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sensor-based sorting of waste digital devices by CNN-based image recognition using composite images created from mass and 2D/3D appearances

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JOURNAL OF MATERIAL CYCLES AND WASTE MANAGEMENT 2023年第2期25卷 851-862页

作者： Koyanaka, Shigeki Kobayashi, Kenichiro Natl Inst Adv Ind Sci & Technol Environm Management Res Inst 16-1 Onogawa Tsukuba Ibaraki 3058569 Japan

To improve the recycling process of waste from electrical products, we developed a sensor-based sorting system using convolutional neural network (CNN)-based image recognition. We sampled a total of 1571 models in three categories of waste digital devices (484 smartphones, 580 non-smart mobile phones, and 507 digital cameras) and examined a method of fusing information from 2D/3D appearance, volume, and mass into a single input image to VGG16 (a well-known CNN). The identification performance of the VGG16 was improved by pre-image-processing that placed the digital device at a fixed orientation and fixed location. In the three-category classification of 628 untrained digital devices, the accuracy was 97.7% for using the "4ch " image that results from inserting the 3D image fusing density information of the digital device into a fourth channel of the BGR color image. In the individual product model identification of trained 943 digital devices using the output vector from the fully connected layer of VGG16, the correctly identified rate was 90.5% for using the "BG + 3D " image that results from overwriting the third channel of the BGR color image with the 3D image fusing density information of the digital device under "fine-tuning " of the VGG16 pre-trained by the ImageNet dataset.

关键词： sensor-based sorting Image recognition Convolutional neural network (CNN) VGG16 Waste digital device

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Explainable AI for sensor-based sorting systems

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TM-TECHNISCHES MESSEN 2023年第3期90卷 154-165页

作者： Anneken, Mathias Veerappa, Manjunatha Huber, Marco F. Kuehnert, Christian Kronenwett, Felix Maier, Georg Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning Karlsruhe Germany Fraunhofer IPA Dept Cyber Cognit Intelligence CCI Stuttgart Germany Univ Stuttgart Inst Ind Mfg & Management IFF Stuttgart Germany

Explainable artificial intelligence (XAI) can make machine learning based systems more transparent. This additional transparency can enable the use of machine learning in many different domains. In our work, we show how XAI methods can be applied to an autoencoder for anomaly detection in a sensor-based sorting system. The setup of the sorting system consists of a vibrating feeder, a conveyor belt, a line-scan camera and an array of fast-switching pneumatic valves. It allows the separation of a material stream into two fractions, realizing a binary sorting task. The autoencoder tries to mimic the normal behavior of the nozzle array and thus can detect abnormal behavior. The XAI methods are used to explain the output of the autoencoder. As XAI methods global and local approaches are used, which means we receive explanations for both a single result and the whole autoencoder. Initial results for both approaches are shown, together with possible interpretations of these results.

关键词： autoencoder explainable artificial intelligence sensor-based sorting

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Simulation study and experimental validation of a neural network-based predictive tracking system for sensor-based sorting

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TM-TECHNISCHES MESSEN 2023年第7-8期90卷 489-499页

作者： Maier, Georg Reith-Braun, Marcel Bauer, Albert Gruna, Robin Pfaff, Florian Kruggel-Emden, Harald Laengle, Thomas Hanebeck, Uwe D. Beyerer, Juergen Fraunhofer IOSB Inst Optron Syst Technol & Image Exploitat Karlsruhe Germany Karlsruhe Inst Technol KIT Intelligent Sensor Actuator Syst Lab Karlsruhe Germany TU Berlin Chair Mech Proc Engn & Solids Proc MVTA Berlin Germany Karlsruhe Inst Technol KIT Vis & Fus Lab IES Karlsruhe Germany

sensor-based sorting offers cutting-edge solutions for separating granular materials. The line-scanning sensors currently in use in such systems only produce a single observation of each object and no data on its movement. According to recent studies, using an area-scan camera has the potential to reduce both characterization and separation error in a sorting process. A predictive tracking approach based on Kalman filters makes it possible to estimate the followed paths and parametrize a unique motion model for each object using a multiobject tracking system. While earlier studies concentrated on physically-motivated motion models, it has been demonstrated that novel machine learning techniques produce predictions that are more accurate. In this paper, we describe the creation of a predictive tracking system based on neural networks. The new algorithm is applied to an experimental sorting system and to a numerical model of the sorter. Although the new approach does not yet fully reach the achieved sorting quality of the existing approaches, it allows the use of the general method without requiring expert knowledge or a fundamental understanding of the parameterization of the particle motion model.

