Generic prediction and optimization of plastic recyclability using statistical entropy analysis
The complexity and diversity of plastics is at the heart of the plastic recycling challenge. And yet, all efforts to predict their recyclability thus far are based on material and energy balances of supposedly useful technologies within a certain waste management system. Hence, these methods are not very generic and the results mainly say something about the system rather than about the innate characteristics of the materials. At iPRACS we believe to have finally found a way to quantify the complexity and diversity of plastics by using statistical entropy analysis. The method builds on the (multi-level) statistical entropy analysis first used by Rechberger (TU Wien) for waste management purposes. When coupled with more generic energy indicators, this may yield a true, unbiased, universal recyclability predictor. This predictor will be an innate material property rather than a characteristic of a specific waste management system. With this method, we can now support in a more quantitative way design-for-recycling, but also study optimal collection schemes, and quantify the value of sorting/refining technologies in view of their true purpose; reducing entropy. Future research at iPRACS consists of further refinement of the method using realistic case studies as well as complex processes such as reactive extrusion. Different relevant levels not considered in the current framework, but necessary for addressing recyclability as the geospatial and molecular level entropies will also be studied.