AI-Driven Recycling Centers for Disadvantaged Communities: Improving Recovery, Safety, and Costs

Creating the Foundation for Modern Recycling Facilities: The processing technology used in the recycling industry has remained unchanged since the 1980s, relying on manual labor and rudimentary machinery to solve our waste crisis. For the past 40 years, municipalities have shelled out egregious tipping fees for ineffective and dangerous sorting practices. Cities across the U.S. need a new recycling processing system to enter the waste industry and correct the backward economic model plaguing the sector. This project will create a proof of concept of a rapidly deployable, autonomous robotic sorting system to build the foundatio n for the next era of recycling facilities. Rather than relying on slow-moving waste haulers, KLAW Industries will develop, deploy, and operate this technology to reduce municipal tipping fees by 31%. With an autonomous sorting system to match the modern waste stream, we can reduce product contamination to 0.5%, lower capital costs by 65%, and decrease excess hauling by 94%. Technical Feasibility & Comparative Performance: With backing from the City of Binghamton, KLAW Industries' researchers have made a breakthrough innovation in feature extract ion and convolutional neural networks to make rapidly deployable autonomous sorting possible at a local level, resulting in higher recovery rates and a significant savings potential for City residents. This project will develop a prototype to demonstrate this technology can be used in an industrial setting to process regular volumes of recyclables from citizens of the disadvantaged community of the East Side of Binghamton. Our target market is cities and commissioners of public works who face the unpleasant dilemma between paying more for recycling or saving money, time, and headaches by dropping the program entirely, as many low-income towns and villages already have. Market Size & Performance Benefits & Environmental Benefits: With a total available market (TAM) of $37.8B in the U.S., the achievements in computer science present an opportunity to rectify one of the most extensive economic calamities in history. With drastic reductions in capital costs, we can shuffle diesel-powered hauling enterprises out and usher in technology companies focused on long-term material recovery. The recycling sector injury rate is 3.2 per 100 full-time equivalent workers. Autonomous sorting protects critical workers by moving away from conveyor belts and toward computer terminals. With an increased sorting accuracy of 90% and a 31% lower tipping fee, this project will begin sorting, classifying, and reusing waste locally to achieve a recycling rate of 50% by 2030.