RESUMO
In response to the COVID-19 pandemic, some vaccines have been developed requiring ultralow-temperature refrigeration, and the number of these freezers has been increased worldwide. Ultralow-temperature refrigeration operates with a significant temperature lift and, hence, a massive decrease in energy performance. Therefore, cascade cycles based on two vapor compression single-stage cycles are traditionally used for these temperatures. This paper proposes the combination of six different cycles (single-stage with and without internal heat exchanger, vapor injection, liquid injection, and parallel compression with and without economizer) in two-stage cascades to analyze the operational and energy performance in ultralow-temperature freezers. All this leads to 42 different configurations in which the intermediate cascade temperature is optimized to maximize the coefficient of performance. Ultra-low global warming potential natural refrigerants such as R-290 (propane) and R-170 (ethane) for the cascade high- and low-temperature stage have been considered. From the thermodynamic analysis, it can be concluded that liquid and vapor injection cascade configurations are the most energy-efficient. More specifically, those containing a vapor injection in the low-temperature stage (0.89 coefficient of performance, 40 % higher than traditional configurations). Then, using an internal heat exchanger for such low temperatures is unnecessary in terms of energy performance. The optimum intermediate cascade temperature varies significantly among cycles, from -37 °C to 2 °C, substantially impacting energy performance. Parallel compression configuration improves energy performance over single-stage cycles, but not as much as multi-stage (between 20 % and 30 % lower coefficient of performance). For most of low-temperature cycles, the high-temperature stage can be based on a single-stage cycle while keeping the maximum coefficient of performance.
RESUMO
Refrigeration systems are complex, non-linear, multi-modal, and multi-dimensional. However, traditional methods are based on a trial and error process to optimize these systems, and a global optimum operating point cannot be guaranteed. Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm. The system is modeled using response surface methods (RSM) to investigate the impacts of design variables on the set responses. Firstly, the interaction between the system components and their cycle behavior is analyzed by building four surrogate models using RSM. The model fit statistics indicate that they are statistically significant and agree with the design data. Three conflicting scenarios in bi-objective optimization are built focusing on the overall system following the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) decision-making methods. The optimal solutions indicate that for the first to third scenarios, the exergetic efficiency (EE) and capital expenditure (CAPEX) are optimized by 33.4% and 7.5%, and the EE and operational expenditure (OPEX) are improved by 27.4% and 19.0%. The EE and global warming potential (GWP) are also optimized by 27.2% and 19.1%, where the proposed HMOGWO outperforms the MOGWO and NSGA-II. Finally, the K-means clustering technique is applied for Pareto characterization. Based on the research outcomes, the combined RSM and HMOGWO techniques have proved an excellent solution to simulate and optimize two-stage VCRS.