Mechanical recycling of polymers – New developments in sensor-based sorting


Karl Friedrich (Montanuniversität Leoben, Chair of Waste Processing Technology and Waste Management)

Marton Bredacs (PCCL)

Part 1: Introduction to sensor-based sorting – State-of-the-Art & Challenges

A circular economy is the stated aim of current technological and political developments in the waste management sector. Achieving the goal of a circular economy requires significant improvements in waste treatment technologies. For this reason, this paper summarises the relevant technologies, detailing the developments in the significant sensor-based sorting technologies. This review analyses the key spectral analysis methods like Near-Infrared Spectroscopy, Visual Spectroscopy, X-Ray Transmission, X-Ray fluorescence analysis and Laser-Induced Breakdown Spectroscopy. This study further contains a detailed analysis of the standard sensor-based sorting construction types chute sorting, belt sorting and robot-aided sorting.

Further insights in the branch of sensor-based sorting are permitted by describing the key players and stakeholders in sensor-based sorting, detailing the area of expertise and current fields of study for primary sensor and sorting machine suppliers. A convenient lookup table detailing the capabilities of these significant suppliers is provided. The last chapter summarises relevant trends and developments in digitalisation and industry 4.0 in the waste and recycling sector, elaborating on relevant technology like digital waste management, sorting robots in waste management, innovative waste, smart villages and recyclable materials scanners. The reviewed data portrays the waste management industry’s substantial developments. While new technologies, like machine learning and convolutional neural networks and robot sorting, are increasingly implemented, a substantial discrepancy exists between technological capabilities and the current State-of-the-Art.

Part 2: Improved mechanical sorting of post-consumed plastics products with multivariate data analysis

Multivariate data analysis (MVDA) of current state-of-the-art NIR data collected with plastic sorting lines can substantially improve the sorting degree of post-consumer products. Determination and separation of polymers based on their properties such as density, MFR and molecular weight would improve the quality of recycled products. Advanced data processing methods such as principal component analysis (PCA) and partial least square (PLS) models showed that processing and density-based differentiation of various PE and PP grades can be achieved. In fact, PE density can be predicted accurately from IR and Raman spectroscopic data. Moreover, separation based on processing method and molecular weight of PET products seems to be possible. Additionally, separation of waste electronic and electrical equipment into various polymer classes and determination of subclasses in a given polymer class were performed based on NIR. This work gives an overview on the possible improvements of mechanical sorting of post-consumed plastic products applying MVDA approaches.

Author Bio Karl Friedrich:

Karl Friedrich received an MSc. Degree in Energy and Environmental Management in 2017 at University for Applied Sciences Burgenland. In 2018 he started his PhD thesis addressing the increase of efficiency in sensor-based sorting efficiency. Now he focuses since 2018 on the research in identification and modelling of sensor-based sorting processes at the Chair of Waste Processing Technology and Waste Management at the Montanuniversitaet Leoben. His research field covers all types of waste materials and products to be sorted, although his PhD thesis is focused only on different types of plastic waste.

Author Bio Márton Bredács:

Dr. Márton Bredács received a MSc. Degree in Polymer and Fibre Technology in 2013 at Budapest University of Technology and Economics. In 2020 he finished his PhD thesis addressing aging mechanisms of polyethylene pipe grades in chlorine dioxide and hypochlorous acid at University of Leoben. His expertise lies in polymer characterization including mechanical, fracture mechanical, thermal, morphological and chemical analyses. His research interest covers various field of plastic recycling, IR-spectroscopy, polyolefin aging and multivariate data analysis.