Modeling and multi-objective optimization of an R450A vapor compression refrigeration system

Abstract

The main objective of this work is to comprehensively investigate R450A behavior in refrigeration systems and subsequently optimize the main operating variables for the first time to reach the maximum performance. For this purpose, a hybrid multi-objective optimization model coupling response surface method and non-dominated sorted genetic algorithm II is established. The regression analysis results reveal a good agreement of experimental data samples with the quadratic polynomial models with a coefficient of determination exceeding 0.97. The optimum results for the first scenario indicate that the reduction in the motor-compressor electrical power consumption and discharge temperature is 19.72% and 54.21%, respectively, and percentage of growth in the refrigerant mass flow rate is 230.52% when the middle evaporator temperature, middle condenser temperature, superheating degree, and subcooling degree change from -14.95 o C to 8.71 o C, 31.28 o C to 24.50 o C, 13.12 K to 10.49 K, and 15.65 K to 15.66 K, respectively.

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

“…This process is inefficient because the interaction between multiple variables cannot be analyzed simultaneously [18,19]. By contrast, the RSM is a statistical technique that overcomes these limitations by allowing users to simultaneously analyze the interaction between multiple independent variables and their effects on the dependent variables using a few experimental data sets [14]. The Central Composite Design (CCD) of RSM is a robust design technique that works better for a small number of data sets, where the built models are not sensitive to missing data.…”

“…The application of the intelligent algorithm PSO led to better results, achieving 123.09 kW and 11.24 kW more energy savings than non-linear programming (NLP). Zendehboudi et al [14] performed modeling and multiobjective optimization (MOO) of the R450A single-stage VCRS using RSM and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The authors established the robustness of their designed NSGA II by testing it for various types of objective functions with different…”

Superstructure-Based Optimization of Vapor Compression-Absorption Cascade Refrigeration Systems

“…Regarding VCRSs, recent results on modeling and optimization considering single-objective functions can be found in [ 13 , 14 , 15 , 16 , 17 ] and multi-objective functions in [ 18 , 19 ]. By using energy, exergy, and economic analyses, Baakeem et al [ 14 ] theoretically investigated a multi-stage VCRS considering eight refrigerants (R407C, R22, R717, R134a, R1234yf, R1234ze(E), R410A, and R404A).…”

“…For a cooling capacity of 1 kW and a temperature of 273 K at the evaporator, the refrigerant R717 showed the highest COP value; the refrigerant R407C was not suggested to use due to the low exergy efficiency and high operating cost. Zendehboudi et al [ 18 ] investigated the performance of VCRSs operating with R450A for cooling capacity values ranging between 0.5 and 2.5 kW. To this end, they developed a multi-objective optimization (MOO) approach coupling the response surface method (RSM) with the non-dominated sorting genetic algorithm II (NSGA-II) method to perform simulation-based optimizations.…”

Learning Feedforward Control of a One-Stage Refrigeration System

“…Recently, there has been extensive research adopted linear techniques regarding the control of vapor compression refrigeration systems. For example, decentralized PID control [2], decoupling multivariable control [3], optimal control [4], LQG control [5], [6], model predictive control (MPC) [7], [8], and robust control [9], [10].…”