Open Lab uses AI to turn old clothes into new yarns

Open Lab, a sustainable fashion initiative, has introduced an artificial intelligence-powered sorting system to help speed up the garment recycling process, in a collaboration backed by cross-industry parties.

The smart system can automatically categorize the fabric of the old clothes it processes by pattern before they are turned into new yarns, which helps reduce time and manual effort of the recycling process. 

It is one of the solutions put forward by the Lab, which was founded by the Hong Kong Research Institute of Textiles and Apparel (HKRITA) and the H&M Foundation to come up with innovative recycling methods.

“When running a recycling business, the first step involves collecting large quantities of used garments and classifying them. Manual sorting is highly labor-intensive, and we must also consider factors like cleanliness, hygiene standards, and potential health risks for workers,” Jake Koh, chief executive officer of HKRITA, explained.

Koh noted that the system utilizes advanced visual technology to rapidly scan and classify garments with high accuracy. The sorted materials will then be fed into HKRITA’s flagship Green Machine 2.0, which employs hydrothermal separation technology to efficiently break down polyester-cotton blended fabrics, converting them into high-quality recycled yarns.

The initiative has gained significant momentum through strategic partnerships with two key players: ISS Facility Services Hong Kong, a facility management provider, and Redress, an environmental non-governmental organization focused on circular fashion. 

The partnership with ISS enabled the Lab to process over 1,200 retired cotton-polyester blended polo shirts last year. 

Redress has provided a steady supply of 750 kilograms of post-consumer garments monthly since December 2024, helping validate the Lab’s recycling technology at scale.

The Lab vows commitment to continue exploring environmentally friendly solutions for textile waste and welcomes partnerships with organizations from different industries.

(Staff Reporter)