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.

Most of the time, photovoltaic (PV) cells' datasheets don't list their model factors. Getting the right PV settings is a very important problem that experts are very interested in. There is a new way to guess the one diode model (ODM) parameters in this study. It is called the iterative chaotic Aeo-1 optimization method (ICAeo-1). The current method uses only addition and multiplication to make the original AEO-1 algorithm better at exploring. It does this through a repeated map-based learning process. The Root-Mean-Square-Error (RMSE) numbers get better, which is a good sign. It can be seen that the ICAeo-1 algorithm is better at predicting the PV parameters than a lot of other well-known algorithms when it comes to being accurate and stable.

The increasing demand for electricity, driven by sustained population growth, has placed considerable pressure on Algeria’s power generation infrastructure to expand its output. In parallel, the sharp decline in the cost of solar photovoltaic (PV) electricity and the rising tariffs on conventional energy sources have intensified interest in PV-based electricity generation. Accordingly, precise evaluation of the annual and monthly performance of solar PV plants has become essential for the optimal design and deployment of new facilities. This study presents a comprehensive performance assessment of a 12 MW grid-connected photovoltaic power plant located in Sidi Bel Abbès, Algeria. Real-time operational data were collected over a one-year period, from January 1st to December 31st, 2023. Key performance indicators analyzed include energy output, final yield (Yf), and performance ratio (PR), among others. The plant delivered a total of 20,780.67 MWh to the grid in 2023. The final yield ranged from 3.25 to 5.88 kWh/kWp, while the performance ratio varied between 79% and 89.71%. The annual capacity factor was calculated to be 19.44%. These empirical results are compared against simulation outputs obtained using PVsyst and the Solar GIS-PV Planner tools.

This investigation presents a comprehensive optimization framework for ozone generation in dielectric barrier discharge (DBD) systems applied to water treatment processes. The study employs a Venturi injection configuration to systematically analyze the influence of three critical operational parameters: applied voltage (6-8 kV), oxygen flow rate (3-5 L/min), and water flow rate (8-16 L/min) on dissolved ozone concentration in treated water. Experimental design methodology utilizing MODDE 5.0 software enabled the development of a second-order polynomial mathematical model correlating input variables with ozone concentration response. The derived model demonstrated excellent statistical validity with coefficient of determination (R²) of 99.2% and predictive capability (Q²) of 90.3%, confirming robust model accuracy and reliability.Parameter optimization was achieved through implementation of Particle Swarm Optimization (PSO) algorithm, a bio-inspired metaheuristic approach that mimics collective intelligence behavior observed in natural swarms. The PSO methodology employed a population of 50 particles over 100 iterations to minimize prediction errors between theoretical model outputs and experimental measurements. Convergence analysis revealed rapid optimization performance, achieving optimal parameter extraction within 90 iterations. Comparative analysis between PSO-optimized parameters and conventional MODDE 5.0 results demonstrated superior performance of the proposed approach. Optimal operating conditions were identified as: applied voltage of 7.95 kV, oxygen flow rate of 3.38 L/min, and water flow rate of 8.27 L/min. The PSO-based optimization yielded enhanced ozone generation efficiency through reduced water flow rates and optimized voltage levels, confirming the voltage parameter as the dominant factor in ozone production.