Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs.
Gaussian process regression
Genetic algorithm
Marine predators algorithm
Microencapsulated PCM
Particle swarm optimization
Thermal energy storage
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
31 Aug 2024
31 Aug 2024
Historique:
received:
16
05
2024
accepted:
23
08
2024
medline:
1
9
2024
pubmed:
1
9
2024
entrez:
31
8
2024
Statut:
epublish
Résumé
Suspensions containing microencapsulated phase change materials (MPCMs) play a crucial role in thermal energy storage (TES) systems and have applications in building materials, textiles, and cooling systems. This study focuses on accurately predicting the dynamic viscosity, a critical thermophysical property, of suspensions containing MPCMs and MXene particles using Gaussian process regression (GPR). Twelve hyperparameters (HPs) of GPR are analyzed separately and classified into three groups based on their importance. Three metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and marine predators algorithm (MPA), are employed to optimize HPs. Optimizing the four most significant hyperparameters (covariance function, basis function, standardization, and sigma) within the first group using any of the three metaheuristic algorithms resulted in excellent outcomes. All algorithms achieved a reasonable R-value (0.9983), demonstrating their effectiveness in this context. The second group explored the impact of including additional, moderate-significant HPs, such as the fit method, predict method and optimizer. While the resulting models showed some improvement over the first group, the PSO-based model within this group exhibited the most noteworthy enhancement, achieving a higher R-value (0.99834). Finally, the third group was analyzed to examine the potential interactions between all twelve HPs. This comprehensive approach, employing the GA, yielded an optimized GPR model with the highest level of target compliance, reflected by an impressive R-value of 0.999224. The developed models are a cost-effective and efficient solution to reduce laboratory costs for various systems, from TES to thermal management.
Identifiants
pubmed: 39217234
doi: 10.1038/s41598-024-71027-9
pii: 10.1038/s41598-024-71027-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
20271Subventions
Organisme : science and technology foundation of Guizhou Province
ID : ZK[2024]661
Organisme : Open Fund of Key Laboratory of Advanced Manufacturing Technology, Ministry of Education
ID : GZUAMT2022KF[07]
Informations de copyright
© 2024. The Author(s).
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