Optical sorting technology helps material recovery facilities (MRFs) reduce their reliance on manpower to sort various materials. They are masters at sorting efficiently and quickly.
Yet, as do human sorters, optical sorters have some limitations. For instance, an optical sorter is a pro at sorting polyethylene terephthalate (PET) from high-density polyethylene (HDPE). Yet, the optical sorter starts to run into problems when sorting nonfood-grade HDPE from food-grade HDPE.
Most mixed rigids are made of HDPE, and mixed rigid plastic has a much lower value than what pure HDPE has in a container form. Traditional optical sorters are not able differentiate between these two materials.
However, this limitation and others can be overcome when optical sorters are paired with artificial intelligence (AI) technology.
All optical sorters have a very basic level of AI, or machine learning, using near-infrared (NIR) technology and cameras. AI takes that machine learning a step further by offering deep-learning skills and the ability to analyze more data.
AI is very much a tool that elevates optical sorters and provides a way to sort more specifically. AI complements traditional optical sorters in that it allows it to select materials in a more refined way.
Going back to the example of sorting mixed rigids from HDPE bottles, AI can solve that problem for optical sorters. Purely see what the chemical composition is but not other things, and that’s where AI sees a huge dividend, pairing AI with optical sorters can help create new opportunities.
Coupling optical sorting with AI provides MRFs a new level of flexibility. Both technologies have their specialties. Optical sorters are speedy and handle basic sorting but can miss some objects; AI and robotics technologies are slower, but they are great at quality control applications and providing additional data on the material stream.
Overall, optical sorters do an adequate job with sorting, according to sources, but incorporating AI takes that sorting to the next level. Certain sorting tasks can be challenging for the standard NIR technology on optical sorters.
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