Exergy Analysis of a Solar Vapor Compression Refrigeration System Using R1234ze(E)as an Environmentally Friendly Replacement of R134a

Refrigeration plays a significant role across various aspects of human life and consumes substantial amounts of electrical energy.The rapid advancement of green cooling technology presents numerous solar-powered refrigeration systems as viable alternatives to traditional refrigeration equipment.Exergy analysis is a key in identifying actual thermodynamic losses and improving the environmental and economic efficiency of refrigeration systems.In this study exergy analyze has been conducted for a solar-powered vapor compression refrigeration(SP-VCR)system in the region of Gharda颽(Southern Algeria)utilizing R1234ze(E)fluid as an eco-friendly substitute for R134a refrigerant.A MATLAB-based numerical model was developed to evaluate losses in different system components and the exergy efficiency of the SP-VCR system.Furthermore,a parametric study was carriedout to analyze the impact of various operating conditions on the system’s exergy destruction and efficiency.The obtained results revealed that,for both refrigerants,the compressor exhibited the highest exergy destruction,followed by the condenser,expansion valve,and evaporator.However,the system using R1234ze(E)demonstrated lower irreversibility compared to that using R134a refrigerant.The improvements made with R1234ze are 71.95%for the compressor,39.13%for the condenser,15.38%for the expansion valve,5%for the evaporator,and 54.76%for the overall system,which confirm the potential of R1234ze(E)as a promising alternative to R134a for cooling applications.

Cooling High Power Dissipating Artificial Intelligence (AI) Chips Using Refrigerant

High power dissipating artificial intelligence (AI) chips require significant cooling to operate at maximum performance. Current trends regarding the integration of AI, as well as the power/cooling demands of high-performing server systems pose an immense thermal challenge for cooling. The use of refrigerants as a direct-to-chip cooling method is investigated as a potential cooling solution for cooling AI chips. Using a vapor compression refrigeration system (VCRS), the coolant temperature will be sub-ambient thereby increasing the total cooling capacity. Coupled with the implementation of a direct-to-chip boiler, using refrigerants to cool AI server systems can materialize as a potential solution for current AI server cooling demands. In this study, a comparison of 8 different refrigerants: R-134a, R-153a, R-717, R-508B, R-22, R-12, R-410a, and R-1234yf is analyzed for optimal performance. A control theoretical VCRS model is created to assess variable refrigerants under the same operational conditions. From this model, the coefficient of performance (COP), required mass flow rate of refrigerant, work required by the compressor, and overall heat transfer coefficient is determined for all 8 refrigerants. Lastly, a comprehensive analysis is provided to determine the most optimal refrigerants for cooling applications. R-717, commonly known as Ammonia, was found to have the highest COP value thus proving to be the optimal refrigerant for cooling AI chips and high-performing server applications.

Energy Efficient Predictive Control for Vapor Compression Refrigeration Cycle Systems

Abstract--Vapor compression refrigeration cycle （VCC） system is a high dimensional coupling thermodynamic system for which the controller design is a great challenge. In this paper, a model predictive control based energy efficient control strategy which aims at maximizing the system efficiency is proposed. Firstly, according to the mass and energy conservation law, an analysis on the nonlinear relationship between superheat and cooling load is carried out, which can produce the maximal effect on the system performance. Then a model predictive control （MPC） based controller is developed for tracking the calculated setting curve of superheat degree and pressure difference based on model identified from data which can be obtained from an experimental rig. The proposed control strategy maximizes the coefficient of performance （COP） which depends on operating conditions, in the meantime, it meets the changing demands of cooling capacity. The effectiveness of the control performance is validated on the experimental rig. Index Terms--Cooling load, model predictive control （MPC）, superheat, vapor compression refrigeration cycle （VCC）.

Performance comparison and analysis of a combined power and cooling system based on organic Rankine cycle

A novel power and cooling system combined system which coupled organic Rankine cycle(ORC) with vapor compression refrigeration cycle(VCRC) was proposed. R245 fa and butane were selected as the working fluid for the power and refrigeration cycle, respectively. A performance comparison and analysis for the combined system was presented. The results show that dual-pressure ORC-VCRC system can achieve an increase of 7.1% in thermal efficiency and 6.7% in exergy efficiency than that of basic ORC-VCRC. Intermediate pressure is a key parameter to both net power and exergy efficiency of dual-pressure ORC-VCRC system. Combined system can produce maximum net power and exergy efficiency at 0.85 MPa for intermediate pressure and 2.4 MPa for high pressure, respectively. However, superheated temperature at expander inlet has little impact on the two indicators. It can achieve higher overall COP, net power and exergy efficiency at smaller difference between condensation temperature and evaporation temperature of VCRC.

Economy of Energy during Refrigerating Machine Operation

Idealized cycles of refrigerating machines with adiabatic and isothermal compression of refrigerant vapor were investigated. Energetic characteristics of cycles： specific mass and volume cooling capacity q0 and qv, work of compression 1, refrigerating coefficient of performance e and power N for drive of compressor were compared. These characteristics were calculated for eight refrigerants at temperature of their condensation 30 ℃ and temperatures of boiling -15℃ and -30 ℃. The calculations show that the use of isothermal compression of refrigerant vapor ensures economy of energy during refrigerating machine operation.

Model Predictive Control for Vapor Compression Cycle of Refrigeration Process

A model predictive controller based on a novel structure selection criterion for the vapor compression cycle （VCC） of refrigeration process is proposed in this paper. Firstly, those system variables are analyzed which exert significant influences on the system performance. Then the structure selection criterion, a trade-off between computation complexity and model performance, is applied to different model structures, and the results are utilized to determine the optimized model structure for controller design. The controller based on multivariable model predictive control （MPC） strategy is designed, and the optimization problem for the reduced order models is formulated as a constrained minimization problem. The effectiveness of the proposed MPC controller is verified on the experimental rig.