IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v320y2025ics0360544225008540.html

Multi-criteria optimization of nanofluid-based solar collector for enhanced performance: An explainable machine learning-driven approach

Author

Listed:
  • Sankar, Anjana
  • Gupta, Kritesh Kumar
  • Bhalla, Vishal
  • Pandey, Daya Shankar

Abstract

This study presents a novel hybrid framework that leverages machine learning to enhance the performance of nanofluid-based solar collectors (NBSCs). The framework is designed to identify the optimal control variables required to meet multiple performance criteria (such as simultaneously maximizing outlet temperature, thermal efficiency, and optical efficiency). This study introduces an end-to-end multi-criteria optimization framework that combines numerical simulations with a Gaussian process regression (GPR) and genetic algorithm (GA) for designing optimized NBSCs. In this approach, a minimal number of random samples are selected using Monte-Carlo sampling to perform numerical simulations. The control variables of the system are varied within practical ranges, and key performance metrics such as outlet temperature [To (°C)], thermal efficiency (ηt), and optical efficiency (ηo) are recorded. The input and output data are utilized to develop a computationally efficient GPR model. The generalization capability of the developed explainable machine learning (xML) models allowed for various data-intensive analyses, including sensitivity analysis, uncertainty quantification, interactive influence of control variables, and multi-objective optimization. The proposed computational framework helped explore previously unknown territory, leading to the identification of optimal settings for simultaneously maximizing all the responses. The optimal parameters led to a simultaneous improvement in the responses, with a 23.44 °C rise in outlet temperature, a 37.48 % increase in thermal efficiency, and a 28.62 % boost in optical efficiency, compared to the base dataset. The developed framework is rigorously tested to ensure its robust generalization and its applicability to calibrate other physical systems. The results of this study offer valuable insights for designing optimal NBSCs with improved operational performance.

