IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v129y2018ipap473-485.html
   My bibliography  Save this article

Using artificial neural network and quadratic algorithm for minimizing entropy generation of Al2O3-EG/W nanofluid flow inside parabolic trough solar collector

Author

Listed:
  • Ebrahimi-Moghadam, Amir
  • Mohseni-Gharyehsafa, Behnam
  • Farzaneh-Gord, Mahmood

Abstract

Entropy generation minimization approach, quadratic optimization algorithm and artificial neural network (ANN) have been applied to find optimal condition of the turbulent Al2O3-60:40% EG/W nanofluid flow inside the absorber tube of a parabolic trough solar collector (PTSC). A three-input ANN has been employed for predicting optimal volume fraction (ϕopt). The process is carried out for optimizing nanoparticle concentration, nanoparticle diameter, nanofluid average flow temperature and Reynolds number. Results show that the rate of the entropy generation decreases by decreasing volume fraction, increasing particle diameter and increasing average flow temperature. Adding the nanoparticles to the base-fluid increases frictional entropy generation and decreases thermal entropy generation. It causes an improvement in heat transfer but an increase in viscous irreversibility too. Finally, it was observed that for each particle sizes and average flow temperatures, there is a specific amount of optimal volume fraction, ϕopt; which is not dependent on the Re number. There is an optimal volume fraction for all Re numbers at constant particle size and mean flow temperature. Also, the optimum values of nanoparticle size, nanofluid average flow temperature and Reynolds number are found to be 90 nm, 360 K and 4000, respectively.

