Publication Date: 2024/02/05
Abstract: This literature review delves into the utilization of computational intelligence techniques, such as Simulated Annealing (SA), Differential Evolution (DE), Heat Transfer Search (HTS), Chemical Reaction Optimization (CRO), Multi-Objective GA (MOGA), and Nondominated Sorting Genetic Algorithm II (NSGA II), for modeling and optimizing vapor absorption refrigeration systems. The inherent complexity of modern refrigeration systems, characterized by their multi-modal, non-linear, and time-consuming optimization problems, necessitates the application of advanced computational tools. These techniques have demonstrated success in overcoming the challenges posed by the intricate nature of refrigeration system optimization. Through trend analysis, the primary focus of optimization is identified as the COP, followed by considerations for total cost, exergetic and energetic efficiency, energy consumption, and cooling capacity. Computational intelligence methods prove effective in addressing these objectives. This review critically evaluates the outcomes of employing such techniques, emphasizing both advancements and shortcomings in existing methodologies. As the demand for energy-efficient refrigeration solutions grows, this comprehensive literature review contributes valuable insights into state-of-the-art computational intelligence approaches for optimizing vapor absorption refrigeration systems. The findings serve as a foundation for future research directions, underscoring the significance of intelligent optimization strategies in addressing the multifaceted challenges within the field of refrigeration technology.
Keywords: Simulated Annealing (SA), Differential Evolution (DE), Heat Transfer Search (HTS), Chemical Reaction Optimization (CRO), Multi-Objective GA (MOGA), Nondominated Sorting Genetic Algorithm II (NSGA II), COP, Optimization.
DOI: https://doi.org/10.5281/zenodo.10617653
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN1721.pdf
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