Photovoltaic–thermal (PVT) systems suffer from reduced electrical efficiency due to heat accumulation on PV surfaces. This study numerically investigates anovelstaircase-configured PVT air system with passive, perforated V-shaped fins—a geometric combination not previously reported in the PVT literature. This design disrupts thermal boundary layers and induces micro‑turbulence, enhancing convective cooling. Using validated 3D CFD simulations, the proposed system achieves a peak thermal efficiency of 31.0% (vs. 29.4% for a conventional design) and an electrical efficiency of 14.8% (vs. 14.6%), while reducing PV surface temperature by 9 K. Peak outputs reach approximately 400 W (thermal) and 180 W (electrical) per panel. Sensitivity analysis shows that a 7 mm perforation diameter and 0.04 kg/s airflow optimize performance, yielding 32.5% thermal efficiency and a 10 K temperature drop. An integrated energy‑economic‑environmental (3E) assessment estimates a 5.8‑year payback period (clear‑sky), 80 tons CO2 mitigation over 25 years, and an energy payback time of 0.86 years. The passive, modular design is scalable from residential to commercial applications, offering a cost‑effective and sustainable solution for arid, high‑irradiance regions.
This study investigates the use of biochar derived from municipal sewage sludge, pyrolyzed at 450 degrees C in an O2-free atmosphere, as an adsorbent for the removal of Cr(VI) from wastewater. The biochar was physicochemically characterized by FTIR, SEM, XRD, XPS, BET analysis, and pH at the zero point charge (pHzpc). Batch adsorption experiments were conducted at room temperature to evaluate its performance for Cr(VI) removal. The effects of initial Cr(VI) concentration, adsorbent dose, agitation time, and pH on the adsorption were explored. The adsorption capacity increased from 23.19 to 81.75 mg/g as the initial Cr(VI) concentration rose from 30 to 100 mg/L. Optimal adsorption occurred at pH similar to 5, with an agitation time of 60 min and a sorbent dose of 5 g/L. Linear regression analysis indicated that the Freundlich model provided a better fit than the Langmuir model for describing Cr(VI) adsorption onto the biochar. The adsorption kinetics followed a pseudo-second-order model, suggesting that the process is governed by chemisorption. In addition, the biochar showed excellent recyclability and reusability over four regeneration-adsorption cycles, demonstrating its potential for suitable long-term applications. This study provides an efficient, low-cost and eco-friendly adsorbent for treating Cr(VI)-containing wastewater, while also offering a sustainable solution for sewage sludge management.
The transportation sector's decarbonization represents one of the most critical challenges in achieving global climate targets. This study presents a comprehensive analysis of an integrated renewable energy system that produces green hydrogen through a hybrid solar photovoltaic (PV) and wind power configuration. The proposed system combines a 1.2 MWp solar array with 800 kW wind turbines, feeding a 1 MW proton exchange membrane (PEM) electrolyzer for hydrogen production. The hydrogen is subsequently compressed, stored at 350 (for trucks and buses) and 700 bar (for cars), and then utilized either directly for fuel cell electric vehicles (FCEVs) or reconverted to electricity via a 250 kW stationary PEM fuel cell to support electric vehicle (EV) charging infrastructure. Through detailed techno-economic simulation using HOMER Pro and MATLAB/Simulink 2022a, we demonstrate that the hybrid configuration achieves a 71% electrolyzer capacity factor, producing 55.8 tonnes of hydrogen annually with a levelized cost of 5.82 & euro;/kg. The system ensures over 60 h of grid-independent operation while reducing CO2 emissions by 1656 tones annually compared to conventional grid-powered alternatives. Results indicate that hybrid renewable hydrogen systems can provide economically viable solutions for sustainable mobility infrastructure, with projected cost reductions making them competitive with fossil fuel alternatives by 2030.
Solar energy is one of the most prominent renewable energy sources that effectively contributes to reducing carbon emissions and promoting environmental sustainability. In the quest to improve the efficiency of energy conversion systems, photovoltaic/thermal (PV/T) collectors emerge as an innovative solution that combines electricity and thermal energy production through an integrated system. This research aims to analyze the thermal performance of a finned bi-fluid PV/T collector by developing a three-dimensional numerical model using computational fluid dynamics (CFD), combined with artificial neural network (ANN) techniques to improve thermal efficiency and identify the factors affecting system performance. The model is based on simulating a forced water flow at rates ranging from 0.01 to 0.02 kg/s, with natural air convection. Realistic boundary conditions and climatic conditions were applied to simulate operational performance under varying solar radiation levels. The ANN algorithms were used to analyze the nonlinear relationships between key operational variables and predict the system's thermal performance. The model demonstrated very high predictive accuracy, with a coefficient of determination (R2) of 0.998, confirming its ability to represent the complex interactions between heat transfer and fluid flow within the collector. Sensitivity analyses using the Garson and Proportional Factor Influence (PFI) methods revealed that solar radiation intensity was the most influential factor, contributing 42.3% and 39.8%, respectively, followed by water flow mass at 28.7% and 32.5%. Ambient air temperature and engineering design had relatively smaller influences, contributing 18.1% and 10.9% according to the Garson method, and 16.4% and 11.3% according to the PFI analysis. These results confirm that the integration of high-resolution numerical modeling and artificial intelligence techniques represents a sophisticated and effective approach to analyzing and improving the performance of finned bi-fluid PV/T systems, enhancing the possibility of designing more efficient solar systems capable of meeting various operational and climatic challenges.
This study presents a comprehensive performance evaluation of an installed rooftop hybrid photovoltaic (PV)-battery system, controlled using multiple Maximum Power Point Tracking (MPPT) algorithms. The system comprises a rooftop PV array integrated with a DC-DC boost converter and a bidirectional converter interfaced with a battery storage unit, ensuring optimal energy flow and power balance under real operating conditions. Five MPPT strategies, Perturb and Observe (P&O), Incremental Conductance (INC), a proposed hybrid P&O-INC, a Neural Network (NN)-based controller, and Sliding Mode Control (SMC), are implemented and comparatively assessed using MATLAB/Simulink. The boost converter regulates the PV voltage to ensure accurate MPP tracking, while the bidirectional converter manages battery charging and discharging cycles. Performance is evaluated under both controlled irradiance profiles and actual atmospheric variations to capture the system's dynamic behavior, energy harvesting efficiency, and operational stability. The results demonstrate that the proposed hybrid P&O-INC-based MPPT controllers outperform conventional methods by offering faster convergence, reduced steady-state oscillations, and improved charging stability. Overall, the findings confirm the effectiveness of hybrid and intelligent MPPT techniques in enhancing energy conversion efficiency and reliability of rooftop PV-battery systems in arid climates.
