Dr.-Ing.

Gruna, Robin

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  • Publication
    Sensor-based characterization of construction and demolition waste at high occupancy densities using synthetic training data and deep learning
    ( 2024)
    Kronenwett, Felix
    ;
    Maier, Georg
    ;
    Leiss, Norbert
    ;
    Gruna, Robin
    ;
    Thome, Volker
    ;
    Längle, Thomas
    Sensor-based monitoring of construction and demolition waste (CDW) streams plays an important role in recycling (RC). Extracted knowledge about the composition of a material stream helps identifying RC paths, optimizing processing plants and form the basis for sorting. To enable economical use, it is necessary to ensure robust detection of individual objects even with high material throughput. Conventional algorithms struggle with resulting high occupancy densities and object overlap, making deep learning object detection methods more promising. In this study, different deep learning architectures for object detection (Region-based CNN/Region-based Convolutional Neural Network (Faster R-CNN), You only look once (YOLOv3), Single Shot MultiBox Detector (SSD)) are investigated with respect to their suitability for CDW characterization. A mixture of brick and sand-lime brick is considered as an exemplary waste stream. Particular attention is paid to detection performance with increasing occupancy density and particle overlap. A method for the generation of synthetic training images is presented, which avoids time-consuming manual labelling. By testing the models trained on synthetic data on real images, the success of the method is demonstrated. Requirements for synthetic training data composition, potential improvements and simplifications of different architecture approaches are discussed based on the characteristic of the detection task. In addition, the required inference time of the presented models is investigated to ensure their suitability for use under real-time conditions.
  • Publication
    Particle-Specific Deflection Windows for Optical Sorting by Uncertainty Quantification
    ( 2024)
    Reith-Braun, Marcel
    ;
    Liang, Kevin
    ;
    Pfaff, Florian
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Bauer, Albert
    ;
    Kruggel-Emden, Harald
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    ;
    Hanebeck, Uwe D.
    In current state of the art sensor-based sorting systems, the length of the deflection windows, i.e., the period of nozzle activation and the number of nozzles to be activated, is commonly determined solely by the size of the particles. However, this comes at the cost of the sorting process not accounting for model discrepancies between actual and presumed particle motion, as well as for situations where the available information does not allow for precise determination of nozzle activations. To achieve a desired sorting accuracy, in practice, one is therefore usually forced to enlarge the deflection window to a certain degree, which increases the number of falsely co-deflected particles and compressed air consumption. In this paper, we propose incorporating the uncertainty of the prediction of particle motion of each individual particle into the determination of the deflection windows. The method is based on the predictive tracking approach for optical sorting, which tracks the particles while they move toward the nozzle array based on images of an area-scan camera. Given the extracted motion information from the tracking, we propose an approximation for the distribution of arrival time and location of the particle at the nozzle array assuming nearly constant-velocity or nearly constantacceleration particle motion behavior. By evaluating the quantile function of both distributions, we obtain a confidence interval for the arrival time and location based on prediction uncertainty, which we then combine with the particle size to form the final deflection window. We apply our method to a real sorting task using a pilot-scale chute sorter. Our results obtained from extensive sorting trials show that sorting accuracies can be remarkably improved compared with state-of-the-art industrial sorters and enhanced even further compared with predictive tracking while having the potential to reduce compressed air consumption.
  • Publication
    A survey of the state of the art in sensor-based sorting technology and research
    ( 2024)
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Sensor-based sorting describes a family of systems that enable the removal of individual objects from a material stream. The technology is widely used in various industries such as agriculture, food, mining, and recycling. Examples of sorting tasks include the removal of fungus-infested grains, the enrichment of copper content in copper mining or the sorting of plastic waste according to the type of plastic. Sorting decisions are made based on information acquired by one or more sensors. A particular strength of the technology is the flexibility in sorting decisions, which is achieved by using various sensors and programming the data analysis. However, a comprehensive understanding of the process is necessary for the development of new sorting systems that can address previously unresolved tasks. This survey is aimed at innovative researchers and practitioners who are unfamiliar with sensor-based sorting or have only encountered certain aspects of the overall process. The references provided serve as starting points for further exploration of specific topics.
  • Publication
    Improving Accuracy of Optical Sorters Using Closed-Loop Control of Material Recirculation
    ( 2023)
    Vieth, Jonathan
    ;
    Reith-Braun, Marcel
    ;
    Bauer, Albert
    ;
    Pfaff, Florian
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Kruggel-Emden, Harald
    ;
    Hanebeck, Uwe D.
    Optical sorting is a key technology for the circular economy and is widely applied in the food, mineral, and recycling industries. Despite its widespread use, one typically resorts to expensive means of adjusting the accuracy, e.g., by reducing the mass flow or changing mechanical or software parameters, which typically requires manual tuning in a lengthy, iterative process. To circumvent these drawbacks, we propose a new layout for optical sorters along with a controller that allows re-feeding of controlled fractions of the sorted mass flows. To this end, we build a dynamic model of the sorter, analyze its static behavior, and show how material recirculation affects the sorting accuracy. Furthermore, we build a model predictive controller (MPC) employing the model and evaluate the closed-loop sorting system using a coupled discrete element–computational fluid dynamics (DEM-CFD) simulation, demonstrating improved accuracy.
  • Publication
    GridSort: Image-based Optical Bulk Material Sorting Using Convolutional LSTMs
    ( 2023)
    Reith-Braun, Marcel
    ;
    Bauer, Albert
    ;
    Staab, Maximilian
    ;
    Pfaff, Florian
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    ;
    Kruggel-Emden, Harald
    ;
    Hanebeck, Uwe D.
