925 research outputs found

    1-[2-(2,6-Dichloro­benz­yloxy)-2-(2-fur­yl)eth­yl]-1H-benzimidazole

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    In the mol­ecule of the title compound, C20H16Cl2N2O2, the planar benzimidazole ring system is oriented with respect to the furan and dichloro­benzene rings at dihedral angles of 53.39 (6) and 31.04 (5)°, respectively. In the crystal structure, inter­molecular C—H⋯Cl hydrogen bonds link the mol­ecules into centrosymmetric R 2 2(8) dimers. These dimers are connected via a C—H⋯π contact between the benzimidazole and the furan rings, and π–π contacts between the benz­imidazole and dichloro­benzene ring systems [centroid–centroid distances = 3.505 (1), 3.567 (1), 3.505 (1) and 3.567 (1) Å]

    1-[2-(3,4-Dichloro­benz­yloxy)-2-phenyl­ethyl]-1H-benzimidazole

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    In the mol­ecule of the title compound, C22H18Cl2N2O, the planar benzimidazole ring system is oriented with respect to the phenyl and dichloro­benzene rings at dihedral angles of 12.73 (3) and 36.57 (4)°, respectively. The dihedral angle between the dichloro­benzene and phenyl rings is 29.95 (6)°. There are C—H⋯π contacts between the benzimidazole and dichloro­benzene rings, between the benzimidazole and phenyl rings, and between a methylene group and the dichlorobenzene ring

    1-{2-Phenyl-2-[4-(trifluoro­meth­yl)­benzyl­oxy]eth­yl}-1H-benzimidazole

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    The asymmetric unit of the crystal structure of the title compound, C23H19F3N2O, contains two independent mol­ecules. In the two mol­ecules the planar benzimidazole ring systems are oriented with respect to the phen­yl/trifluoro­methyl­benzene rings at dihedral angles of 9.62 (6)/78.63 (7) and 2.53 (8)/83.83 (9)°. In the crystal structure, inter­molecular C—H⋯N hydrogen bonds link the mol­ecules into R 2 2(6) dimers. The mol­ecules are elongated along [001] and stacked along the b axis

    Assessing the efficiency of Laser-Induced Breakdown Spectroscopy (LIBS) based sorting of post-consumer aluminium scrap

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    The aluminium Twitch fraction of a Belgian recycling facility could be further sorted by implementing Laser-Induced Breakdown Spectroscopy (LIBS). To achieve this goal, the presented research identifies commercially interesting output fractions and investigates machine learning methods to classify the post-consumer aluminium scrap samples based on the spectral data collected by the LIBS sensor for 834 aluminium scrap pieces. The classification performance is assessed with X-Ray Fluorescence (XRF) reference measurements of the investigated aluminium samples, and expressed in terms of accuracy, precision, recall, and f1 score. Finally, the influence of misclassifications on the composition of the desired output fractions is evaluated.Peer ReviewedPostprint (published version

    Techno-economic assessment of robotic sorting of aluminium scrap

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    Due to shifting material use in several sectors, such as the automotive sector, the demand for wrought aluminium alloys is significantly increasing. Because of their low weight and desirable mechanical properties, wrought aluminium alloys find their use in many different applications. However, the primary production of aluminium is extremely energy intensive. Therefore, using secondary aluminium yields major environmental benefits. Hence, in order to avoid degradation of the aluminium quality during recycling, sorting aluminium alloys, based on their alloying elements, is necessary. Today, various non-ferrous metal fractions are either still sorted manually in unhealthy working conditions, resulting in either high labour costs, or the export of this waste stream to countries with a lower labour cost. With the emergence of novel spectrometric techniques, such as laser-induced breakdown spectrometry (LIBS) and deep learning computer vision techniques, the technical feasibility of classifying different aluminium alloys has been demonstrated. Therefore, the techno-economic viability of a robotic sorting process, that could be combined with such advanced classification systems, is presented. This study presents the development and evaluation of a robotic sorting system consisting of; a vision system, a conveyor, a SCARA robot and a pneumatic gripper. The vision system recognises the dimensions and positions of the objects on the conveyor and communicates with an innovative sequence planning algorithm. The use of experimental data enables to obtain realistic insights in the sorting efficiencies that can be obtained. The initial economic analysis illustrates the substantial potential of the proposed robotic sorting approach. To overcome saturation of the conveyor belt, two of the proposed systems are assumed to be capable of sorting 20.000 tons of aluminium annually each equipped with 6 robots creating a total added revenue up to 1,95 million euro per year.Peer ReviewedPostprint (published version

