Renewable energy sources are undoubtedly necessary, considering global electricity demand is expected to rise dramatically in the coming years. This research looks at a unique multi-generation plant from the perspectives of exergy, energy, and economics; also, an environmental evaluation is performed to estimate the systems' CO2 emissions. The unit is made up of a biomass digester and gasifier, a Multi effect Desalination unit, and a supercritical CO2 (SCO2) cycle. In this study, two methods for using biomass are considered: the first is using synthesis gas generated by the gasifier, and the second is utilizing a digester to generate biogas. A comprehensive parametric study is performed on the designed energy unit to assess the influence of compressor pressure ratio, Gas turbine inlet temperature, supercritical CO2 cycle pressure ratio, and the number of effects of multi-effect distillation on the system performance. Furthermore, the exergy study revealed that the exergy destruction in the digestion unit was 11,337 kW, which was greater than the exergy destruction in the gasification unit, which was 9629. Finally, when compared to the gasifier, the amount of exergy efficiency, net output power, and freshwater production in the digester was greater.
The study of blood flow in obstructed arteries is a significant focus in computational fluid dynamics, particularly in the field of biomedicine. The primary objective of this research is to investigate the impact of pulsating blood velocity on heat transfer within biological systems, with a specific focus on blood flow in obstructed arteries. To achieve this goal, a comprehensive 3D model representing a straight, constricted blood vessel has been developed. This model incorporates periodic, unsteady, Newtonian blood flow along with the presence of gold and silver nanoparticles. Leveraging the Finite Element Method (FEM), the Navier-Stokes and energy equations have been rigorously solved. Through the investigation, it is aim to shed light on how alterations in the pulsation rate and the volume fraction of nanoparticles influence both temperature distribution and velocity profiles within the system. The present study findings unequivocally highlight that the behavior of pulsatile nanofluid flow significantly impacts the velocity field and heat transfer performance. However, it is imperative to note that the extent of this influence varies depending on the specific volume fractions involved. Specifically, higher volume fractions of nanofluids correlate with elevated velocities at the center of the vessel and decreased velocities near the vessel walls. This pattern also extends to the temperature distribution and heat flux within the vessel, further underscoring the paramount importance of pulsatile flow dynamics in biomedicine and computational fluid dynamics research. Besides, results revealed that the presence of occlusion significantly affects the heat transfer and fluid flow.
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.
Optimization of thermophysical properties (TPPs) of MXene-based nanofluids is essential to increase the performance of hybrid solar photovoltaic and thermal (PV/T) systems. This study proposes a hybrid approach to optimize the TPPs of MXene-based Ionanofluids. The input variables are the MXene mass fraction (MF) and temperature. The optimization objectives include three TPPs: specific heat capacity (SHC), dynamic viscosity (DV), and thermal conductivity (TC). In the proposed hybrid approach, the powerful group method of data handling (GMDH)-type ANN technique is used to model TPPs in terms of input variables. The obtained models are integrated into the multi-objective particle swarm optimization (MOPSO) and multi-objective thermal exchange optimization (MOTEO) algorithms, forming a three-objective optimization problem. In the final step, the TOPSIS technique, one of the well-known multi-criteria decision-making (MCDM) approaches, is employed to identify the desirable Pareto points. Modeling results showed that the developed models for TC, DV, and SHC demonstrate a strong performance by R-values of 0.9984, 0.9985, and 0.9987, respectively. The outputs of MOPSO revealed that the Pareto points dispersed a broad range of MXene MFs (0-0.4%). However, the temperature of these optimal points was found to be constrained within a narrow range near the maximum value (75 °C). In scenarios where TC precedes other objectives, the TOPSIS method recommended utilizing an MF of over 0.2%. Alternatively, when DV holds greater importance, decision-makers can opt for an MF ranging from 0.15 to 0.17%. Also, when SHC becomes the primary concern, TOPSIS advised utilizing the base fluid without any MXene additive.