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Hyperspectral imaging in the near infrared range (1000-1700 nm) was evaluated to identify different polyolefin flakes for quality assessment of recycled products. According to market requirements, the output of the recycling process of polyolefins must be high purity secondary polypropylene and polyethylene granulates. Hyperspectral images were acquired for selected plastic flakes coming from household waste, classified according to their typology. Spectra were analysed using principal component analysis to reduce the high dimensionality of data and for selecting effective wavelengths. Partial least square discriminant analysis was applied for classification of the two polyolefin typologies. Prediction results showed that it is possible to recognise the different plastic flakes with sensitivity and specificity ranging from 0.90 to 0.99. The developed procedures based on hyperspectral imaging can be utilised for quality control of the two pure polypropylene and polyethylene flow streams obtained by the innovative recycling process based on magnetic density separation. © IM Publications LLP 2012. All rights reserved

The systematic identification of impurities inside secondary plastics flow streams can be considered as one of the key issues to certify and to classify waste plastics fed to recycling plants and to perform a full control of the resulting processed fractions, that have to comply with market demands on grade and purity of the recovered products when compared with virgin streams. HyperSpectral Imaging (HSI) can represent an optimal, reliable and low costs answer to reach the previous mentioned goals. HSI is based on the utilization of an integrated hardware and software architecture able to digitally capture and handle spectra, as an image sequence, as they results along a pre-defined alignment on a surface sample properly energized. According to the different wavelengths of the source and the different spectral sensitivity of the device, different physical-chemical superficial characteristics of the sample can be investigated and analyzed. Such an approach, if fully investigated and implemented, could allow to perform a big-step-forward inside quality-inspection-control-strategies/logics (QICSL) applied to the entire waste sector. The identification of contaminants in secondary plastics, adopting HSI, thus can represent a first attempt to introduce new QICSL in an innovative polyolefins separation process from end-of-life product. Such an approach well fit the goals that are at the base of a the application of a non-linear Cyclic Innovation Model (CIM) addressed to connect technical capabilities (contaminants identification) with societal market needs (quality of the recycled products).

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The inverse scattering problem of non-spherical particle size estimation is solved using a series of supervised machine learning models trained on a library of light scattering data. By establishing a large library with spheres and spheroids as fundamental shapes and through optimization of model hyperparameters, the trained models are able to accurately estimate a precise equivalent volume sphere radius of particles from an external database and simulations, with root mean square errors of 2.6% and 1.9% for the external and simulated particles, respectively. It was found that classification via a k -nearest neighbor model and refinement via a trained ensemble regression model performed best for equivalent volume measurements.

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