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Energy analysis of a domestic refrigerator system with ANN using LPG/TiO2–lubricant as replacement for R134a

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Abstract

This paper experimentally investigated and also modeled using artificial neural networks (ANN) approach the energy analysis of a domestic refrigerator using selected charges of LPG refrigerants (40, 50, 60 and 70 g) and different concentrations (0, 0.2, 0.4 and 0.6 gL−1) of TiO2-based nanolubricants as replacement for R134a refrigerant. The parameters for energy analysis include: compressor power consumption, cooling capacity, COP, compressor discharge temperature and pressure ratio. The findings showed that cooling capacity and COP of the domestic refrigerator using LPG refrigerant with TiO2 nanoparticles dispersed in a mineral oil lubricant was found to be higher than that of R134a by around 18.74–32.72 and 10.15–61.49%, respectively. Furthermore, compressor power consumption and pressure ratio of the domestic refrigerator using LPG refrigerant with TiO2–mineral oil lubricant were also found to be lower than that of R134a by about 3.20–18.1 and 2.33–8.45%, respectively, under similar conditions. The compressor discharge temperature was also found to be lower using LPG refrigerant with lubricant TiO2–mineral oil than R134a. The results further suggested that the best energetic performance of the domestic refrigerator was obtained using 40 g charge of LPG refrigerant with TiO2–mineral oil lubricant at 0.4 gL−1 of TiO2 concentration under similar operating conditions. The predictions from ANN models had excellent alignment with experimental results giving a range of values for R2 from 0.914 to 0.970, RMSE from 0.111 to 2.317, and MAPE from 0.865 to 3.148%.

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Abbreviations

GWP:

Global warming potential

P :

Pressure, bar

h :

Enthalpy, kJ kg−1

W :

Compressor power kW

Q C :

Cooling capacity, kW

MO:

Mineral oil

POE:

Polyol ester oil

LPG:

Liquefied petroleum gas

T :

Temperature, K

COP:

Coefficient of performance

ANN:

Artificial neural network

comp:

Compressor

r :

Ratio

d :

Discharge

η :

Isentropic efficiency

References

  1. Adrian MB, Joaquin NE, Angel BC, Francisco M, Bernado P. Analysis based on EU regulation No 517/2014 of new HFC/HFO mixtures as alternatives of high GWP refrigerants in refrigeration and HVAC systems. Int J Refrig. 2015;52:21–31.

    Article  Google Scholar 

  2. UNEP (2016). The emission Gap Report 2016. United Nations Environment Programme (UNEP), Nairobi.

  3. UNEP (2015). The emission Gap Report 2015. United Nations Environment Programme (UNEP), Nairobi.

  4. UNEP (2014). The emission Gap Report 2014. United Nations Environment Programme (UNEP), Nairobi.

  5. Sanchez D, Cabello R, Llopis R, Arauzo I, Catalán-Gil J, Torrella E. Energy performance evaluation of R1234yf, R1234ze (E), R600a, R290 and R152a as low-GWP R134a alternatives. Int J Refrig. 2017;74:267–80.

    Article  Google Scholar 

  6. Gill J, Singh J. Adaptive neuro-fuzzy inference system approach to predict the mass flow rate of R134a/LPG refrigerant for straight and helical coiled adiabatic capillary tubes in the vapor compression refrigeration system. Int J Refrig. 2017;78:166–75.

    Article  CAS  Google Scholar 

  7. Gill J, Singh J. Performance analysis of vapor compression refrigeration system using an adaptive neuro-fuzzy inference system. Int J Refrig. 2017;82:436–46.

    Article  CAS  Google Scholar 

  8. Gill J, Singh J. Energy analysis of vapor compression refrigeration system using mixture of R134a and LPG as refrigerant. Int J Refrig. 2017;84:287–99.

    Article  CAS  Google Scholar 

  9. Gill J, Singh J. Experimental analysis of R134a/LPG as replacement of R134a in a vapor-compression refrigeration system. Int J Air-Cond Refrig. 2017;25(02):1750015.

    Article  CAS  Google Scholar 

  10. Gill J, Singh J. An applicability of ANFIS approach for depicting energetic performance of VCRS using mixture of R134a and LPG as refrigerant. Int J Refrig. 2018;85:353–75.

    Article  CAS  Google Scholar 

  11. Gill J, Singh J. Use of artificial neural network approach for depicting mass flow rate of R134a/LPG refrigerant through straight and helical coiled adiabatic capillary tubes of vapor compression refrigeration system. Int J Refrig. 2018;86:228–38.

