Analytical design and optimization of a new hybrid solar-driven micro gas turbine/stirling engine, based on exergo-enviro-economic concept
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- @Article{BABAELAHI:2020:SETA,
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author = "Mojtaba. Babaelahi and Hamed. Jafari",
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title = "Analytical design and optimization of a new hybrid
solar-driven micro gas turbine/stirling engine, based
on exergo-enviro-economic concept",
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journal = "Sustainable Energy Technologies and Assessments",
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volume = "42",
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pages = "100845",
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year = "2020",
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ISSN = "2213-1388",
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DOI = "
doi:10.1016/j.seta.2020.100845",
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URL = "
https://www.sciencedirect.com/science/article/pii/S2213138820312728",
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keywords = "genetic algorithms, genetic programming, Solar, Micro
gas turbine, Exergoeconomic, Environmental, Particle
swarm optimization",
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abstract = "One of the crucial problems in the power systems is
the selection of energy-efficient systems with suitable
efficiency, cost, and environmental performance.
Accordingly, this paper introduces a new power
generation system that supplies a significant part of
the required energy from solar energy and uses
liquefied natural gas (LNG) fuel as an auxiliary
source. To evaluation of the system,
exergo-enviro-economic analysis and thermohydraulic
design of are performed using Matlab code. A comparison
of the governed results with the base cycle (ThermoFlex
simulation) shows good improvement in exergy efficiency
fuel consumption. Since the preparation of an
analytical model has a practical effect on the
selection of optimum configuration, an analytical model
for objective functions is provided based on the
exergoeconomic and environmental numerical model. For
this analytical model, A large data bank from the
numerical simulation results is obtained, and the
artificial intelligence tool known as Genetic
Programming is used for multivariate fitting. Finally,
to find the optimal configuration, various
optimizations (using the particle swarm optimization)
have been made, and the final optimal design has been
selected. The results indicated that the thermal and
exergetic efficiencies in the ultimate optimum point
increased about 6.252 and 8.842 percent, respectively",
- }
Genetic Programming entries for
Mojtaba Babaelahi
Hamed Jafari
Citations
