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Potential application of Response Surface Methodology (RSM) for the prediction and optimization of thermal conductivity of aqueous CuO (II) nanofluid: A statistical approach and experimental validation

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  • Peng, Yeping
  • Khaled, Usama
  • Al-Rashed, Abdullah A.A.A.
  • Meer, Rashid
  • Goodarzi, Marjan
  • Sarafraz, M.M.

Abstract

In this article, we report the results of a robust model developed for estimating the thermal conductivity of a copper oxide-water nano-suspensions using Response Surface Methodology (RSM) at different temperatures and various mass fractions of nanoparticles. To increase the reliability, an ANOVA statistical approach was utilised to further identify the F-value and P-value of the model. To rank the operating parameters, a normal probability assessment was applied to the data to calculate the value of the noise and potential error distribution of the model. Results revealed that the developed model is robust, reliable and the achieved outcome is in a reasonable agreement with those data obtained by the experiments for the thermal conductivity of CuO (II)/water NF within 25 °C to 40 °C and mass concentrations of 1000 ppm to 4000 ppm. The values of R-squared (R2) and average absolute deviation (AAD%) were 0.9939 and 0.615%, respectively showing the accuracy and robustness of the established model. We also validated the RSM model by conducting further experiments. It was shown that the model can predict the thermal conductivity of the NF with less than 2% error.

Suggested Citation

  • Peng, Yeping & Khaled, Usama & Al-Rashed, Abdullah A.A.A. & Meer, Rashid & Goodarzi, Marjan & Sarafraz, M.M., 2020. "Potential application of Response Surface Methodology (RSM) for the prediction and optimization of thermal conductivity of aqueous CuO (II) nanofluid: A statistical approach and experimental validatio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    • Handle: RePEc:eee:phsmap:v:554:y:2020:i:c:s0378437120301217
    DOI: 10.1016/j.physa.2020.124353
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    1. Cai, Weihong & Yu, Ding & Wu, Ziyu & Du, Xin & Zhou, Teng, 2019. "A hybrid ensemble learning framework for basketball outcomes prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C).
    2. Jiang, Yu & Bahrami, Mehrdad & Bagherzadeh, Seyed Amin & Abdollahi, Ali & Sulgani, Mohsen Tahmasebi & Karimipour, Arash & Goodarzi, Marjan & Bach, Quang-Vu, 2019. "Propose a new approach of fuzzy lookup table method to predict Al2O3/deionized water nanofluid thermal conductivity based on achieved empirical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    3. Kurt, Hüseyin & Kayfeci, Muhammet, 2009. "Prediction of thermal conductivity of ethylene glycol-water solutions by using artificial neural networks," Applied Energy, Elsevier, vol. 86(10), pages 2244-2248, October.
    4. Ghasemi, Ali & Hassani, Mohsen & Goodarzi, Marjan & Afrand, Masoud & Manafi, Sahebali, 2019. "Appraising influence of COOH-MWCNTs on thermal conductivity of antifreeze using curve fitting and neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 36-45.
    5. Rostamian, Hossein & Lotfollahi, Mohammad Nader, 2019. "A novel statistical approach for prediction of thermal conductivity of CO2 by Response Surface Methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    6. Hemmat Esfe, Mohammad & Rostamian, Hossein & Esfandeh, Saeed & Afrand, Masoud, 2018. "Modeling and prediction of rheological behavior of Al2O3-MWCNT/5W50 hybrid nano-lubricant by artificial neural network using experimental data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 625-634.
    7. Khodabandeh, Erfan & Safaei, Mohammad Reza & Akbari, Soheil & Akbari, Omid Ali & Alrashed, Abdullah A.A.A., 2018. "Application of nanofluid to improve the thermal performance of horizontal spiral coil utilized in solar ponds: Geometric study," Renewable Energy, Elsevier, vol. 122(C), pages 1-16.
    8. Moradikazerouni, Alireza & Hajizadeh, Ahmad & Safaei, Mohammad Reza & Afrand, Masoud & Yarmand, Hooman & Zulkifli, Nurin Wahidah Binti Mohd, 2019. "Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 138-145.
    9. Wu, Huawei & Al-Rashed, Abdullah A.A.A. & Barzinjy, Azeez A. & Shahsavar, Amin & Karimi, Ali & Talebizadehsardari, Pouyan, 2019. "Curve-fitting on experimental thermal conductivity of motor oil under influence of hybrid nano additives containing multi-walled carbon nanotubes and zinc oxide," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    10. Al-Rashed, Abdullah A.A.A., 2019. "Optimization of heat transfer and pressure drop of nano-antifreeze using statistical method of response surface methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 531-542.
    11. Tawfik, Mohamed M., 2017. "Experimental studies of nanofluid thermal conductivity enhancement and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1239-1253.
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