Analytical design and optimization of a new hybrid solar-driven micro gas turbine/stirling engine, based on exergo-enviro-economic concept

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{BABAELAHI:2020:SETA,

• author = "Mojtaba. Babaelahi and Hamed. Jafari",
• title = "Analytical design and optimization of a new hybrid solar-driven micro gas turbine/stirling engine, based on exergo-enviro-economic concept",
• journal = "Sustainable Energy Technologies and Assessments",
• volume = "42",
• pages = "100845",
• year = "2020",
• ISSN = "2213-1388",
• DOI = "doi:10.1016/j.seta.2020.100845",
• URL = "https://www.sciencedirect.com/science/article/pii/S2213138820312728",
• keywords = "genetic algorithms, genetic programming, Solar, Micro gas turbine, Exergoeconomic, Environmental, Particle swarm optimization",
• 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