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Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks

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  • Baklacioglu, Tolga
  • Turan, Onder
  • Aydin, Hakan

Abstract

Genetic algorithm is utilized to design the optimum initial value of parameters and topology of the artificial neural network which is trained by applying the improved backpropagation algorithm using momentum factor so as to minimize the spent time and effort. In this study, a comprehensive dynamic modeling of turboprop engine components plant is accomplished using hybrid GA (genetic algorithm) ANN (artificial neural networks) strategy. The turboprop engine is equipped with main components such as compressor, combustor, gas turbine and power turbine. Newly derived GA-ANN model takes into account five independent engine variables (i.e., torque, power, gas generator speed, engine mass air flow and fuel flow). These dynamic variables are used as inputs of the ANN while exergy efficiencies of the components are considered as the output parameter of the network. The results show that the hybridization with the genetic algorithm has improved the accuracy even further compared to the trial-and-error case, and the estimated values of exergy efficiencies of the components obtained by the derived model provide a close fit with the reference data.

Suggested Citation

  • Baklacioglu, Tolga & Turan, Onder & Aydin, Hakan, 2015. "Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks," Energy, Elsevier, vol. 86(C), pages 709-721.
    • Handle: RePEc:eee:energy:v:86:y:2015:i:c:p:709-721
    DOI: 10.1016/j.energy.2015.04.025
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    References listed on IDEAS

