Application of Bat Algorithm and Fuzzy Systems to Model Exergy Changes in a Gas Turbine

  • A. L. Tamiru
  • F. M. Hashim
Part of the Studies in Computational Intelligence book series (SCI, volume 427)

Abstract

Exergy analysis plays a major role in thermal systems. Using exergy, apart from finding components for a potential for further improvement, fault detection and diagnosis, performance optimization, and environmental impact assessment can be conducted. This chapter addresses the use of fuzzy systems for modeling exergy destructions in the main components of an industrial gas turbine. The details include: (i) system description and the challenges in developing first principle models, (ii) thermodynamic models for part load and full load operating conditions, (iii) model identification technique that uses fuzzy systems and a meta-heuristic nature inspired algorithm called Bat Algorithm, (iv) validation graphs for semi-empirical models, and (v) validation test for fuzzy models. In the validation of the fuzzy model, the inputs to the model are considered the same as the inputs as experienced by the gas turbine generator. The comparison tests between actual data and prediction demonstrate how promising the combined method is as compared to separate use of the fuzzy systems trained by a heuristic approach.

Keywords

Root Mean Square Error Fuzzy System Exergy Analysis Exergy Destruction Heat Recovery Steam Generator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Lazzaretto, A., Toffolo, A.: Energy, economy and environment as objectives in multi-criterion optimization of thermal systems design. Energy 29, 1139–1157 (2004)CrossRefGoogle Scholar
  2. 2.
    Sayyaadi, M.B., Farmani, M.R.: Implementing of the multi-objective particle swarm optimizer and fuzzy decision-maker in exergetic, exergoeconomic and environmental optimization of a benchmark cogeneration system. Energy 36, 4774–4789 (2011)CrossRefGoogle Scholar
  3. 3.
    Verda, V., Borchiellini, R.: Exergy method for the diagnosis of energy systems using measured data. Energy 32, 490–498 (2007)CrossRefGoogle Scholar
  4. 4.
    Zhang, C., Chen, S., Zheng, C., Lou, X.: Thermoeconomic diagnosis of a coal fired power plant. Energy Conversion and Management 48, 405–419 (2007)CrossRefGoogle Scholar
  5. 5.
    Dincer, I., Rosen, M.A.: Exergy: Energy, Environment, and Sustainable Development. Elsevier Ltd. (2007)Google Scholar
  6. 6.
    Cybenko, G.: Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems (MCSS) 2, 303–314 (1989)MathSciNetMATHGoogle Scholar
  7. 7.
    Funahashi, K.-I.: On the Approximate Realization of Continuous Mappings by Neural Networks. Neural Networks 2, 183–192 (1989)CrossRefGoogle Scholar
  8. 8.
    Hornik, K., Stinchcombe, M., White, H.: Multilayer Feedforward Networks are Universal Approximators. Neural Networks 2, 359–366 (1989)CrossRefGoogle Scholar
  9. 9.
    Nelles, O.: Nonlinear System Identification. Springer, Heideberg (2001)MATHGoogle Scholar
  10. 10.
    Das, S.K., Nanda, P.: Use of artificial neural network and leveque analogy for the exergy analysis of regenerator beds. Chemical Engineering and Processing 39, 113–120 (2000)CrossRefGoogle Scholar
  11. 11.
    Yoru, Y., Karakoc, T.H., Hepbasli, A.: Exergy analysis of a cogeneration system through Artificial Neural Network (ANN) method. International Journal of Energy 7, 178–192 (2010)Google Scholar
  12. 12.
    Babuska, R.: Fuzzy Modeling and Identification. The Netherlands, Deft University of Technology (1996)Google Scholar
  13. 13.
    Korbicz, J., Koscielny, J.M., Kowalczuk, Z.: Fault diagnosis: models, artificial intelligence, applications. Springer (2004)Google Scholar
  14. 14.
    Gandomi, A., Yang, X.-S., Alavi, A.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 1–19 (2011)Google Scholar
  15. 15.
    Hashimoto, Y., Murase, H., Morimoto, T., Torii, T.: Intelligent systems for agriculture in Japan. IEEE Control Systems 21, 71–85 (2001)CrossRefGoogle Scholar
  16. 16.
    Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems 22, 52–67 (2002)CrossRefGoogle Scholar
  17. 17.
    Yang, X.-S.: Firefly Algorithms for Multimodal Optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  18. 18.
