Abstract
Although traditional compression absorption refrigeration cycle (CARC) addresses the respective limitations of the Absorption Refrigeration Cycle (ARC) and Vapor Compression Refrigeration (VCR) systems while harnessing their individual strengths. But this arrangement faces challenges related to power wastage, insufficient thermal energy absorption, and high compressor power requirements. To address these issues, in this study, an advanced RAC (Recompression Absorption System) is integrated with enhanced VCR incorporated with ejector to develop advanced proposed Ejector-Compression Recompression Absorption cycle (E-CRAC) and Ejector enhanced Vapor-Injection Compression Recompression Absorption Cycle (EI-CRAC). A computational model is developed in the Engineering Equation Solver (EES) employing energy, mass, and exergy conservation principles to conduct a thorough 1st and 2nd law analysis. An Artificial Neural Network (ANN) model, combined with a Genetic algorithm, enabled multi-objective optimization to pinpoint optimal operational conditions. The COP of the proposed systems is nearly three times higher than the traditional CARC system, showing an improved efficiency in cooling operations. Additionally, EI-CRAC and E-CRAC demonstrate near 10% and 20% COP enhancement, as well as 15% and 25% increase of Exergy efficiency over benchmark basic CRAC respectively. Furthermore, the research highlights the sensitivity of these systems to various operating conditions, such as pressure drop across ejector nozzle, evaporator temperature, condenser pressure, absorber temperature etc., emphasizing the need for precise control to maximize efficiency. The results of this detailed theoretical thermodynamic analysis with optimization provide a comprehensive understanding of the proposed systems while offering valuable insights for further scope of improvements.
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