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Exergy analysis and thermodynamic optimisation of a steam power plant-based Rankine cycle system using intelligent optimisation algorithms

Elahifar, S ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1080/14484846.2019.1661807
  3. Publisher: Taylor and Francis Ltd , 2019
  4. Abstract:
  5. In this paper, exergy analysis of a steam power plant located in southern Iran named Zarand power plant has been studied. In order to optimize the performance of the Rankine cycle and achieve higher exergy efficiency, several parameters have been considered as decision variables. Knowing that there is the ability to change some of the parameters in the specific range in the process of electricity production in power plant, temperature and output pressure of the boiler and output pressure of four steps of extraction turbine have been selected as six decision variables. Also, exergy efficiency has been considered as the objective function. For this purpose, the exergy efficiency of the system is optimized using intelligent algorithms including bees, fireflies, and algorithm based on teaching and learning and they are compared with each other. The results show that in the case of suitable changes of decision variables and applying appropriate algorithms, exergy efficiency of the studied thermal power plant can be increased from 30.1% to 30.68047%, 30.70368%, and 30.70369%, respectively. It means using optimization algorithms of bees, fireflies, training-learning, exergy efficiency of the Rankine cycle of the studied power plant can be increased by 0.58047%, 0.60368%, and 0.60369%, respectively. © 2019, © 2019 Engineers Australia
  6. Keywords:
  7. Exergy efficiency ; Rankine cycle ; Thermal power plant ; Bioluminescence ; Decision making ; Efficiency ; Exergy ; Learning algorithms ; Optimization ; Rankine cycle ; Thermoelectric power plants ; Decision variables ; Electricity production ; Exergy efficiencies ; Intelligent Algorithms ; Objective functions ; Optimization algorithms ; Teaching and learning ; Thermal power plants ; Steam power plants
  8. Source: Australian Journal of Mechanical Engineering ; 2019 ; 14484846 (ISSN)
  9. URL: https://www.tandfonline.com/doi/abs/10.1080/14484846.2019.1661807