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, Volume 10, Issue 5, pp 2341–2351 | Cite as

Experimental Study on Heat Insulation Performance of Ceramic Additives Paint (CAP) in an Indoor Closed Media

  • M. EsfandyariEmail author
  • H. Salehi
  • D. Jafari
  • M. Koolivand-salooki
  • J. R. Esfandyari
Original Paper
  • 27 Downloads

Abstract

The objective of this research is to study the heat insulation performance of ceramic additives paint (CAP) with the variation of electric power. The thickness of the closed media was 13 cm and the internal dimensions were 1m × 1m × 1m. The two closed media were painted by the regular paint and the ceramic content of insulation layer was 18%. The temperature of internal closed media was controlled by thermostats to be in two levels, namely 28 °C and 40 °C. The results indicated that a considerable electric energy gain can be achieved by CAP in comparison with the regular paint. Additionally, it was observed that in indoor closed media with ceramic additive paint, the internal temperature reached 40 °C and 28 °C in a sufficient short period of time. In this paper, adaptive neuro-fuzzy inference system (ANFIS) is used to predict the energy consumption and closed media temperature. The ANFIS model has been applied for the training of the fuzzy system and the test set was used to evaluate the performance of the system using statistical parameters, which were R2 > 0.95, MAE < 0.96, MSE < 1.53, RMSE < 1.28, and MAPE < 0.98. The results approved that the predicted values from the model were in a good agreement with the experimental data.