Suggested Citation

  • Ebrahimi-Moghadam, Amir & Mohseni-Gharyehsafa, Behnam & Farzaneh-Gord, Mahmood, 2018. "Using artificial neural network and quadratic algorithm for minimizing entropy generation of Al2O3-EG/W nanofluid flow inside parabolic trough solar collector," Renewable Energy, Elsevier, vol. 129(PA), pages 473-485.
    • Handle: RePEc:eee:renene:v:129:y:2018:i:pa:p:473-485
    DOI: 10.1016/j.renene.2018.06.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118306542
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.06.023?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. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2014. "Performance analysis of turbulent convection heat transfer of Al2O3 water-nanofluid in circular tubes at constant wall temperature," Energy, Elsevier, vol. 77(C), pages 403-413.
    2. Loni, R. & Askari Asli-ardeh, E. & Ghobadian, B. & Kasaeian, A.B. & Gorjian, Sh., 2017. "Thermodynamic analysis of a solar dish receiver using different nanofluids," Energy, Elsevier, vol. 133(C), pages 749-760.
    3. Mwesigye, Aggrey & Huan, Zhongjie & Meyer, Josua P., 2015. "Thermodynamic optimisation of the performance of a parabolic trough receiver using synthetic oil–Al2O3 nanofluid," Applied Energy, Elsevier, vol. 156(C), pages 398-412.
    4. Potenza, Marco & Milanese, Marco & Colangelo, Gianpiero & de Risi, Arturo, 2017. "Experimental investigation of transparent parabolic trough collector based on gas-phase nanofluid," Applied Energy, Elsevier, vol. 203(C), pages 560-570.
    5. Li, Jingrui & Wang, Rui & Wang, Jianzhou & Li, Yifan, 2018. "Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms," Energy, Elsevier, vol. 144(C), pages 243-264.
    6. Fuqiang, Wang & Zhexiang, Tang & Xiangtao, Gong & Jianyu, Tan & Huaizhi, Han & Bingxi, Li, 2016. "Heat transfer performance enhancement and thermal strain restrain of tube receiver for parabolic trough solar collector by using asymmetric outward convex corrugated tube," Energy, Elsevier, vol. 114(C), pages 275-292.
    7. Kasaiean, Alibakhsh & Sameti, Mohammad & Daneshazarian, Reza & Noori, Zahra & Adamian, Armen & Ming, Tingzhen, 2018. "Heat transfer network for a parabolic trough collector as a heat collecting element using nanofluid," Renewable Energy, Elsevier, vol. 123(C), pages 439-449.
    8. Mohammad Zadeh, P. & Sokhansefat, T. & Kasaeian, A.B. & Kowsary, F. & Akbarzadeh, A., 2015. "Hybrid optimization algorithm for thermal analysis in a solar parabolic trough collector based on nanofluid," Energy, Elsevier, vol. 82(C), pages 857-864.
    9. Purohit, Nilesh & Jakhar, Sanjeev & Gullo, Paride & Dasgupta, Mani Sankar, 2018. "Heat transfer and entropy generation analysis of alumina/water nanofluid in a flat plate PV/T collector under equal pumping power comparison criterion," Renewable Energy, Elsevier, vol. 120(C), pages 14-22.
    10. Bellos, Evangelos & Tzivanidis, Christos & Tsimpoukis, Dimitrios, 2017. "Multi-criteria evaluation of parabolic trough collector with internally finned absorbers," Applied Energy, Elsevier, vol. 205(C), pages 540-561.
    11. Mwesigye, Aggrey & Meyer, Josua P., 2017. "Optimal thermal and thermodynamic performance of a solar parabolic trough receiver with different nanofluids and at different concentration ratios," Applied Energy, Elsevier, vol. 193(C), pages 393-413.
    12. Yousefi, Tooraj & Veysi, Farzad & Shojaeizadeh, Ehsan & Zinadini, Sirus, 2012. "An experimental investigation on the effect of Al2O3–H2O nanofluid on the efficiency of flat-plate solar collectors," Renewable Energy, Elsevier, vol. 39(1), pages 293-298.
    13. Tagle-Salazar, Pablo D. & Nigam, K.D.P. & Rivera-Solorio, Carlos I., 2018. "Heat transfer model for thermal performance analysis of parabolic trough solar collectors using nanofluids," Renewable Energy, Elsevier, vol. 125(C), pages 334-343.
