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.

Sensor-based sorters operating in the Near-Infrared (NIR) range are commonly used to sort post-consumer plastics. However, this method fails, when the NIR light is fully absorbed by carbon black pigments, which are present in black plastics. Mid-Wave Infrared (MWIR) is less absorbed by carbon black and therefore provides a promising alternative wavelength range for analyzing black polymers. This study compares MWIR to NIR hyperspectral imaging for classifying black and colored plastic waste. We collected spectral data from five common polymers found in post-consumer packaging, namely High-Density Polyethylene (HDPE), Low-Density Polyethylene (LDPE), Polyethylene Terephthalate (PET), Polypropylene (PP), and Polystyrene (PS). We classified the spectra using several chemometric methods, including a convolutional neural network (CNN). The results quantitatively verify the superior performance of MWIR over NIR for classifying black plastics. MWIR achieved a balanced accuracy of 83.4% compared to 47.5% for NIR, when using a CNN, which outperformed other chemometric methods for all sensors and sample sets. On the other hand, NIR surpasses MWIR by 7 percentage points for colored plastics in balanced accuracy. These findings suggest that MWIR hyperspectral imaging is an effective alternative to NIR hyperspectral imaging for sorting post-consumer packaging waste, especially when the share of black plastics is high.

Pneumatic ejection has dominated sensor-based sorting for decades, yet diffuse air jets cause bycatch at high mass throughput, and compressed-air generation accounts for a large share of operating cost. We propose (i) a grid-based sorting architecture replacing the conventional single nozzle bar with a two-dimensional grid, and (ii) impact-based mechanical actuators within the grid. Together, these eliminate compressed air entirely while increasing spatio-temporal resolution by leveraging redundant particle–actuator assignments. To coordinate online ejection—deciding which actuators to trigger and when—we formulate sorting as a finite-horizon soc problem and introduce mtcr. mtcr integrates olf soc, mixed discrete–continuous-time modeling, situation-aware action-space reduction, and a greedy rollout heuristic, supported by analytically tractable evaluation of expected sorting costs. In high-mass-flow simulations with equal accept/reject ratios, mtcr maintains both tnr and tpr above 90 , demonstrating effective actuator coordination and bycatch reduction. Laboratory experiments on a 56-solenoid prototype show that the grid significantly exceeds a state-of-the-art pneumatic chute sorter in rejection performance at matched bycatch level, with actuation energy being at least 79 times lower. Grid-based mechanical sorting thus offers a promising route toward precise, energy-efficient separation for cost-sensitive industries such as recycling.