Semnan University Press Journal of Modeling in Engineering 2008-4854 15 50 2017 09 23 Parametric analysis and optimization of the supercritical ejector refrigeration cycle with different working fluids using Artificial neural network and particle swarm optimization algorithm Parametric analysis and optimization of the supercritical ejector refrigeration cycle with different working fluids using Artificial neural network and particle swarm optimization algorithm 121 133 2556 10.22075/jme.2017.2556 FA Navid Freidoonimehr Foad Nazari Journal Article 2013 10 30 In this paper, parametric analysis and optimization of the transcritical ejector refrigeration cycle using different working fluids have been proposed which can be employed in the parts of solar energy processes. The main advantages of using ejector in the refrigeration cycles, which often use instead of the compressor, are simplicity in construction and maintenance, high reliability and low cost. In this study, the transcritical ejector refrigeration cycle is modelled using EES software and the effects of different parameters such as temperature and pressure of different parts of cycle on the coefficient of performance and entrainment ratio are investigated. In continued, the coefficient of performance of the transcritical ejector refrigeration cycle for four different working fluids is optimized using the combination the Artificial Neural Network and Particle Swarm Optimization algorithm. In this paper, parametric analysis and optimization of the transcritical ejector refrigeration cycle using different working fluids have been proposed which can be employed in the parts of solar energy processes. The main advantages of using ejector in the refrigeration cycles, which often use instead of the compressor, are simplicity in construction and maintenance, high reliability and low cost. In this study, the transcritical ejector refrigeration cycle is modelled using EES software and the effects of different parameters such as temperature and pressure of different parts of cycle on the coefficient of performance and entrainment ratio are investigated. In continued, the coefficient of performance of the transcritical ejector refrigeration cycle for four different working fluids is optimized using the combination the Artificial Neural Network and Particle Swarm Optimization algorithm. https://modelling.semnan.ac.ir/article_2556_709107f009a2c73b39c81fd0ddf76043.pdf