Innovative Waste Management Technologies

Innovative Waste Management Technologies

Innovative Waste Management Technologies : Waste Industry is in Need of an Ai Shakeup!

The recycling process is in desperate need of improvement, and several companies are utilizing AI and machine learning to shake up the industry. Less than 14 percent of plastic products are recycled globally according to the EPA. Of the 9 billion plastic tons produced since 1950, about 7 billion tons are no longer in use, according to a study the University of California at Santa Barbara published in the journal Science Advances. This means that there is a long runway for anyone trying to solve this problem.

Municipal Recycling Facilities (MRFs) located throughout the country have been in need dire of technological advancements. MRFs have seen an increase from 180 tons of daily waste to 220 tons over the past ten years. It is difficult to staff these facilities as the jobs are messy and incredibly tedious. This industry is clearly ready to be overhauled by technology, and there are already steps being taken to incorporate this.

AMP Robotics is a US based technology company seeking to improve the world of recycling using their AI powered robots that can detect plastic in trash sorting facilities. Their robot named Clarke is currently being used in Colorado to assist a municipal waste facility. Clarke has taken over a decades-old sorting system by picking 60 recyclable items per minute, with 90 percent accuracy.

AMP claims they have cut sorting costs by 50 percent while improving efficiency. Clarke can do even more than help out with recycling. While its camera is used to identify plastic, it also sees non-plastic items and can create actionable data about which products are coming through facilities in what quantities. According to AMP robotics executive Matanya Horowitz,“[n]ot only can the technology identify and sort materials, but it can see how much aluminum is getting lost or notice spikes in paper and track and manage pre-emptively and that’s exciting.” This efficiency is not possible without the use of AI.

Simple sorting is not the endgame for AI in this field. Using data provided by the camera and sensors could allow MRFs to redesign their facilities. According to Mary O’Connor at GE, “the same computer vision cameras that power robotic sorters could be used to configure the conveyor layout, optimizing the sorting equipment matrix in real-time to match the flow of materials coming into the facility. As the percentage of plastic waste grows, so would the capacity to sort it, but if the amount of paper increases, more paper sorting would come online.”

AI is also helping sort all trash so that what ends up in the MRFs is actually recyclable. According to, only 20% of what gets put in blue bins makes it to the MRFs. Companies like Clean Robotics are seeking to fix that with the use of “smart” trash cans. A smart trash can analyze what is thrown away using sensors and cameras to determine if it is recyclable.

This process ensures that the materials that end up at MRFs are actually recyclable. Their robot TrashBot is customizable so that it can adjust to the different standards for recyclable items based on where it is used. TrashBot uses machine learning to improve over time and is only getting more accurate. It also has the same advantage as the AI used in MRFs: data collection. TrashBot collects data about what an organization using their trash cans is throwing away and in what volumes, so they can better understand their waste.

TrashBot was a finalist for the IBM Watson AI XPRIZE which recognizes the AI products that are tackling huge world challenges. TrashBot is a great example of how AI and machine learning can clean up our world.

It is clear that we need to recycle better as a society, and AI and machine learning will expedite the process to a greener society.Written by John Martin, Edited by Jack Vasquez & Alexander Fleiss

Innovative Waste Management Technologies

Environmental Risk : Our environment is at serious painful risk globally! (