    Yang, X.-S., Deb, S.: Cuckoo Search via Levy Flights. In: Proc. of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), India, pp. 210–214 (2009)Google Scholar
  19. 19.
    Chu, S.-C., Tsai, P.-W.: Computational Intelligence based on the behavior of cats. International Journal of Innovative Computing, Information and Control 3 (2007)Google Scholar
  20. 20.
    Simon, D.: Biogeography-Based Optimization. IEEE Transactions on Evolutionary Computation 12, 702–713 (2008)CrossRefGoogle Scholar
  21. 21.
    Yang, X.-S.: A New Metaheuristic Bat-Inspired Algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  22. 22.
    Krishnanand, K.N., Ghose, D.: Glowwarm Swarm Optimization: A New Method for Optimizing Multi-Modal Functions. International Journal of Computational Intelligence Studies 1 (2009)Google Scholar
  23. 23.
    Dorigo, M., Stutzle, T.: Anty Colony Optimization: Massachusetts Institute of Technology (2004)Google Scholar
  24. 24.
    Hackel, S., Dippold, P.: The bee colony-inspired algorithm (BCiA): a two-stage approach for solving the vehicle routing problem with time windows. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation. ACM, Montreal (2009)Google Scholar
  25. 25.
    Mucherino, A., Seref, O.: Monkey Search: A Novel Meta-Heuristic Search for Global Optimization. In: AIP Conference Proceedings 953, Data Mining, System Analysis and Optimization in Biomedicine, pp. 162–173 (2007)Google Scholar
  26. 26.
    Oftadeh, R., Mahjoob, M.J., Shariatpanahi, M.: A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search. Computers & Mathematics with Applications 60, 2087–2098 (2010)MATHCrossRefGoogle Scholar
  27. 27.
    Erol, O.K., Eksin, I.: A new optimization method: Big Bang-Big Crunch. Advances in Engineering Software 37, 106–111 (2006)CrossRefGoogle Scholar
  28. 28.
    Kaveh, A., Talatahari, S.: A novel heuristic optimization method: charged system search. Acta Mechanica 213, 267–289 (2010)MATHCrossRefGoogle Scholar
  29. 29.
    Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In: IEEE Congress on Evolutionary Computation, pp. 4661–4667 (2007)Google Scholar
  30. 30.
    Shah-Hosseini, H.: Intelligent water drops algorithm: A new optimization method for solving the multiple knapsack problem. International Journal of Intelligent Computing and Cybernetics 1, 193–212 (2008)MathSciNetMATHCrossRefGoogle Scholar
  31. 31.
    Yamamoto, L.: Evaluation of a Catalytic Search Algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 75–87. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  32. 32.
    Alatas, B.: ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization. Expert Systems with Applications 38, 13170–13180 (2011)CrossRefGoogle Scholar
  33. 33.
    Boyce, M.P.: Gas Turbine Engineering Handbook. Gulf Professional Publishing (2006)Google Scholar
  34. 34.
    Walsh, P.P., Fletcher, P.: Gas Turbine Performance. Blackwell Science Ltd. (2004)Google Scholar
  35. 35.
    Kakimoto, N., Baba, K.: Performance of gas turbine-based plants during frequency drops. IEEE Transactions on Power Systems (2003)Google Scholar
  36. 36.
    Verda, V.: Thermoeconomic Diagnosis of an Urban District Heating System based on Cogeneration Steam and Gas Turbines. PhD Dissertation. Dipartmento Di Energetica, Politecnico Di Torino (2001)Google Scholar
  37. 37.
    Kurzke, J.: GasTurb 9–A Program to Calculate Design and Off-Design Performance of Gas Turbines, Germany (2001), http://www.gasturb.de
  38. 38.
    Kim, T.S., Hwang, S.H.: Part load performance analysis of recuperated gas turbines considering engine configuration and operation strategy. Energy 31, 260–277 (2006)CrossRefGoogle Scholar
  39. 39.
    Celis, C., Pinto, P.d.M.R., Barbosa, R.S., Ferreira, S.B.: Modeling of Variable Inlet Guide Vanes Affects on a One Shaft Industrial Gas Turbine Used in a Combined Cycle Application. In: ASME Conference Proceedings, vol. 2, pp. 1–6 (2008)Google Scholar
  40. 40.
    Muir, D.E., Saravanamuttoo, H.I.H., Marshall, D.J.: Health Monitoring of Variable Geometry Gas Turbines for the Canadian Navy. Journal of Engineering for Gas Turbines and Power 111, 244–250 (1989)CrossRefGoogle Scholar
  41. 41.
    Dixon, S.L.: Fluid Mechanics, Thermodynamics of Turbomachinery. Elsevier Butterworth-Heinemann (2005)Google Scholar
  42. 42.
