IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i5p1190-d145240.html
   My bibliography  Save this article

Measurement and Artificial Neural Network Modeling of Electrical Conductivity of CuO/Glycerol Nanofluids at Various Thermal and Concentration Conditions

Author

Listed:
  • Reza Aghayari

    (Department of Chemistry, Payame Noor University (PNU), P.O. Box, Tehran 19395-3697, Iran)

  • Heydar Maddah

    (Department of Chemistry, Payame Noor University (PNU), P.O. Box, Tehran 19395-3697, Iran)

  • Mohammad Hossein Ahmadi

    (Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran)

  • Wei-Mon Yan

    (Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
    Research Center of Energy Conservation for New Generation of Residential, Commercial, and Industrial Sectors, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Nahid Ghasemi

    (Department of Chemistry, Arak Branch, Islamic Azad University, Arak 38361119131, Iran)

Abstract

In this work, the electrical conductivity of CuO/glycerol nanofluid was measured at a temperature range of 20–60 °C, volume fraction of 0.1–1.5% and nanoparticle size of 20–60 nm. The experimental data were predicted by the perceptron neural network. The results showed that the electrical conductivity increases with temperature, especially in higher volume fractions. These results are attributed to the accumulation of nanoparticles in the presence of the field and their Brownian motion at different temperatures and the reduction of electrical conductivity at higher nanoparticle sizes is attributed to the decreased mobility of nanoparticles as load carriers as well as to their decrease in volume unit per constant volume fraction. The results revealed that sonication time up to 70 min increases the nanofluid stability, while further increase in the sonication time decreases the nanofluid stability. In the modeling, input data to perceptron artificial neural network are nanofluid temperature, nanoparticle size, sonication time and volume fraction and electrical conductivity is considered as output. The results obtained from self-organizing map (SOM) showed that the winner neuron which has the most data is neuron 31. The values of the correlation coefficient (R 2 ), the mean of squared errors (MSE) and maximum error(e max ) used to evaluate the perceptron artificial neural network with 2 hidden layers and 31 neurons are 1, 2.3542 × 10 −17 and 0 respectively, indicating the high accuracy of the network.

Suggested Citation

  • Reza Aghayari & Heydar Maddah & Mohammad Hossein Ahmadi & Wei-Mon Yan & Nahid Ghasemi, 2018. "Measurement and Artificial Neural Network Modeling of Electrical Conductivity of CuO/Glycerol Nanofluids at Various Thermal and Concentration Conditions," Energies, MDPI, vol. 11(5), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1190-:d:145240
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/5/1190/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/5/1190/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rashidi, Saman & Akar, Shima & Bovand, Masoud & Ellahi, Rahmat, 2018. "Volume of fluid model to simulate the nanofluid flow and entropy generation in a single slope solar still," Renewable Energy, Elsevier, vol. 115(C), pages 400-410.
    2. Wahiba Yaïci & Michela Longo & Evgueniy Entchev & Federica Foiadelli, 2017. "Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System," Sustainability, MDPI, vol. 9(8), pages 1-14, August.
    3. Mahesh Suresh Patil & Jae-Hyeong Seo & Suk-Ju Kang & Moo-Yeon Lee, 2016. "Review on Synthesis, Thermo-Physical Property, and Heat Transfer Mechanism of Nanofluids," Energies, MDPI, vol. 9(10), pages 1-17, October.
    4. Maytham S. Ahmed & Azah Mohamed & Raad Z. Homod & Hussain Shareef, 2016. "Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy," Energies, MDPI, vol. 9(9), pages 1-20, September.
    5. Junjie Lu & Feng Lu & Jinquan Huang, 2018. "Performance Estimation and Fault Diagnosis Based on Levenberg–Marquardt Algorithm for a Turbofan Engine," Energies, MDPI, vol. 11(1), pages 1-18, January.
    6. Juan Velasco & Ricardo Frascella & Ricardo Albarracín & Juan Carlos Burgos & Ming Dong & Ming Ren & Li Yang, 2018. "Comparison of Positive Streamers in Liquid Dielectrics with and without Nanoparticles Simulated with Finite-Element Software," Energies, MDPI, vol. 11(2), pages 1-16, February.
    7. Zhanxiao Kang & Liqiu Wang, 2017. "Effect of Thermal-Electric Cross Coupling on Heat Transport in Nanofluids," Energies, MDPI, vol. 10(1), pages 1-13, January.
    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. Mahmoodi-Eshkaftaki, Mahmood & Mahbod, Mehdi & Ghenaatian, Hamid Reza, 2024. "Non-destructive estimation of biomass characteristics: Combining hyperspectral imaging data with neural networks," Renewable Energy, Elsevier, vol. 224(C).
    2. Hemmat Esfe, Mohammad & Reiszadeh, Mahdi & Esfandeh, Saeed & Afrand, Masoud, 2018. "Optimization of MWCNTs (10%) – Al2O3 (90%)/5W50 nanofluid viscosity using experimental data and artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 731-744.
    3. Behzad Maleki & Mahyar Ghazvini & Mohammad Hossein Ahmadi & Heydar Maddah & Shahaboddin Shamshirband, 2019. "Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network," Mathematics, MDPI, vol. 7(11), pages 1-12, November.
    4. Farzaneh-Gord, Mahmood & Mohseni-Gharyehsafa, Behnam & Arabkoohsar, Ahmad & Ahmadi, Mohammad Hossein & Sheremet, Mikhail A., 2020. "Precise prediction of biogas thermodynamic properties by using ANN algorithm," Renewable Energy, Elsevier, vol. 147(P1), pages 179-191.
    5. Toghraie, Davood & Sina, Nima & Jolfaei, Niyusha Adavoodi & Hajian, Mehdi & Afrand, Masoud, 2019. "Designing an Artificial Neural Network (ANN) to predict the viscosity of Silver/Ethylene glycol nanofluid at different temperatures and volume fraction of nanoparticles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

