Commenced in January 2007
Paper Count: 30127
Type–2 Fuzzy Programming for Optimizing the Heat Rate of an Industrial Gas Turbine via Absorption Chiller Technology
Abstract:Terms set in power purchase agreements (PPA) challenge power utility companies in balancing between the returns (from maximizing power production) and securing long term supply contracts at capped production. The production limitation set in the PPA has driven efforts to maximize profits through efficient and economic power production. In this paper, a combined industrial-scale gas turbine (GT) - absorption chiller (AC) system is considered to cool the GT air intake for reducing the plant’s heat rate (HR). This GT-AC system is optimized while considering power output limitations imposed by the PPA. In addition, the proposed formulation accounts for uncertainties in the ambient temperature using Type-2 fuzzy programming. Using the enhanced chaotic differential evolution (CEDE), the Pareto frontier was constructed and the optimization results are analyzed in detail.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129171Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 512
 Wu, O.Q. and Babich, V., 2012. Unit-contingent power purchase agreement and asymmetric information about plant outage. Manufacturing & Service Operations Management, 14(2), pp.245-261.
 Cory, K., Canavan, B. and Koenig, R., 2009. Power Purchase Agreement Checklist for State and Local Governments (No. NREL/FS-6A2-46668). National Renewable Energy Laboratory (NREL), Golden, CO.
 Najjar, Y.S. and Balawneh, I.A., 2015. Optimization of gas turbines for sustainable turbojet propulsion. Propulsion and Power Research, 4(2), pp.114-121.
 Silva, V.V., Khatib, W. and Fleming, P.J., 2005. Performance optimization of gas turbine engine. Engineering Applications of Artificial Intelligence, 18(5), pp.575-583.
 Mohammadi, E., Montazeri-Gh, M. and Khalaf, P., 2014. Metaheuristic Design and Optimization of Fuzzy-Based Gas Turbine Engine Fuel Controller Using Hybrid Invasive Weed Optimization/Particle Swarm Optimization Algorithm. Journal of Engineering for Gas Turbines and Power, 136(3), p.031601.
 Ganesan, T., Elamvazuthi, I., Zilati Ku Shaari, K. and Vasant, P., 2014. Hopfield differential evolution for multi-objective optimization of a cement-bonded sand mould system. International Journal of Management Science and Engineering Management, 9(1), pp.40-47.
 Ganesan, T., Elamvazuthi, I. and Vasant, P., 2015a. Multiobjective design optimization of a nano-CMOS voltage-controlled oscillator using game theoretic-differential evolution. Applied Soft Computing, 32, pp.293-299.
 Ganesan, T., Vasant, P. and Elamvazuthi, I., 2016. Multiobjective optimization using particle swarm optimization with non-Gaussian random generators. Intelligent Decision Technologies, 10(2), pp.93-103.
 García-Revillo, F.J., Jimenez-Octavio, J.R., Sanchez-Rebollo, C. and Cantizano, A., 2014. Efficient multi-objective optimization for gas turbine discs. In Design and Computation of Modern Engineering Materials (pp. 227-255). Springer International Publishing.
 Yazdi, B.A., Yazdi, B.A., Ehyaei, M.A. and Ahmadi, A., 2015. Optimization of Micro Combined Heat and Power Gas Turbine by Genetic Algorithm. Thermal Science, 19(1), pp.207-218.
 Gong, W., Cai, Z. and Liang, D., 2014. Engineering optimization by means of an improved constrained differential evolution. Computer Methods in Applied Mechanics and Engineering, 268, pp.884-904.
 Zitzler, E., Brockhoff, D. and Thiele, L., 2007, March. The hypervolume indicator revisited: On the design of Pareto-compliant indicators via weighted integration. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 862-876). Springer Berlin Heidelberg.
 Ganesan, T., Elamvazuthi, I., Shaari, K.Z.K. and Vasant, P., 2013. Multiobjective optimization of green sand mould system using chaotic differential evolution. In Transactions on Computational Science XXI (pp. 145-163). Springer Berlin Heidelberg.
 Fayek, H.M., Elamvazuthi, I., Perumal, N. and Venkatesh, B., 2014. A controller based on optimal type-2 fuzzy logic: systematic design, optimization and real-time implementation. ISA transactions, 53(5), pp.1583-1591.
 Mendel, J., and John, R., ‘Type-2 fuzzy sets made simple’, IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, (2002), pp. 117–127.
 Lucas, L., Centeno, T., and Delgado, M., ‘General type-2 fuzzy inference systems: Analysis, design and computational aspects’, in Proceedings of IEEE International Conference of Fuzzy Systems, London, U.K., (2007), pp. 1107–1112.
 Hamrawi, H. and Coupland, S. (2009) Type-2 fuzzy arithmetic using Alpha-planes. Proceedings of the Joint 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology conference, pp. 606-611.
 Greenfield, S., John, R., and Coupland, S., ‘A novel sampling method for type-2 defuzzification’, in Proc. UKCI 2005, Sep., pp. 120–127.
 Rao, D.H. and Saraf, S.S., Study of defuzzification methods of fuzzy logic controller for speed control of a DC motor, Proceedings of the 1996 International Conference on Power Electronics, Drives and Energy Systems for Industrial Growth, 1996, Vol 2, pp 782 - 787.
 Weather Underground (WE), (2016), https://www.wunderground.com/history/airport/WMKK/2013/6/14/MonthlyHistory.html?&reqdb.zip=&reqdb.magic=&reqdb.wmo Accessed on 20th August 2015.
 Rommelfanger, H., ‘Interactive decision making in fuzzy linear optimization problems’, European Journal of Operational Research, 41, 2, 1989, pp. 210–217.
 Mo, H., Wang, F.Y., Zhou, M., Li, R. and Xiao, Z., 2014, Footprint of uncertainty for type-2 fuzzy sets, Information Sciences, Vol. 272, pp 96–110.
 Klir, G.J. and Yuan, B., Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, Upper Saddle River, NJ, 1995.
 Storn, R. and Price, K. V., ‘Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces’, ICSI, Technical Report TR-95-012.
 Flake, G.W., ‘The computational beauty of nature: Computer explorations of fractals, chaos, complex systems, and adaptation’, MIT Press, Cambridge, Massachusetts, pp 469-482, 1998.
 Liu, Y., Passino, K. M., and Simaan, M. A., ‘Biomimicry of social foraging bacteria for distributed optimization: Models, principles, and emergent behaviors’, Journal of Optimization Theory and Applications, Vol. 115, No. 3, pp. 603–628, 2002.
 Yildiz, A.R., Cuckoo search algorithm for the selection of optimal machining parameters in milling operations, International Journal of Advanced Manufacturing Technology, 2013, Vol 64 (1-4), pp 55-61.
 Fidanova, S., Paprzycki, M. and Roeva, O., 2014, September. Hybrid GA-ACO algorithm for a model parameters identification problem. In Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on (pp. 413-420). IEEE.