Journal of Thermal Analysis and Calorimetry

, Volume 135, Issue 1, pp 475–488 | Cite as

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

  • Jatinder GillEmail author
  • Jagdev Singh
  • Olayinka S. Ohunakin
  • Damola S. Adelekan


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%.


R134a LPG TiO2 nanoparticle Cooling capacity Compressor power consumption COP ANN 



Global warming potential


Pressure, bar


Enthalpy, kJ kg−1


Compressor power kW


Cooling capacity, kW


Mineral oil


Polyol ester oil


Liquefied petroleum gas


Temperature, K


Coefficient of performance


Artificial neural network









Isentropic efficiency



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

Compliance with ethical standards

Conflict of interest

The authors declare no competing financial interest.


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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  • Jatinder Gill
    • 1
    Email author
  • Jagdev Singh
    • 2
  • Olayinka S. Ohunakin
    • 3
    • 4
  • Damola S. Adelekan
    • 3
  1. 1.Department of Mechanical Engineering, Ph.D. Research ScholarIKGPTUKapurthalaIndia
  2. 2.Faculty of Mechanical Engineering DepartmentBCETGurdaspurIndia
  3. 3.The Energy and Environment Research Group (TEERG), Mechanical Engineering DepartmentCovenant UniversityOtaNigeria
  4. 4.Center for African StudiesUniversity of CaliforniaBerkeleyUSA

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