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.
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References
Lazzaretto, A., Toffolo, A.: Energy, economy and environment as objectives in multi-criterion optimization of thermal systems design. Energy 29, 1139–1157 (2004)
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)
Verda, V., Borchiellini, R.: Exergy method for the diagnosis of energy systems using measured data. Energy 32, 490–498 (2007)
Zhang, C., Chen, S., Zheng, C., Lou, X.: Thermoeconomic diagnosis of a coal fired power plant. Energy Conversion and Management 48, 405–419 (2007)
Dincer, I., Rosen, M.A.: Exergy: Energy, Environment, and Sustainable Development. Elsevier Ltd. (2007)
Cybenko, G.: Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems (MCSS) 2, 303–314 (1989)
Funahashi, K.-I.: On the Approximate Realization of Continuous Mappings by Neural Networks. Neural Networks 2, 183–192 (1989)
Hornik, K., Stinchcombe, M., White, H.: Multilayer Feedforward Networks are Universal Approximators. Neural Networks 2, 359–366 (1989)
Nelles, O.: Nonlinear System Identification. Springer, Heideberg (2001)
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)
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)
Babuska, R.: Fuzzy Modeling and Identification. The Netherlands, Deft University of Technology (1996)
Korbicz, J., Koscielny, J.M., Kowalczuk, Z.: Fault diagnosis: models, artificial intelligence, applications. Springer (2004)
Gandomi, A., Yang, X.-S., Alavi, A.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 1–19 (2011)
Hashimoto, Y., Murase, H., Morimoto, T., Torii, T.: Intelligent systems for agriculture in Japan. IEEE Control Systems 21, 71–85 (2001)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems 22, 52–67 (2002)
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)
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)
Chu, S.-C., Tsai, P.-W.: Computational Intelligence based on the behavior of cats. International Journal of Innovative Computing, Information and Control 3 (2007)
Simon, D.: Biogeography-Based Optimization. IEEE Transactions on Evolutionary Computation 12, 702–713 (2008)
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)
Krishnanand, K.N., Ghose, D.: Glowwarm Swarm Optimization: A New Method for Optimizing Multi-Modal Functions. International Journal of Computational Intelligence Studies 1 (2009)
Dorigo, M., Stutzle, T.: Anty Colony Optimization: Massachusetts Institute of Technology (2004)
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)
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)
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)
Erol, O.K., Eksin, I.: A new optimization method: Big Bang-Big Crunch. Advances in Engineering Software 37, 106–111 (2006)
Kaveh, A., Talatahari, S.: A novel heuristic optimization method: charged system search. Acta Mechanica 213, 267–289 (2010)
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)
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)
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)
Alatas, B.: ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization. Expert Systems with Applications 38, 13170–13180 (2011)
Boyce, M.P.: Gas Turbine Engineering Handbook. Gulf Professional Publishing (2006)
Walsh, P.P., Fletcher, P.: Gas Turbine Performance. Blackwell Science Ltd. (2004)
Kakimoto, N., Baba, K.: Performance of gas turbine-based plants during frequency drops. IEEE Transactions on Power Systems (2003)
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)
Kurzke, J.: GasTurb 9–A Program to Calculate Design and Off-Design Performance of Gas Turbines, Germany (2001), http://www.gasturb.de
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)
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)
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)
Dixon, S.L.: Fluid Mechanics, Thermodynamics of Turbomachinery. Elsevier Butterworth-Heinemann (2005)
Razak, A.M.Y.: Industrial Gas Turbines Performance and Operability. Woodhead Publishing Limited and CRC Press, LLC (2007)
Lefebvre, A.H., Ballal, D.R.: Gas Turbine Combustion: Alternation Fuels and Emissions. CRC Press, Taylor and Francis Group (2010)
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)
Tournier, J.M., El-Genk, M.S.: Axial flow, multi-stage turbine and compressor models. Energy Conversion and Management 51, 16–29 (2010)
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)
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)
Johnsen, I.A., Bullock, R.O.: Aerodynamic design of axial-flow compressors. NASA SP–36 (1965)
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)
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)
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)
Haglind, F.: Variable Geometry Gas Turbines for Improving the Part-Load Performance of Marine Combined Cycles - Gas Turbine Performance. Energy 31, 467–476 (2010)
Saravanamutto, H.I.H., Rogers, G.F.C., Cohen, H.: Gas Turbine Theory. Longman Group Limited (1996)
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)
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)
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)
Yang, X.S.: Bat algorithm for multi-objective optimisation. International Journal of Bio-Inspired Computation 3 (2011)
Khan, K., Sahai, A.: A Levy-flight Neuro-biosonar Algorithm for Improving the Design of eCommerce Systems. Journal of Artificial Intelligence 4 (2011)
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)
Converse, G.L., Giffin, R.G.: Extended Parametric Representation of Compressor Fans and Turbines. CMGEN User’s Manual, NASA–CR–174645, vol. 1 (1984)
Yeh, W.C., Hsieh, T.J.: Solving reliability redundancy allocation problems using an artificial bee colony algorithm. Computers & Operations Research 38, 1465–1473 (2011)
Khan, Z., Prasad, B., Singh, T.: Machining condition optimization by genetic algorithms and simulated annealing. Computers & Operations Research 24, 647–657 (1997)
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)
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Tamiru, A.L., Hashim, F.M. (2013). Application of Bat Algorithm and Fuzzy Systems to Model Exergy Changes in a Gas Turbine. In: Yang, XS. (eds) Artificial Intelligence, Evolutionary Computing and Metaheuristics. Studies in Computational Intelligence, vol 427. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29694-9_26
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DOI: https://doi.org/10.1007/978-3-642-29694-9_26
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