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

Abstract

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

4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer

“…Therefore, there is always the risk that the designed results are not the optimal ones. This uncertainty leads researchers to apply intelligent optimization algorithms such as GA, PSO, and differential evolution (DE) to refrigeration systems [25,26]. Subsequently, these algorithms can handle a large amount of data and non-linearities and do not require detailed information about the system and the differentiability of the model.…”