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.
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.
Hybrid photovoltaic/thermal (PV/T) systems offer efficient cogeneration of electricity and heat but suffer from significant efficiency losses at high irradiance due to photovoltaic cell overheating, with conventional cooling methods facing limitations in cost or complexity. This study addresses this challenge by introducing a novel Vshaped perforated fin-tube (VSPFT) cooling design to enhance thermal management and overall performance. Utilizing a rigorously validated three-dimensional computational fluid dynamics (CFD) model alongside comprehensive techno-economic and environmental assessments, the system's efficacy was evaluated. The results demonstrate that the VSPFT design reduces the PV surface temperature by 6 K under peak irradiance (995 W/ m2), elevating electrical efficiency from 13.2% to 15.9% and thermal efficiency from 45% to 50%. For a 10-panel array (6.48 m2), this configuration achieves a promising payback period of 4.27 years and reduces COQ emissions by 71.8 kg annually. These findings confirm the VSPFT system as a scalable, cost-effective solution for enhanced solar energy utilization, suitable for residential and industrial applications, and that contributes to broader decarbonization goals.
Accurate determination of the temperature coefficient of maximum power (TCPmax) is essential for evaluating photovoltaic (PV) module performance. This work compares two techniques for TCPmax determination: a conventional outdoor method and a novel approach based on long-term monitoring. The research was conducted on grid-connected PV installation located at Algiers. Three representative PV modules exhibiting different failure severity levels were selected for analysis. Results show good agreement between two methods, with deviations not exceeding ± 5% in most cases. However, TCPmax values were found to decrease as the severity of module degradation increased, indicating a strong correlation between failure severity and temperature sensitivity. The proposed method, while requiring a longer data collection period, offers a cost-effective, non-intrusive alternative suitable for in-field applications. The comparative analysis highlights the advantages and limitations of each technique and demonstrates the potential of the proposed method for real-time performance diagnostics and degradation assessment in PV systems.
