Volume 55

Optimizing Packed Bed Latent Heat Storage Systems: A Machine Learning and Metaheuristic Approach Theofilos Xenitopoulos, Argyrios Anagnostopoulos, Panos Seferlis, Yulong Ding

Abstract

With the increase in industrial carbon emissions, the efficient recovery of waste heat has become imperative. Thermal Energy Storage (TES) systems utilizing Phase Change Materials (PCMs) present a viable solution, with Packed Bed Latent Heat Storage Systems (PBLHS) being particularly noted for their effectiveness. However, current PBLHS designs face challenges in terms of accuracy and adaptability. This research introduces a MachineLearning (ML) approach to overcome these obstacles. By leveraging data from a validated Computational Fluid Dynamics (CFD) model, a deep ML model was developed and trained, achieving an R2value of 0.975 and a MAPE of less than 9.14%. The Harmony Search algorithm emerged as the most effective optimization technique, which, after refinement, enhanced design efficiency by over 40%. The optimized model improved existing experimental setups by up to 84%. This study underscores the potential of ML in advancing TES system designs for efficient waste heat recovery.

Keywords Waste heat recovery, Thermal Energy Storage, Phase Change Materials, Packed Bed Latent Heat Storage Systems, Machine Learning, Harmony Search.