    Article  CAS  Google Scholar 

  12. Mohanraj M, Muraleedharan C, Jayaraj S. A review of recent developments in new refrigerant mixtures for vapor compression based refrigeration, air conditioning and heat pump units. Int J Energy Res. 2011;35(8):647–69.

    Article  CAS  Google Scholar 

  13. El-Morsi M. Energy and exergy analysis of LPG (liquefied petroleum gas) as a drop in replacement for R134a in domestic refrigerators. Energy. 2015;86:344–53.

    Article  CAS  Google Scholar 

  14. Akash BA, Said SA. Assessment of LPG as a possible alternative to R-12 in a domestic refrigerator. Energy Conversat Manag. 2003;44:381–8.

    Article  CAS  Google Scholar 

  15. Fatouh M, Kafafy M El. Experimental evaluation of a domestic refrigerator working with LPG. Appl Therm Eng. 2006;26:1593–603.

    Article  CAS  Google Scholar 

  16. Ahamed JU, Saidur R, Masjuki HH, Sattar MA. Energy and thermodynamic performance of LPG as an alternative refrigerant to R-134a in a domestic refrigerator. Energy Sci Res. 2012;29(1):597–610.

    CAS  Google Scholar 

  17. Srinivas P, Chandra RP, Kumar MR, Reddy N. Experimental investigation of LPG as refrigerant in a domestic refrigerator. J Mech Eng Res Technol. 2014;2(1):470–6.

    Google Scholar 

  18. Babarinde TO, Ohunakin OS, Adelekan DS, Aasa SA, Oyedepo SO. Experimental study of LPG and R134a refrigerants in vapor compression refrigeration. Int J Energy Clean Environ. 2015;16(1–4):71–80.

    Article  Google Scholar 

  19. Adelekan DS, Ohunakin OS, Babarinde TO, Odunfa MK, Leramo RO, Oyedepo SO, Badejo DC. Experimental performance of LPG refrigerant charges with varied concentration of TiO2 nano-lubricants in a domestic refrigerator. Case Stud Therm Eng. 2017;9:55–61.

    Article  Google Scholar 

  20. Botha S. Sythesis and characterization of nanofluids for cooling applications (doctor of philosophy). South Africa: University of the Western Cape; 2007.

    Google Scholar 

  21. Yu W, Xie H. A review on nanofluids: preparation, stability mechanisms, and applications. J. Nanomater. 2012. https://doi.org/10.1155/2012/435873.

    Article  Google Scholar 

  22. Sundar LS, Sharma KV, Naik MT, Singh MK. Empirical and theoretical correlations on viscosity of nanofluids: a review. Renew. Sust. Energy Rev. 2013;25:670–86.

    Article  CAS  Google Scholar 

  23. Wang RX, Hao B, Xie GZ, Li HQ. A refrigerating system using HFC134a and mineral lubricant appended with n-TiO2 (R) as working fluids. Proceedings of the 4th International Symposium on HAVC, Tsinghua University Press, Beijing, China 2003, pp. 888–92.

  24. Bi S, Shi L, Zhang L. Application of nanoparticles in domestic refrigerators. Appl Therm Eng. 2008;28:1834–43.

    Article  CAS  Google Scholar 

  25. Jwo CS, Jeng LY, Teng TP, Chang H. Effects of nanolubricant on performance of hydrocarbon refrigerant system. J Vac Sci Technol, B. 2009;27(3):1473–7.

    Article  CAS  Google Scholar 

  26. Bobbo S, Fedele L, Fabrizio M, Barison S, Battiston S, Pagura C. Influence of nanoparticles dispersion in POE oils on lubricity and R134a solubility. Int J Refrig. 2010;33:1180–6.

    Article  CAS  Google Scholar 

  27. Subramani N, Prakash MJ. Experimental studies on a vapour compression system using nanorefrigerants. Int J Eng Sci Technol. 2011;3(9):95–102.

    Google Scholar 

  28. Padmanabhan VMV, Palanisamy S. The use of TiO2 nanoparticles to reduce refrigerator IR-reversibility. Energy Convers Manag. 2012;59:122–32.

    Article  CAS  Google Scholar 

  29. Sabareesh RK, Gobinath N, Sajith V, Das S, Sobhan CB. Application of TiO2 nanoparticles as a lubricant-additive for vapor compression refrigeration systems—an experimental investigation. Int J Refrig. 2012;35:1989–96.

    Article  Google Scholar 

  30. Kumar DS, Elansezhian RD. Experimental study on Al2O3–R134a nanorefrigerant in refrigeration system. Int J Mod Eng Res. 2012;2(5):3927–9.

