EverestLabs, a leading robotics supplier headquartered in Fremont, California, has released a recent report shedding light on the fate of recyclable materials at Material Recovery Facilities (MRFs). According to their findings, approximately 27 per cent of aluminium cans, PET, HDPE, and PP collected at MRFs are disposed of rather than recycled.
{alcircleadd}This data stems from a comprehensive analysis conducted over two years, during which EverestLabs gathered sample data from MRFs across its customer base. The study focused on estimating the yield loss at the MRF level across different material streams.
About the findings
The scale of the problem becomes even more apparent when we consider the extrapolation of the sample results. The data was collected from the 'last-chance' lines of single-stream facilities, and EverestLabs' findings are alarming: of the 3.6 million tonnes of aluminium, PET, HDPE, and PP collected and sent to MRFs annually, approximately 960,000 tonnes end up in the disposal stream. Within this disposal volume, PET constitutes 66 per cent, HDPE accounts for 16 per cent, PP represents 14 per cent, and aluminium cans make up 4 per cent.
EverestLabs has underscored the potential of equipment upgrades, particularly the integration of artificial intelligence-equipped robotic sorting systems, to address missorting issues and enhance material recovery rates. This aligns with a growing consensus among industry stakeholders who recognize the power of artificial intelligence in generating valuable data, a crucial aspect in a sector often lacking precise metrics for material generation and recovery.
These findings contribute to an expanding body of knowledge concerning recycling capture rates in the United States. Earlier this year, The Recycling Partnership conducted an analysis estimating that 21 per cent of all recyclables are successfully captured and processed nationwide, pinpointing a 3 per cent loss rate at MRFs across all material types.
EverestLabs highlighted the transformative impact of artificial intelligence adoption in recycling facilities, enabling the collection of accurate and consistent data regarding household recyclables destined for landfills.
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