Machine-learning system sorts compostable from conventional plastic waste
Image credit: DT
Researchers have used machine learning to automatically sort different types of compostable and biodegradable plastics and differentiate them from conventional plastics.
Compostable plastics, which are engineered to biodegrade under controlled conditions, have been increasing in popularity in recent years, but often look identical to conventional plastics and therefore get recycled incorrectly.
Conventional plastic samples included PP and PET, often used for food containers and drinking bottles, as well as LDPE which is often used for plastic bags and packaging.
Compostable plastic samples included PLA and PBAT, used for cup lids, tea bags and magazine wraps, as well as palm-leaf and sugarcane, both biomass-derived materials used to produce packaging.
The samples were divided into a training set, used to build classification models, and a testing set, used to check accuracy. The researchers worked with different types of plastics measuring between 50mm by 50mm and 5mm by 5mm.
The model achieved perfect accuracy for all materials when the samples measured more than 10mm by 10mm. For sugarcane-derived or palm leaf-based materials measuring 10mm by 10mm or less, however, the misclassification rate was 20 per cent and 40 per cent, respectively.
“The accuracy is very high and allows the technique to be feasibly used in industrial recycling and composting facilities in the future,” said professor Mark Miodownik, corresponding author of the study and researcher at University College London (UCL).
Looking at pieces measuring 5mm by 5mm, some materials were identified more reliably than others. For LDPE and PBAT pieces the misclassification rate was 20 per cent and both biomass-derived materials were misidentified at rates of 60 per cent (sugarcane) and 80 per cent (palm leaf). The model was, however, able to identify PLA, PP and PET pieces without error, regardless of sample measurements.
“Currently, most compostable plastics are treated as a contaminant in the recycling of conventional plastics, reducing their value,” Miodownik said. “The advantages of compostable packaging are only realised when they are industrially composted and do not enter the environment or pollute other waste streams or the soil.”
To improve accuracy, the team tested different types of conventional, compostable and biodegradable plastics, using hyperspectral imaging (HSI) for classification model development.
HSI is an imaging technique that detects the invisible chemical signature of different materials while scanning them, producing a pixel-by-pixel chemical description of a sample. AI models were used to interpret these descriptions and make a material identification.
Plastic mismanagement in recycling and industrial composting processes is high, making reliable sorting mechanisms essential.
“Currently, the speed of identification is too low for implementation at industrial scale,” Miodownik admitted, but said the speed of the technology can be improved, which would make it a key technology to make compostable plastics a sustainable alternative to recycling.
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