Machine Learning for Design Optimization and PCM-Based Storage in Plate Heat Exchangers: A Review

Fatemeh Isania () and Antonio Galgaro
Additional contact information
Fatemeh Isania: Geoscience Department, Padova University, 35131 Padova, Italy
Antonio Galgaro: Geoscience Department, Padova University, 35131 Padova, Italy

Energies, 2025, vol. 18, issue 19, 1-39

Abstract: This review critically examines the intersection of machine learning (ML), plate heat exchangers (PHEs), and latent heat thermal energy storage (LHTES) using phase-change materials (PCMs)—a combination not comprehensively addressed in the existing literature. Covering more than 120 peer-reviewed studies published between 2015 and 2025, we analyze the deployment of ML methods—including artificial neural networks, ensemble models, physics-informed neural networks, and hybrid optimization techniques—for modeling, performance enhancement, and real-time control of PCM-integrated PHE systems. Particular attention is given to ML-driven geometry optimization, flow prediction, and surrogate modeling for computational fluid dynamics (CFD) simulations. The review also explores digital twin development and nanofluid-enhanced storage strategies. By addressing key gaps in dataset availability, model interpretability, and integration challenges, we provide a structured roadmap for future research, emphasizing hybrid ML–physics models, explainable AI, and standardized benchmarking. This work offers a data-driven and focused perspective on advancing the design of intelligent and sustainable thermal systems.

Keywords: plate heat exchanger; machine learning; phase-change material; latent heat storage; surrogate modeling; digital twin (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/19/5115/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/19/5115/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:19:p:5115-:d:1758469

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().