Dynamical modeling and Thermo-Economic Optimization of a Cold room Using Various Working Fluids

A cold room assisted vapor-compression refrigeration cycle is dynamically modelled and optimized. The design parameters are evaporator pressure, condenser pressure, length, wall’ thickness and height of cold room, mass flow rate of refrigerant as well as the value of superheating/subcooling in evaporator/condenser. To find the optimum value of design parameters Multi Objective Genetic Algorithm (MOGA) is applied. Moreover three refrigerants included R22, R134a and R407c are used as working refrigerant in this system. Total annual cost (TACO) and coefficient of performance (COP) are selected as two objective functions. The optimum solutions show that R407c is the best refrigerant in both thermo dynamical and economical view points with 9148.2 $/year as total annual cost and 6.12 for COP. The optimum result of R407c showed the total annual cost improved 50.74%, and 8.68% in comparison with R134a and R22 respectively. Furthermore, COP improved 9.97% and 13.72% in comparison with R134a and R22, respectively. It is worth mentioning the optimum results reveal the compatible between two objectives, the coefficient of performance and the total annual cost (in maximum point of COP have minimum point of TACO). However by using R407c as refrigerant of system have the minimum area of cold room, the refrigerants R134a and R22 are in the next ranking, respectively.