Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy

Abstract

In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.

Identification of cellulose textile fibers

“…1 Used cotton, viscose and lyocell fibers are important raw materials for chemical recycling and are challenging to identify quickly and accurately. Most recent studies on non-destructive optical methods for textile identification have focused on classifying synthetic and natural fibers, such as polyester, [8][9][10][11] cotton, [8][9][10][11] viscose, [9][10][11] and wool. 8,9,12 These results are important for developing automated textile identification for efficient separation and sorting once the upcoming EU regulation on textile collection will be enforced.…”

The Impact of Textile Waste on the Features of High-Density Polyethylene (HDPE) Composites

“…The share of unidentified material can be explained by the material color, because NIR is an unfeasible technology for dark materials, especially black wool, polyamide, and old textile materials [14]. Additionally, the sensitivity to humidity and the spectral similarity limited the identification capacity of NIR [15], but those effects were assumed to be minor in this study.…”

Classification of Material Type from Optical Coherence Tomography Images Using Deep Learning

“…ese data can be used to train deep learning networks, test existing machine learning algorithms, or help develop new deep learning systems for automated material classification. With advances in technology, we foresee that optical imaging-based methods combined with artificial intelligence will be used more frequently in the textile industry [44,45].…”