    Modeling the interstellar aromatic infrared bands with co-added spectra of PAHs

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    The observed variations in profiles of the interstellar aromatic infrared bands correlate with the object type and are indicative of PAH populations existing i n different sources. Spectroscopic studies on PAHs can provide tools for the int erpretation of variations accompanying the AIBs. As the observed spectra results from a mix of possible species in the region attempt is made to model this comp osite spectra by co-adding emissions from PAHs in different size groups. Theoretical IR data of PAHs having 10 to 96 carbon atoms is used to obtain emis sion spectra. The models are taken in size groups making up of small, medium and large PAHs. The models show good profile match with observations for the 7.7 μm\mu m complex having sub-features at 7.6 and 7.8 μm\mu m. The 7.6 μm\mu m sub-feature dominates in the spectra of medium sized PAH cations matching observations from UV rich interstellar environments. The 7.8 μm\mu m component is more intense in the spectra of large PAH cations (model III) correlating with observations from benign astrophysical regions. A possible interpretation for the observations of CHC-H out-of-plane bend modes and the weak outliers on the blue side of the intense 11.2 μm\mu m band is proposed. The models provide pointers to possible PAH populations in different regions.Comment: accepted for publication in A&

    Enhanced plastic recycling using RGB+depth fusion with massFaster and massMask R-CNN

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    © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe rapid increase in waste generation from electrical and electronic equipment (WEEE) has created the need for more advanced sensor-based systems to sort this complex type of waste. Therefore, this study proposes a method for object detection, instance segmentation, and mass estimation of plastics and contaminants using the fusion of RGB and depth (D) images. The methodology is based on the Faster and Mask R-CNN with an extra head for the mass estimation. In addition, a pre-processing method to enhance the depth image (ED) is proposed. To evaluate the data fusion and pre-processing method, two data sets of plastics and impurities were created containing images with and without overlapping samples. The first data set contains 174 RGB images and depth (D) maps of 3146 samples, excluding their mass value, while the second data set contains 42 RGB and D images of 766 pieces together with their mass. The first and second data sets were used to evaluate the performance of Mask and Faster R-CNN. Further, the second data set was used to evaluate the network’s performance with the additional head for mass estimation.The proposed method achieved 0.75 R 2 , 1.39 RMSE, and 0.81 MAE with an IoU greater than 50% using the network Resnet50_FPN_RGBED. Hence, it can be concluded that the presented method can distinguish plastics from other materials with reasonable accuracy. Furthermore, the mass of each detected particle can be estimated individually, which is of great relevance for the recycling sector. Knowing the mass distribution and the percentage of contaminants in a waste stream of mixed plastics can be valuable for adjusting the parameters of upstream and downstream sorting processes.Peer ReviewedPostprint (author's final draft

    Simultaneous mass estimation and class classification of scrap metals using deep learning

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    © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksWhile deep learning has helped improve the performance of classification, object detection, and segmentation in recycling, its potential for mass prediction has not yet been explored. Therefore, this study proposes a system for mass prediction with and without feature extraction and selection, including principal component analysis (PCA). These feature extraction methods are evaluated on a combined Cast (C), Wrought (W) and Stainless Steel (SS) image dataset using state-of-the-art machine learning and deep learning algorithms for mass prediction. After that, the best mass prediction framework is combined with a DenseNet classifier, resulting in multiple outputs that perform both object classification and object mass prediction. The proposed architecture consists of a DenseNet neural network for classification and a backpropagation neural network (BPNN) for mass prediction, which uses up to 24 features extracted from depth images. The proposed method obtained 0.82 R2, 0.2 RMSE, and 0.28 MAE for the regression for mass prediction with a classification performance of 95% for the C&W test dataset using the DenseNet+BPNN+PCA model. The DenseNet+BPNN+None model without the selected feature (None) used for the CW&SS test data had a lower performance for both classification of 80% and the regression (0.71 R2, 0.31 RMSE, and 0.32 MAE). The presented method has the potential to improve the monitoring of the mass composition of waste streams and to optimize robotic and pneumatic sorting systems by providing a better understanding of the physical properties of the objects being sorted.Peer ReviewedPostprint (author's final draft
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