关键词： DEM-CFD machine learning multiobject tracking sensor-based sorting visual inspection

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Coarse Beneficiation of Trona Ore by sensor-based sorting

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MINING METALLURGY & EXPLORATION 2022年第5期39卷 2179-2185页

作者： Bellusci, Nicholas Taylor, Patrick R. Spiller, D. Erik Braman, Victor Colorado Sch Mines Kroll Inst Extract Met Golden CO 80401 USA Chem North Amer Inc Green River WY USA

Approximately 30% of the world's production of soda ash comes from mining and processing of naturally occurring deposits of sodium carbonate containing species. Of these deposits, the largest and most profitable have been the trona beds found near Green River, Wyoming. Processing of trona ores is water-energy intensive, particularly due to unit operations of calcination, dissolution, and evaporative crystallization to wield a high-quality dense soda ash product and reject the insoluble gangue species, primarily in the form of oil shale. However, it is speculated that the glass-manufacturing industry, the largest consumer of soda ash, may be able to use a product of a lower grade and consistent level of the insoluble constituents. Thus, pre-concentration of trona ore was investigated using sensor-based sorting, to produce a beneficiated trona stream that would be fed to calcination. The study encompassed preparation of a run-of-mine (ROM) Green River ore sample, selection of 25-75-mm-sized specimens, imaging of the specimens by x-ray transmission (XRT), color, and laser-induced fluorescence (LIF)-based sensors, insoluble analysis of the specimens, and a bench-scale sorting test. Results of the study established that a correlation between the color of the individual ore particles and their insoluble content existed and that a surface detection-based sensor, particularly LIF, was apparently more suitable than XRT to produce a beneficiated trona stream. The bench-scale sorting test demonstrated that a 65% separation efficiency and a 70% recovery of trona could be achieved.

关键词： Trona Soda ash Beneficiation sensor-based sorting

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Influence of material alterations and machine impairment on throughput related sensor-based sorting performance

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WASTE MANAGEMENT & RESEARCH 2021年第1期39卷 122-129页

作者： Kueppers, Bastian Schloegl, Sabine Friedrich, Karl Lederle, Laura Pichler, Celestine Freil, Julia Pomberger, Roland Vollprecht, Daniel Univ Leoben Chair Waste Proc Technol & Waste Management Franz Josef Str 18 A-8700 Leoben Austria

Experiments with sensor-based sorting (SBS) machinery provide insight into the effect of throughput rate and input composition on the sorting performance. For this purpose, material mixtures with certain compositions and particle size distributions were created from waste fractions and sorted at various throughput rates. To evaluate the sorting performance of the SBS unit (using near infrared technology) in dependence of the applied load, four assessment factors concerning the output fractions were studied: yield, product purity, recovery/product quantity and incorrectly discharged share of reject particles. The influences on the assessment parameters of light twodimensional (2D) particles in the input of a sorting stage and failing air valves in an SBS unit were evaluated for various input compositions at different throughput rates. It was found that a share of approximately 5 wt% 2D particles in the input had a similar negative effect on the yield as the malfunction of 20% of all air valves in an SBS machine at high throughput rates. Additionally, the failure of the air valves reduced the product purity of the sorting stage at increased throughput rates. Furthermore, qualitative observations concerning systematic effects of prior studies could be confirmed. Resulting graphs for a specific input composition of an SBS unit at varying throughput rates could be used to adjust the throughput rate to meet the exact demands for a sorting stage.

关键词： sensor-based sorting sorting performance purity yield recovery throughput rate input composition NIR

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Experimental Evaluation of a Novel sensor-based sorting Approach Featuring Predictive Real-Time Multiobject Tracking

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第2期68卷 1548-1559页

作者： Maier, Georg Pfaff, Florian Pieper, Christoph Gruna, Robin Noack, Benjamin Kruggel-Emden, Harald Langle, Thomas Hanebeck, Uwe D. Wirtz, Siegmar Scherer, Viktor Beyerer, Jurgen Fraunhofer IOSB D-76131 Karlsruhe Germany Inst Optron Syst Technol & Image Exploitat D-76131 Karlsruhe Germany Karlsruhe Inst Technol Intelligent Sensor Actuator Syst Lab D-76131 Karlsruhe Germany Ruhr Univ Bochum Dept Energy Plant Technol D-44801 Bochum Germany TU Berlin Mech Proc Engn & Solids Proc D-10623 Berlin Germany