    Razak, A.M.Y.: Industrial Gas Turbines Performance and Operability. Woodhead Publishing Limited and CRC Press, LLC (2007)CrossRefGoogle Scholar
  43. 43.
    Lefebvre, A.H., Ballal, D.R.: Gas Turbine Combustion: Alternation Fuels and Emissions. CRC Press, Taylor and Francis Group (2010)Google Scholar
  44. 44.
    Ainley, D.G., Mathieson, G.C.R.: A Method of Performance Estimation for Axial-Flow Turbines. British Aeronautical Research Council, Reports and Memoranda No. 2974 (1951)Google Scholar
  45. 45.
    Tournier, J.M., El-Genk, M.S.: Axial flow, multi-stage turbine and compressor models. Energy Conversion and Management 51, 16–29 (2010)CrossRefGoogle Scholar
  46. 46.
    Ordys, A.W., Pike, A.W., Johnson, M.A., Katebi, R.M., Grimble, M.J.: Modelling and Simulation of Power Generation Plant. Springer, London (1994)CrossRefGoogle Scholar
  47. 47.
    Sellers, J.F., Daniele, C.J.: DYNGEN: A program for calculating steady-state and transient performance of turbojet and turbofan engines. NASA–TN–D–7901 (1975)Google Scholar
  48. 48.
    Johnsen, I.A., Bullock, R.O.: Aerodynamic design of axial-flow compressors. NASA SP–36 (1965)Google Scholar
  49. 49.
    Tamiru, A.L., Hashim, F.M., Rangkuti, C.: Generating Gas Turbine Component Maps Relying on Partially Known Overall System Characteristics. Journal of Applied Sciences 11, 1885–1894 (2011)CrossRefGoogle Scholar
  50. 50.
    Kong, C., Ki, J., Kang, M.: A New Scaling Method for Component Maps of Gas Turbine Using System Identification. Journal of Engineering for Gas Turbines and Power 125, 979–985 (2003)CrossRefGoogle Scholar
  51. 51.
    Kong, C., Ki, J.: Components Map Generation of Gas Turbine Engine Using Genetic Algorithms and Engine Performance Deck Data. Journal of Engineering for Gas Turbines and Power 129, 312–317 (2007)CrossRefGoogle Scholar
  52. 52.
    Haglind, F.: Variable Geometry Gas Turbines for Improving the Part-Load Performance of Marine Combined Cycles - Gas Turbine Performance. Energy 31, 467–476 (2010)Google Scholar
  53. 53.
    Saravanamutto, H.I.H., Rogers, G.F.C., Cohen, H.: Gas Turbine Theory. Longman Group Limited (1996)Google Scholar
  54. 54.
    Kim, J.H., Kim, T.S., Sohn, J.L., Ro, S.T.: Comparative Analysis of Off-Design Performance Characteristics of Single and Two-Shaft Industrial Gas Turbines. Journal of Engineering for Gas Turbines and Power 125, 954–960 (2003)CrossRefGoogle Scholar
  55. 55.
    Lee, J.J., Kang, D.W., Kim, T.S.: Development of a gas turbine performance analysis program and its application. Energy 36, 5274–5285 (2011)CrossRefGoogle Scholar
  56. 56.
    Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Printice Hall (1997)Google Scholar
  57. 57.
    Yang, X.S.: Bat algorithm for multi-objective optimisation. International Journal of Bio-Inspired Computation 3 (2011)Google Scholar
  58. 58.
    Khan, K., Sahai, A.: A Levy-flight Neuro-biosonar Algorithm for Improving the Design of eCommerce Systems. Journal of Artificial Intelligence 4 (2011)Google Scholar
  59. 59.
    Tsai, P.W., Pan, J.S., Liao, B.Y., Tsai, M.J., Istanda, V.: Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems. Applied Mechanics and Materials 148, 134–137 (2011)CrossRefGoogle Scholar
  60. 60.
    Converse, G.L., Giffin, R.G.: Extended Parametric Representation of Compressor Fans and Turbines. CMGEN User’s Manual, NASA–CR–174645, vol. 1 (1984)Google Scholar
  61. 61.
    Yeh, W.C., Hsieh, T.J.: Solving reliability redundancy allocation problems using an artificial bee colony algorithm. Computers & Operations Research 38, 1465–1473 (2011)MathSciNetCrossRefGoogle Scholar
  62. 62.
    Khan, Z., Prasad, B., Singh, T.: Machining condition optimization by genetic algorithms and simulated annealing. Computers & Operations Research 24, 647–657 (1997)MATHCrossRefGoogle Scholar
  63. 63.
    Biswas, S., Mahapatra, S.: Modified particle swarm optimization for solving machine-loading problems in flexible manufacturing systems. The International Journal of Advanced Manufacturing Technology 39, 931–942 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • A. L. Tamiru
    • 1
  • F. M. Hashim
    • 1
  1. 1.Mechanical Engineering DepartmentUniversiti Teknologi PETRONASTronohMalaysia

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