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.
In this research, aligned with global policies aimed at reducing CO2 emissions from traditional power plants, we developed a holistic energy system utilizing solar, wind, and ocean thermal energy sources, tailored to regions optimal for ocean thermal energy conversion (OTEC). The selected site, characterized by favorable wind and solar conditions close to areas with high OTEC potential, is designed to meet the electricity needs of a coastal community. The system's core components include an Organic Rankine Cycle, turbines, thermoelectric elements, pumps, a heat exchanger, a wind turbine, and a solar collector. A detailed system analysis and thermodynamic evaluation based on thermodynamic principles were carried out using the Engineering Equation Solver (EES) software. Key factors such as wind speed, solar radiation, and collector area were critical in determining system performance. To enhance the system's effectiveness, we conducted a comprehensive comparison of optimization algorithms, incorporating the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and utilizing a Pareto front for value optimization. This approach significantly outperformed other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) in terms of system efficiency and cost-effectiveness. The developed system achieved an exergy efficiency of 14.46 % and a cost rate of $74.98 per hour, demonstrating its suitability for its intended functions. Moreover, exergoenvironmental evaluation was conducted for the proposed plant. The findings revealed that key component HEX has a high exergoenvironmental factor due to their use of hot water, which has zero unit exergoenvironmental impact. Additionally, pumps demonstrated a zero exergoenvironmental impact factor, indicating negligible component-related environmental impacts. Sensitivity analysis further evaluated critical performance parameters, revealing that increases in solar irradiation lead to decreased total system cost rates, while higher turbine temperatures resulted in a remarkable 14.08 % reduction in the system's cost rate. These results underscore the economic viability of operating the system at higher temperatures and strengthen the argument for its adoption from a financial perspective.