China: AI scanner analyses textiles for recycling in seconds

A recycling plant has been developed in China that uses artificial intelligence to quickly and automatically analyse textile waste according to its material composition. The “Fastsort-Textile” machine was developed by the Chinese company DataBeyond and named one of the best inventions of 2025 by The Times Magazine.

The plant combines a conveyor belt system with an AI scanner measuring approximately five by two metres. There, the material composition of each individual garment is analysed in less than a second, with the results displayed live. The system then automatically sorts the textiles by fibre type, such as polyester or nylon, and directs them into the appropriate recycling streams. Items that do not meet defined quality criteria are sorted out. Previously, up to 50 percent of the textiles processed were considered non-recyclable and were disposed of or incinerated. With the Fastsort-Textile machine, this proportion is said to have fallen to 30 percent.

The machine achieves a high level of efficiency. Around 100 kilograms of clothing are sorted in two to three minutes, totalling up to two tonnes per hour. This is significantly faster and more precise than the manual sorting that has been common practice until now. The Fastsort machine is also said to be able to sort by details such as colours and neckline types on T-shirts. For shredded textiles, it sorts out contaminants like sequins, buttons, and zips.

The machine is located in an industrial park in Zhangjiagang, a small city on the east coast of China. The company, DataBeyond, is a Chinese technology firm founded in 2018 that now operates globally. It specialises in AI-supported sorting solutions for recycling processes. Its European branch is located in Italy.

The company primarily develops optical and AI-based sorting systems for various waste streams, such as plastics and textiles, and is one of the leading providers of such technologies in China.

The company's goal is to make recycling processes more efficient through automation and to advance the transition to a more digitised circular economy.

This article was translated to English using an AI tool.

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