IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v554y2020ics0378437120301217.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120301217
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2020.124353?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Grzegorz Czerwiński & Jerzy Wołoszyn, 2021. "Numerical Study of a Cooling System Using Phase Change of a Refrigerant in a Thermosyphon," Energies, MDPI, vol. 14(12), pages 1-22, June.
    2. Mohammed Algarni & Mashhour A. Alazwari & Mohammad Reza Safaei, 2021. "Optimization of Nano-Additive Characteristics to Improve the Efficiency of a Shell and Tube Thermal Energy Storage System Using a Hybrid Procedure: DOE, ANN, MCDM, MOO, and CFD Modeling," Mathematics, MDPI, vol. 9(24), pages 1-30, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Tian, Zhe & Rostami, Sara & Taherialekouhi, Roozbeh & Karimipour, Arash & Moradikazerouni, Alireza & Yarmand, Hooman & Zulkifli, Nurin Wahidah Binti Mohd, 2020. "Prediction of rheological behavior of a new hybrid nanofluid consists of copper oxide and multi wall carbon nanotubes suspended in a mixture of water and ethylene glycol using curve-fitting on experim," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    3. Hemmat Esfe, Mohammad & Esfandeh, Saeed, 2020. "The statistical investigation of multi-grade oil based nanofluids: Enriched by MWCNT and ZnO nanoparticles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    4. Peng, Yeping & Parsian, Amir & Khodadadi, Hossein & Akbari, Mohammad & Ghani, Kamal & Goodarzi, Marjan & Bach, Quang-Vu, 2020. "Develop optimal network topology of artificial neural network (AONN) to predict the hybrid nanofluids thermal conductivity according to the empirical data of Al2O3 – Cu nanoparticles dispersed in ethy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    5. Arani, Ali Akbar Abbasian & Alirezaie, Ali & Kamyab, Mohammad Hassan & Motallebi, Sayyid Majid, 2020. "Statistical analysis of enriched water heat transfer with various sizes of MgO nanoparticles using artificial neural networks modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    6. Xu, Yanyan & Xue, Yanqin & Qi, Hong & Cai, Weihua, 2021. "An updated review on working fluids, operation mechanisms, and applications of pulsating heat pipes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    7. Chen, Zhixiong & Ashkezari, Abbas Zarenezhad & Tlili, Iskander, 2020. "Applying artificial neural network and curve fitting method to predict the viscosity of SAE50/MWCNTs-TiO2 hybrid nanolubricant," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    8. Rostamian, Hossein & Lotfollahi, Mohammad Nader, 2020. "Statistical modeling of aspirin solubility in organic solvents by Response Surface Methodology and Artificial Neural Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    9. Xiaohong, Dai & Huajiang, Chen & Bagherzadeh, Seyed Amin & Shayan, Masoud & Akbari, Mohammad, 2020. "Statistical estimation the thermal conductivity of MWCNTs-SiO2/Water-EG nanofluid using the ridge regression method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    10. 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).
    11. Jiang, Ping & Liu, Zhenkun & Niu, Xinsong & Zhang, Lifang, 2021. "A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting," Energy, Elsevier, vol. 217(C).
    12. Ahmadi, Mohammad Hossein & Ghazvini, Mahyar & Maddah, Heydar & Kahani, Mostafa & Pourfarhang, Samira & Pourfarhang, Amin & Heris, Saeed Zeinali, 2020. "Prediction of the pressure drop for CuO/(Ethylene glycol-water) nanofluid flows in the car radiator by means of Artificial Neural Networks analysis integrated with genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 546(C).
    13. Wang, Xianling & Luo, Liang & Xiang, Jinwei & Zheng, Senlin & Shittu, Samson & Wang, Zhangyuan & Zhao, Xudong, 2021. "A comprehensive review on the application of nanofluid in heat pipe based on the machine learning: Theory, application and prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    14. 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.
    15. Tian, Zhe & Arasteh, Hossein & Parsian, Amir & Karimipour, Arash & Safaei, Mohammad Reza & Nguyen, Truong Khang, 2019. "Estimate the shear rate & apparent viscosity of multi-phased non-Newtonian hybrid nanofluids via new developed Support Vector Machine method coupled with sensitivity analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    16. Sarafraz, M.M. & Tlili, I. & Tian, Zhe & Bakouri, Mohsen & Safaei, Mohammad Reza, 2019. "Smart optimization of a thermosyphon heat pipe for an evacuated tube solar collector using response surface methodology (RSM)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    17. Wei, Li & Arasteh, Hossein & abdollahi, Ali & Parsian, Amir & Taghipour, Abdolmajid & Mashayekhi, Ramin & Tlili, Iskander, 2020. "Locally weighted moving regression: A non-parametric method for modeling nanofluid features of dynamic viscosity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    18. Bahrami, Mehrdad & Akbari, Mohammad & Bagherzadeh, Seyed Amin & Karimipour, Arash & Afrand, Masoud & Goodarzi, Marjan, 2019. "Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: Measure MSEs between targets & ANN for Fe–CuO/Eg–Water nanofluid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 159-168.
    19. Ghazvini, Mahyar & Maddah, Heydar & Peymanfar, Reza & Ahmadi, Mohammad Hossein & Kumar, Ravinder, 2020. "Experimental evaluation and artificial neural network modeling of thermal conductivity of water based nanofluid containing magnetic copper nanoparticles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    20. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:554:y:2020:i:c:s0378437120301217. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.