The current study objective is to optimize the integration of a solar ejector cycle with cold storage by applying a multi-objective modified particle swarm optimization technique. Various collector and working fluid\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s effects are evaluated on dynamic system behavior, which zeotropic fluid and parabolic trough collector have been the most effective choices. Hot and cold storage tanks are applied to overcome the instability of solar energy and ensure sustainable access to produced cold. Design parameter\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s optimum values are estimated through sensitivity analysis and genetic algorithm optimization which are decreased up to 70% compared to initial guesses. An artificial neural network is employed to train exergo-economic-environmental data which fed into the multi-objective modified particle swarm optimization algorithm. With using of cold storage tank, the system\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s coefficient of performance is enhanced up to three times compared to the ejector cycle at optimum design variable. By employing of parabolic trough collector, the system has its maximum coefficient of performance and exergy efficiency, as well as the minimum size of the ejector cycle and cold storage tank. The parabolic trough collector, air handling unit, and cold storage tank waste over 80% of the overall system\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s exergy. The payback period of system is estimated about 3.5 years.

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