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Modelling of energy and exergy analysis of ORC integrated systems in terms of sustainability by applying artificial neural network
[Thermodynamic performance evaluation of a novel solar energy based multigeneration system]

Author

Listed:
  • Zafer Utlu
  • Mert Tolon
  • Arif Karabuga

Abstract

The present study focuses on the organic Rankine cycle (ORC) integrated into an evacuated tube heat pipe (ETHP), whose systems are an alternative solar energy system to low-efficiency planary collectors. In this work, a detailed thermodynamic and artificial neural network (ANN) analysis was conducted to evaluate the solar energy system. One of the key parameters of sustainable approaches focused on exergy efficiency is application of thermal engineering. In addition to this, sustainable engineering approaches nowadays are a necessity for improving the efficiency of all of the engineering research areas. For this reason, the ANN model is used to forecast different types of energy efficiency problems in thermodynamic literature. The examined system consists of two main parts such as the ETHP system and the ORC system used for thermal energy production. With this system, it is aimed to evaluate energy and exergy analysis results by the ANN method in the case of integrating the ORC system to ETHP, which is one of the planar collectors suitable for the roofs of the buildings. Within the scope of this study, the exergy efficiency was evaluated on the developed ANN algorithm. The effect rates of parameters such as pressure, temperature and ambient temperature affecting the exergy efficiency of ORC integrated ETHP were calculated. Ambient temperature was found to be the most influential parameter on exergy efficiency. The exergy efficiency of the whole system has been calculated as ~23.39%. The most suitable BPNN architecture for this case study is recurrent networks with dampened feedback (Jordan–Elman nets). The success rate of the developed BPNN model is 95.4%.

Suggested Citation

  • Zafer Utlu & Mert Tolon & Arif Karabuga, 2021. "Modelling of energy and exergy analysis of ORC integrated systems in terms of sustainability by applying artificial neural network [Thermodynamic performance evaluation of a novel solar energy base," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 16(1), pages 156-164.
  • Handle: RePEc:oup:ijlctc:v:16:y:2021:i:1:p:156-164.
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    References listed on IDEAS

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    1. Lili Wei & Zhenjun Ma & Xuemei Gong & Xiujuan Guo, 2019. "Experimental investigation and performance analysis of an Organic Rankine Cycle for low-temperature heat to electricity generation," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 14(4), pages 500-507.
    2. Tong, Yijie & Kim, Jinhyun & Cho, Honghyun, 2015. "Effects of thermal performance of enclosed-type evacuated U-tube solar collector with multi-walled carbon nanotube/water nanofluid," Renewable Energy, Elsevier, vol. 83(C), pages 463-473.
    3. Muhyiddine Jradi & Jinxing Li & Hao Liu & Saffa Riffat, 2014. "Micro-scale ORC-based combined heat and power system using a novel scroll expander," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 9(2), pages 91-99.
    4. Ersöz, Mustafa Ali, 2016. "Effects of different working fluid use on the energy and exergy performance for evacuated tube solar collector with thermosyphon heat pipe," Renewable Energy, Elsevier, vol. 96(PA), pages 244-256.
    5. Jorge Facão & Ana Palmero-Marrero & Armando C. Oliveira, 2008. "Analysis of a solar assisted micro-cogeneration ORC system," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 3(4), pages 254-264, October.
    6. Huang, Xiaona & Wang, Qiliang & Yang, Honglun & Zhong, Shuai & Jiao, Dongsheng & Zhang, Kaili & Li, Mujun & Pei, Gang, 2019. "Theoretical and experimental studies of impacts of heat shields on heat pipe evacuated tube solar collector," Renewable Energy, Elsevier, vol. 138(C), pages 999-1009.
    7. 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.
    8. Marugán, Alberto Pliego & Márquez, Fausto Pedro García & Perez, Jesus María Pinar & Ruiz-Hernández, Diego, 2018. "A survey of artificial neural network in wind energy systems," Applied Energy, Elsevier, vol. 228(C), pages 1822-1836.
    9. Zafer Utlu & Büşra Selenay Önal, 2018. "Thermodynamic analysis of thermophotovoltaic systems used in waste heat recovery systems: an application," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 13(1), pages 52-60.
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