This paper discusses the efficient implementation of a new hybrid approach to forecasting short-term PV power production for four different PV plants in Algeria. The developed model incorporates a time-varying filter-empirical mode decomposition (TVF-EMD) and an extreme learning machine (ELM) as an essence regression. The TVF-EMD technique is used to deal with the fluctuation of PV power data by splitting it into a series of more stable and constant subseries. The specified set of features (intrinsic mode functions (IMFs)) is utilized for training and improving our forecasting extreme learning machine model. The adjusted ELM model is used to evaluate prediction efficiency. The suggested TVF-EMD-ELM model is assessed and verified in various Algerian locations with varying climate conditions. In all examined regions, the TVF-EMD-ELM model generates less than 4% error in terms of normalized root mean square error (nRMSE).

Dyes are responsible for major environmental issues globally due to their toxicity, large-scale production, and extensive use in various industrial sectors. Pollution caused by hazardous dyes is mainly due to textile waste, which is constantly discharged into the aquatic system, often causing harm to humans and affecting water quality. In recent years, the removal of dyes from industrial textile wastewater has been a major challenge. Numerous technologies and methods have been developed to remove dyes from wastewater and meet clean water requirements. In this study, the effectiveness of activated carbon prepared by chemical activation of jujube stones for textile wastewater treatment was investigated. The effects of the concentration of H₃PO₄ and the carbonization temperature on the activated carbon’s properties were studied. Several physicochemical methods, including Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction, methylene blue index, Boehm titration, iodine index and pH point of zero charge, were considered to characterize the produced adsorbents. To assess the quality of the two studied textile effluents (Mustard and Violet), the following parameters were used: biological oxygen demand (BOD), chemical oxygen demand (COD), turbidity, suspended particles and dissolved solids, before and after treatment with the produced activated carbon. Untreated wastewater analysis revealed high values for almost all parameters: pH > 9, COD of 302.72 mg/L and 230.68 mg/L for Mustard and Violet effluent, respectively. Both effluents from an industrial textile factory exhibited a COD/BOD ratio higher than three, which restricts their biodegradability. Examination of the effect of contact time and activated carbon dosage on the treatment of the two effluents showed that 4 g/L of activated carbon and 60 min of contact time were sufficient for optimal treatment, resulting in pollutant removal rates of 81.03 and 84.65% for the Violet and Mustard effluents, respectively. The results of this research highlight the efficiency of activated carbon derived from jujube stones as a cost-effective adsorbent for the treatment of real textile wastewater.

The current study proposed a novel simple and environmentally friendly approach for producing silver nanoparticles (AgNPs) using an extract of Moringa oleifera leaves (MOL) and optimizing the different experimental factors required for the formation and stability of AgNPs. The formation of nanoparticles was confirmed by a color change from yellow to reddish-brown with a surface plasmon resonance band at 412 nm. The morphology, size, and elemental composition of AgNPs were investigated by zeta potential dynamic light scattering, field emission scanning electron microscopy, energy dispersive X-ray, X-ray diffraction, and transmission electron microscopy analysis, which showed crystalline and spherical AgNPs. The identification of functional groups was supported by Fourier-transform infrared spectroscopy. The photocatalytic activities of AgNPs were assessed in the degradation of organic Malachite green (MG) dye in the aqueous solution. Two kinetic adsorption models, the pseudo-first-order model and the pseudo-second-order model, and three isotherm models, the Langmuir, Freundlich, and Temkin, were used to mathematically characterize the MG degradation process. The pseudo-second-order kinetics and the Freundlich isotherm model were found to be in good agreement with the experimental data. As a result of their synergistic interaction with the MOL extract solution, the photocatalytic activity of AgNPs increases and they can successfully adapt to the photodegradation of organic dyes in industrial effluents.