    Google Scholar 

  31. Lou JF, Zhang H, Wang R. Experimental investigation of graphite nanolubricant used in a domestic refrigerator. Adv Mech Eng 2015;7, [1687814015571011].

  32. Azmi WH, Sharma KV, Mamat R, Najafi G, Mohamad MS. The enhancement of effective thermal conductivity and effective dynamic viscosity of nanofluids: a review. Renew Sustain Energy Rev. 2016;53:1046–58.

    Article  Google Scholar 

  33. Azmi WH, Sharifa MZ, Yusof TM, Mamat R, Redhwan AAM. Potential of nanorefrigerant and nanolubricant on energy saving in refrigeration system: a review. Renew Sustain Energy Rev. 2017;69:415–28.

    Article  CAS  Google Scholar 

  34. Ohunakin OS, Adelekan DS, Babarinde TO, Leramo RO, Abam FI, Diarra CD. Experimental Investigation of TiO2-, SiO2-and Al2O3-lubricants for a domestic refrigerator system using LPG as working fluid. Appl Therm Eng. 2017;127:1469–77.

    Article  CAS  Google Scholar 

  35. Bi S, Guo K, Liu Z, Wu J. Performance of a domestic refrigerator using TiO2-R600a nano-refrigerant as working fluid. Energy Convers Manag. 2011;52(1):733–7.

    Article  CAS  Google Scholar 

  36. Cao X, Li ZY, Shao LL, Zhang CL. Refrigerant flows through electronic expansion valve: experiment and neural network modeling. Appl Therm Eng. 2016;92:210–8.

    Article  CAS  Google Scholar 

  37. Hosoz M, Ertunc HM. Modelling of a cascade refrigeration system using artificial neural network. Int J Energy Res. 2016;30:1200–15.

    Article  Google Scholar 

  38. Rashidi MM, Aghagoli A, Raoofi R. Thermodynamic analysis of the ejector refrigeration cycle using the artificial neural network. Energy. 2017;129:201–15.

    Article  CAS  Google Scholar 

  39. Sendil Kumar D, Elansezhain R. Experimental study of Al2O3-R134a nano-refrigerant in refrigeration system. Int J Mod Eng Res. 2012;2(5):3927–9.

    Google Scholar 

  40. R. Schultz, R. Cole, Uncertainty analysis in boiling nucleation, in: AICHE Symposium Series, 1979.

  41. Sheikholeslami M, Ganji DD. Heat transfer enhancement in an air to water heat exchanger with discontinuous helical turbulators; experimental and numerical studies. Energy. 2016;116:341–52.

    Article  CAS  Google Scholar 

  42. Ledesma S, Ibarra-Manzano MA, Garcıa-Hernandez MG, Almanza-Ojeda DL. Neural Lab a Simulator for Artificial Neural Networks, Computing Conference IEEE, 2017, pp. 716–21.

  43. Masters T. Practical neural network recipes in C ++, 1993. San Diego: Academic Press Inc; 1993.

    Google Scholar 

  44. Kumar DS, Elansezhian R. ZnO nanorefrigerant in R152a refrigeration system for energy conservation and green environment. Front Mech Eng. 2014;9:75–80.

    Article  Google Scholar 

  45. Gill J, Singh J. Energetic and Exergetic performance analysis of the vapor compression refrigeration system using adaptive neuro-fuzzy inference system approach. Exp Thermal Fluid Sci. 2017;88:246–60.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the IKG PTU, Kapurthala, BCET Gurdaspur, and Covenant University, Ogun State, Nigeria, for their excellent support.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Ph.D. Research Scholar, IKGPTU, Kapurthala, Punjab, India

    Jatinder Gill

  2. Faculty of Mechanical Engineering Department, BCET, Gurdaspur, Punjab, India

    Jagdev Singh

  3. The Energy and Environment Research Group (TEERG), Mechanical Engineering Department, Covenant University, Ota, Ogun State, Nigeria

    Olayinka S. Ohunakin & Damola S. Adelekan

  4. Center for African Studies, University of California, Berkeley, CA, USA

    Olayinka S. Ohunakin

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  1. Jatinder Gill
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  2. Jagdev Singh
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  4. Damola S. Adelekan
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Correspondence to Jatinder Gill.

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Gill, J., Singh, J., Ohunakin, O.S. et al. Energy analysis of a domestic refrigerator system with ANN using LPG/TiO2–lubricant as replacement for R134a. J Therm Anal Calorim 135, 475–488 (2019). https://doi.org/10.1007/s10973-018-7327-3

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