Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters
American Journal of Energy Engineering
Volume 6, Issue 4, December 2018, Pages: 44-49
Received: Nov. 9, 2018; Accepted: Dec. 10, 2018; Published: Jan. 16, 2019
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Ugwuoke Philip Emeka, Department of Mechanical Engineering, Petroleum Training Institute, Effurun, Nigeria
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A comparative analysis for improving the efficiency of 100MW Delta IV Ughelli gas turbine power plant is performed. The study used non-dominated sorting genetic and pattern search algorithms to minimize the objective function by optimally adjusting the operating parameters (decision variables). The adjusted operating variables were compressor inlet temperature (T1), compressor pressure ratio (rp), compressor isentropic efficiency (ɳic), turbine isentropic efficiency (ɳit), turbine exhaust temperature (T4) and air mass flow rate (ma), fuel mass flow rate (mf) and fuel supply temperature (Tf). The ambient temperature and pressure were held constant at 304K and 1.01325bar respectively because of location limitation. The optimization code was written in Matlab programming language. The decision variables (constraints) were obtained randomly within the admission range. The GA and PS optimal values of the decision variables were obtained by minimizing the objective function. The determined GA and PS optimum operating variables have the same values which were compressor pressure ratio (rn) = 9.76, compressor isentropic efficiency (ɳic) = 86.40%, turbine isentropic efficiency (ɳit) = 89.12%, combustion chamber outlet temperature (T3) = 1481.8K, air mass flow rate = 530kg/s, fuel mass flow rate = 7.00kg/s. The total exergy destruction cost rate (D) for PS and GAvaries by +0.00004% and the total investment cost rate for PS and GAvaries by +0.00038%. The results show that there is slight increase in total exergy destruction cost rate and total capital investment cost rate in PS optimum when compared to GA optimum. This shows that GA is better than PS as an optimization algorithm.
Comparative Analysis, Optimizing, Genetic Algorithm, Pattern Search
To cite this article
Ugwuoke Philip Emeka, Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters, American Journal of Energy Engineering. Vol. 6, No. 4, 2018, pp. 44-49. doi: 10.11648/j.ajee.20180604.12
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Bejan, A., Tsatsaronis, G. and Moran, M. (1996).Thermal Design and Optimization.Wiley, New York.
Coley, A. D. (1999); An Introduction to Genetic Algorithms for Scientists and Engineers, 2nd Edition, World Scientific Publishing Co. Pte. Ltd, Singapore, 211pp.
Malhotra, R.; Singh, N. and Singh, Y. (2011); Genetic Algorithms: Concepts, Design for Optimization of Process Controllers, Computer and Information Science, Vol. 4, No. 2, pp. 39-54.
Al-Sumait, J.S. Al-Othman A.K., Sykulski J.K. (2007). Application of Pattern Search Method to Power System Valve-Point Economic Load Dispatch. Journal of Electrical Power and Energy Systems. Pp 720-730.
PHCN (2015); Ughelli Power Plant Logbook, Ughelli, Delta State, Nigeria.
Obodeh, O. and Ugwuoke, P.E. (2017); Optimal Operating Parameters Of 100mw Delta IvUghelli Gas Turbine Power Plant Unit, in press.
Moran, M. J. and Shapiro, H. (2000). Fundamentals of Engineering Thermodynamics, 4th Edition, Wiley, New York.
Khosravi, A.; Gorji-Bandpy, M. and Fazelpour, F. (2014); Optimization of a Gas Turbine Cycle by Genetic and PSO Algorithms, Journal of Middle East Applied Science and Technology (JMEAST), Issue 21, pp. 706-711.
Emefiele, G. (2016). MPR: Banks Raise Interest Rates on Existing Loans. Punch Newspapers, July 29.
Moran, M. J. (1982). Availability Analysis; A Guide to Efficient Energy Use, USA: Prentice Hall, Englewood Cliffs, N.J.
Ebadi, M. and Gorji-Bandpy, M. (2005). Exergetic Analysis of Gas Turbine Plants. International Journal of Exergy 2 (4), 31-39.
Gorji-Bandpy, M. and Goodarzian, H. (2011). Exergoeconomic Optimization of Gas Turbine Power Plant Operating Parameters Using Genetic Algorithm: A Case Study. J Thermal Science, 15, 43-54.
Kumar, M. and Guria, C. (2017). The elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and its jumping gene adaptations for multi-objective optimization. Information Science, vol. 382-383, pp. 15-37.
Goldberg DE. Genetic algorithms insearch,optimization, and machine learning. Reading, Mass, Harlow:Addison-Wesley;1989.
MichalewiczZ.Geneticalgorithms+ datastructures= evolution programs. 3rd ed. Berlin, New York:Springer-Verlag;1996.
Jomison Janawitz, James Masso and Christopher Childs (2015).Heavy-Duty Gas Turbine Operating and Maintenance Consideration Ger 3620M.GE Power and Water, Atlanta, Georgia, February.
Ogbe, P. O.; Anosike, N. B. and Okonkwo, U. C. (2 Type equation here.016); Exergoeconomic Evaluation of Transcorp Power Plant Ughelli, International Research Journal of Engineering and Technology, Vol. 3, Issue 11, pp. 36-44.
Igbong, D. I. and Fakorede, D. O. (2014); Exergoeconomic Analysis of 100 MW Unit GE Frame 9 Gas Turbine Plant at Ughelli, Nigeria, International Journal of Engineering and Technology, Vol. 4, No. 8, pp. 463-468.
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