Parametric Optimization of a Two Stage Vapor Compression Refrigeration System by Comparative Evolutionary Techniques

Multistage refrigeration system plays a vital role in industrial for the chemical, petrochemical, pharmaceuticals and food industries. Modern chemical industries are complex, problems commonly multi-dimensional, non-linear time-consuming. This study presents application of evolutionary computation techniques, namely PSO (particle swarm optimization), GA (Genetic Algorithm) SA (Simulated Annealing) to solve design problem two-stage vapor compression system. Two objectives evaluated, minimization total energy consumption maximization coefficient performance (COP) The basis is built from validated against data published literature. mass flow ratio, evaporator condenser temperature, parameters subcooling desuperheating, show acceptable results. errors below 5% literature, which within significant figures calculations. In this work, optimum solutions reduction required amount by 30.8% an increase COP nearly 77% with respect design. Further improvements made optimization procedures prevent early convergence search efficiency finding global optima. findings PSO, agreement, all techniques achieved proper two objective functions. It also found that requires lower computational effort, less time easier implement compared SA.