Sensor-based material flow monitoring has great potential in sorting plants in
general, an implementation based on the data of a sensor-based sorter (SBS)
provides an economic advantage compared to the use of additional sensors. In
this work, we provide insights into the data stream collected by a SBS positioned at
the beginning of the sorting cascade for 3D-shaped particles in a sorting plant for
lightweight packaging waste in Austria. The data was analyzed in three regards: A
first analysis identifies plant downtimes within a year and allocates them to events
such as weekends or public holidays. A second analysis presents the composition
of the lightweight packaging material over a year. Here, the mean of the dominating
fractions is 45.1% for PET, 16.1% for PE, and 15.4% for PP. Lastly, we examine
the seasonal variability of the material composition. Here, no major changes in the
composition in terms of mean and standard deviation were observed. Only the share
of clear PET bottles increases in summer, whereas a lower share of blue PET bottles
at the same time compensates the overall share of PET bottles. The results of this
research indicate the variety of possible applications for monitoring based on SBS
data to improve the operational efficiency of the plant.