A survey conducted by the Carton Council of North America in 2018 showed that 94% of Americans support recycling. That same year, the Environmental Protection Agency (EPA) reported that the recycling rate was only 32.1%. Why is this the case?
Local governments are responsible for creating recycling programs. Cities that have invested in recycling infrastructure, education and incentive programs, like San Francisco and Los Angeles, claim recycling rates of over 70%. Contrarily, cities with smaller budgets and staff and contamination issues have eliminated curbside recycling altogether. (Chesapeake, VA Pembroke Pines, FL are two examples.)
The adoption of single-stream recycling, where various recyclables are placed in a single container, has significantly increased household participation. But it has also contributed to a 25% contamination rate of recycled material. Contamination occurs when non-recyclable items are mixed with recyclables, making it challenging or impossible to sort and safely process these materials. Common contaminants include non-recyclable plastics (bubble wrap, trash bags, cling wrap, etc.) and food residue.
Contamination is more than a mere inconvenience. In 2016, China received over 16 million tons of plastic, paper and metals from the U.S., 30% of which was contaminated and later dumped in the Chinese countryside and waterways. In 2017, China passed the National Sword Policy, banning the importation of materials that the U.S. had previously sent in for recycling. As a result, U.S. recycling facilities have had to make substantial improvements in the quality of their recyclables.
How does AI play a role in improving recycling? The 1990s saw the introduction of optical sensing and computational intelligence to distinguish between various types of plastic and paper. These systems typically achieved 80 to 95% purity, with human workers tasked to manually remove contaminants. Enter artificial intelligence! Recycling requires rapid identification of objects with diverse shapes, sizes and orientations on conveyor belts. AI-driven systems demonstrate near-100% accuracy by relying on image analysis of attributes, including color, opacity and form. A vast dataset of recyclable material images, collected globally and meticulously annotated, are regularly updated to improve reliability.
One company, AMP Robotics, has pioneered in the AI-recycling industry since 2014. Equipped with a powerful network, their 1,800 pound “pick-and-place” robots are twice as efficient as human employees, identifying and sorting 80 items per minute. Now recycling facilities equipped with artificial intelligence robots are able to sort greater quantities of trash while reducing operating costs.
Perhaps we can even stop contamination at the point of disposal, right at home. CleanRobotics has created a receptacle named TrashBot that uses imaging, AI algorithms, and robotics to detect and sort waste as it is being thrown away. This prevents contamination and makes the sorting process easier down the line.