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The study addresses the application of the supercritical water technology in the simultaneous recycling of obsolete solar panels and treatment of persistent organic compounds. The obsolete solar panels samples were characterized by TEM-EDS, SEM, TG-DTA, XRD, WDXRF, MP-AES and elemental analysis. Initially, the optimized parameters for the degradation of solid organic polymers present in residual solar panels via oxidation in supercritical water were defined by an experimental design. Under optimized conditions, 550 °C, reaction time of 60 min, volumetric flow rate of 10 mL min-1 and hydrogen peroxide as oxidant agent, real laboratory liquid wastewater was used as feed solution to achieve 99.6% of polymers degradation. After the reaction, the solid product free of organic matter was recovered and characterized. On average, a metal recovery efficiency of 76% was observed. Metals such as aluminum, magnesium, copper, and silver, that make up most of the metallic fraction, were identified. Only H2, N2 and CO2 were observed in the gaseous fraction. Then, initial data on the treatment of the liquid decomposition by-products, generated during ScW processing, were reported. A total organic carbon reduction of 99.9% was achieved after the subsequential treatment via supercritical water oxidation using the same experimental apparatus. Finally, insights on the scale-up, energy integration and implementation costs of a ScW solid processing industrial unit were presented using the Aspen Plus V9 software.
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Cobre , Água , Águas Residuárias , Compostos Orgânicos , Reciclagem , Polímeros , MagnésioRESUMO
Photovoltaic panels can be colonized by a highly diverse microbial diversity, despite life-threatening conditions. Although they are distributed worldwide, the microorganisms living on their surfaces have never been profiled in tropical regions using 16S rRNA high-throughput sequencing and PICRUst metagenome prediction of functional content. In this work, we investigated photovoltaic panels from two cities in southeast Brazil, Sorocaba and Itatiba, using these bioinformatics approach. Results showed that, despite significant differences in microbial diversity (p < 0.001), the taxonomic profile was very similar for both photovoltaic panels, dominated mainly by Proteobacteria, Bacteroidota and lower amounts of Cyanobacteria phyla. A predominance of Hymenobacter and Methylobacterium-Methylorubrum was observed at the genus level. We identified a microbial common core composed of Hymenobacter, Deinococcus, Sphingomonas, Methylobacterium-Methylorubrum, Craurococcus-Caldovatus, Massilia, Noviherbaspirillum and 1174-901-12 sharing genera. Predicted metabolisms focused on specific genes associated to radiation and desiccation resistance and pigments, were detected in members of the common core and among the most abundant genera. Our results suggested that taxonomic and functional profiles investigated were consistent with the harsh environment that photovoltaic panels represent. Moreover, the presence of stress genes in the predicted functional content was a preliminary evidence that microbes living there are a possibly source of metabolites with biotechnological interest.
Assuntos
Cianobactérias , Extremófilos , Microbiota , Energia Solar , Materiais de Construção/microbiologia , Cianobactérias/genética , Extremófilos/classificação , Extremófilos/genética , Metagenoma , Microbiota/genética , RNA Ribossômico 16S/genética , Clima TropicalRESUMO
The installation of solar plants everywhere in the world increases year by year. Automated diagnostic methods are needed to inspect the solar plants and to identify anomalies within these photovoltaic panels. The inspection is usually carried out by unmanned aerial vehicles (UAVs) using thermal imaging sensors. The first step in the whole process is to detect the solar panels in those images. However, standard image processing techniques fail in case of low-contrast images or images with complex backgrounds. Moreover, the shades of power lines or structures similar to solar panels impede the automated detection process. In this research, two self-developed methods are compared for the detection of panels in this context, one based on classical techniques and another one based on deep learning, both with a common post-processing step. The first method is based on edge detection and classification, in contrast to the second method is based on training a region based convolutional neural networks to identify a panel. The first method corrects for the low contrast of the thermal image using several preprocessing techniques. Subsequently, edge detection, segmentation and segment classification are applied. The latter is done using a support vector machine trained with an optimized texture descriptor vector. The second method is based on deep learning trained with images that have been subjected to three different pre-processing operations. The postprocessing use the detected panels to infer the location of panels that were not detected. This step selects contours from detected panels based on the panel area and the angle of rotation. Then new panels are determined by the extrapolation of these contours. The panels in 100 random images taken from eleven UAV flights over three solar plants are labeled and used to evaluate the detection methods. The metrics for the new method based on classical techniques reaches a precision of 0.997, a recall of 0.970 and a F1 score of 0.983. The metrics for the method of deep learning reaches a precision of 0.996, a recall of 0.981 and a F1 score of 0.989. The two panel detection methods are highly effective in the presence of complex backgrounds.
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Urban environments in Latin America must begin decarbonizing their activities to avoid increasing greenhouse gases (GHGs) emissions rates due to their reliance on fossil fuel-based energy to support economic growth. In this context, cities in Latin America have high potential to convert sunlight into energy. Hence, the main objective of this study was to determine the potential of electricity self-sufficiency production and mitigation of GHG emissions in three medium-sized cities in Peru through the revalorization of underutilized rooftop areas in urban environments. Each city represented a distinct natural area of Peru: Pacific coast, Andean region and Amazon basin. More specifically, photovoltaic solar systems were the technology selected for implementation in these rooftop areas. Data on incident solar energy, temperature and energy consumption were collected. Thereafter, ArcGis10.3 was used to quantify the total usable area in the cities. A series of correction factors, including tilt, orientation or roof profiles were applied to attain an accurate value of usable area. Finally, Life Cycle Assessment was the methodology chosen to calculate the reduction of environmental impacts as compared to the current context of using electricity from the regional grids. Results showed that the cities assessed have the potential to obtain their entire current electricity demand for residential, commercial and public lighting purposes, augmenting energy security and resilience to intermittent natural disasters, with the support of decentralized storage systems. This approach would also translate into substantial reductions in terms of GHG emissions. Annual reductions in GHG emissions ranged from 112ton CO2eq in the city of Ayacucho to over 523kton CO2eq in Pucallpa, showing that cities in the Amazon basin would be the ones that benefit the most in terms of climate change mitigation.
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Photovoltaic panels have a limited lifespan and estimates show large amounts of solar modules will be discarded as electronic waste in a near future. In order to retrieve important raw materials, reduce production costs and environmental impacts, recycling such devices is important. Initially, this article investigates which silicon photovoltaic module's components are recyclable through their characterization using X-ray fluorescence, X-ray diffraction, energy dispersion spectroscopy and atomic absorption spectroscopy. Next, different separation methods are tested to favour further recycling processes. The glass was identified as soda-lime glass, the metallic filaments were identified as tin-lead coated copper, the panel cells were made of silicon and had silver filaments attached to it and the modules' frames were identified as aluminium, all of which are recyclable. Moreover, three different components segregation methods have been studied. Mechanical milling followed by sieving was able to separate silver from copper while chemical separation using sulphuric acid was able to detach the semiconductor material. A thermo gravimetric analysis was performed to evaluate the use of a pyrolysis step prior to the component's removal. The analysis showed all polymeric fractions present degrade at 500 °C.