A photovoltaic–thermal (PVT)-assisted electro-Fenton (EF) system (PVT–EF) was developed for temperature-regulated methylene blue (MB) treatment in a 400 mL batch reactor equipped with a foam-nickel cathode and graphite anode. The PVT subsystem supplies electricity to the EF reactor, while recovered low-grade heat regulates wastewater temperature, coupling oxidant generation with the reaction environment. After 20 min, MB decolorization increased from 81.6 ± 1.1 % at 15 °C to 86.8 ± 0.6 % at 30 °C and then decreased to 72.2 ± 1.2 % at 40 °C. At 30 °C, chemical oxygen demand (COD) and total organic carbon (TOC) removals reached 70.6 ± 2.0 % and 62.1 ± 1.7 %, respectively, after 60 min. The bottom exchanger position gave significantly higher 20-min removal than the top position, whereas copper tubing and higher circulation velocity showed the highest observed means without statistically significant overall effects at the α = 0.05 level (material: p = 0.054; velocity: p = 0.288). Computational fluid dynamics (CFD) suggested that a middle exchanger position may provide greater robustness under variable inlet conditions. For the 6.198 MWp engineering scenario, a Global Solar Atlas site-screening yield of 811.5 kWh kWp−1 yr−1 gave an estimated annual AC electricity output of 5.030 × 106 kWh yr−1. This is 1.299 GWh yr−1 below the identified conventional annual loads of 6.329 × 106 kWh yr−1, before additional EF, storage, curtailment, and auxiliary-heating terms are included. Because plant-scale useful-heat delivery and the complete industrial life-cycle and cost inventories were not closed, energy payback time (EPBT), greenhouse-gas payback time (GPBT), net project economics, and annual grid export are not reported numerically for the engineering scenario. Quantitative environmental and cost results are reported only as a laboratory-scale operational screening (Section 4.4), where the operating cost is USD 3.13 m−3 and the operational-screening global-warming potential is 5.44 kg CO2-eq m−3 under the stated photovoltaic–thermal electricity scenario. The results support the feasibility of using recovered PVT heat as an active EF temperature-control variable while defining the remaining requirements for real-wastewater and pilot-scale validation. All degradation results reported here were obtained on a synthetic single-pollutant methylene-blue solution (20 mg L−1) in a 400 mL batch reactor; no real industrial wastewater was treated, and the bench-scale validation protocol and acceptance criteria required to extend the findings to a real effluent are specified in Section 3.6.
Lead-free inorganic perovskites have emerged as promising alternative absorbers to conventional lead-based absorbers for photovoltaics because of their compositional tunability and potential environmental advantage. Among these materials, transition-metal-based CsMBr3 (M = Mn, Fe, Ni) perovskites offer opportunities to tune optoelectronic and charge-transport properties through B-site cation selection. In this work, CsMBr3 (M = Mn, Fe, Ni) perovskites were integrated with m-TiO2 electron-transport layers to investigate composition-dependent structural, morphological, optical, electronic, and interfacial properties. X-ray diffraction, electron microscopy, atomic force microscopy, Kelvin probe force microscopy, UV–vis, steady-state and time-resolved photoluminescence, four-point probe, and photovoltaic measurements were employed. The estimated charge-transfer efficiencies were 57.4%, 88.4%, and 88.5% for CsMnBr3/m-TiO2, CsFeBr3/m-TiO2, and CsNiBr3/m-TiO2, respectively, while the corresponding conductivities increased from 0.00861 to 0.0655 and 0.203 S cm−1. Among the compositions investigated, CsNiBr3 delivered the highest power conversion efficiency of 3.2% and retained 66% of its initial efficiency after 20 days under ambient conditions. Estimated band-edge analysis suggested a more balanced energetic alignment for charge extraction in the CsNiBr3/m-TiO2 architecture. The results demonstrate that B-site transition-metal selection strongly influences interfacial charge transfer, electrical transport, and photovoltaic performance, and provide insight into the design of lead-free photovoltaic systems.
