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

Abstract

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.

Sensor-based and Robot Sorting Processes and their Role in Achieving European Recycling Goals - A Review

“…Moreover, the global circular economy and waste management market volume is expected to rise from around 100 billion euros in 2013 to 170 billion euros by 2025, further encouraging becoming involved in the digital transformation [26,46]. This conclusion is supported by another recently published survey in which 83 % of all surveyed companies announce that they have implemented a company strategy for managing data and 75 % make efforts to ensure high qualities in their transaction data [47].…”

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

“…Some of these articles summarize waste sorting systems and approaches for different waste source domains, including WEEE [13], [21], [25], [35], [37], municipal [1], [2], [14], [16], [17], [19], [34], and CND [23], [30], [33], [36] waste. Furthermore, several assessment and evaluation methods for sustainability in construction automation and robotics [81], technological developments for robotic sorting of plants [82], performance in sorting technology [39], and waste treatment systems [83] have also been proposed.…”

“…7 shows a heat-map superimposed on the world map with the json data of geographic information. 2 Recently, 15 developed countries: Australia [20], [30], Austria [58], [82], Canada [13], China [26], [31], [38], [91], [92], India [18], [29], Italy [61], Korea [28], Malaysia [65], Malta [93], Poland [83], Portugal [94], Russia [19], Slovenia [17], UK [55], and USA [49] have worked on this topic on a large scale.…”

Application of Digital Methods in Polymer Science and Engineering

“…To become more resource-efficient, it is important to understand the handling and processing of polymer materials while recycling. This includes i) the sensor-based sorting process in general, [196] ii) the use of spectra databases that enable to extract certain commodity plastics (e.g., polystyrene, polyolefines, and poly(ethylene terephthalate) from a mixed waste stream via spectra recognition (most commonly (N)IR) in real time, iii) the use of artificial intelligence when using image recognition to detect contaminated products that would disrupt the recycling process or at least would reduce its quality, and iv) the digital material-or product passes that enable the tracking of the life cycle. [197] Digital methods can increase the efficiency of these processes, in particular for the separation of plastic waste, for example, by the application of a ML algorithms to separate plastic and cardboard by a robot [198] or by using a deep learning approach to categorize post-consumer waste (poly[ethylene terephthalate]-PET bottles) in a pre-recycling process to classify the waste (e.g., with label, with residues, with cap) already during the bottle collection.…”

“…To become more resource‐efficient, it is important to understand the handling and processing of polymer materials while recycling. This includes i) the sensor‐based sorting process in general, [ 196 ] ii) the use of spectra databases that enable to extract certain commodity plastics (e.g., polystyrene, polyolefines, and poly(ethylene terephthalate) from a mixed waste stream via spectra recognition (most commonly (N)IR) in real time, iii) the use of artificial intelligence when using image recognition to detect contaminated products that would disrupt the recycling process or at least would reduce its quality, and iv) the digital material‐ or product passes that enable the tracking of the life cycle. [ 197 ]…”