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Adaptive neuro-fuzzy inference system modeling to predict the performance of graphene nanoplatelets nanofluid-based direct absorption solar collector based on experimental study

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  • Vakili, Masoud
  • Yahyaei, Masood
  • Ramsay, James
  • Aghajannezhad, Pouria
  • Paknezhad, Behnaz

Abstract

In this study, an artificial neural network (ANN) adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the performance of a low-temperature direct absorption solar collector (DASC) enhanced with nanofluids. The accuracy and the effect of different types of clustering methods such as Subtractive Clustering and Fuzzy c-means (FCM) clustering investigated. Various inlet temperatures, weight percentage of graphene nanoplatelets nanofluids based deionized water (0.0005, 0.001, 0.005 wt%) and flow rates (0.0075, 0.015, 0.0225 kgs−1) were tested. Five inputs used for the ANFIS model: the inlet temperature, rate of solar radiation, ambient temperature, mass flow rate and weight percentage of nanofluid. The ANN provides a single output: the efficiency of the collector. In total, 1536 data points utilized for modeling, including 1075 for the ANN training process and 461 for testing. Overall, the ANFIS model was very accurate at predicting the collector performance. This was indicated by the root mean square error (RSME), the mean absolute percentage error (MAPE), coefficient of determination (R2), and mean bias error (MBE), which calculated to be 0.008, 0.031, 0.999 and 7e-4 respectively. The ANFIS model agrees well with the experimental results.

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  • Vakili, Masoud & Yahyaei, Masood & Ramsay, James & Aghajannezhad, Pouria & Paknezhad, Behnaz, 2021. "Adaptive neuro-fuzzy inference system modeling to predict the performance of graphene nanoplatelets nanofluid-based direct absorption solar collector based on experimental study," Renewable Energy, Elsevier, vol. 163(C), pages 807-824.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:807-824
    DOI: 10.1016/j.renene.2020.08.134
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    as
    1. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    2. Leong, K.Y. & Ong, Hwai Chyuan & Amer, N.H. & Norazrina, M.J. & Risby, M.S. & Ku Ahmad, K.Z., 2016. "An overview on current application of nanofluids in solar thermal collector and its challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1092-1105.
    3. Mahbubul, I.M. & Khan, Mohammed Mumtaz A. & Ibrahim, Nasiru I. & Ali, Hafiz Muhammad & Al-Sulaiman, Fahad A. & Saidur, R., 2018. "Carbon nanotube nanofluid in enhancing the efficiency of evacuated tube solar collector," Renewable Energy, Elsevier, vol. 121(C), pages 36-44.
    4. Kalogirou, Soteris A & Panteliou, Sofia & Dentsoras, Argiris, 1999. "Artificial neural networks used for the performance prediction of a thermosiphon solar water heater," Renewable Energy, Elsevier, vol. 18(1), pages 87-99.
    5. 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.
    6. Choudhary, Suraj & Sachdeva, Anish & Kumar, Pramod, 2020. "Influence of stable zinc oxide nanofluid on thermal characteristics of flat plate solar collector," Renewable Energy, Elsevier, vol. 152(C), pages 1160-1170.
    7. Diallo, Thierno M.O. & Yu, Min & Zhou, Jinzhi & Zhao, Xudong & Shittu, Samson & Li, Guiqiang & Ji, Jie & Hardy, David, 2019. "Energy performance analysis of a novel solar PVT loop heat pipe employing a microchannel heat pipe evaporator and a PCM triple heat exchanger," Energy, Elsevier, vol. 167(C), pages 866-888.
    8. Peng, Hao & Guo, Wenhua & Li, Meilin, 2020. "Thermal-hydraulic and thermodynamic performances of liquid metal based nanofluid in parabolic trough solar receiver tube," Energy, Elsevier, vol. 192(C).
    9. Bhalla, Vishal & Beejawat, Sachin & Doshi, Jay & Khullar, Vikrant & Singh, Harjit & Tyagi, Himanshu, 2020. "Silicone oil envelope for enhancing the performance of nanofluid-based direct absorption solar collectors," Renewable Energy, Elsevier, vol. 145(C), pages 2733-2740.
    10. Goel, Nipun & Taylor, Robert A. & Otanicar, Todd, 2020. "A review of nanofluid-based direct absorption solar collectors: Design considerations and experiments with hybrid PV/Thermal and direct steam generation collectors," Renewable Energy, Elsevier, vol. 145(C), pages 903-913.
    11. Bhusal, Yogesh & Hassanzadeh, Ali & Jiang, Lun & Winston, Roland, 2020. "Technical and economic analysis of a novel low-cost concentrated medium-temperature solar collector," Renewable Energy, Elsevier, vol. 146(C), pages 968-985.
    12. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    13. Kalogirou, S.A. & Mathioulakis, E. & Belessiotis, V., 2014. "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, Elsevier, vol. 63(C), pages 90-97.
    14. Pandey, Krishna Murari & Chaurasiya, Rajesh, 2017. "A review on analysis and development of solar flat plate collector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 641-650.
    15. Mojumder, Juwel Chandra & Ong, Hwai Chyuan & Chong, Wen Tong & Izadyar, Nima & Shamshirband, Shahaboddin, 2017. "The intelligent forecasting of the performances in PV/T collectors based on soft computing method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1366-1378.
    16. Raj, Pankaj & Subudhi, Sudhakar, 2018. "A review of studies using nanofluids in flat-plate and direct absorption solar collectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 54-74.
    17. Choudhary, Suraj & Sachdeva, Anish & Kumar, Pramod, 2020. "Investigation of the stability of MgO nanofluid and its effect on the thermal performance of flat plate solar collector," Renewable Energy, Elsevier, vol. 147(P1), pages 1801-1814.
    18. Gorji, Tahereh B. & Ranjbar, A.A., 2017. "Thermal and exergy optimization of a nanofluid-based direct absorption solar collector," Renewable Energy, Elsevier, vol. 106(C), pages 274-287.
    19. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    20. Delfani, S. & Karami, M. & Behabadi, M.A. Akhavan-, 2016. "Performance characteristics of a residential-type direct absorption solar collector using MWCNT nanofluid," Renewable Energy, Elsevier, vol. 87(P1), pages 754-764.
    21. Kalogirou, Soteris A., 2000. "Long-term performance prediction of forced circulation solar domestic water heating systems using artificial neural networks," Applied Energy, Elsevier, vol. 66(1), pages 63-74, May.
    22. Evangelisti, Luca & De Lieto Vollaro, Roberto & Asdrubali, Francesco, 2019. "Latest advances on solar thermal collectors: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    23. Karami, M. & Akhavan-Bahabadi, M.A. & Delfani, S. & Raisee, M., 2015. "Experimental investigation of CuO nanofluid-based Direct Absorption Solar Collector for residential applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 793-801.
    24. 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.
    25. Kalogirou, Soteris A. & Neocleous, Constantinos C. & Schizas, Christos N., 1998. "Artificial neural networks for modelling the starting-up of a solar steam-generator," Applied Energy, Elsevier, vol. 60(2), pages 89-100, June.
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