Determination of Unit Fuel Cost Effect on Optimal Designed Parameters of Delta IV Ughelli Gas Turbine Power Plant Unit
Science Journal of Energy Engineering
Volume 6, Issue 4, December 2018, Pages: 49-53
Received: Nov. 10, 2018; Accepted: Dec. 21, 2018; Published: Jan. 22, 2019
Views 141      Downloads 10
Author
Ugwuoke Philip Emeka, Mechanical Engineering Department, Petroleum Training Institute, Effurun, Nigeria
Article Tools
Follow on us
Abstract
The effect of variation on optimal decision variables with respect to unit cost of fuel (sensitivity analysis) for optimal performance of 100MW Delta IV ughelli gas turbine power plant unit was determined using optimal operating parameters and exergoeconomics. The optimization tool is an evolutionary algorithm known as Genetic Algorithm (GA). The computer application used in this work is written in matlab programming language. Eight optimal operating parameters of the plant were used: compressor inlet temperature (T1), compressor pressure ratio (rp), compressor isentropic efficiency (ɳic), turbine isentropic efficiency (ɳit), turbine exhaust temperature (Tt). Air mass flow rate, fuel mass flow rate and fuel supply Temperature (Tf). These decision variables were optimally adjusted by the Genetic Algorithm (GA) to minimize the objective function. The objective function representing the total operating cost of the plant defined in terms of $ per hour is the sum of operating cost (i.e fuel consumption cost rate), rate of capital cost (i.e optimal investment and maintenance expenses) and rate of exergy destruction cost. The optimal values of the decision variables were obtained by minimizing the objective function. The determined values of the optimal operating variables were rp = 9.76, ɳic = 86.4%, ɳit = 89.12%, T3 = 1,481.8K, ɳε = 29%, ɳ E = 31%, Total Cost Rate = 13292$/hr, Wt = 277.11MW, Wc = 169.63MW, air mass flow rate = 530kg/s and fuel mass flow rate = 7.00kg/s. The variation of optimal decision variables with unit cost of fuel showed that by increasing the unit fuel cost, the pressure ratio (r p), compressor isentropic efficiency (ɳic), exergy efficiency (ɳε), Energy efficiency (ɳ E), total cost rate, turbine output power (Wt) and compressor input power (Wc) increase. The increase in ɳic, ɳε, ɳE and Wt guarantees less exergy destruction in compressor and turbine as well as less net cycle fuel consumption and operating cost.
Keywords
Sensitivity Analysis, Unit Fuel Cost, Optimal Parameters, Genetic Algorithm
To cite this article
Ugwuoke Philip Emeka, Determination of Unit Fuel Cost Effect on Optimal Designed Parameters of Delta IV Ughelli Gas Turbine Power Plant Unit, Science Journal of Energy Engineering. Vol. 6, No. 4, 2018, pp. 49-53. doi: 10.11648/j.sjee.20180604.11
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Bejan, A., Tsatsaronis, G. and Moran, M. (1996). Thermal Design and Optimization. Wiley, New York.
[2]
Coley, A. D. (1999); An Introduction to Genetic Algorithims for Scientists and Engineers, 2th Edition, World Scientific Publishing Co. Pte. Ltd, Singapore, 211pp.
[3]
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.
[4]
PHCN (2015); Ughelli Power Plant Logbook, Ughelli, Delta State, Nigeria.
[5]
Obodeh, O. and Ugwuoke, P. E. (2017); Optimal Operating Parameters Of 100mw Delta Iv Ughelli Gas Turbine Power Plant Unit, in press.
[6]
Moran, M. J. and Shapiro, H. (2000). Fundamentals of Engineering Thermodynamics, 4th Edition, Wiley, New York.
[7]
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.
[8]
Emefiele, G. (2016). MPR: Banks Raise Interest Rates on Existing Loans. Punch Newspapers, July 29.
[9]
Moran, M. J. (1982). Availability Analysis; A Guide to Efficient Energy Use, USA: Prentice Hall, Englewood Cliffs, N. J.
[10]
Ebadi, M. and Gorji-Bandpy, M. (2005). Exergetic Analysis of Gas Turbine Plants. International Journal of Exergy 2 (4), 31-39.
[11]
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.
[12]
Srinivas, N. and Deb, K. (2002); Multi-Objective Optimization using Non-Dominated Sorting in Genetic Algorithms, Journal of Evolutional Computation, Vol. 2, No. 3, pp. 221-248.
[13]
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.
[14]
Almasi A, Barzegra Avval H. Ahmadi P, Najafi A. F (2011) Thermodynamic modeling, energy and exergoeconomic analysis and optimization of mahshar gas turbine power plant.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186