Expert Systems and Artificial Intelligence Importance in Using Phase Change Materials in Thermal Applications: A Review

Abstract

The accelerated development of energy requirements and climate change reduction brought about an increase in the study on novel thermal energy storage systems. With its latent heat capacity, Phase Change Materials (PCMs) have become a promising medium, as it is capable of absorbing and releasing large amounts of latent heat during the process of changing phase. This is a critical review of the involvement of expert systems and artificial intelligence (AI) in developing the selection, design, and use of PCMs in several thermal processes. The paper classifies PCMs into the following types; organic, inorganic, and eutectic and discusses their thermal characteristics, advantages and limitations. The issues that receive special consideration are the low thermal conductivity, supercooling, phase segregation and cost obstacle. The involvement of expert systems in PCM can be seen to support its deployment by replicating human experience, supporting material selection, and configuration of a system across different thermal and economic situations. At the same time, AI technologies such as machine learning, neural networks, and fuzzy logic allow predicting the behavior of PCM correctly, optimizing the working system in real-time, and managing uncertainty. Moreover, when combined with the use of nanotechnology and IoT, AI has also brought smart PCM systems that can predict maintenance services and apply adaptive thermal control. The paper uses this as its culmination at the end to indicate the future directions with respect to AI-assisted material discovery and superior hybrid PCM systems placing them as critical to the next-generation sustainable power solutions.