    14. Islam, Mohammad Rafiqul & Shabani, Bahman & Rosengarten, Gary, 2016. "Nanofluids to improve the performance of PEM fuel cell cooling systems: A theoretical approach," Applied Energy, Elsevier, vol. 178(C), pages 660-671.
    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. Deymi-Dashtebayaz, Mahdi & Ebrahimi-Moghadam, Amir & Pishbin, Seyyed Iman & Pourramezan, Mahdi, 2019. "Investigating the effect of hydrogen injection on natural gas thermo-physical properties with various compositions," Energy, Elsevier, vol. 167(C), pages 235-245.
    2. Ramezanizadeh, Mahdi & Ahmadi, Mohammad Hossein & Nazari, Mohammad Alhuyi & Sadeghzadeh, Milad & Chen, Lingen, 2019. "A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    3. Abubakr, Mohamed & Amein, Hamza & Akoush, Bassem M. & El-Bakry, M. Medhat & Hassan, Muhammed A., 2020. "An intuitive framework for optimizing energetic and exergetic performances of parabolic trough solar collectors operating with nanofluids," Renewable Energy, Elsevier, vol. 157(C), pages 130-149.
    4. Alawi, Omer A. & Kamar, Haslinda Mohamed & Homod, Raad Z. & Yaseen, Zaher Mundher, 2024. "Incorporating artificial intelligence-powered prediction models for exergy efficiency evaluation in parabolic trough collectors," Renewable Energy, Elsevier, vol. 225(C).
    5. Alirahmi, Seyed Mojtaba & Behzadi, Amirmohammad & Ahmadi, Pouria & Sadrizadeh, Sasan, 2023. "An innovative four-objective dragonfly-inspired optimization algorithm for an efficient, green, and cost-effective waste heat recovery from SOFC," Energy, Elsevier, vol. 263(PA).
    6. Yue Hua & Jiang-Zhou Peng & Zhi-Fu Zhou & Wei-Tao Wu & Yong He & Mehrdad Massoudi, 2022. "Thermal Performance in Convection Flow of Nanofluids Using a Deep Convolutional Neural Network," Energies, MDPI, vol. 15(21), pages 1-16, November.
    7. Hemmat Esfe, Mohammad & Sadati Tilebon, Seyyed Mohamad, 2020. "Statistical and artificial based optimization on thermo-physical properties of an oil based hybrid nanofluid using NSGA-II and RSM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    8. Nidal H. Abu-Hamdeh & Radi A. Alsulami & Muhyaddin J. H. Rawa & Mashhour A. Alazwari & Marjan Goodarzi & Mohammad Reza Safaei, 2021. "A Significant Solar Energy Note on Powell-Eyring Nanofluid with Thermal Jump Conditions: Implementing Cattaneo-Christov Heat Flux Model," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    9. Ma, Ting & Guo, Zhixiong & Lin, Mei & Wang, Qiuwang, 2021. "Recent trends on nanofluid heat transfer machine learning research applied to renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    10. Ajbar, Wassila & Parrales, A. & Huicochea, A. & Hernández, J.A., 2022. "Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    11. Tavakoli, Ali & Farzaneh-Gord, Mahmood & Ebrahimi-Moghadam, Amir, 2023. "Using internal sinusoidal fins and phase change material for performance enhancement of thermal energy storage systems: Heat transfer and entropy generation analyses," Renewable Energy, Elsevier, vol. 205(C), pages 222-237.
    12. 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.
    13. Kebir, Anouer & Woodward, Lyne & Akhrif, Ouassima, 2019. "Real-time optimization of renewable energy sources power using neural network-based anticipative extremum-seeking control," Renewable Energy, Elsevier, vol. 134(C), pages 914-926.
    14. 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.
    15. Sayantan Mukherjee & Nawaf F. Aljuwayhel & Sasmita Bal & Purna Chandra Mishra & Naser Ali, 2022. "Modelling, Analysis and Entropy Generation Minimization of Al 2 O 3 -Ethylene Glycol Nanofluid Convective Flow inside a Tube," Energies, MDPI, vol. 15(9), pages 1-24, April.

