INTELLIGENT LABELS AS A BASIS FOR AUTO-SORTING OF PLASTIC PACKAGING

摘要

Polypropylene (PP) from packaging is a significant polymer in the mixed plastics waste stream and closed loop recycling of it back into packaging would enable considerable carbon savings to be realised and generate significant revenues. Recent estimates suggest that 143,000 tonnes of the total PP packaging is used for food- grade application. One of the remaining barriers to closing the loop on the recycling of PP food packaging waste back to food grade applications is the absence of an automated method for sorting PP packaging waste to separate a stream consisting of at least 99% PP packaging that has been previously used for food from other non- food PP packaging. Machine readable inks (including fluorescent pigments) have shown great potential for the identification and separation of plastic packaging. Unlike existing NIR sorting practices, these technologies are not polymer specific and could be applied to targeted streams like food grade PP packaging and others, using commercial labelling and decoration methods and sorted using MRF infrastructure with only minor modification. This report describes the work of an identification technique which is based on fluorescent pigments that can be applied to labels and packaging as a machine readable ink (MRI) to enable the automatic separation of target materials such as the sorting of food grade PP packaging for closed loop recycling. Based on the audit of commercial of PP waste indicating that 55% detectability of existing packages together with potential yields of 98% from the sorting trials, it can be estimated that of the 143,000 tonne of PP food packaging, 77,077 tonnes could be recovered each year in the UK. This would increase dramatically if label design and application was modified to better accommodate identification and sorting requirements.