**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**30127

##### Type–2 Fuzzy Programming for Optimizing the Heat Rate of an Industrial Gas Turbine via Absorption Chiller Technology

**Authors:**
T. Ganesan,
M. S. Aris,
I. Elamvazuthi,
Momen Kamal Tageldeen

**Abstract:**

**Keywords:**
Absorption chillers,
turbine inlet air cooling,
power purchase agreement,
multiobjective optimization,
type-2 fuzzy programming,
chaotic differential evolution.

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1129171

**References:**

[1] 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.

[2] 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.

[3] 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.

[4] 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.

[5] 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.

[6] 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.

[7] 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.

[8] 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.

[9] 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.

[10] 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.

[11] 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.

[12] 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.

[13] 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.

[14] 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.

[15] Mendel, J., and John, R., ‘Type-2 fuzzy sets made simple’, IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, (2002), pp. 117–127.

[16] 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.

[17] 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.

[18] Greenfield, S., John, R., and Coupland, S., ‘A novel sampling method for type-2 defuzzification’, in Proc. UKCI 2005, Sep., pp. 120–127.

[19] 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.

[20] 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.

[21] Rommelfanger, H., ‘Interactive decision making in fuzzy linear optimization problems’, European Journal of Operational Research, 41, 2, 1989, pp. 210–217.

[22] 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.

[23] Klir, G.J. and Yuan, B., Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, Upper Saddle River, NJ, 1995.

[24] 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.

[25] 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.

[26] 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.

[27] 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.

[28] 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.