    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. Azharul Karim & M. Masum Billah & M. T. Talukder Newton & M. Mustafizur Rahman, 2017. "Influence of the Periodicity of Sinusoidal Boundary Condition on the Unsteady Mixed Convection within a Square Enclosure Using an Ag–Water Nanofluid," Energies, MDPI, vol. 10(12), pages 1-21, December.
    2. Wei-Tao Wu & Mehrdad Massoudi & Hongbin Yan, 2017. "Heat Transfer and Flow of Nanofluids in a Y-Type Intersection Channel with Multiple Pulsations: A Numerical Study," Energies, MDPI, vol. 10(4), pages 1-18, April.
    3. Yubai Li & Hongbin Yan & Mehrdad Massoudi & Wei-Tao Wu, 2017. "Effects of Anisotropic Thermal Conductivity and Lorentz Force on the Flow and Heat Transfer of a Ferro-Nanofluid in a Magnetic Field," Energies, MDPI, vol. 10(7), pages 1-19, July.
    4. Homod, Raad Z., 2018. "Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings," Renewable Energy, Elsevier, vol. 126(C), pages 49-64.
    5. Mikhail A. Sheremet & Hakan F. Oztop & Dmitriy V. Gvozdyakov & Mohamed E. Ali, 2018. "Impacts of Heat-Conducting Solid Wall and Heat-Generating Element on Free Convection of Al 2 O 3 /H 2 O Nanofluid in a Cavity with Open Border," Energies, MDPI, vol. 11(12), pages 1-17, December.
    6. Ali O. Al-Sulttani & Amimul Ahsan & Basim A. R. Al-Bakri & Mahir Mahmod Hason & Nik Norsyahariati Nik Daud & S. Idrus & Omer A. Alawi & Elżbieta Macioszek & Zaher Mundher Yaseen, 2022. "Double-Slope Solar Still Productivity Based on the Number of Rubber Scraper Motions," Energies, MDPI, vol. 15(21), pages 1-34, October.
    7. Baxter L. M. Williams & R. J. Hooper & Daniel Gnoth & J. G. Chase, 2025. "Residential Electricity Demand Modelling: Validation of a Behavioural Agent-Based Approach," Energies, MDPI, vol. 18(6), pages 1-22, March.
    8. Adnan Ahmad & Asif Khan & Nadeem Javaid & Hafiz Majid Hussain & Wadood Abdul & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources," Energies, MDPI, vol. 10(4), pages 1-35, April.
    9. Shoeibi, Shahin & Rahbar, Nader & Esfahlani, Ahad Abedini & Kargarsharifabad, Hadi, 2021. "Energy matrices, exergoeconomic and enviroeconomic analysis of air-cooled and water-cooled solar still: Experimental investigation and numerical simulation," Renewable Energy, Elsevier, vol. 171(C), pages 227-244.
    10. Rashid, M. & Shahzadi, I. & Nadeem, S., 2020. "Significance of Knudsen number and corrugation on EMHD flow under metallic nanoparticles impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    11. Lei Chen & Hongkun Chen & Jun Yang & Yanjuan Yu & Kaiwei Zhen & Yang Liu & Li Ren, 2017. "Coordinated Control of Superconducting Fault Current Limiter and Superconducting Magnetic Energy Storage for Transient Performance Enhancement of Grid-Connected Photovoltaic Generation System," Energies, MDPI, vol. 10(1), pages 1-23, January.
    12. Botman, Lola & Lago, Jesus & Fu, Xiaohan & Chia, Keaton & Wolf, Jesse & Kleissl, Jan & De Moor, Bart, 2024. "Building plug load mode detection, forecasting and scheduling," Applied Energy, Elsevier, vol. 364(C).
    13. 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).
    14. Salah L. Zubaidi & Sadik K. Gharghan & Jayne Dooley & Rafid M. Alkhaddar & Mawada Abdellatif, 2018. "Short-Term Urban Water Demand Prediction Considering Weather Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4527-4542, November.
    15. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    16. Hossein Moayedi & Amir Mosavi, 2021. "An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework," Energies, MDPI, vol. 14(4), pages 1-18, February.
    17. Al-Waeli, Ali H.A. & Chaichan, Miqdam T. & Kazem, Hussein A. & Sopian, K. & Ibrahim, Adnan & Mat, Sohif & Ruslan, Mohd Hafidz, 2018. "Comparison study of indoor/outdoor experiments of a photovoltaic thermal PV/T system containing SiC nanofluid as a coolant," Energy, Elsevier, vol. 151(C), pages 33-44.
    18. Muhammad, Noor & Nadeem, S. & Issakhov, Alibek, 2020. "Finite volume method for mixed convection flow of Ag–ethylene glycol nanofluid flow in a cavity having thin central heater," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    19. Tembhare, Saurabh P. & Barai, Divya P. & Bhanvase, Bharat A., 2022. "Performance evaluation of nanofluids in solar thermal and solar photovoltaic systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    20. Nizami, Sohrab & Tushar, Wayes & Hossain, M.J. & Yuen, Chau & Saha, Tapan & Poor, H. Vincent, 2022. "Transactive energy for low voltage residential networks: A review," Applied Energy, Elsevier, vol. 323(C).

    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:gam:jeners:v:11:y:2018:i:5:p:1190-:d:145240. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.