GW Work


Self-Sorting Recycling Machine Open Access

In America, the concept of recycling as a means of waste management has gained more traction over recent time. Typically, this process involves consumers sorting their garbage into trash cans and their recyclables into a separate recycling can. When all recyclables are meant to be placed in one bin, this is referred to as "single-stream recycling." These recycled items are taken to a facility where they are sorted into separate streams to be repurposed, with any non-recyclable items being sorted back into trash and sent to landfills. Within this system, when recycling bins contain more than 50% trash, the entire receptacle is treated as waste rather than being sorted apart. The challenge here is inherent to the method: the brunt of the responsibility of recycling falls on the consumer. The average person who is trying to dispose of their waste has to know, based on their current location, whether or not items are recyclable, or if they are too contaminated to be recycled by their nearest recycling facility. While currently the responsibility of recycling falls on the consumer, implementing green technology into this process would reduce the rates of human error and increase recycling rates. Artificial intelligence integrated anywhere into the waste stream process would likely increase recycling rates, and a local device attached directly to a trash can could increase the accuracy of the recycled items in a given can. In 2014, The George Washington University launched its Zero Waste Initiative to increase the sustainability and recycling rate around campus. GW's Zero Waste Plan set the goal to minimize the university's trash output and, conversely, maximize the recycling rate. New, easily readable signs were installed on the sides of the cans with the appropriate materials clearly advertised. The university also began the process of standardizing all trash/recycling cans located across campus, distributing cans in places around their Foggy Bottom campus where they had been deemed to be lacking, and ensuring that all trash cans have a recycle bin located close by [7]. While this initiative has slightly improved recycling rates and accuracy on campus, a self-sorting recycling bin that could sort items as either trash or recycling would remove any potential for human error in the process. On The George Washington University's campus, an ideal self-sorting recycling machine would utilize the trash cans and recycling bins already located around campus, and would fit on top of the containers to be able to sort items into one bin or the other. Users would be able to place their items into the lid, and the Smart technology would do the rest of the work. This project aimed to create a self-contained lid for the trash receptacles located around GW's campus to reduce the contamination rate within the recycling containers on campus. The self-sorting recycling machine uses Google's Cloud Vision API to identify a picture of a given item and determine recyclability. The user places the item onto a platform and presses a button, which signals a Raspberry Pi to capture a picture and cross-reference its annotations with a list of recyclable and non-recyclable items. After determining recyclability, the platform rotates to drop the item into the designated can. While GW's campus recycling currently has a contamination rate of about 30%, this machine consistently has lower than a 15% contamination rate, indicating success [Appendix 9].

Author Keyword Date created Type of Work Rights statement GW Unit Persistent URL

Notice to Authors

If you are the author of this work and you have any questions about the information on this page, please use the Contact form to get in touch with us.