In our efforts to maximize resource recovery from PET bottles, we are piloting a quality control system powered by an AI/ML-based vision system. This initiative is in collaboration with Google and is leveraging CircularNet, the hashtag#AI model developed by Google.
Recovery of post-consumer waste is complicated. Let’s take the example of PET bottles, which have relatively high rates of recycling in India. However, only 5% of PET bottles are truly recycled (closed-loop, i.e., bottle-to-bottle)*.
In our efforts to maximize resource recovery from PET bottles, we are piloting a quality control system powered by an AI/ML-based vision system. This initiative is in collaboration with Google and is leveraging CircularNet, the hashtag#AI model developed by Google. Here are further details published by Google.
How does this work? When PET bottles enter the Materials Recovery Facility (MRF), they are manually sorted on a conveyor belt into three grades based on their contamination levels. The AI-driven CircularNet system will identify contaminated bottles to ensure that good-quality feedstock is supplied to recyclers.
Saahas Zero Waste carefully chooses tech-driven solutions, and we believe that CircularNet adds significant value to the system because recyclers are now willing to pay a 10% higher value for such quality feedstock. Looking ahead, we hope to bring this shift in the business case for all plastic streams other than PET.
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