Within the framework of Circular Textile Intelligence (CRTX), the TU Berlin, and the Freie Universität of Berlin are researching a solution that aims toward automating the sorting of used garments and textile waste for high quality purposes in a circular economy.

The goal is to close the gap between the collection of used textiles and specific sorting for second hand and fiber-to-fiber recycling by means of AI-backed spectroscopy and image analysis, thus enabling a continuous material cycle. High quality recycling techniques such as fiber-to-fiber recycling require material specific feedstock with high purity to recover yarn of the same quality. So far, available sorting solutions have not delivered the required precision to allow recyclers to work with post-consumer textile waste.

CRTX brings together a team of experts in the field of optics and data-science and sustainability to develop a new data-driven multisensor sorting solution to solve this challenge.

Reusable textiles, on the other hand, will be processed with methods of computer vision to support sorting personshuman sorters achieving a more fine grained and objective classification. Using a deep learning with state-of-the-art training methods the team develops solutions that are optimized for conveyor belt garment identification. To make second hand products more attractive and curated according to the target groups, their needs and wishes curate and carefully select products in a more targeted way put the for second-hand markets in a better position, trends from the first-hand market are analyzed and constantly matched with the sorting algorithm. This allows to target more fashion-aware consumer groups, increasing the overall usage of second-hand garments.

A market analysis of the first-hand identifies trends and matches these with the detailed descriptors as prevents fiber-to-fiber recycling, which would allow yarn of the same quality to be recovered. This is one of the main reasons for the low recycling rate (less thanaround one percent) of textiles.

The spectroscopic methods, which are being developed under the direction of Dr. Karsten Pufahl from the Department of Nonlinear Optics, should enable an exact determination of the material composition and pollutant load.

An AI-based evaluation is planned, which will overcome the previous hurdles in sorting technology and enable a closing of the material cycle. At the same time, image analysis methods are to be used to achieve more precise sorting, also for reusable garments. will coordinate the development and contribute essential expertise and partner networks from the industry.