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Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network

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  • Rashidi, M.M.
  • Galanis, N.
  • Nazari, F.
  • Basiri Parsa, A.
  • Shamekhi, L.

Abstract

The present work concerns the parametric study and optimization of regenerative Clausius and organic Rankine cycles (ORC) with two feedwater heaters. For the parametric optimization, thermal efficiency, exergy efficiency and specific work are selected as the objective functions, so the mentioned parameters are calculated for different values of the outlet pressures from the second and third pumps by using EES (Engineering Equation Solver) software. Aiming at optimizing these functions, a procedure based on artificial neural network (ANN) and artificial bees colony (ABC) is proposed. The procedure includes two stages. According to the obtained data from the parametric analysis, in the first stage three different multi-layer perceptron neural networks are trained. In the next stage, three distinct artificial neural networks are used to optimize the specific network, the thermal efficiency and the exergy efficiency. Variables and fitness functions in these algorithms are the inputs and the outputs of the corresponding trained neural network, respectively. This optimization process is applied to water for a Clausius Rankine cycle and also to R717 for an ORC. It is shown that some interesting features among optimal objective functions and decision variables involved in this power cycle can be discovered consequently.

Suggested Citation

  • Rashidi, M.M. & Galanis, N. & Nazari, F. & Basiri Parsa, A. & Shamekhi, L., 2011. "Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network," Energy, Elsevier, vol. 36(9), pages 5728-5740.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:9:p:5728-5740
    DOI: 10.1016/j.energy.2011.06.036
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    11. Scardigno, Domenico & Fanelli, Emanuele & Viggiano, Annarita & Braccio, Giacobbe & Magi, Vinicio, 2015. "A genetic optimization of a hybrid organic Rankine plant for solar and low-grade energy sources," Energy, Elsevier, vol. 91(C), pages 807-815.
    12. Martelli, Emanuele & Capra, Federico & Consonni, Stefano, 2015. "Numerical optimization of Combined Heat and Power Organic Rankine Cycles – Part A: Design optimization," Energy, Elsevier, vol. 90(P1), pages 310-328.
    13. Wang, Xiao-Qiong & Li, Xiao-Ping & Li, You-Rong & Wu, Chun-Mei, 2015. "Payback period estimation and parameter optimization of subcritical organic Rankine cycle system for waste heat recovery," Energy, Elsevier, vol. 88(C), pages 734-745.
    14. Çetin, Gürcan & Keçebaş, Ali, 2021. "Optimization of thermodynamic performance with simulated annealing algorithm: A geothermal power plant," Renewable Energy, Elsevier, vol. 172(C), pages 968-982.
    15. Feng, Yongqiang & Zhang, Yaning & Li, Bingxi & Yang, Jinfu & Shi, Yang, 2015. "Sensitivity analysis and thermoeconomic comparison of ORCs (organic Rankine cycles) for low temperature waste heat recovery," Energy, Elsevier, vol. 82(C), pages 664-677.
    16. Rossi, Francesco & Velázquez, David, 2015. "A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant," Energy, Elsevier, vol. 89(C), pages 528-544.
    17. Li, Tailu & Fu, Wencheng & Zhu, Jialing, 2014. "An integrated optimization for organic Rankine cycle based on entransy theory and thermodynamics," Energy, Elsevier, vol. 72(C), pages 561-573.
    18. Zhou, Qian & Du, Dongmei & Lu, Chang & He, Qing & Liu, Wenyi, 2019. "A review of thermal energy storage in compressed air energy storage system," Energy, Elsevier, vol. 188(C).
    19. Mikulandrić, Robert & Lončar, Dražen & Cvetinović, Dejan & Spiridon, Gabriel, 2013. "Improvement of existing coal fired thermal power plants performance by control systems modifications," Energy, Elsevier, vol. 57(C), pages 55-65.
    20. Palagi, Laura & Pesyridis, Apostolos & Sciubba, Enrico & Tocci, Lorenzo, 2019. "Machine Learning for the prediction of the dynamic behavior of a small scale ORC system," Energy, Elsevier, vol. 166(C), pages 72-82.
    21. Wang, Jiangfeng & Yan, Zhequan & Wang, Man & Ma, Shaolin & Dai, Yiping, 2013. "Thermodynamic analysis and optimization of an (organic Rankine cycle) ORC using low grade heat source," Energy, Elsevier, vol. 49(C), pages 356-365.
    22. Rahbar, Kiyarash & Mahmoud, Saad & Al-Dadah, Raya K. & Moazami, Nima, 2015. "Parametric analysis and optimization of a small-scale radial turbine for Organic Rankine Cycle," Energy, Elsevier, vol. 83(C), pages 696-711.

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