During a school sustainability-focused hackathon, North Carolina State engineering students Nick Conlon, Hartley LeRoy, Nick Sischo and Goutham Subramanian were taken aback by the lack of efficiency within the waste removal market.
Garbage trucks average 2.5 miles per gallon and each burns about $42,000 of fuel a year, but often they’re picking up nearly-empty dumpsters, since they don’t have data to see if they’re full. With fuel accounting for about 20 percent of total costs in the waste collection industry, dumpster sensor startup Trashr aims to help waste collection be more efficient by alerting companies to the optimal time for pickup.
“Picking up those dumpsters at optimal times can reduce time on the road and improved service,” says Sischo.
“What that means for the producer of the trash is that they get better pickups, meaning their trash is never full, stinking up the place,” he says. “The institution that’s collecting benefits because they reduced their fuel usage and they also make less trips to the dump where they have to pay five pounds for the waste.”
Trashr’s battery-powered smart sensors attach to the dumpster and let the hauler know the best time to add it to their pickup route while adhering to health and safety restrictions, such as that food waste shouldn’t sit outside for more than 2-3 days. The machine learning and behavioral analytics components of the dashboard can help an organization like a university “generate an optimum path and collection efforts based on the waste cycle of the entire semester.”
“Our current sensor model collects the volumetric fill data using an ultrasonic sensor, meaning it creates a sound profile of the dumpster depending on how full it is and then sends those readings back to our online dashboard,” says Sischo.
Following installation, the sensor generates a profile of the dumpster’s measurements over the next week and uses that to compare once the dumpster has trash in it. The accompanying platform allows the organization to track their dumpsters, see on a map small icons indicating fullness level, and access accompanying reports with the sensor data. Organizations can obtain Trashr’s service through a subscription model with optional add-ons like data analysis.
Sischo shares that most of their current customers monitor dumpsters that are far off the route of most of their waste collection. They have two sensor models, one for open rectangular dumpsters and one for residential closed dumpsters. They’re currently working on an API of the software to be able to quickly integrate with the organization’s already existing software.
The entire team is currently in their senior year, and they’re using this time to take advantage of school programs and grants to continue validating their customer base while bootstrapping. They will pursuing a seed round earmarked for growth within the next couple of months.
The startup is currently working with the city of Durham through their Innovate Durham program — a 12-week stint where they gain access to the city’s resources to solve local waste management issues.
“We’re currently monitoring the city’s dumpsters and helping them improve and optimize their waste collection system,” says Sischo. “Not only does that benefit the citizens in the city of Durham, but it could also reduce tax load on them in the long run.”