Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs

• Published in: Scientific reports Vol. 14; no. 1; pp. 20271 - 24
• Main Authors: Hai, Tao, Basem, Ali, Alizadeh, As’ad, Sharma, Kamal, jasim, Dheyaa J., Rajab, Husam, Ahmed, Mohsen, Kassim, Murizah, Singh, Narinderjit Singh Sawaran, Maleki, Hamid
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 31-08-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/301; 639/4077; 639/925; Algorithms; Cooling systems; Energy storage; Gaussian process regression; Genetic algorithm; Humanities and Social Sciences; Marine predators algorithm; Microencapsulated PCM; multidisciplinary; Particle swarm optimization; Predators; Science; Science (multidisciplinary); Thermal energy; Thermal energy storage; Viscosity; Gaussian process regression; Thermal energy storage; Genetic algorithm; Marine predators algorithm; Particle swarm optimization; Microencapsulated PCM
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-71027-9

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.