sensor-based sorting is a machine vision application that has found industrial application in various fields. An accept-or-reject task is executed by separating a material stream into two fractions. Current systems use line-scanning sensors, which is convenient as the material is perceived during transportation. However, line-scanning sensors yield a single observation of each object and no information about their movement. Due to a delay between localization and separation, assumptions regarding the location and point in time for separation need to be made based on the prior localization. Hence, it is necessary to ensure that all objects are transported at uniform velocities. This is often a complex and costly solution. In this article, we propose a new method for reliably separating particles at nonuniform velocities. The problem is transferred from a mechanical to an algorithmic level. Our novel advanced image processing approach includes equipping the sorter with an area-scan camera in combination with a real-time multiobject tracking system, which enables predictions of the location of individual objects for separation. For the experimental validation of our approach, we present a modular sorting system, which allows comparing sorting results using a line-scan and area-scan camera. Results show that our approach performs reliable separation and hence increases sorting efficiency.

关键词： Automated visual inspection machine vision real-time multiobject tracking sensor-based sorting

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Feasibility Study for Finding Mathematical Approaches to Describe the Optimal Operation Point of sensor-based sorting Machines for Plastic Waste

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POLYMERS 2023年第21期15卷 4266-4266页

作者： Friedrich, Karl Kuhn, Nikolai Pomberger, Roland Koinig, Gerald Univ Leoben Mineral Proc Dept Mineral Resources Engn Franz Josef Str 18 A-8700 Leoben Austria Univ Leoben Dept Environm & Energy Proc Engn Waste Proc Technol & Waste Management Franz Josef Str 18 A-8700 Leoben Austria

At present, sensor-based sorting machines are usually not operated at the optimal operation point but are either overrun or underrun depending on the availability of waste streams. Mathematical approaches for predefined ideal mixtures can be found based on the input stream composition and the throughput rate. This scientific article compares whether and under what conditions these approaches can be applied to sensor-based sorting machines. Existing data for predefined ideal mixtures are compared with newly generated data of real waste on three sensor-based sorting setups in order to make significant statements. Five samples of 3D plastics at regular intervals were taken in a processing plant for refuse-derived fuels. With the comparison of all these results, four hypotheses were validated, related to whether the same mathematical approaches can be transferred from ideal mixtures to real waste and whether they can be transferred to sensor-based sorting machines individually or depending on the construction type. The developed mathematical approaches are regression models for finding the optimal operation point to achieve a specific sensor-based sorting result in terms of purity and recovery. For a plant operator, the main benefit of the findings of this scientific article is that purity could be increased by 20% without substantially adapting the sorting plant.

关键词： sensor-based sorting NIR sorting optimal operation point throughput rate input composition purity recovery regression model

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Investigation of sensor-based sorting and selective comminution for pre-concentration of an unusual parisite-rich REE ore, South Namxe, Vietnam

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MINERALS ENGINEERING 2022年第0期177卷 107371-107371页

作者： Cardenas-Vera, Alan Hesse, Max Moeckel, Robert Merker, R. Gerhard Heinig, Thomas Quang Van Phan Helmholtz Zentrum Dresden Rossendorf Helmholtz Inst Freiberg Resource Technol Chemnitzer Str 40 D-09599 Freiberg Germany TU Bergakad Freiberg Inst Mineral Proc Machines Lampadiusstr 4 D-09599 Freiberg Germany Merker Mineral Proc MMP Oertenroeder Str 21 D-35329 Gemuenden Germany Hanoi Univ Min & Geol Fac Environm Hanoi 100000 Vietnam Univ British Columbia Norman B Keevil Inst Min Engn 517-6350 Stores Rd Vancouver BC V6T 1Z4 Canada Umwelt & Ingenieurtech GmbH Dresden UIT Zum Windkanal 21 D-01109 Dresden Germany

The South Namxe deposit in Vietnam contains unusually high quantities of parisite, in addition to minor quantities of other fluorocarbonates, such as synchysite and bastnaesite. Together these minerals constitute the main REE-bearing minerals. This carbonatite-related deposit presents tabular (dykes) or lens-form ore bodies, up to several tens of meters wide and tens to hundreds of meters in length, that are hosted in Permian limestone and Triassic basalts. The main gangue minerals in the ore bodies are from the barite-celestine solid solution series, ankerite and calcite. It was considered necessary to assess the different grain characteristics presented by the presence of parisite intergrowths and its association with minerals of the barite-celestine solid solution series to structure a probable initial beneficiation strategy. Mineral liberation analysis revealed that an optimal liberation particle size between 45 mu m and 90 mu m could be achieved without overgrinding the material. Additionally, it showed a strong mineral association between fluorocarbonates, baryto-celestine, and calcite. Grinding below 100 mu m is necessary. One substantial energy saving potential is removing the host rock prior to grinding. The potential for pre-concentration with sensor-based sorting and selective comminution is investigated in a prestudy. sensor-based sorting using X-ray transmission resulted in an enrichment of REE by a factor of 4, a reduction of the mass stream down to approximately 30%, and a loss of rare earths below 2%. Selective comminution using a pin mill, at a circumferential speed of 20 m/s, yielded a recovery of 96.5% of REE minerals and allowed rejection of 27% of barren material. This shows that both selective comminution and sensor-based sorting can be considered as complementary beneficiation steps when processing these REE ores.

