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

Author

Listed:
  • Fatemeh Isania

    (Geoscience Department, Padova University, 35131 Padova, Italy)

  • Antonio Galgaro

    (Geoscience Department, Padova University, 35131 Padova, Italy)

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.

Suggested Citation

  • Fatemeh Isania & Antonio Galgaro, 2025. "Machine Learning for Design Optimization and PCM-Based Storage in Plate Heat Exchangers: A Review," Energies, MDPI, vol. 18(19), pages 1-39, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:19:p:5115-:d:1758469
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    References listed on IDEAS

    as
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    4. Rishikesh Sharma & Dipti Prasad Mishra & Marek Wasilewski & Lakhbir Singh Brar, 2023. "Application of Response Surface Methodology and Artificial Neural Network to Optimize the Curved Trapezoidal Winglet Geometry for Enhancing the Performance of a Fin-and-Tube Heat Exchanger," Energies, MDPI, vol. 16(10), pages 1-30, May.
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    12. Bin Yang & Xin Zhu & Boan Wei & Minzhang Liu & Yifan Li & Zhihan Lv & Faming Wang, 2023. "Computer Vision and Machine Learning Methods for Heat Transfer and Fluid Flow in Complex Structural Microchannels: A Review," Energies, MDPI, vol. 16(3), pages 1-24, February.
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