Influence of Throughput Rate and Input Composition on Sensor-Based Sorting Efficiency

Abstract

According to the Directive (EU) 2018/851 of the European Union, higher recycling rates for municipal waste will have to be met in the near future. Beside improvements to the collection systems, the efficiency of mechanical processing and sorting will have to be increased to reach the EU´s targets. Sensor-based sorting (SBS) plants constitute an integral part of today's sorting processes. Two main factors determine the sorting performance: throughput rate and input composition. To improve recycling efficiencies, especially SBS machines need to be optimized. Three evaluation criteria are used to describe the performance of these processes: recovery (content of input material -both eject and reject material discharged into the product fraction) or product quantity (amount of product generated via sorting within a specific interval -calculated by multiplying throughput rate and yield), yield (amount of eject material discharged into the product fraction), and product purity. For this study, 160 sorting experiments each with 1,000 red and white low-density polyethylene (LDPE) chips were conducted to investigate the effects of throughput rate and input composition on sorting processes. This simplified approach reduced the influence of other factors on the sorting performance, giving precise information on the effect of throughput rate and input composition. The testing results can enter process optimization. With increasing throughput rates, product quantity rises following a saturation graph (despite exponential decrease in recovery). In the experiments a higher throughput rate also resulted in an exponential decrease of the yield while a change to the input composition had no such effect. The third evaluation criteria, product purity, decreases linearly with increasing occupation density. The slope of this function depends on the input composition.

Influence of material alterations and machine impairment on throughput related sensor-based sorting performance

“…However, trials with coarser, rectangular particles created an exponential decrease in yield ( Küppers et al, 2020 ). The form of the respective yield function might be dependent on the particle size distribution of the input material ( Figure 3 ) in dependence of the sorting algorithm.…”

“…The current research at the Chair of Waste Processing Technology and Waste Management of the Montanuniversität Leoben aims to quantify the impact of input composition and throughput rate (occupation density) on SBS. Küppers et al (2020) found the following systematic effects from prior SBS trials:…”

A Survey of the State of the Art in Sensor-Based Sorting Technology and Research

“…According to the above-mentioned definition, however, it is not so much the mass flow that is responsible for the influences on sorting quality shown above, but rather the occupancy density [163]. The density of occupancy describes the proportion of the sensory image that is actually occupied by material, compare Fig.…”

“…This is partly because energy is required for the deflection process and partly because the deflection process itself potentially leads to sorting errors and should therefore be performed as rarely as possible [161] (see Sections III-D and V). Based on the Monte Carlo simulation [162] and experimental studies [163], an exponential decrease in the achievable mass flow is obtained with an increasing fraction of particles to be deflected while fixing the sorting quality.…”

The EU Training Network for Resource Recovery through Enhanced Landfill Mining—A Review

“…Within NEW-MINE, the influence of surface roughness and surface moisture of plastics on NIR sorting was investigated [48], and the findings were used outside NEW-MINE for material flow characterization in sensor-based sorting systems using an instrumented particle [49] and for a study of the influence of material alterations and machine impairment [50]. NEW-MINE learnings on the effects of throughput rate and input composition on sensor-based sorting [51] were subsequently used within the above mentioned ReWaste 4.0 project [47,52].…”