TY - BOOK

T1 - Increasing Efficiency in Sensor-Based Sorting Processes for Waste Streams consisting of Plastics

AU - Friedrich, Karl

N1 - no embargo

PY - 2024

Y1 - 2024

N2 - This doctoral thesis aims to validate new methods that increase the efficiency of sensor-based sorting processes for waste streams consisting of plastics. It deals with set boundaries on aggregate level; the plant level is not considered. The used equipment is the experimental sensor-based sorting setup at the Chair of Waste Processing Technology and Waste Management at Montanuniversität Leoben and the used sensor technology near-infrared spectroscopy.Increasing the sorting efficiency can be done by optimizing the identification of the mechanical discharge of particles. Data analytics is shown as a solution to achieve optimization, therefore this thesis focuses on using data-analytics-related methods.For optimizing the identification of particles, research is conducted in the fields:•Influence of surface roughness•Influence of reflectors as background material•Usage of machine learning approachesFor optimizing the mechanical discharge of particles, research is conducted in the fields:•Correlations between the input parameters (input composition, throughput rate) and the output parameters (purity, recovery, yield, incorrect discharged particles) of a sensor-based sorting process•Mathematical approaches to describe the optimal operation point of a sensor-based sorting machine to achieve a specific sorting resultIt is stated that this outcome allows a sorting plant to increase purity by using machine learning approaches to optimize the identification or running the plant on the optimal operation point, both without substantially adapting the plant. Superordinate considered these solutions help to increase the amount of recycled plastic so that less plastic waste is thermally treated.

AB - This doctoral thesis aims to validate new methods that increase the efficiency of sensor-based sorting processes for waste streams consisting of plastics. It deals with set boundaries on aggregate level; the plant level is not considered. The used equipment is the experimental sensor-based sorting setup at the Chair of Waste Processing Technology and Waste Management at Montanuniversität Leoben and the used sensor technology near-infrared spectroscopy.Increasing the sorting efficiency can be done by optimizing the identification of the mechanical discharge of particles. Data analytics is shown as a solution to achieve optimization, therefore this thesis focuses on using data-analytics-related methods.For optimizing the identification of particles, research is conducted in the fields:•Influence of surface roughness•Influence of reflectors as background material•Usage of machine learning approachesFor optimizing the mechanical discharge of particles, research is conducted in the fields:•Correlations between the input parameters (input composition, throughput rate) and the output parameters (purity, recovery, yield, incorrect discharged particles) of a sensor-based sorting process•Mathematical approaches to describe the optimal operation point of a sensor-based sorting machine to achieve a specific sorting resultIt is stated that this outcome allows a sorting plant to increase purity by using machine learning approaches to optimize the identification or running the plant on the optimal operation point, both without substantially adapting the plant. Superordinate considered these solutions help to increase the amount of recycled plastic so that less plastic waste is thermally treated.

KW - Sensorgestützte Sortierung

KW - Sortiereffizienz

KW - NIR-Sortierung

KW - Datenanalytik

KW - Maschinelles Lernen

KW - Regressionsmodell

KW - Optimaler Betriebspunkt

KW - Durchsatz

KW - Transflektion

KW - Oberflächenrauigkeit

KW - Sensor-Based Sorting

KW - Sorting Efficiency

KW - NIR-Sorting

KW - Data Analytics

KW - Machine Learning

KW - Regression Model

KW - Optimal Operation Point

KW - Throughput Rate

KW - Transflection

KW - Surface Roughness

U2 - 10.34901/mul.pub.2024.052

DO - 10.34901/mul.pub.2024.052

M3 - Doctoral Thesis

ER -