Performance Evaluation of Pin-on-Disc Tribometer for Friction and Wear Measurement in Engineering Materials

Abstract

The refrigeration industry is undergoing a transformative phase with the advent of Artificial Intelligence (AI) and data-driven control systems. Conventional refrigeration systems often rely on fixed control strategies, which can lead to inefficiencies in energy consumption, maintenance, and overall performance. The integration of AI techniques such as Machine Learning (ML), Deep Learning (DL), and Predictive Analytics is enabling intelligent automation, fault diagnosis, load forecasting, and energy optimization in both commercial and industrial refrigeration applications. AI-based models can analyze large datasets from sensors and IoT devices to predict system behavior, identify leaks, optimize compressor cycles, and maintain temperature stability with minimal human intervention. Moreover, reinforcement learning and neural network approaches are being developed to improve refrigerant flow control and adapt to varying environmental and load conditions dynamically. The incorporation of AI not only enhances system reliability and reduces operational costs but also contributes to environmental sustainability by minimizing greenhouse gas emissions through optimized refrigerant use. This review discusses recent advances, challenges, and prospects of AI applications in refrigeration, highlighting its potential to create smart, autonomous, and eco-efficient systems. The study concludes that AI-driven refrigeration will play a crucial role in achieving the global goals of energy efficiency, sustainability, and digital transformation of the HVACR sector.

Cite as:

V. Jamadar. (2025). Integration of Artificial Intelligence in Refrigeration Systems: A Review. Research and Reviews: Journal of Mechanics and Machines, 7(3), 9–17. 

https://doi.org/10.5281/zenodo.17838646