Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters

Ugwuoke Philip Emeka

doi:doi:10.11648/j.ajee.20180604.12

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Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters

Ugwuoke Philip Emeka

Published in American Journal of Energy Engineering (Volume 6, Issue 4)

Received: 9 November 2018 Accepted: 10 December 2018 Published: 16 January 2019

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Abstract

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 (r_p), compressor isentropic efficiency (ɳ_ic), turbine isentropic efficiency (ɳ_it), turbine exhaust temperature (T₄) and air mass flow rate (m_a), fuel mass flow rate (m_f) and fuel supply temperature (T_f). 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 (r_n) = 9.76, compressor isentropic efficiency (ɳ_ic) = 86.40%, turbine isentropic efficiency (ɳ_it) = 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.

Published in	American Journal of Energy Engineering (Volume 6, Issue 4)
DOI	10.11648/j.ajee.20180604.12
Page(s)	44-49
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

Comparative Analysis, Optimizing, Genetic Algorithm, Pattern Search

References

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APA Style

Ugwuoke Philip Emeka. (2019). Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters. American Journal of Energy Engineering, 6(4), 44-49. https://doi.org/10.11648/j.ajee.20180604.12

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Ugwuoke Philip Emeka. Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters. Am. J. Energy Eng. 2019, 6(4), 44-49. doi: 10.11648/j.ajee.20180604.12

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AMA Style

Ugwuoke Philip Emeka. Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters. Am J Energy Eng. 2019;6(4):44-49. doi: 10.11648/j.ajee.20180604.12

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@article{10.11648/j.ajee.20180604.12,
  author = {Ugwuoke Philip Emeka},
  title = {Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters},
  journal = {American Journal of Energy Engineering},
  volume = {6},
  number = {4},
  pages = {44-49},
  doi = {10.11648/j.ajee.20180604.12},
  url = {https://doi.org/10.11648/j.ajee.20180604.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajee.20180604.12},
  abstract = {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.},
 year = {2019}
}

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TY  - JOUR
T1  - Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters
AU  - Ugwuoke Philip Emeka
Y1  - 2019/01/16
PY  - 2019
N1  - https://doi.org/10.11648/j.ajee.20180604.12
DO  - 10.11648/j.ajee.20180604.12
T2  - American Journal of Energy Engineering
JF  - American Journal of Energy Engineering
JO  - American Journal of Energy Engineering
SP  - 44
EP  - 49
PB  - Science Publishing Group
SN  - 2329-163X
UR  - https://doi.org/10.11648/j.ajee.20180604.12
AB  - 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.
VL  - 6
IS  - 4
ER  -

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Author Information

Ugwuoke Philip Emeka

Department of Mechanical Engineering, Petroleum Training Institute, Effurun, Nigeria

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APA Style

Ugwuoke Philip Emeka. (2019). Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters. American Journal of Energy Engineering, 6(4), 44-49. https://doi.org/10.11648/j.ajee.20180604.12

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ACS Style

Ugwuoke Philip Emeka. Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters. Am. J. Energy Eng. 2019, 6(4), 44-49. doi: 10.11648/j.ajee.20180604.12

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AMA Style

Ugwuoke Philip Emeka. Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters. Am J Energy Eng. 2019;6(4):44-49. doi: 10.11648/j.ajee.20180604.12

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@article{10.11648/j.ajee.20180604.12,
  author = {Ugwuoke Philip Emeka},
  title = {Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters},
  journal = {American Journal of Energy Engineering},
  volume = {6},
  number = {4},
  pages = {44-49},
  doi = {10.11648/j.ajee.20180604.12},
  url = {https://doi.org/10.11648/j.ajee.20180604.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajee.20180604.12},
  abstract = {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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Comparison of GA and PS Optimization Mechanisms for Optimizing 100MW Delta IV Ughelli Gas Turbine Power Plant Operating Parameters
AU  - Ugwuoke Philip Emeka
Y1  - 2019/01/16
PY  - 2019
N1  - https://doi.org/10.11648/j.ajee.20180604.12
DO  - 10.11648/j.ajee.20180604.12
T2  - American Journal of Energy Engineering
JF  - American Journal of Energy Engineering
JO  - American Journal of Energy Engineering
SP  - 44
EP  - 49
PB  - Science Publishing Group
SN  - 2329-163X
UR  - https://doi.org/10.11648/j.ajee.20180604.12
AB  - 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.
VL  - 6
IS  - 4
ER  -

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