    Optical sorters separate particles of different classes by first detecting them while they are transported, e.g., on a conveyor belt, and subsequently bursting out particles of undesired classes using compressed air nozzles. Currently, the most promising results are achieved by predictive tracking, a multitarget tracking approach based on extracted midpoints from area-scan camera images that analyzes the particles’ motion and activates the nozzles accordingly. However, predictive tracking requires expert knowledge for setup and preceding object detection. Moreover, particle shapes are only considered implicitly, and the need to solve an association problem rises the computational complexity of the algorithm. In this paper, we present GridSort, an image-based approach that forecasts the scene at the nozzle array using a convolutional long short-term memory neural network and subsequently extracts nozzle activations, thus circumventing the aforementioned weaknesses. We show how GridSort can be trained in an unsupervised fashion and evaluate it using a coupled discrete element–computational fluid dynamics simulation of an optical sorter. We compare our method with predictive tracking in terms of sorting accuracy and demonstrate that it is an easy-to-apply alternative while achieving state-of-the-art results.
  • Publication
    Simulation study and experimental validation of a neural network-based predictive tracking system for sensor-based sorting
    ( 2023)
    Maier, Georg
    ;
    Reith-Braun, Marcel
    ;
    Bauer, Albert
    ;
    Gruna, Robin
    ;
    Pfaff, Florian
    ;
    Kruggel-Emden, Harald
    ;
    Längle, Thomas
    ;
    Hanebeck, Uwe D.
    ;
    Beyerer, Jürgen
    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.
  • Publication
    Detecting Tar Contaminated Samples in Road-rubble using Hyperspectral Imaging and Texture Analysis
    ( 2023)
    Bäcker, Paul
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Polycyclic aromatic hydrocarbons (PAH) containing tar-mixtures pose a challenge for recycling road rubble, as the tar containing elements have to be extracted and decontaminated for recycling. In this preliminary study, tar, bitumen and minerals are discriminated using a combination of color (RGB) and Hyperspectral Short Wave Infrared (SWIR) cameras. Further, the use of an autoencoder for detecting minerals embedded inside tar- and bitumen mixtures is proposed. Features are extracted from the spectra of the SWIR camera and the texture of the RGB images. For classification, linear discriminant analysis combined with a k-nearest neighbor classification is used. First results show a reliable detection of minerals and positive signs for separability of tar and bitumen. This work is a foundation for developing a sensor-based sorting system for physical separation of tar contaminated samples in road rubble.
  • Publication
    Benchmarking a DEM‐CFD Model of an Optical Belt Sorter by Experimental Comparison
    ( 2023)
    Bauer, Albert
    ;
    Maier, Georg
    ;
    Reith-Braun, Marcel
    ;
    Kruggel-Emden, Harald
    ;
    Pfaff, Florian
    ;
    Gruna, Robin
    ;
    Hanebeck, Uwe
    ;
    Längle, Thomas
    A DEM-CFD (discrete element method - computational fluid dynamics) model of an optical belt sorter was extensively compared with experiments of a laboratory-scale sorter to assess the model's accuracy. Brick and sand-lime brick were considered as materials. First, the transport characteristics on the conveyor belt, involving mass flow, lateral particle distribution and proximity, were compared. Second, sorting results were benchmarked for varying mixture proportions at differing mass flows. It was found that the numerical model is able to reproduce the experimental results with high accuracy.
  • Publication
    Towards a feed material adaptive optical belt sorter: A simulation study utilizing a DEM-CFD approach
    ( 2022-09-09)
    Bauer, Albert
    ;
    Maier, Georg
    ;
    Reith-Braun, Marcel
    ;
    Kruggel-Emden, Harald
    ;
    Pfaff, Florian
    ;
    Gruna, Robin
    ;
    Hanebeck, Uwe
    ;
    Längle, Thomas
    In this investigation, a DEM-CFD model of an optical belt sorter is modified to become adaptive to varying belt speeds. For that, the positions and orientations of the nozzle bar and collecting containers are rearranged. Also, the duration of nozzle activation and optimal position of particle ejection are adjusted. For the derivation of optimal velocity-dependent parameters, a two-dimensional model is derived and optimized as a pre-processing step. The derived parameters are applied to the three-dimensional DEM-CFD model. Two optically distinguishable types of demolition waste materials are considered. All conveyor belt velocities are investigated with instantaneously and lagged activated nozzles, which represent fast and realistic triggered nozzle activations. The application of optimized sorting setups shows promising sorting results for a broad range of conveyor belt velocities. The obtained results are discussed in terms of their feasibility in being applied to real optical belt sorters.
  • Publication
    Systematic Determination of the Influence of Factors Relevant to Operating Costs of Sensor-based Sorting Systems
    ( 2022)
    Ludwig, Jan Niklas
    ;
    Flitter, Merle
    ;
    Maier, Georg
    ;
    Bauer, Albert
    ;
    Reith-Braun, Marcel
    ;
    Gruna, Robin
    ;
    Kruggel-Emden, Harald
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Within the next decade, the recycling rates of all waste streams in the European Union are to be consistently increased (EU, 2018). Sensor-based sorting plays a crucial role in achieving those aims. However, the composition of the operating costs of sensor-based sorting systems (SBS), which are made up of compressed air and electricity costs, for example, has not yet been adequately investigated. The main cause is the massive experimental effort required to investigate these costs. In this paper, we pro-pose a systematic approach for determining the operating costs of SBS systems by using Design of Experiments (DoE). For this purpose, experiments are carried out to investigate whether the methodology of DoE is applicable to the use case of SBS. The resulting models are validated with statistical measures and additional experimental runs. For comparability of the results, two different materials, namely construction and demolition waste as well as plastic flakes, with grain sizes between 0 - 10 mm are investigated. With the presented approach high coefficients of determination of the regression models are reached. Consequently, the results show that precise regres-sion models can be derived with reduced effort.