Predictive model for the Dutch post-consumer plastic packaging recycling system and implications for the circular economy

摘要

Highlights•A model of the Dutch post-consumer plastic packaging recycling system has been established.•Both the amounts of produced milled goods and their polymeric compositions can be predicted.•The end-of-life fates of 35 individual packaging types have been resolved.•The origins of the polymeric contaminants in recycled plastics have been determined.•Most contaminants originate from packaging components and are hence design-related.AbstractThe Dutch post-consumer plastic packaging recycling network has been described in detail (both on the level of packaging types and of materials) from the household potential to the polymeric composition of the recycled milled goods. The compositional analyses of 173 different samples of post-consumer plastic packaging from different locations in the network were combined to indicatively describe the complete network with material flow analysis, data reconciliation techniques and process technological parameters. The derived potential of post-consumer plastic packages in the Netherlands in 2014 amounted to 341 Gg net (or 20.2 kg net.cap−1.a−1). The complete recycling network produced 75.2 Gg milled goods, 28.1 Gg side products and 16.7 Gg process waste. Hence the net recycling chain yield for post-consumer plastic packages equalled 30%. The end-of-life fates for 35 different plastic packaging types were resolved. Additionally, the polymeric compositions of the milled goods and the recovered masses were derived with this model. These compositions were compared with experimentally determined polymeric compositions of recycled milled goods, which confirmed that the model predicts these compositions reasonably well. Also the modelled recovered masses corresponded reasonably well with those measured experimentally. The model clarified the origin of polymeric contaminants in recycled plastics, either sorting faults or packaging components, which gives directions for future improvement measures.