In consequence of the electrification and the increased adoption of lightweight structures in the automotive industry, global demand for wrought Aluminum (Al) is expected to rise while demand for cast Al will stagnate...
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In consequence of the electrification and the increased adoption of lightweight structures in the automotive industry, global demand for wrought Aluminum (Al) is expected to rise while demand for cast Al will stagnate. Since cast alloys can only be converted to wrought alloys by energy-intensive processes, the most promising strategy to avoid the emergence of excess Al cast alloys scrap is to sort cast from wrought alloys. To date, the separation of complex mixes of non-ferrous metals often implies the use of either or both sink-float techniques and/or X-ray fluorescence (XRF) based sorting. Therefore, the presented research develops an efficient method to classify cast and wrought (C&W) alloys in a real-time system with a conveyor belt using transfer learning methods, such as fine-tuning and feature extraction. Five CNNs are evaluated to classify C&W alloys using colour and depth images and transfer learning methods. In addition, the early fusion and late fusion of colour and depth images of C&W Al are investigated. For early fusion, data is added as an extra input channel to the first convolution layer of the CNN, and for later fusion, the images are fed in two separate subnetworks with the same architecture, where the parameters of the fully-connected layers are concatenated in both subnetworks. Our approach shows that late fusion CNN DenseNet allows obtaining the best performances and can achieve up to 98% accuracy.
Integrating multi-sensor systems to sort and monitor complex waste streams is one of the most recent innovations in the recycling industry. The complementary strengths of Laser-Induced Breakdown Spectroscopy (LIBS) an...
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Integrating multi-sensor systems to sort and monitor complex waste streams is one of the most recent innovations in the recycling industry. The complementary strengths of Laser-Induced Breakdown Spectroscopy (LIBS) and computervision systems offer a novel multi-sensor solution for the complex task of sorting aluminum (Al) post -consumer scrap into alloy groups. This study presents two novel methods for fusing RGB and Depth images with LIBS using deeplearning models. The first method is a single-output model that combines LIBS UNET and two DenseNets in a late fusion framework. The second method is a multiple-output model that uses the structure of the single-output model to enhance learning and avoid overfitting. In particular, the network has two outputs that enable the regularization of the individual sensors. A data set of 773 aluminum scrap pieces was created with two sets of ground truth-values, corresponding to the two envisaged sorting tasks, to train and evaluate the developed models. The first sorting task is separating Cast and Wrought (C&W) aluminum. The second is the division of the post-consumer aluminum scrap into three commercially interesting fractions. The single-output model performs best for separating C&W, with a Precision, Recall, and F1-score of 99%. The multiple-output model performs best for classifying the three selected commercial fractions, with a Precision, Recall, and F -score of 86%, 83%, and 84%, respectively. The presented data fusion method for LIBS and computervision images encompasses the great potential for sorting post-consumer aluminum scrap. By sorting mixed post -consumer aluminum scrap in alloy groups, more wrought-to-wrought recycling can occur, and quality losses can be mitigated during recycling.
While deeplearning 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 ...
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While deeplearning 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 deeplearning 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.
The trend of increased use of lithium-ion batteries, challenges the cost-effectiveness and safety of manual battery separation during the end-of-life treatment of Waste Electric and Electronic Equipment (WEEE). Theref...
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The trend of increased use of lithium-ion batteries, challenges the cost-effectiveness and safety of manual battery separation during the end-of-life treatment of Waste Electric and Electronic Equipment (WEEE). Therefore, the need for novel techniques to separate and sort batteries from WEEE is increasingly important. For this reason, the presented research investigates the potential to facilitate the development of novel techniques for battery extraction and sorting by examining the technical feasibility of predicting the presence, location, and type of batteries inside electronic devices with a deeplearning object detection network using X-Ray images of the internal structure of WEEE. To determine the required X-ray imaging parameters, 532 electronic devices were arbitrarily collected from a recycling facility. From each product, two X-Ray Transmission (XRT) images were captured at two different X-Ray source configurations. Results obtained with the limited dataset are promising, demonstrating a 91% true positive rate and only a 6% false positive rate for classifying battery-containing devices. Moreover, a precision of 89% and a recall of 81% are demonstrated for battery detection, and an average precision of 85% and an average recall of 76% are demonstrated to distinguish amongst the following six battery technologies: cylindrical nickel-metal hydride or nickel-cadmium, cylindrical alkaline, cylindrical zinc-carbon, cylindrical lithium-ion, pouch lithium-ion, and button cell batteries. These results demonstrate the potential of using deeplearning object detection on XRT-generated images for both automated battery extraction and sorting, regardless of the condition or shape of the products.
Because of the low concentrations of Tantalum on printed wiring boards, today it is not economically viable to recycle Tantalum in pyro- and hydrometallurgical processes. However, in case the Tantalum can be up concen...
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Because of the low concentrations of Tantalum on printed wiring boards, today it is not economically viable to recycle Tantalum in pyro- and hydrometallurgical processes. However, in case the Tantalum can be up concentrated, the recycling of the Tantalum can become economically viable in the multi-stage process used to refine Tantalum from ores. Therefore, the presented research investigates the techno-economic potential of selectively removing Tantalum capacitors from PWBs prior to treating the complete PWBs in integrated precious metal smelting and refining installations. In the presented research the economic potential is analysed using return on investment analysis for automated selective dismantling and the viability of manual selective dismantling. These analyses are based on the measured metal concentration of Tantalum capacitors, the composition of a typical PWB and the results of lab experiments for the removal and detection of components using deep learning computer vision.
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