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Determination of Unit Fuel Cost Effect on Optimal Designed Parameters of Delta IV Ughelli Gas Turbine Power Plant Unit

Received: 10 November 2018     Accepted: 21 December 2018     Published: 22 January 2019
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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.

Published in Science Journal of Energy Engineering (Volume 6, Issue 4)
DOI 10.11648/j.sjee.20180604.11
Page(s) 49-53
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Sensitivity Analysis, Unit Fuel Cost, Optimal Parameters, Genetic Algorithm

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.
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    Ugwuoke Philip Emeka. (2019). Determination of Unit Fuel Cost Effect on Optimal Designed Parameters of Delta IV Ughelli Gas Turbine Power Plant Unit. Science Journal of Energy Engineering, 6(4), 49-53. https://doi.org/10.11648/j.sjee.20180604.11

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    Ugwuoke Philip Emeka. Determination of Unit Fuel Cost Effect on Optimal Designed Parameters of Delta IV Ughelli Gas Turbine Power Plant Unit. Sci. J. Energy Eng. 2019, 6(4), 49-53. doi: 10.11648/j.sjee.20180604.11

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    AMA Style

    Ugwuoke Philip Emeka. Determination of Unit Fuel Cost Effect on Optimal Designed Parameters of Delta IV Ughelli Gas Turbine Power Plant Unit. Sci J Energy Eng. 2019;6(4):49-53. doi: 10.11648/j.sjee.20180604.11

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  • @article{10.11648/j.sjee.20180604.11,
      author = {Ugwuoke Philip Emeka},
      title = {Determination of Unit Fuel Cost Effect on Optimal Designed Parameters of Delta IV Ughelli Gas Turbine Power Plant Unit},
      journal = {Science Journal of Energy Engineering},
      volume = {6},
      number = {4},
      pages = {49-53},
      doi = {10.11648/j.sjee.20180604.11},
      url = {https://doi.org/10.11648/j.sjee.20180604.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjee.20180604.11},
      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.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Determination of Unit Fuel Cost Effect on Optimal Designed Parameters of Delta IV Ughelli Gas Turbine Power Plant Unit
    AU  - Ugwuoke Philip Emeka
    Y1  - 2019/01/22
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    N1  - https://doi.org/10.11648/j.sjee.20180604.11
    DO  - 10.11648/j.sjee.20180604.11
    T2  - Science Journal of Energy Engineering
    JF  - Science Journal of Energy Engineering
    JO  - Science Journal of Energy Engineering
    SP  - 49
    EP  - 53
    PB  - Science Publishing Group
    SN  - 2376-8126
    UR  - https://doi.org/10.11648/j.sjee.20180604.11
    AB  - 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.
    VL  - 6
    IS  - 4
    ER  - 

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Author Information
  • Mechanical Engineering Department, Petroleum Training Institute, Effurun, Nigeria

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