Developing a prediction model in a lightweight packaging waste sorting plant using sensor-based sorting data combined with data of external near-infrared and LiDAR sensors

Waste Management & Research The Journal for a Sustainable Circular Economy, vol. 42, pp. 747–758

Abstract

Sensor-based material flow monitoring allows for continuously high output qualities, through quality management and process control. The implementation in the waste management sector, however, is inhibited by the heterogeneity of waste and throughput fluctuations. In this study, challenges and possibilities of using different types of sensors in a lightweight packaging waste sorting plant are investigated. Three external sensors have been mounted on different positions in an Austrian sorting plant: one Light Detection and Ranging (LiDAR) sensor for monitoring the volume flow and two near-infrared (NIR) sensors for measuring the pixel-based material composition and occupation density. Additionally, the data of an existing sensor-based sorter (SBS) were evaluated. To predict the newly introduced parameter material-specific occupation density (MSOD) of multi-coloured polyethylene terephthalate (PET) preconcentrate, different machine learning models were evaluated. The results indicate that using SBS data for both monitoring of throughput fluctuations caused by different bag opener settings as well as monitoring the material composition is feasible, if the pre-set teach-in is suitable. The ridge regression model based on SBS was comparable (RMSE = 3.59 px%, R² = 0.57) to the one based on NIR and LiDAR (RMSE = 3.1 px%, R² = 0.68). The demonstrated feasibility of the implementation at plant scale highlights the opportunities of sensor-based material flow monitoring for the waste management sector and paves the way towards a more circular plastics economy.

Authors 6

1. S.M. Schlögl corresponding

   Montanuniversität Leoben

   Affiliation as printed

   Chair of Waste Processing Technology and Waste Management, Montanuniversität Leoben, Leoben, Austria

2. Josef Kamleitner

   Siemens (Austria)

   Affiliation as printed

   Siemens AG Österreich, Vienna, Austria

   Siemens Advanta Solutions GmbH, Vienna, Austria

3. Nils Kroell Aachen Department of Anthropogenic Material Cycles

   RWTH Aachen University

   Affiliation as printed

   Department of Anthropogenic Material Cycles, RWTH Aachen University, Aachen, Germany

4. Xiaozheng Chen

   Altana (Germany)

   Affiliation as printed

   Stadler Anlagenbau GmbH, Altshausen, Germany

5. Daniel Vollprecht

   University of Augsburg

   Affiliation as printed

   Institute for Materials Resource Management, Chair of Resource and Chemical Engineering, University of Augsburg, Augsburg, Germany

6. Alexia Tischberger-Aldrian

   Montanuniversität Leoben

   Affiliation as printed

   Chair of Waste Processing Technology and Waste Management, Montanuniversität Leoben, Leoben, Austria