This paper aims to analyse the effect of climate conditions on the performance of infinity PV organic solar cells in a Saharan setting in the region of Adrar (southern Algeria). The first part of this study consists of an analysis and assessment of organic photovoltaic (OPV) module degradation following a prolonged exposure to these open-air conditions (> 2 years). Inspection of the OPV module revealed various degradation modes such as solar cell deformation and coating degradation. A degradation assessment was conducted on the modules via I–V/P-V characteristics together with the rate of degradation of P_max,I_mp,V_mp,I_sc,V_oc and FF parameters under Standard Test Conditions (STC) to draw a comparison with the reference data provided by the photovoltaic panel manufacturer. Last, the impacts of partial shading, illumination, temperature, and dust accumulation were investigated to evaluate the evolution of both I–V and P-V curves.

The identification of water status is the foundation for fuel cell water management, which is helpful to fuel cell reliability and longevity. In this article, a novel and reliable method for diagnosing the hydration condition of proton exchange membrane fuel cells (PEMFCs) was presented using a fractional-order model (FOM) to represent the PEMFCs impedance. The results show that the mean root-mean-squared error (RMSE) and mean absolute percentage error (MAPE) between the proposed model and experimental data (in normal, drying, or flooding cases) are about 0.034 and 0.473, respectively. The fast Fourier transform–electrochemical impedance spectroscopy technique (FFT-EIS) was used as an alternative technique that is simple and efficient to electrochemical impedance spectroscopy (EIS). The PEMFCs hydration state is monitored by observing the changing effect of the physical resistor values (membrane resistance, polarization, and diffusion resistances) of the proposed model. These resistors, characterized by their high sensitivity to the drying and flooding of PEMFCs, affect the Nyquist impedance spectra and frequency spectrum amplitudes at low and high frequencies. Based on the obtained results, it is concluded that the proposed strategy can be used to develop new domains in which the PEMFCs’ hydration states can be properly predicted.

The water treatment process with ozone is influenced by various operating parameters and environmental factors that can impact its efficiency. In this study, experiments were conducted using a Venturi pumping frame to investigate the effects of three controllable variables: oxygen flow height, applied voltage level, and water flow rate. The tests aimed to develop a mathematical model that accurately represents the relationship between these input variables and the resulting ozone concentration in the treated water. The experimental data was analyzed using the MODDE 5.0 software, a specialized application for statistical modeling and design of experiments. By fitting the data to appropriate model equations, a mathematical model was obtained that quantifies the influence of each variable and their interactions on the ozone concentration response. To optimize the process performance, a particle swarm optimization (PSO) algorithm was employed to extract the best-fit parameters for the mathematical model. PSO is a computational technique inspired by the social behavior of bird flocks or fish schools, utilizing a population of candidate solutions that evolve iteratively to converge on the global optimum solution. In this case, PSO searched for the model parameter values that minimized the error between predicted and experimentally measured ozone concentrations, rapidly converging to an accurate solution. The optimized mathematical model enables predicting the ozone concentration under any combination of oxygen flow height, voltage, and water flow rate within the experimental range. This predictive capability facilitates identifying the optimum operating conditions to maximize ozone concentration, thereby enhancing the efficiency of the water treatment process. The model serves as a valuable tool for process control, monitoring, and optimization, ensuring consistent treatment quality while minimizing resource consumption and operational costs.

The elaboration and characterization of transparent electrodes of ZnO:Al type (aluminum-doped zinc oxide) are crucial steps in the development of organic photovoltaic cells (OPV) due to their essential role in collecting the current generated by the organic active layer. In this work, we have make realization and electrically characterized of transparent electrodes based on ZnO at various doping levels of Al. Subsequently, we created different metal-semiconductor polymer junctions (PV cells) and determined their I(V) characteristics in both darkness and under illumination.

To conduct exact performance investigations and control studies on solar PV systems, it is necessary to extract relevant circuit model characteristics. In this work, we have proposed for the identification of the parameters of the single-diode model of the amorphous PV module, a numerical modeling approach presented by the determinist based on the “Levenberg–Marquardt (LM)” gradient descent, combined with the intelligent method based on artificial neural networks (ANN) taking into account the variation of the solar radiation and the temperature of the cell, in real working conditions