IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v235y2021ics0360544221015371.html
   My bibliography  Save this article

A hybrid adaptive neuro-fuzzy inference system integrated with equilibrium optimizer algorithm for predicting the energetic performance of solar dish collector

Author

Listed:
  • Zayed, Mohamed E.
  • Zhao, Jun
  • Li, Wenjia
  • Elsheikh, Ammar H.
  • Elaziz, Mohamed Abd

Abstract

Solar energy exploitation has a vital role to fulfill sustainability and decrease the usage of non-renewable energy resources. Solar parabolic dish collector (SPDC) is an effective alternative to fossil fuels due to its high efficiency. Nevertheless, performance prediction, optimization, and working fluid selection of SPDCs are highly complex problems and need complicated calculations and/or costly time-consuming experiments. Artificial intelligence-based algorithms have been proven to be beneficial in modeling different solar systems. Therefore, this study proposes an improved method to predict the thermal performance parameters of SPDC with a cylindrical cavity receiver using a modified algorithm of the adaptive neuro-fuzzy inference system (ANFIS) integrated with equilibrium optimizer (EO). In the developed algorithm, EO is employed as a new metaheuristic approach to enhance the prediction accuracy of ANFIS via determining the optimal values of ANFIS parameters. To evaluate the performance of the developed method, ANFIS-EO is compared with ANFIS and the conventional artificial neural network. The three models were applied to compare and predict the temperature difference of working fluid, heat gain, and energy efficiency of cylindrical receiver for SPDC operating with two different solar working fluids, namely, multi-wall carbon nanotubes/thermal oil nanofluid and pure thermal oil. Moreover, five statistical criteria are utilized to evaluate the performance of the investigated algorithms. The statistical performance results showed that the ANFIS-EO technique had the best prediction accuracy among the three models, and can be regarded as a powerful optimization tool for predicting the energetic performance of SPDC. The predicted results obtained by the ANFIS-EO have an excellent determination coefficient of 0.99999 for all predicted responses.

