Authors :
Kapil Jain; Basant Kumar Chourasia
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/3wn44bkk
Scribd :
http://tinyurl.com/3abhkfer
DOI :
https://doi.org/10.5281/zenodo.10617653
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.
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.
