Exergy analysis and optimization of Rankine cycle in steam power plants using Bees Algorithm
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Abstract: (1073 Views) |
In this study, the exergy analysis of a steam power plant generating electricity with Rankine thermodynamic cycle is studied. Exergy efficiency is a suitable criterion for the analysis of a thermodynamic cycle. To optimize the processes and obtaining higher exergy efficiency, some parameters were considered as decision variables, and by changing these parameters, it was tried to improve the exergy efficiencyOutput temperature and pressure of the boiler, and the output pressures of the four stages of turbine extraction, were selected as six decision variables. In other words, the exergy efficiency function was considered as the objective function, and the six aforementioned variables were considered as decision variables. Optimization algorithm using the behavior of bees, which is one of the relatively new intelligent algorithms for optimization problems, performs the optimization by inspiring from the behavior and action of bees to find food. In this study, firstly, the optimization of exergy efficiency function was done for the steam power plant, and then the results were compared with the results obtained using the genetic and particle swarm optimization algorithms. Results showed that by appropriate changes in decision variables and using bees algorithm, exergy efficiency of the thermal power plant increased from 30.1% to 30.6675%. This increase was equivalent to 0.6675% for the cycle, and compared to the use of genetic and swarm particle optimization algorithms it was 0.0038% and 0.0036% higher, respectively.
Keywords: Exergy efficiency; Bees algorithm; Rankine cycle; thermal power plant |
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Keywords: Exergy efficiency, Bees algorithm, Rankine cycle, thermal power plant |
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Full-Text [PDF 1095 kb]
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Type of Study: Research |
Subject:
سایر (برق، عمران، معماری، هیدرولیک، ماشین آلات کشاورزی، ساخت و تولید، متالوژی، ریاضی، فیزیک، شیمی و ...) Received: 2019/05/5 | Accepted: 2019/06/5 | Published: 2019/06/16
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