Dcard

Advanced Sorting Technologies

Sorting clothes for reuse and recycling is a difficult process, often with more than 300 categories. New technologies like visual identification AI can be used to help sorters identify and sort materials into recycling and reuse by material and brand. Using phone cameras, wearers could be encouraged to dispose their clothes at an optimal time.

VET: How can using new technology to sort and recycle clothes make recycling easier and better?
HEI: How can visual identification technologies improve the fashion ecosystem processes and encourage sustainable behavior?


Advanced sorting technologies refer to innovative and technologically advanced systems and processes used to sort and categorize fashion products for the purpose of digital recycling and upcycling. These technologies leverage cutting-edge methods, such as machine vision, artificial intelligence, and data analytics, to efficiently and accurately identify, classify, and separate garments and materials based on their suitability for recycling or upcycling.

Digital recycling and upcycling involve the transformation of discarded or unused fashion products into new, valuable items through digital processes. Advanced sorting technologies play a crucial role in this context by enabling the automated identification and separation of materials that can be recycled or repurposed, thereby streamlining the digital recycling and upcycling workflows.

One of the key components of advanced sorting technologies for digital recycling and upcycling is the use of machine vision systems. These systems employ cameras, sensors, and advanced image recognition algorithms to capture detailed visual information about fashion products. Through machine vision, these technologies can identify specific garment types, materials, colors, patterns, and other attributes that are relevant for determining their recycling or upcycling potential.

Furthermore, advanced sorting technologies integrate artificial intelligence and data analytics capabilities. By analyzing vast amounts of data, including historical records, product specifications, and material characteristics, these technologies can learn and improve their sorting accuracy over time. They can identify patterns, trends, and correlations that help in making informed decisions regarding the recycling or upcycling potential of fashion items.

The benefits of advanced sorting technologies in the context of digital recycling and upcycling are significant. They enable the efficient and precise sorting of fashion products, reducing the reliance on manual labor and minimizing errors. This not only saves time but also enhances the overall quality and value of the recycled or upcycled materials.

Moreover, these technologies contribute to the circular economy in the fashion industry. By facilitating the identification and separation of recyclable or upcyclable materials, they support the transition from a linear “take-make-dispose” model to a more sustainable and circular approach. Advanced sorting technologies promote resource efficiency, waste reduction, and the reuse of materials, thereby reducing the environmental impact associated with fashion production and utilization.

Case studies

The Renewal Workshop

The Renewal Workshop is a company that partners with fashion brands to implement a circular system for their garments. They utilize digitalization and data to track and manage the entire lifecycle of a garment. Through their proprietary software, they collect data on garment quality, repairs needed, and material composition. This data helps them determine the best course of action for each garment, whether it’s repairing, upcycling, or recycling. By using data-driven insights, they enable fashion brands to extend the lifespan of their products and reduce waste. More about The Renewal Workshop

Reflaunt

Reflaunt is a technology company that works with luxury fashion brands to facilitate the resale of their products. They integrate their digital solution into the brand’s e-commerce platform, allowing customers to easily resell their pre-owned items. Through the use of data, Reflaunt provides a transparent and trusted resale experience by verifying the authenticity and quality of the items. This data-driven approach promotes circularity by encouraging the reuse and circulation of luxury fashion products. More about Reflaunt

Reverse Resources

Reverse Resources is a company that uses advanced sorting technologies to enable digital recycling in the fashion industry. They have developed a digital platform that utilizes machine vision and data analytics to identify and sort textile waste materials. By connecting textile waste generators with recycling facilities, they facilitate the efficient recycling and repurposing of discarded textiles. More about Reverse Resources

I:CO (I Collect)

I:CO is a global recycling solution provider for the fashion industry. They have implemented advanced sorting technologies in their sorting facilities to process used garments and footwear. These technologies enable the identification and separation of different materials, such as cotton, polyester, and nylon, allowing for efficient recycling and upcycling of the collected items. More about I:CO (I Collect)

References

Charnley, Fiona, et al. “Can Digital Technologies Increase Consumer Acceptance of Circular Business Models? The Case of Second Hand Fashion.”
Sustainability 14.8 (2022): 4589.
This paper explores the role of digital technologies in enhancing consumer acceptance of circular business models, particularly focusing on the second-hand fashion market.
https://doi.org/10.3390/su14084589

Colombi, Chiara, and Erminia D’Itria. “Fashion Digital Transformation: Innovating Business Models toward Circular Economy and Sustainability.”
Sustainability 15.6 (2023): 4942.
This study examines how digital transformation in the fashion industry can innovate business models to support circular economy practices and enhance sustainability.
https://doi.org/10.3390/su15064942

Alpert, Cirrus, Michaela Turkowski, and Tahiya Tasneem. “Scalability solutions for automated textile sorting: a case study on how dynamic capabilities can overcome scalability challenges.” (2021).
This case study addresses the scalability challenges in automated textile sorting and discusses how dynamic capabilities can provide effective solutions.
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Bonifazi, Giuseppe, et al. “End-of-Life Textile Recognition in a Circular Economy Perspective: A Methodological Approach Based on Near Infrared Spectroscopy.”
Sustainability 14.16 (2022): 10249.
The paper presents a methodological approach using Near Infrared Spectroscopy to recognize end-of-life textiles, supporting circular economy initiatives.
https://doi.org/10.3390/su141610249

Humpston, G., et al. “Technologies for sorting end of life textiles.”
A technical and economic evaluation of the options applicable to clothing and household textiles, WRAP, UK (2014).
This evaluation provides a comprehensive overview of technologies available for sorting end-of-life textiles, assessing both technical and economic aspects.
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Nørup, Nynne, et al. “Development and testing of a sorting and quality assessment method for textile waste.”
Waste Management 79 (2018): 8-21.
This research focuses on developing and testing a new method for sorting and assessing the quality of textile waste to improve recycling efficiency.
https://doi.org/10.1016/j.wasman.2018.07.029