By George George Idowu
Despite increasing awareness of the environmental damage caused by plastic waste, global plastic production continues to surge, with predictions suggesting it could double by mid-century.
Addressing this challenge, Cycled Technologies and Khalifa University in Abu Dhabi are pioneering an AI-operated method to enhance plastic waste sorting and boost recycling rates.
Their prototype, housed at the Masdar Institute Field Station, employs near-infrared (NIR) spectroscopy to identify and sort various types of plastic based on their light absorption or scattering properties.
This cutting-edge system can accurately distinguish between polypropylene (PP), high-density polyethylene (HDPE), and polyethylene terephthalate (PET).
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Dr. Ayoola Brimmo, co-founder and COO of Cycled Technologies, highlighted the system’s cost-effectiveness, which allows for its deployment in community settings, unlike traditional large-scale industrial methods.
The device’s low cost and nearly 100% accuracy could make recycling more financially viable on a smaller scale.
Given the alarming amount of plastic waste generated per person, such as 221 kg annually in the US, improving recycling methods is essential.
Dr. Brimmo emphasised that recycling reduces environmental pollution and significantly lowers carbon emissions compared to producing virgin plastic.
In parallel, Amit Goyal from the University of Buffalo is developing a barcode system to identify plastic types, aiming to reduce contamination and improve recycling rates. Combined with AI and robotics, these efforts are expected to enhance recycling effectiveness.
As these technologies evolve, researchers remain optimistic about achieving higher recycling rates, which would offer a promising solution to the plastic waste crisis.