关键词： sensor-based sorting Selective Comminution Mineral Liberation X-ray Transmission Parisite

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Qualitative analysis of post-consumer and post-industrial waste via near-infrared, visual and induction identification with experimental sensor-based sorting setup

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METHODSX 2022年 9卷 101686页

作者： Friedrich, K. Koinig, G. Pomberger, R. Vollprecht, D. Univ Leoben Dept Environm & Energy Proc Engn Chair Waste Proc Technol & Waste Management Franz Josef Str 18 A-8700 Leoben Austria

A B S T R A C T sensor-based sorting in waste management is a method to separate valuable material or contaminants from a waste stream. Depending on the separation property different types of sensors are used. Separation properties and their corresponding sensors are e.g. molecular composition with near-infrared sensors, colour with visual spectroscopy or colour line scan cameras, or electric conductivity with electromagnetic *** methods described in this paper deal with the development of sorting models for a specific near -infrared, a visual spectroscopy and an induction sensor . For near-infrared and visual spectroscopy software is required to create sorting models, while for induction only machine settings have to be adjusted and optimized for a specific sorting task. These sensors are installed in the experimental sensor-based sorting setup at the Chair of Waste Processing Technology and Waste Management located at the Montanuniversitaet Leoben. This sorting stand is a special designed machine for the university to make experiments on sensor-based sorting in lab scale. It can be used for a variety of waste streams depending on the grain size and the pre-conditioning for the sensor-based sorting machine. In detail the methods to create these sorting models are described and validated with plastic, glass and metal *** dot Near-infrared spectroscopy measures the molecular composition of near-infrared-active *** dot Visual spectroscopy measures the absorption of visible light by chemical *** dot Induction sensors use induced currents to detect nearby metal objects.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： sensor-based sorting Identification model Near-infrared sorting (NIR sorting) Visual-spectroscopy sorting (VIS sorting) Induction sorting

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Influence of surface roughness and surface moisture of plastics on sensor-based sorting in the near infrared range

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WASTE MANAGEMENT & RESEARCH 2019年第8期37卷 843-850页

作者： Kueppers, Bastian Schloegl, Sabine Oreski, Gernot Pomberger, Roland Vollprecht, Daniel Univ Leoben Waste Proc Technol & Waste Management AVAW Franz Josef Str A-188700 Leoben Austria Polymer Competence Ctr Leoben GmbH Leoben Austria

In the project 'NEW-MINE' the use of sensor-based sorting machinery in the field of 'landfill mining' is investigated. Defilements pose a particular challenge in the treatment and sorting of plastics contained in landfills. For this reason, the effects of various pollutants caused by the interactions in the landfill body or the mechanical treatment steps in landfill mining are examined. In the following elaboration, the focus is on the influences of surface moisture and surface roughness of plastics on sensor-based sorting by means of near-infrared technology. Near-infrared radiation (NIR) in a wavelength range of 990 nm to 1500 nm has been used for the detection and classification of plastic particles. The experiments demonstrate that increased surface roughness reduces signal noise and thereby improves the classification of both spectrally similar and transparent plastics, but reduces the yield of low-softening plastics because their sliding speed on a sensor-based chute sorter varies as a result of the heating of the chute. Surface moisture causes the absorption of radiation from 1115 nm (high density polyethylene [HDPE], linear low density polyethylene [LLDPE], polyethylen terephthalate [PET] and polyvinylchloride [PVC]) or from 1230 nm (low density polyethylene [LDPE], polypropylene [PP] and thermoplastic polyurethane [TPU]) up to at least 1680 nm, which causes amplification or attenuation of various extremes in the derivative. However, the influence of surface moisture on the yield of plastics is usually very low and depends on the spectral differences between the different plastics.

关键词： sensor-based sorting hyperspectral imaging near-infrared spectroscopy influence of defilements surface roughness surface water

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