Suggested Citation

  • Zayed, Mohamed E. & Zhao, Jun & Li, Wenjia & Elsheikh, Ammar H. & Elaziz, Mohamed Abd, 2021. "A hybrid adaptive neuro-fuzzy inference system integrated with equilibrium optimizer algorithm for predicting the energetic performance of solar dish collector," Energy, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:energy:v:235:y:2021:i:c:s0360544221015371
    DOI: 10.1016/j.energy.2021.121289
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221015371
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.121289?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. Ghasemi-Mobtaker, Hassan & Mostashari-Rad, Fatemeh & Saber, Zahra & Chau, Kwok-wing & Nabavi-Pelesaraei, Ashkan, 2020. "Application of photovoltaic system to modify energy use, environmental damages and cumulative exergy demand of two irrigation systems-A case study: Barley production of Iran," Renewable Energy, Elsevier, vol. 160(C), pages 1316-1334.
    2. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    3. Jiao, Shan & Chong, Guoshuang & Huang, Changcheng & Hu, Hanqing & Wang, Mingjing & Heidari, Ali Asghar & Chen, Huiling & Zhao, Xuehua, 2020. "Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models," Energy, Elsevier, vol. 203(C).
    4. Zou, Chongzhe & Zhang, Yanping & Falcoz, Quentin & Neveu, Pierre & Zhang, Cheng & Shu, Weicheng & Huang, Shuhong, 2017. "Design and optimization of a high-temperature cavity receiver for a solar energy cascade utilization system," Renewable Energy, Elsevier, vol. 103(C), pages 478-489.
    5. Deshmukh, M.K. & Deshmukh, S.S., 2008. "Modeling of hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(1), pages 235-249, January.
    6. Bretado de los Rios, Mariana Soledad & Rivera-Solorio, Carlos I. & García-Cuéllar, Alejandro J., 2018. "Thermal performance of a parabolic trough linear collector using Al2O3/H2O nanofluids," Renewable Energy, Elsevier, vol. 122(C), pages 665-673.
    7. Ghodbane, Mokhtar & Said, Zafar & Hachicha, Ahmed Amine & Boumeddane, Boussad, 2020. "Performance assessment of linear Fresnel solar reflector using MWCNTs/DW nanofluids," Renewable Energy, Elsevier, vol. 151(C), pages 43-56.
    8. Sun, Lulening & Zong, Chenggang & Yu, Liang & Huang, Weidong, 2019. "Evaluation of solar brightness distribution models for performance simulation and optimization of solar dish," Energy, Elsevier, vol. 180(C), pages 192-205.
    9. Prado, Francisco & Minutolo, Marcel C. & Kristjanpoller, Werner, 2020. "Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework," Energy, Elsevier, vol. 197(C).
    10. Shen, Chao & Lv, Guoquan & Wei, Shen & Zhang, Chunxiao & Ruan, Changyun, 2020. "Investigating the performance of a novel solar lighting/heating system using spectrum-sensitive nanofluids," Applied Energy, Elsevier, vol. 270(C).
    11. Loni, R. & Kasaeian, A.B. & Askari Asli-Ardeh, E. & Ghobadian, B., 2016. "Optimizing the efficiency of a solar receiver with tubular cylindrical cavity for a solar-powered organic Rankine cycle," Energy, Elsevier, vol. 112(C), pages 1259-1272.
    12. Morshedizadeh, Majid & Kordestani, Mojtaba & Carriveau, Rupp & Ting, David S.-K. & Saif, Mehrdad, 2017. "Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production," Energy, Elsevier, vol. 138(C), pages 394-404.
    13. Loni, R. & Askari Asli-Ardeh, E. & Ghobadian, B. & Kasaeian, A.B. & Bellos, Evangelos, 2018. "Thermal performance comparison between Al2O3/oil and SiO2/oil nanofluids in cylindrical cavity receiver based on experimental study," Renewable Energy, Elsevier, vol. 129(PA), pages 652-665.
    14. Omar, Amr & Nashed, Amir & Li, Qiyuan & Leslie, Greg & Taylor, Robert A., 2020. "Pathways for integrated concentrated solar power - Desalination: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    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. Lujano-Rojas, Juan M. & Zubi, Ghassan & Dufo-López, Rodolfo & Bernal-Agustín, José L. & García-Paricio, Eduardo & Catalão, João P.S., 2019. "Contract design of direct-load control programs and their optimal management by genetic algorithm," Energy, Elsevier, vol. 186(C).
    17. Azzouzi, Djelloul & Boumeddane, Boussad & Abene, Abderahmane, 2017. "Experimental and analytical thermal analysis of cylindrical cavity receiver for solar dish," Renewable Energy, Elsevier, vol. 106(C), pages 111-121.
    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. Mona A. S. Ali & Fathimathul Rajeena P. P. & Diaa Salama Abd Elminaam, 2022. "A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem," Mathematics, MDPI, vol. 10(15), pages 1-34, July.
    2. Hamzah Ali Alkhazaleh & Navid Nahi & Mohammad Hossein Hashemian & Zohreh Nazem & Wameed Deyah Shamsi & Moncef L. Nehdi, 2022. "Prediction of Thermal Energy Demand Using Fuzzy-Based Models Synthesized with Metaheuristic Algorithms," Sustainability, MDPI, vol. 14(21), pages 1-14, November.
    3. Miloš Milašinović & Damjan Ivetić & Milan Stojković & Dragan Savić, 2023. "Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1153-1182, February.