This study investigates the enhancement of phase change heat transfer in molten salts using a combined approach of structural optimization and electrostatic fields. The variations in phase change parameters for single and double tubes were analyzed. The research finds that reducing the center distance between double tubes generally elevates the average temperature and shortens the phase change duration, with the most pronounced effect occurring at a 20 mm spacing: its complete melting time reaches 4065 s, corresponding to a 22.33% reduction compared with the single-tube model. For the optimized 20 mm double-tube structure, applying a 200 V electrostatic field delivers further significant intensification, the peak average temperature rises from 420 K to 424.25 K, the total phase change duration drops to 2762 s, exhibits a 47.23% reduction relative to the single tube and a 32.05% reduction relative to the uncharged double tube. This remarkable improvement arises from the combined effects of electric field-induced Joule heating and electric body force. To further homogenize and focus the electric field distribution, suspended conductive fine strips are introduced as floating conductive structures. This modification generates local high-intensity electric field near the tube wall, increases the total Joule heating by approximately 4%, and reduces the phase change duration to 2321 s. Compared with the uncharged double tube and the conventional electric field scheme, the phase change time is shortened by 42.9% and 15.97%, respectively. This work quantitatively reveals the multi-physics coupling enhancement mechanism of electrostatic field on molten salt phase change.
Sustainable solar drying technologies have been of considerable interest due to their potential to decrease post-harvest losses, cut down on reliance on fossil fuels, and enhance energy-efficient agricultural preservation. In the present study, thermodynamic and machine learning analysis with sustainability perspective was conducted for three passive solar dryer configurations for fenugreek leaves drying: (C1) conventional passive dryer, (C2) chimney assisted multi-pass V-groove absorber dryer and (C3) chimney assisted multi-pass V-groove absorber dryer integrated with phase change material (PCM) thermal storage. The experimental results showed that the PCM-assisted configuration could significantly improve the thermal absorption, air circulation and heat storage at low radiation hours. The moisture ratio of C3 decreased to nearly 0.11 at the end of drying and the product temperature and maximum chamber temperature were 50 °C and 68 °C. The maximum values of thermal efficiency, exergy efficiency, Heat Utilization Factor (HUF) and Specific Moisture Extraction Rate(SMER) for C3 were about 58.6%, 31.4%, 0.76 and 1.14 kg/kWh. The sustainability analysis showed that the potential for CO2 mitigation is almost 58% higher than the potential of the conventional dryer. The PCM assisted system also performed better in terms of thermal retention as it saw an almost 37% higher chamber temperature stability than C1 in the evening mode of operation. To predict the performance, a Gradient-Boasted Regression Tree (GBRT) model was built with good prediction accuracy (R2 values >0.98) and very low RMSE and MAE values. The proposed passive solar dryer thus had good potential for sustainable agriculture drying applications with energy efficiency.
This study mobilizes machine learning (ML) and statistical models to predict Co concentrations and classify anomalies in the Bou Azzer ultramafic Complex of the, which hosts significant Co-Ni-Fe mineralization. Based on a dataset comprising geochemical analyses, enriched with ASTER-derived spectral indices (e.g., Serpentinite index, Ferric Index), we applied three regression models: LR as statistical baseline, PLS regression as multivariate statistical method and RF regression as primary ML algorithm in order to predict Co concentrations, while their corresponding version enabled binary anomaly. A 5-fold nested cross-validation framework, with internal feature selection, was used to ensure solid performance and unbiaised performance evaluation, addressing challenges such as small sample size, multicollinearity (e.g., Sc vs. MgO: r = 0.85), and outliers (e.g., 69 ppm Co). PLS Regression outperformed the others models, achieving a MAE of 5–7 ppm, an Area AUC-PR of 0.94, an AUC-ROC of 0.98, and an R2 of 0.60–0.70. Predictive maps align closely with known Co anomalies along the Bou Azzer Fault, highlighting the critical role of serpentinization as a process creating favorable lithological and alteration conditions for Co mineralization. Feature importance analysis confirms Sc (r = 0.883), MgO (0.867), and Ni (0.786) as key drivers of Co distribution. Despite limitations from the small dataset and complex mineralization, this study demonstrates ML's potential to enhance cost-effective, sustainable exploration, offering a scalable framework for targeting critical metals in ultramafic terrains globally.
