A Review on Machine Learning Applications in Vapor Compression Refrigeration (VCR) Systems
Abstract: The increasing demand for energy-efficient systems in various industrial and commercial applications has prompted a surge in the development of smart technologies, particularly in the field of Vapor Compression Refrigeration (VCR). Machine learning (ML), when integrated with Internet of Things (IoT) technology, is revolutionizing the optimization of VCR systems, enhancing energy efficiency, predictive maintenance, and fault detection. This paper reviews recent advancements in ML applications for VCR systems, emphasizing real-time system optimization, energy consumption reduction, and autonomous operational strategies. By leveraging ML techniques such as supervised learning, reinforcement learning, and deep learning, VCR systems can dynamically adapt to environmental fluctuations, improve system performance, and reduce operational costs. Furthermore, the integration of IoT sensors facilitates continuous data collection, providing valuable insights into system behavior and enabling predictive maintenance. The paper also explores the future of autonomous VCR systems, where machine learning algorithms will control and optimize system parameters in real time. This paper concludes that the ongoing advancements in ML and IoT integration will continue to drive the evolution of VCR systems, leading to more sustainable, energy-efficient, and reliable refrigeration solutions.

Authors: Arun Solanki, Khemraj Beragi

File Name: Arun_13006-25-111.pdf
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Recent Advances in Heat Exchanger Technologies: A Review on Machine Learning and Thermal Management Techniques
Abstract: Heat exchangers are critical components in thermal management systems across diverse industries, playing a vital role in the efficient transfer of thermal energy between fluids. With the growing need for improved energy efficiency and system reliability, recent advancements have focused on the incorporation of innovative materials and computational techniques. This review explores the integration of machine learning (ML) algorithms, nanofluids, and phase change materials (PCMs) in heat exchanger design. Machine learning has emerged as a powerful tool in optimizing heat exchanger performance by predicting heat transfer rates, identifying optimal design configurations, and enhancing maintenance practices. Nanofluids, with their enhanced thermal conductivity, and PCMs, offering thermal energy storage capabilities, represent significant advancements in improving heat exchanger efficiency and sustainability. The combination of these technologies, along with computational fluid dynamics (CFD) and optimization algorithms, paves the way for the next generation of heat exchangers that are more efficient, compact, and adaptable to modern industrial needs. The findings in this review highlight the importance of these technologies in advancing thermal management systems and offer insights into their future applications.

Authors: Manglesh Dubey, Khemraj Beragi

File Name: Manglesh_13006-25-107.pdf
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