Suggested Citation

  • Sankar, Anjana & Gupta, Kritesh Kumar & Bhalla, Vishal & Pandey, Daya Shankar, 2025. "Multi-criteria optimization of nanofluid-based solar collector for enhanced performance: An explainable machine learning-driven approach," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225008540
    DOI: 10.1016/j.energy.2025.135212
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225008540
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135212?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    2. Mausam, Kuwar & Singh, Shiva & Ghosh, Subrata Kumar & Singh, Ravindra P., 2024. "Thermal performance modelling of solar flat plate parallel tube collector using ANN," Energy, Elsevier, vol. 303(C).
    3. Sourav Diwania & Maneesh Kumar & Rajeev Kumar & Arun Kumar & Varun Gupta & Pavan Khetrapal, 2024. "Machine learning-based thermo-electrical performance improvement of nanofluid-cooled photovoltaic–thermal system," Energy & Environment, , vol. 35(4), pages 1793-1817, June.
    4. Tyagi, Praveen Kumar & Kumar, Rajan, 2024. "Thermodynamic modeling and performance optimization of nanofluid-based photovoltaic/thermal system using central composite design scheme of response surface methodology," Renewable Energy, Elsevier, vol. 225(C).
    5. Zhou, Yuekuan & Zheng, Siqian, 2020. "Multi-level uncertainty optimisation on phase change materials integrated renewable systems with hybrid ventilations and active cooling," Energy, Elsevier, vol. 202(C).
    6. Ataee, Sadegh & Ameri, Mehran & Askari, Ighball Baniasad & Keshtegar, Behrooz, 2024. "Evaluation and intelligent forecasting of energy and exergy efficiencies of a nanofluid-based filled-type U-pipe solar ETC using three machine learning approaches," Energy, Elsevier, vol. 298(C).
    7. Bhalla, Vishal & Tyagi, Himanshu, 2018. "Parameters influencing the performance of nanoparticles-laden fluid-based solar thermal collectors: A review on optical properties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 12-42.
    8. L, Chilambarasan & Thangarasu, Vinoth & Ramasamy, Prakash, 2024. "Solar flat plate collector's heat transfer enhancement using grooved tube configuration with alumina nanofluids: Prediction of outcomes through artificial neural network modeling," Energy, Elsevier, vol. 289(C).
    9. Bhalla, Vishal & Khullar, Vikrant & Parupudi, Ranga Vihari, 2022. "Design and thermal analysis of nanofluid-based compound parabolic concentrator," Renewable Energy, Elsevier, vol. 185(C), pages 348-362.
    10. Bertocchi, Rudi & Karni, Jacob & Kribus, Abraham, 2004. "Experimental evaluation of a non-isothermal high temperature solar particle receiver," Energy, Elsevier, vol. 29(5), pages 687-700.
    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. Mandal, Dipak Kumar & Vaishali, & Gupta, Kritesh Kumar & Biswas, Nirmalendu & Manna, Nirmal K. & Santra, Somnath & Cuce, Erdem, 2025. "A novel comparative study of machine learning surrogate models for solar chimney (SC) plant performance evaluation: Thermo-physical insights," Energy, Elsevier, vol. 330(C).
    2. Mandal, Dipak Kumar & Gupta, Kritesh Kumar & Biswas, Nirmalendu & Manna, Nirmal K. & Santra, Somnath & Benim, Ali Cemal, 2025. "Optimization of hybrid solar chimney power plants (HSCPPs): A review of multi-objective approaches," Applied Energy, Elsevier, vol. 396(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. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. Ren, Kezheng & Liu, Jun & Wu, Zeyang & Liu, Xinglei & Nie, Yongxin & Xu, Haitao, 2024. "A data-driven DRL-based home energy management system optimization framework considering uncertain household parameters," Applied Energy, Elsevier, vol. 355(C).
    3. Gohar Gholamibozanjani & Mohammed Farid, 2021. "A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings," Energies, MDPI, vol. 14(7), pages 1-39, March.
    4. Sun, Chunlei & Zou, Yuan & Qin, Caiyan & Chen, Meijie & Li, Xiaoke & Zhang, Bin & Wu, Xiaohu, 2022. "Solar absorption characteristics of SiO2@Au core-shell composite nanorods for the direct absorption solar collector," Renewable Energy, Elsevier, vol. 189(C), pages 402-411.
    5. Liu, Zhengxuan & Sun, Pengchen & Xie, Mingjing & Zhou, Yuekuan & He, Yingdong & Zhang, Guoqiang & Chen, Dachuan & Li, Shuisheng & Yan, Zhongjun & Qin, Di, 2021. "Multivariant optimization and sensitivity analysis of an experimental vertical earth-to-air heat exchanger system integrating phase change material with Taguchi method," Renewable Energy, Elsevier, vol. 173(C), pages 401-414.
    6. Qin, Di & Liu, Zhengxuan & Zhou, Yuekuan & Yan, Zhongjun & Chen, Dachuan & Zhang, Guoqiang, 2021. "Dynamic performance of a novel air-soil heat exchanger coupling with diversified energy storage components—modelling development, experimental verification, parametrical design and robust operation," Renewable Energy, Elsevier, vol. 167(C), pages 542-557.
    7. Abd, Hareth Maher & Abdulrazzaq, Nabeel M. & Soheel, Ammar Hassan, 2024. "Solar air heater energy and exergy enhancement using a v-corrugated wire mesh absorber: An experimental comparison," Energy, Elsevier, vol. 309(C).
    8. Zhou, Yuekuan & Zheng, Siqian & Liu, Zhengxuan & Wen, Tao & Ding, Zhixiong & Yan, Jun & Zhang, Guoqiang, 2020. "Passive and active phase change materials integrated building energy systems with advanced machine-learning based climate-adaptive designs, intelligent operations, uncertainty-based analysis and optimisations: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    9. 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.
    10. Zhou, Yuekuan & Zheng, Siqian, 2020. "Uncertainty study on thermal and energy performances of a deterministic parameters based optimal aerogel glazing system using machine-learning method," Energy, Elsevier, vol. 193(C).
    11. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    12. Radwan, Ali & Abo-Zahhad, Essam M. & El-Sharkawy, Ibrahim I. & Said, Zafar & Abdelrehim, Osama & Memon, Saim & Cheng, Ping & Soliman, Ahmed Saad, 2024. "Thermal analysis of a bifacial vacuum-based solar thermal collector," Energy, Elsevier, vol. 294(C).
    13. Zhou, Yuekuan & Zheng, Siqian, 2020. "Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization," Renewable Energy, Elsevier, vol. 153(C), pages 375-391.
    14. Zhou, Yuekuan & Zheng, Siqian, 2020. "Multi-level uncertainty optimisation on phase change materials integrated renewable systems with hybrid ventilations and active cooling," Energy, Elsevier, vol. 202(C).
    15. Shubo Liu & Yi Yang & Kuiyuan Ma & Haichuan Jin & Xin Jin, 2022. "Experimental Study of Pulsating Heat Pipes Filled with Nanofluids under the Irradiation of Solar Simulator," Energies, MDPI, vol. 15(23), pages 1-15, December.
    16. Daghigh, Roonak & Arshad, Siamand Azizi, 2025. "Experimental and theoretical performance analysis of PVT-evacuated U-tube solar collectors with and without CPC integration," Energy, Elsevier, vol. 320(C).
    17. Olmuş, Umutcan & Güzelel, Yunus Emre & Çerçi, Kamil Neyfel & Büyükalaca, Orhan, 2025. "Numerical analysis and comparison of different serpentine-based photovoltaic-thermal collectors," Renewable Energy, Elsevier, vol. 241(C).
    18. Zhou, Yuekuan, 2024. "AI-driven battery ageing prediction with distributed renewable community and E-mobility energy sharing," Renewable Energy, Elsevier, vol. 225(C).
    19. Sharaf, Omar Z. & Al-Khateeb, Ashraf N. & Kyritsis, Dimitrios C. & Abu-Nada, Eiyad, 2019. "Energy and exergy analysis and optimization of low-flux direct absorption solar collectors (DASCs): Balancing power- and temperature-gain," Renewable Energy, Elsevier, vol. 133(C), pages 861-872.
    20. Tyagi, Praveen Kumar & Kumar, Rajan, 2024. "Comprehensive performance assessment of photovoltaic/thermal system using MWCNT/water nanofluid and novel finned multi-block nano-enhanced phase change material-based thermal collector: Energy, exergy, economic, and environmental (4E) perspectives," Energy, Elsevier, vol. 312(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:320:y:2025:i:c:s0360544225008540. 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.