    as
    1. Zhao, Jinxing & Xu, Min, 2013. "Fuel economy optimization of an Atkinson cycle engine using genetic algorithm," Applied Energy, Elsevier, vol. 105(C), pages 335-348.
    2. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    3. Porzio, Giacomo Filippo & Fornai, Barbara & Amato, Alessandro & Matarese, Nicola & Vannucci, Marco & Chiappelli, Lisa & Colla, Valentina, 2013. "Reducing the energy consumption and CO2 emissions of energy intensive industries through decision support systems – An example of application to the steel industry," Applied Energy, Elsevier, vol. 112(C), pages 818-833.
    4. Aydın, Hakan & Turan, Önder & Karakoç, T. Hikmet & Midilli, Adnan, 2013. "Exergo-sustainability indicators of a turboprop aircraft for the phases of a flight," Energy, Elsevier, vol. 58(C), pages 550-560.
    5. Buratti, C. & Barbanera, M. & Palladino, D., 2014. "An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks," Applied Energy, Elsevier, vol. 120(C), pages 125-132.
    6. Smrekar, J. & Potočnik, P. & Senegačnik, A., 2013. "Multi-step-ahead prediction of NOx emissions for a coal-based boiler," Applied Energy, Elsevier, vol. 106(C), pages 89-99.
    7. Rosen, Marc A. & Dincer, Ibrahim & Kanoglu, Mehmet, 2008. "Role of exergy in increasing efficiency and sustainability and reducing environmental impact," Energy Policy, Elsevier, vol. 36(1), pages 128-137, January.
    8. Zeng, M. & Du, L.X. & Liao, D. & Chu, W.X. & Wang, Q.W. & Luo, Y. & Sun, Y., 2012. "Investigation on pressure drop and heat transfer performances of plate-fin iron air preheater unit with experimental and Genetic Algorithm methods," Applied Energy, Elsevier, vol. 92(C), pages 725-732.
    9. Wang, Jiang-Jiang & Jing, You-Yin & Zhang, Chun-Fa, 2010. "Optimization of capacity and operation for CCHP system by genetic algorithm," Applied Energy, Elsevier, vol. 87(4), pages 1325-1335, April.
    10. Atılgan, Ramazan & Turan, Önder & Altuntaş, Önder & Aydın, Hakan & Synylo, Kateryna, 2013. "Environmental impact assessment of a turboprop engine with the aid of exergy," Energy, Elsevier, vol. 58(C), pages 664-671.
    11. Ahmadi, Pouria & Rosen, Marc A. & Dincer, Ibrahim, 2012. "Multi-objective exergy-based optimization of a polygeneration energy system using an evolutionary algorithm," Energy, Elsevier, vol. 46(1), pages 21-31.
    12. Dincer, I. & Hussain, M. M. & Al-Zaharnah, I., 2004. "Energy and exergy use in public and private sector of Saudi Arabia," Energy Policy, Elsevier, vol. 32(14), pages 1615-1624, September.
    13. Toffolo, A. & Lazzaretto, A., 2002. "Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design," Energy, Elsevier, vol. 27(6), pages 549-567.
    14. Piechocki, Janusz & Ambroziak, Dominik & Palkowski, Aleksander & Redlarski, Grzegorz, 2014. "Use of Modified Cuckoo Search algorithm in the design process of integrated power systems for modern and energy self-sufficient farms," Applied Energy, Elsevier, vol. 114(C), pages 901-908.
    15. Jeong, Kwangbok & Koo, Choongwan & Hong, Taehoon, 2014. "An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)," Energy, Elsevier, vol. 71(C), pages 71-79.
    16. Zeng, Chunlei & Wu, Changchun & Zuo, Lili & Zhang, Bin & Hu, Xingqiao, 2014. "Predicting energy consumption of multiproduct pipeline using artificial neural networks," Energy, Elsevier, vol. 66(C), pages 791-798.
    17. Marzband, Mousa & Ghadimi, Majid & Sumper, Andreas & Domínguez-García, José Luis, 2014. "Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode," Applied Energy, Elsevier, vol. 128(C), pages 164-174.
    18. Tsai, Ming-Tang & Yen, Chih-Wei, 2011. "The influence of carbon dioxide trading scheme on economic dispatch of generators," Applied Energy, Elsevier, vol. 88(12), pages 4811-4816.
    19. Turan, Onder, 2012. "Exergetic effects of some design parameters on the small turbojet engine for unmanned air vehicle applications," Energy, Elsevier, vol. 46(1), pages 51-61.
    20. Hu, Mengqi & Cho, Heejin, 2014. "A probability constrained multi-objective optimization model for CCHP system operation decision support," Applied Energy, Elsevier, vol. 116(C), pages 230-242.
    21. Tona, Cesare & Raviolo, Paolo Antonio & Pellegrini, Luiz Felipe & de Oliveira Júnior, Silvio, 2010. "Exergy and thermoeconomic analysis of a turbofan engine during a typical commercial flight," Energy, Elsevier, vol. 35(2), pages 952-959.
    22. Charkaz Aghayeva & Qurban Abushov, 2013. "The maximum principle for the nonlinear stochastic optimal control problem of switching systems," Journal of Global Optimization, Springer, vol. 56(2), pages 341-352, June.
    23. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    24. Niknam, Taher & Taheri, Seyed Iman & Aghaei, Jamshid & Tabatabaei, Sajad & Nayeripour, Majid, 2011. "A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources," Applied Energy, Elsevier, vol. 88(12), pages 4817-4830.
    25. Cho, Heejin & Mago, Pedro J. & Luck, Rogelio & Chamra, Louay M., 2009. "Evaluation of CCHP systems performance based on operational cost, primary energy consumption, and carbon dioxide emission by utilizing an optimal operation scheme," Applied Energy, Elsevier, vol. 86(12), pages 2540-2549, December.
    26. Balli, Ozgur & Hepbasli, Arif, 2014. "Exergoeconomic, sustainability and environmental damage cost analyses of T56 turboprop engine," Energy, Elsevier, vol. 64(C), pages 582-600.
    27. Yu, Shiwei & Wei, Yi-Ming & Fan, Jingli & Zhang, Xian & Wang, Ke, 2012. "Exploring the regional characteristics of inter-provincial CO2 emissions in China: An improved fuzzy clustering analysis based on particle swarm optimization," Applied Energy, Elsevier, vol. 92(C), pages 552-562.
    28. Hepbasli, Arif, 2008. "A key review on exergetic analysis and assessment of renewable energy resources for a sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(3), pages 593-661, April.
    29. Feijoo, Felipe & Das, Tapas K., 2014. "Design of Pareto optimal CO2 cap-and-trade policies for deregulated electricity networks," Applied Energy, Elsevier, vol. 119(C), pages 371-383.
    30. Piacentino, Antonio & Barbaro, Chiara & Cardona, Fabio & Gallea, Roberto & Cardona, Ennio, 2013. "A comprehensive tool for efficient design and operation of polygeneration-based energy μgrids serving a cluster of buildings. Part I: Description of the method," Applied Energy, Elsevier, vol. 111(C), pages 1204-1221.
    31. Ahmadi, Pouria & Dincer, Ibrahim & Rosen, Marc A., 2011. "Exergy, exergoeconomic and environmental analyses and evolutionary algorithm based multi-objective optimization of combined cycle power plants," Energy, Elsevier, vol. 36(10), pages 5886-5898.
    32. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    33. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S. & Kopanos, Georgios M. & Pistikopoulos, Efstratios N. & Georgiadis, Michael C., 2014. "A spatial multi-period long-term energy planning model: A case study of the Greek power system," Applied Energy, Elsevier, vol. 115(C), pages 456-482.
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