Keywords

Energy gain Ceramic additives Paint Thermal insulation ANFIS model 

Abbreviations

ceramic additives paint

CAP

mean Absolute Error

MAE

Mean Squared Error

MSE

Root Mean Squared Error

RMSE

Mean Absolute Percentage Error

MAPE

Adaptive Neuro fuzzy inference system

ANFIS

Thermal Barrier Coatings

TBCs

National Aeronautics and Space Administration

NASA

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References

  1. 1.
    Bynum R (2000) Insulation handbook. McGraw Hill ProfessionalGoogle Scholar
  2. 2.
    Zhang C, Zhou C, Peng H, Gong S, Xu H (2007) Influence of thermal shock on insulation effect of nano-multilayer thermal barrier coatings. Surf Coatings Technol 201(14):6340–6344CrossRefGoogle Scholar
  3. 3.
    Portinha A, Teixeira V, Carneiro J, Martins J, Costa M, Vassen R, Stoever D (2005) Characterization of thermal barrier coatings with a gradient in porosity. Surf Coatings Technol 195(2):245– 251CrossRefGoogle Scholar
  4. 4.
    Teixeira V, Andritschky M, Fischer W, Buchkremer H, Stöver D (1999) Effects of deposition temperature and thermal cycling on residual stress state in zirconia-based thermal barrier coatings. Surf Coatings Technol 120:103–111CrossRefGoogle Scholar
  5. 5.
    DeMasi-Marcin JT, Gupta DK (1994) Protective coatings in the gas turbine engine. Surf Coatings Technol 68:1–9CrossRefGoogle Scholar
  6. 6.
    Johner G, Schweitzer K (1985) Flame rig testing of thermal barrier coatings and correlation with engine results. J Vacuum Sci Technol A 3(6):2516–2524CrossRefGoogle Scholar
  7. 7.
    Nicholls J, Deakin M, Rickerby D (1999) A comparison between the erosion behaviour of thermal spray and electron beam physical vapour deposition thermal barrier coatings. Wear 233:352–361CrossRefGoogle Scholar
  8. 8.
    Meier SM, Gupta DK, Sheffler KD (1991) Ceramic thermal barrier coatings for commercial gas turbine engines. JOM 43(3):50–53CrossRefGoogle Scholar
  9. 9.
    Miller RA (1987) Current status of thermal barrier coatings—an overview. Surf Coatings Technol 30(1):1–11CrossRefGoogle Scholar
  10. 10.
    Movchan B (1996) EB-PVD technology in the gas turbine industry: present and future. JOM 48(11):40–45CrossRefGoogle Scholar
  11. 11.
    Thornton JA (1975) Influence of substrate temperature and deposition rate on structure of thick sputtered Cu coatings. J Vacuum Sci Technol 12(4):830–835CrossRefGoogle Scholar
  12. 12.
    Schulz U, Fritscher K, Rätzer-Scheibe H-J, Kaysser WA, Peters M (1997) Thermocyclic behaviour of microstructurally modified EB-PVD thermal barrier coatings. In: Materials science forum. Trans Tech Publ, pp 957–964Google Scholar
  13. 13.
    Oda T, Nakai T, Toba K, Jianbo H (2015) Measurement of amenity in buildings interiors coated with ceramic insulating paint. Procedia Manufac 3:1728–1733CrossRefGoogle Scholar
  14. 14.
    Yuan Y, Li Z (2017) A novel approach of in-situ synthesis of WC particulate-reinforced Fe-30Ni ceramic metal coating. Surf Coatings Technol 328:256–265CrossRefGoogle Scholar
  15. 15.
    Bozsaky D (2017) Thermodynamic tests with nano-ceramic thermal insulation coatings. Pollack Periodica 12 (1):135–145CrossRefGoogle Scholar
  16. 16.
    Esfandyari M, Amiri M, Salooki MK (2015) Neural network prediction of the Fischer-Tropsch synthesis of natural gas with Co (III)/Al2O3 catalyst. Chem Eng Res Bull 17(1):25–33CrossRefGoogle Scholar
  17. 17.
    Esfandyari M, Fanaei MA, Gheshlaghi R, Mahdavi MA (2016) Neural network and neuro-fuzzy modeling to investigate the power density and Columbic efficiency of microbial fuel cell. J Taiwan Inst Chem Eng 58:84–91CrossRefGoogle Scholar
  18. 18.
    Inan G, Göktepe A, Ramyar K, Sezer A (2007) Prediction of sulfate expansion of PC mortar using adaptive neuro-fuzzy methodology. Build Environ 42(3):1264–1269CrossRefGoogle Scholar
  19. 19.
    Wang Y-M, Elhag TM (2008) An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Syst Appl 34(4):3099–3106CrossRefGoogle Scholar
  20. 20.
    Takassi MA, Kharaji AG, Esfandyari M, Salooki MK (2013) Neuro-fuzzy prediction of alumina-supported cobalt vanadate catalyst behavior in the Fischer-Tropsch process. Eur J Chem 4(2):110–116CrossRefGoogle Scholar
  21. 21.
    Takassi M, Gharibi Kharaji A, Esfandyari M, Koolivand-salooki M (2013) Neuro-fuzzy prediction of Fe-V2O5-promoted γ-alumina catalyst behavior in the reverse water–gas–shift reaction. Energy Technol 1(2-3):144–150CrossRefGoogle Scholar
  22. 22.
    Salehi H, Zeinali-Heris S, Esfandyari M, Koolivand M (2013) Nero-fuzzy modeling of the convection heat transfer coefficient for the nanofluid. Heat Mass Transfer 49(4):575–583CrossRefGoogle Scholar
  23. 23.
    Rahmanian B, Pakizeh M, Mansoori SAA, Esfandyari M, Jafari D, Maddah H, Maskooki A (2012) Prediction of MEUF process performance using artificial neural networks and ANFIS approaches. J. Taiwan Inst Chem Eng 43(4):558–565CrossRefGoogle Scholar
  24. 24.
    Meharrar A, Tioursi M, Hatti M, Stambouli AB (2011) A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference system. Expert Syst Appl 38(6):7659–7664CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • M. Esfandyari
    • 1
    Email author
  • H. Salehi
    • 2
  • D. Jafari
    • 3
  • M. Koolivand-salooki
    • 4
  • J. R. Esfandyari
    • 5
  1. 1.Department of Chemical EngineeringUniversity of BojnordBojnordIran
  2. 2.Faculty of Chemical, Petroleum and Gas EngineeringSemnan UniversitySemnanIran
  3. 3.Department of Chemical Engineering, Bushehr BranchIslamic Azad UniversityBushehrIran
  4. 4.Gas Research Division, Research Institute of Petroleum IndustryTehranIran
  5. 5.Technical teacher at Imam Hassan industrial school of educational OfficeMashhadIran

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