Recent research has reported on the energy and mass transition caused by Casson hybrid nanofluid flow across an extended stretching sheet. Thermal and velocity slip conditions, heat absorption, viscous dissipation, thermal radiation, the Darcy effect, and thermophoresis diffusion have all been considered in the study of fluid flow. Fluid flow is subjected to an angled magnetic field to control the flow stream. Cu and Al 2 O 3 NPs are dispensed into the Casson fluid to create a hybrid nanofluid (blood). The suggested model of flow dynamics is an evolving nonlinear system of PDEs, which is then reduced to a system of dimensionless ODEs using similarity proxies. The resulting set of ODEs is solved using the analytical program "HAM" for further processing. However, it has been found that the effects of the suction parameter and Darcy Forchhemier considerably reduced the energy transference rate of hybrid nanoliquids. It has been discovered that the effects of thermal radiation and heat absorption increase the energy transfer rate. Furthermore, the velocity and energy transmission rate are noticeably amplified by the dispersion of copper and cobalt ferrite nanoparticles in the base fluid.

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Previous theoretical research efforts which were validated by experimental findings demonstrated the thermo-economic benefits of the hybrid concentrated photovoltaic-thermoelectric (CPV-TE) system over the stand-alone CPV. However, the operating conditions and TE material properties for maximum CPV-TE performance may differ from those required in a standalone thermoelectric module (TEM). For instance, a high-performing TEM requires TE materials with high Seebeck coefficients and electrical conductivities, and at the same time, low thermal conductivities ( k ). Although it is difficult to attain these ideal conditions without complex material engineering, the low k implies a high thermal resistance and temperature difference across the TEM which raises the PV backplate’s temperature in a hybrid CPV-TE operation. The increased PV temperature may reduce the overall system’s thermodynamic performance. To understand this phenomenon, a study is needed to guide researchers in choosing the best TE material for an optimal operation of a CPV-TE system. However, no prior research effort has been made to this effect. One method of finding the optimum TE material property is to parametrically vary one or more transport parameters until an optimum point is determined. However, this method is time-consuming and inefficient since a global optimum may not be found, especially when large incremental step sizes are used. This study provides a better way to solve this problem by using a multiobjective optimization genetic algorithm (MOGA) which is fast and reliable and ensures that the global optimum is obtained. After the optimization has been conducted, the best performing conditions for maximum CPV-TE energy, exergy, and environmental (3E) performance are selected using the technique for order performance by similarity to ideal solution (TOPSIS) decision algorithm. Finally, the optimization workflow is deployed for 7000 test cases generated from 10 features using the optimal machine learning (ML) algorithm. The result of the optimization chosen by the TOPSIS decision-making method generated an output power, exergy efficiency, and CO2 saving of 44.6 W, 18.3%, and 0.17 g/day, respectively. Furthermore, among other ML algorithms, the Gaussian process regression was the most accurate in learning the CPV-TE performance dataset, although it required more computational effort than some algorithms like the linear regression model.