    4. Ayman M. Sadoun & Ismail R. Najjar & Ghazi S. Alsoruji & M. S. Abd-Elwahed & Mohamed Abd Elaziz & Adel Fathy, 2022. "Utilization of Improved Machine Learning Method Based on Artificial Hummingbird Algorithm to Predict the Tribological Behavior of Cu-Al 2 O 3 Nanocomposites Synthesized by In Situ Method," Mathematics, MDPI, vol. 10(8), pages 1-14, 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. Yanping, Zhang & Yuxuan, Chen & Chongzhe, Zou & Hu, Xiao & Falcoz, Quentin & Neveu, Pierre & Cheng, Zhang & Xiaohong, Huang, 2021. "Experimental investigation on heat-transfer characteristics of a cylindrical cavity receiver with pressurized air in helical pipe," Renewable Energy, Elsevier, vol. 163(C), pages 320-330.
    2. Kasaeian, Alibakhsh & Kouravand, Amir & Vaziri Rad, Mohammad Amin & Maniee, Siavash & Pourfayaz, Fathollah, 2021. "Cavity receivers in solar dish collectors: A geometric overview," Renewable Energy, Elsevier, vol. 169(C), pages 53-79.
    3. Hassan, Atazaz & Quanfang, Chen & Abbas, Sajid & Lu, Wu & Youming, Luo, 2021. "An experimental investigation on thermal and optical analysis of cylindrical and conical cavity copper tube receivers design for solar dish concentrator," Renewable Energy, Elsevier, vol. 179(C), pages 1849-1864.
    4. Zhang, Yanping & Xiao, Hu & Zou, Chongzhe & Falcoz, Quentin & Neveu, Pierre, 2020. "Combined optics and heat transfer numerical model of a solar conical receiver with built-in helical pipe," Energy, Elsevier, vol. 193(C).
    5. Loni, R. & Kasaeian, A.B. & Askari Asli-Ardeh, E. & Ghobadian, B. & Gorjian, Sh, 2018. "Experimental and numerical study on dish concentrator with cubical and cylindrical cavity receivers using thermal oil," Energy, Elsevier, vol. 154(C), pages 168-181.
    6. 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.
    7. Loni, Reyhaneh & Mahian, Omid & Markides, Christos N. & Bellos, Evangelos & le Roux, Willem G. & Kasaeian, Ailbakhsh & Najafi, Gholamhassan & Rajaee, Fatemeh, 2021. "A review of solar-driven organic Rankine cycles: Recent challenges and future outlook," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    8. Qin, Caiyan & Kim, Joong Bae & Lee, Bong Jae, 2019. "Performance analysis of a direct-absorption parabolic-trough solar collector using plasmonic nanofluids," Renewable Energy, Elsevier, vol. 143(C), pages 24-33.
    9. 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).
    10. Mirzaei, Mohammad Reza & Kasaeian, Alibakhsh & Sadeghi Motlagh, Maryam & Fereidoni, Sahar, 2024. "Thermo-economic analysis of an integrated combined heating, cooling, and power unit with dish collector and organic Rankine cycle," Energy, Elsevier, vol. 296(C).
    11. Soltani, Sara & Bonyadi, Mohammad & Madadi Avargani, Vahid, 2019. "A novel optical-thermal modeling of a parabolic dish collector with a helically baffled cylindrical cavity receiver," Energy, Elsevier, vol. 168(C), pages 88-98.
    12. Zhang, Li & Fang, Jiabin & Wei, Jinjia & Yang, Guidong, 2017. "Numerical investigation on the thermal performance of molten salt cavity receivers with different structures," Applied Energy, Elsevier, vol. 204(C), pages 966-978.
    13. 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).
    14. Wang, Ding & Chen, Yuxuan & Xiao, Hu & Zhang, Yanping, 2022. "Effects of geometric and operating parameters on thermal performance of conical cavity receivers using supercritical CO2 as heat transfer fluid," Renewable Energy, Elsevier, vol. 185(C), pages 804-819.
    15. 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.
    16. Amir Hossein Arkian & Gholamhassan Najafi & Shiva Gorjian & Reyhaneh Loni & Evangelos Bellos & Talal Yusaf, 2019. "Performance Assessment of a Solar Dryer System Using Small Parabolic Dish and Alumina/Oil Nanofluid: Simulation and Experimental Study," Energies, MDPI, vol. 12(24), pages 1-22, December.
    17. Amein, Hamza & Akoush, Bassem M. & El-Bakry, M. Medhat & Abubakr, Mohamed & Hassan, Muhammed A., 2022. "Enhancing the energy utilization in parabolic trough concentrators with cracked heat collection elements using a cost-effective rotation mechanism," Renewable Energy, Elsevier, vol. 181(C), pages 250-266.
    18. Loni, R. & Askari Asli-Ardeh, E. & Ghobadian, B. & Kasaeian, A.B. & Bellos, Evangelos, 2018. "Thermal performance comparison between Al2O3/oil and SiO2/oil nanofluids in cylindrical cavity receiver based on experimental study," Renewable Energy, Elsevier, vol. 129(PA), pages 652-665.
    19. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
    20. Natividade, Pablo Sampaio Gomes & de Moraes Moura, Gabriel & Avallone, Elson & Bandarra Filho, Enio Pedone & Gelamo, Rogério Valentim & Gonçalves, Júlio Cesar de Souza Inácio, 2019. "Experimental analysis applied to an evacuated tube solar collector equipped with parabolic concentrator using multilayer graphene-based nanofluids," Renewable Energy, Elsevier, vol. 138(C), pages 152-160.

    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:energy:v:235:y:2021:i:c:s0360544221015371. 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/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.