Assessment of Technological Developments in Data Analytics for Sensor-Based and Robot Sorting Plants Based on Maturity Levels to Improve Austrian Waste Sorting Plants

Sensor-based and robot sorting are key technologies in the extended value chain of many products such as packaging waste (glass, plastics) or building materials since these processes are significant contributors in reaching the EU recycling goals. Hence, technological developments and possibilities to improve these processes concerning data analytics are evaluated with an interview-based survey. The requirements to apply data analytics in sensor-based sorting are separated into different sections, i.e., data scope or consistency. The interviewed companies are divided into four categories: sorting machine manufacturers, sorting robot manufacturers, recycling plant operators, and sensor technology companies. This paper aims to give novel insights into the degree of implementation of data analytics in the Austrian waste management sector. As a result, maturity models are set up for these sections and evaluated for each of the interview partner categories. Interviewees expressed concerns regarding the implementation such as a perceived loss of control and, subsequently, a supposed inability to intervene. Nevertheless, further comments by the interviewees on the state of the waste management sector conveyed that data analytics in their processes would also be a significant step forward to achieve the European recycling goals.

Construction and Demolition Waste Recycling through Conventional Jig, Air Jig, and Sensor-Based Sorting: A Comparison

The paper presents a comparison of the concentration methods conventional jig, air jig, and sensor-based sorting to treat construction and demolition waste. All tests were made with concrete, brick, and gypsum particles and the tests aim to separate these materials into different size ranges, depending on the method. The equipment tested, conventional jig, air jig, and sensor-based sorting present good results to concentrate construction and demolition waste particles, with different concentrations and mass recoveries. The results show particularly good mass recoveries and particle concentration for conventional jig, especially for concrete and gypsum particles. Sensor-based sorting should preferably use concentration circuits for best results.

Jigging: A Review of Fundamentals and Future Directions

For centuries, jigging has been a workhorse of the mineral processing industry. Recently, it has also found its way into the recycling industry, and the increasing concerns related to water usage has led to a renewed interest in dry jigging. However, the current scenario of increasing ore complexity and the advent of smart sensor technologies, such as sensor-based sorting (SBS), has established increasingly challenging levels for traditional concentration methods, such as jigging. Against this background, the current review attempts to summarize and refresh the key aspects and concepts about jigging available in the literature. The configuration, operational features, applications, types, and theoretical models of jigging are comprehensively reviewed. Three promising paths for future research are presented: (1) using and adapting concepts from granular physics in fundamental studies about the stratification phenomena in jigs; (2) implementing advanced control functions by using machine vision and multivariate data analysis and; (3) further studies to unlock the potential of dry jigs. Pursuing these and other innovations are becoming increasingly essential to keep the role of jigging as a valuable tool in future industry.

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

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.

Characterizing material flow in sensor-based sorting systems using an instrumented particle

Sensor-based sorting is a well-established single particle separation technology. It has found wide application as a quality assurance and control approach in food processing, mining, and recycling. In order to assure high sorting quality, a high degree of control of the motion of individual particles contained in the material stream is required. Several system designs, which are tailored to a sorting task at hand, exist. However, the suitability of a design for a sorting task is assessed by empirical observation. The required thorough experimentation is very time consuming and labor intensive. In this paper, we propose an instrumented bulk material particle for the characterization of motion behavior of the material stream in sensor-based sorting systems. We present a hardware setup including a 9-axis absolute orientation sensor that is used for data acquisition on an experimental sorting system. The presented results show that further processing of this data yields meaningful features of the motion behavior. As an example, we acquire and process data from an experimental sorting system consisting of several submodules such as vibrating conveyor channels and a chute. It is shown that the data can be used to train a model which enables predicting the submodule of a sorting system from which an unknown data sample originates. To our best knowledge, this is the first time that this IIoT-based approach has been applied for the characterization of material flow properties in sensor-based sorting.

INFLUENCE OF THROUGHPUT RATE AND INPUT COMPOSITION ON SENSOR-BASED SORTING EFFICIENCY

According to Directive (EU) 2018/851 of the European Union, higher recycling rates for municipal waste have to be met in the near future. Besides improvements in the collection systems, the mechanical processing and sorting efficiencies need to be increased to reach the EU´s targets. Sensor-based sorting (SBS) plants constitute an integral part of today's sorting processes. Two main factors influence the sorting performance, namely the throughput rate and the input composition. To improve recycling efficiencies, especially SBS machines must be adjusted accordingly to guarantee the highest possible machine efficiency. Three evaluation criteria, yield/product quantity, product yield, and product purity, are used to describe the performance of these processes. Therefore in this study, 160 sorting trials with 1,000 red and white low-density polyethylene (LDPE) chips were conducted to investigate the influence of the throughput rate and input composition on the sorting processes. For each evaluation criteria, the testing results are plotted in graphs enabling the possibility for process optimization. With increasing throughput rates, the product quantity rises (despite an exponential decrease in yield) in the form of a saturation curve. A higher throughput rate also results in an exponential decrease of the product yield, while a change in the input composition has no effect on the product yield. The third evaluation criteria, the product purity, decreases linearly with an increasing occupation density. The slope of this function depends on the input composition.