    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. Ajbar, Wassila & Parrales, A. & Huicochea, A. & Hernández, J.A., 2022. "Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. Yılmaz, İbrahim Halil & Mwesigye, Aggrey, 2018. "Modeling, simulation and performance analysis of parabolic trough solar collectors: A comprehensive review," Applied Energy, Elsevier, vol. 225(C), pages 135-174.
    3. Bellos, Evangelos & Tzivanidis, Christos & Tsimpoukis, Dimitrios, 2018. "Enhancing the performance of parabolic trough collectors using nanofluids and turbulators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 358-375.
    4. Cheng, Ze-Dong & Zhao, Xue-Ru & He, Ya-Ling, 2018. "Novel optical efficiency formulas for parabolic trough solar collectors: Computing method and applications," Applied Energy, Elsevier, vol. 224(C), pages 682-697.
    5. Ambreen, Tehmina & Kim, Man-Hoe, 2020. "Influence of particle size on the effective thermal conductivity of nanofluids: A critical review," Applied Energy, Elsevier, vol. 264(C).
    6. Manikandan, G.K. & Iniyan, S. & Goic, Ranko, 2019. "Enhancing the optical and thermal efficiency of a parabolic trough collector – A review," Applied Energy, Elsevier, vol. 235(C), pages 1524-1540.
    7. Bellos, Evangelos & Tzivanidis, Christos & Tsimpoukis, Dimitrios, 2017. "Multi-criteria evaluation of parabolic trough collector with internally finned absorbers," Applied Energy, Elsevier, vol. 205(C), pages 540-561.
    8. Bellos, Evangelos & Tzivanidis, Christos, 2017. "Parametric analysis and optimization of an Organic Rankine Cycle with nanofluid based solar parabolic trough collectors," Renewable Energy, Elsevier, vol. 114(PB), pages 1376-1393.
    9. Hachicha, Ahmed Amine & Yousef, Bashria A.A. & Said, Zafar & Rodríguez, Ivette, 2019. "A review study on the modeling of high-temperature solar thermal collector systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 280-298.
    10. Loni, Reyhaneh & Asli-Ardeh, E. Askari & Ghobadian, B. & Kasaeian, A.B. & Bellos, Evangelos, 2018. "Energy and exergy investigation of alumina/oil and silica/oil nanofluids in hemispherical cavity receiver: Experimental Study," Energy, Elsevier, vol. 164(C), pages 275-287.
    11. Liu, Peng & Dong, Zhimin & Xiao, Hui & Liu, Zhichun & Liu, Wei, 2021. "Thermal-hydraulic performance analysis of a novel parabolic trough receiver with double tube for solar cascade heat collection," Energy, Elsevier, vol. 219(C).
    12. Kumaresan, G. & Sudhakar, P. & Santosh, R. & Velraj, R., 2017. "Experimental and numerical studies of thermal performance enhancement in the receiver part of solar parabolic trough collectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1363-1374.
    13. Bellos, Evangelos & Tzivanidis, Christos, 2018. "Investigation of a star flow insert in a parabolic trough solar collector," Applied Energy, Elsevier, vol. 224(C), pages 86-102.
    14. Moghimi, M.A. & Ahmadi, G., 2018. "Wind barriers optimization for minimizing collector mirror soiling in a parabolic trough collector plant," Applied Energy, Elsevier, vol. 225(C), pages 413-423.
    15. Rehan, Mirza Abdullah & Ali, Muzaffar & Sheikh, Nadeem Ahmed & Khalil, M. Shahid & Chaudhary, Ghulam Qadar & Rashid, Tanzeel ur & Shehryar, M., 2018. "Experimental performance analysis of low concentration ratio solar parabolic trough collectors with nanofluids in winter conditions," Renewable Energy, Elsevier, vol. 118(C), pages 742-751.
    16. Gong, Jing-hu & Wang, Jun & Lund, Peter D. & Zhao, Dan-dan & Xu, Jing-wen & Jin, Yi-hao, 2021. "Comparative study of heat transfer enhancement using different fins in semi-circular absorber tube for large-aperture trough solar concentrator," Renewable Energy, Elsevier, vol. 169(C), pages 1229-1241.
    17. Mwesigye, Aggrey & Meyer, Josua P., 2017. "Optimal thermal and thermodynamic performance of a solar parabolic trough receiver with different nanofluids and at different concentration ratios," Applied Energy, Elsevier, vol. 193(C), pages 393-413.
    18. Tashtoush, Bourhan M. & Al-Nimr, Moh'd A. & Khasawneh, Mohammad A., 2017. "Investigation of the use of nano-refrigerants to enhance the performance of an ejector refrigeration system," Applied Energy, Elsevier, vol. 206(C), pages 1446-1463.
    19. Jing-hu, Gong & Yong, Li & Jun, Wang & Lund, Peter, 2023. "Performance optimization of larger-aperture parabolic trough concentrator solar power station using multi-stage heating technology," Energy, Elsevier, vol. 268(C).
    20. Mamourian, Mojtaba & Milani Shirvan, Kamel & Mirzakhanlari, Soroush, 2016. "Two phase simulation and sensitivity analysis of effective parameters on turbulent combined heat transfer and pressure drop in a solar heat exchanger filled with nanofluid by Response Surface Methodol," Energy, Elsevier, vol. 109(C), pages 49-61.

    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:renene:v:129:y:2018:i:pa:p:473-485. 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/renewable-energy .

    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.