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Sensors to make waste management more climate-friendly

From the left: Sheng Leslie, who is leading the project, Peter Götlind, manager at Ekonom

The waste stations around Uppsala university’s campus area Ekonomikum now have sensors attached. The sensors measure how full the bins are and when they need to be emptied. Aggregating this data and combining it with learning algorithms make it possible to plan optimal pick-up routes. The project is a pilot study run by Sheng Leslie, student and entrepreneur.

Shen Leslie is studying nanobiotechnology at Uppsala University and has won the CleanTech Challenge, a competition organised by Uppsala Municipality and Uppsala University.

Now he is developing a sensor that measures how much waste is collected, making waste management more data-driven and climate-friendly.

“Logistics account for 75 per cent of the total cost of waste management systems, due to static pick-up schedules and traffic jams in urban living areas. We have developed a sensor that quantifies the bins’ fill-levels, mapping out which ones are necessary to pick up, while skipping unfilled ones, so we can significantly reduce logistics costs,” says Sheng Leslie.

Real-time data

Sheng Leslie shows one of the low power wide area
network sensors that is attached to a dust bin.

The sensors collect real-time data that is combined with machine learning algorithms to create optimal waste collection paths, to make the recycling industry smarter.

“The preliminary results show 36.8 per cent reduced operational costs, and 15.8 per cent improvement in sustainable performance,” says Sheng Leslie.

He hopes his system can contribute to the goals of the Uppsala Climate Protocol and become a robust tool for the recycling industry.

Sheng Leslie turned to university environmental coordinator Karolina Kjellberg with his idea and she in turn spoke to Peter Götlind, manager at Ekonomikum. During the autumn, measurements have been made at Ekonomikum which will be analysed to further the development of the sensor.

Expert on optimal scheduling

Also involved in the project is Di Yuan, Professor of Optimisation at the Computer Science division of the Department of Information Technology and expert on algorithms for optimal scheduling and routing in networks.

The project has received funding from the European Regional Development Fund and UU Innovation.

“These grants will be helpful for strengthening industrial collaboration during the upcoming project development,” says Sheng Leslie.

 

21 December 2017