, de Cervantes GJ. Exergy analysis and optimization of a solar-assisted heat pump. Energy. 1998;23(4):337–44.CrossRefGoogle Scholar
  23. 23.
    Cervantes GJ, Torres-Reyes E. Experiments on a solar-assisted heat pump and an exergy analysis of the system. Appl Therm Eng. 2002;22(12):1289–97.CrossRefGoogle Scholar
  24. 24.
    Mohanraj M, Jayaraj S, Muraleedharan C. Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—a review. Renew Sustain Energy Rev. 2012;16(2):1340–58.CrossRefGoogle Scholar
  25. 25.
    Kalogirou SA. Applications of artificial neural-networks for energy systems. Appl Energy. 2000;67(2):17–35.CrossRefGoogle Scholar
  26. 26.
    Mohanraj M, Jayaraj S, Muraleedharan C. Applications of artificial neural networks for thermal analysis of heat exchangers—a review. Int J Therm Sci. 2015;90:150–72.CrossRefGoogle Scholar
  27. 27.
    Mohanraj M, Muraleedharan C, Jayaraj S. Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks. Appl Energy. 2009;86:1442–9.CrossRefGoogle Scholar
  28. 28.
    Esen H, Inalli M, Sengur A, Esen M. Performance prediction of a ground-coupled heat pump system using artificial neural networks. Expert Syst Appl. 2008;35(4):1940–8.CrossRefGoogle Scholar
  29. 29.
    Gunasekar N, Mohanraj M, Velumuragan V. Artificial neural network modelling of a photovoltaic-thermal evaporator for solar assisted heat pumps. Energy. 2015;91:255–64.CrossRefGoogle Scholar
  30. 30.
    Shariah A, Al-Akhras M, Al-Omar A. Optimizing the tilt angle of solar collectors. Renew Energy. 2002;26:587–98.CrossRefGoogle Scholar
  31. 31.
    Bechtler H, Browne MW, Bansal PK, Kecman V. Neural networks a new approach to model vapour-compression heat pumps. Int J Energy Res. 2001;25(7):591–9.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018
corrected publication September/2018

Authors and Affiliations

  • L. Paradeshi
    • 1
  • M. Mohanraj
    • 2
  • M. Srinivas
    • 1
  • S. Jayaraj
    • 1
  1. 1.Department of Mechanical EngineeringNational Institute of TechnologyCalicutIndia
  2. 2.Department of Mechanical EngineeringHindusthan College of Engineering and TechnologyCoimbatoreIndia

Personalised recommendations