Performing experiments with mixed commercial waste, sampling is unavoidable for material analysis. Thus, the procedure of sampling needs to be defined in a way that guarantees sufficient accuracy regarding the estimation of the examined analytes. In this work, a sampling procedure for coarsely shredded mixed commercial waste, based on the Austrian Standard ÖNORM S 2127, the horizontal sampling standard DS 3077 and the theory of sampling, was established, described and examined through a replication experiment determining the relative sampling variability. The analytes are described through a matrix of nine (9) material classes and nine (9) particle size classes. It turns out that the typical threshold value of 20% can be reached for some fractions of the particle size–material matrix (for example, wood 20–40 mm and cardboard 60–80 mm) but gets as bad as 231% (wood 200–400 mm) for others. Furthermore, a decrease in the relative sampling variability with the mass share of a fraction is observed. Part of the observed variability is explainable through the fundamental sampling error, while contributions of other types of sampling errors are also evident. The results can be used for estimating confidence intervals for experimental outcomes as well as assessing required sample sizes for reaching a target precision when working with mixed commercial waste.

Global waste generation is projected to reach 3.40 billion tons by 2050, necessitating improved waste sorting for effective recycling and progress toward a circular economy. Achieving this transformation requires higher sorting intensity through intensified processes, increased efficiency, and enhanced yield. While manual sorting remains common, smaller plants often use positive sorting to recover recyclables, and larger plants combine automated systems with manual sorting. Negative sorting is employed to remove impurities and improve material quality. However, innovation in manual sorting has stagnated. Advances in Machine Learning and Artificial Intelligence offer transformative potential for waste management, with digitalisation and improved recyclate quality becoming priorities. Despite these trends, manual sorting is still largely treated as a digital black box. The presented research outlines the design of a novel, human-centric AI-powered assistance system to support sorting workers by enhancing decision-making and real-time assistance during the sorting process, driving the digitalisation of manual sorting. Potential use cases, system requirements, and essential components were explored. High-quality use case-specific data is essential for model training. Therefore, publicly available datasets were evaluated but found inadequate, necessitating use-case-specific data acquisition through near-industry-scale experiments. This data was used to train and develop key system components, such as object recognition, classification, and action recognition models. Results indicate that transfer learning with a balanced dataset is effective for waste-sorting applications. The classification model achieved 81% accuracy on an experimental acquired balanced dataset, outperforming the accuracy of the pre-trained model on its original dataset.

Particle size distributions (PSDs) belong to the most critical properties of particulate materials. They influence process behavior and product qualities. Standard methods for describing them are either too detailed for straightforward interpretation (i.e., lists of individual particles), hide too much information (summary values), or are distribution-dependent, limiting their applicability to distributions produced by a small number of processes. In this work the distribution-independent approach of modeling isometric log-ratio-transformed shares of an arbitrary number of discrete particle size classes is presented. It allows using standard empirical modeling techniques, and the mathematically proper calculation of confidence and prediction regions. The method is demonstrated on coarse-shredding of mixed commercial waste from Styria in Austria, resulting in a significant model for the influence of shredding parameters on produced particle sizes (with classes: >80 mm, 30–80 mm, 0–30 mm). It identifies the cutting tool geometry as significant, with a p-value < 10⁻⁵, while evaluating the gap width and shaft rotation speed as non-significant. In conclusion, the results question typically chosen operation parameters in practice, and the applied method has proven to be valuable addition to the mathematical toolbox of process engineers.