The Silica monolith particles (SMP) were prepared from Tetra-Methyl-Ortho-Silicate (TMOS) and characterized by Fourier transforms infrared (FTIR), Scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and surface area analyzer. FTIR analysis showed the Si−O stretching confirming SMP formation. SEM analysis provided information about the mean diameter of SMP (1−5 µm). EDX confirmed the presence of silicon and oxygen in the SMP. Moreover, the calculated surface area for SMP was found to be around 367 m²/g, whereas BJH pore size distributed particles were 87.15 along with the total pore volume and pore radius of 0.073 cm³/g and 16.627 Å, respectively. Besides, the removal efficiency was found to be about 96%. Various kinetic equations were used to calculate the adsorption parameters. Overall, the results show that the most appropriate model for the kinetics data was the pseudo-second order kinetics model while the mechanism of adsorption was best explained by the Langmuir isotherm. The highest removal of Basic Red 5 dye after 120 min at 298 K was 576 mg/g. Moreover, the thermodynamics parameters (Enthalpy, Gibb’s energy, and Entropy) were also estimated. The ΔH° (0.995 kJ/mol) value depicted the endothermic nature of the process. The non-spontaneous aspect of the process was evident from the ΔG° values which were 60.431, 328.93, and 339.5 kJ/mol at 293, 303, and 313 K, respectively. From the high removal efficiency value, it can be concluded that the prepared adsorbent can be a potential adsorbent in the reclamation of dyes from wastewater.

The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-based predictive maintenance is a promising solution for enhancing the reliability and efficiency of renewable energy systems.

The purpose of this article is to present the methods for the development and testing of polymeric hybrid Photovoltaic Thermal (PVT) collectors for indoor experiments. Polymeric material has been used in hybrid PVT collectors and even longer used on flat plate collectors. We have developed a method to make a hybrid PVT collector with an absorber made of a polymethyl-methacrylate (PMMA) and combined with a copper sheet. To understand the details of the thermal and electrical performance, we have developed a method to test this PVT collector by conducting a series of experiments by varying the mass flow rate, irradiance, and inlet water temperature. The results showed that PMMA as a thermal absorber with a copper sheet can provide a beneficial cooling effect of around 80% thermal efficiency and a 0.03 %/oC decrease in electrical efficiency. •This method makes PVT collector hybrids easily and cheaply using simple equipment.•This method provides simple technical testing of hybrid PVT collectors•This method can be applied to make a larger hybrid PVT collector with modular.