An integrated machine learning and metaheuristic approach for advanced packed bed latent heat storage system design and optimization

Argyrios Anagnostopoulos, Theofilos Xenitopoulos, Yulong Ding and Panos Seferlis

Energy, 2024, vol. 297, issue C

Abstract: To tackle the challenge of waste heat recovery in the industrial sector, this research presents a novel design and optimization framework for Packed Bed Latent Heat Storage Systems (PBLHS). This features a Deep Learning (DL) model, integrated with metaheuristic algorithms. The DL model was developed to predict PBLHS performance, trained using data generated from a validated Computational Fluid Dynamics (CFD) model. The model exhibited a high performance with an R2 value of 0.975 and a low Mean Absolute Percentage Error (<9.14%). To enhance the ML model's efficiency and optimized performance, various metaheuristic algorithms were explored. The Harmony Search algorithm emerged as the most effective through an early screening and underwent further refinement. The optimized algorithm demonstrated its capability by rapidly producing designs that showcased an improvement in total efficiency of up to 85% over available optimized experimental PBLHS designs. This research underscores the potential of ML-integrated approaches in laying the groundwork for generalized design frameworks for TES systems, offering efficient and effective solutions for waste heat recovery.

Keywords: Packed bed latent heat storage; Deep learning optimization; Metaheuristic algorithms; Thermal energy storage design; Phase change materials (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224009228
Full text for ScienceDirect subscribers only

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:eee:energy:v:297:y:2024:i:c:s0360544224009228

DOI: 10.1016/j.energy.2024.131149

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().