Influence of Throughput Rate and Input Composition on Sensor-Based Sorting Efficiency
Research output: Contribution to journal › Article › Research › peer-review
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 frac-tion) 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 pu-rity, decreases linearly with increasing occupation density. The slope of this function depends on the input composition.