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Denne idé er en del af Smart & Close — Circular Data Challenge

Smartest bin
13. november 2019

We are contributing for challenge 1: How can we ensure a better waste separation?

Waste separation requires different waste containers, which occupy too much space. The management of which could be more efficient. 

Our solution:
SMARTestBIN. One underground bin. Unlimited possibilites of space management for waste management. This smart bin is not a one size fits all, but based on data it can be delivered in the optimal size. Furthermore the management of waste type space is dynamic based on data. SMARTestBIN is a bin of the future, because it provides data for it self and its stakeholders. 

Key elements:


  1. Filling data regarding volume over time --> Analyses on optimal bin size for local areas --> More quality space and less waste mess
  2. Filling data regarding waste type volume over time --> Prediction of optimal space division for each type of waste, with dynamic changes when needed --> Better use of the space for sorting
  3. Data on actual filling levels --> Utilities have a better understanding of optimal waste picking management in regards to route and frequency --> Reduced costs of operation and lower carbon footprint

Smarter handling of space make it more available to start separating more waste types. Doing so optimize the waste circulation without having to affect the needed space nor more complex waste picking infrastructures.

The muncipality of Copenhagen have data regarding the total amount of waste collected from each type every month in the years 2008-2014. This data will make up the starting dynamics of the bin. The space division for each waste type will change each month based on that data using predictive data analyses. However, we believe that the composition of waste differs in different areas. Areas which is student heavy will have more of one type of waste while areas with primarily families with children will have more of another type of waste. This is why the bin tracks filling levels continuously, which can deliver a sorting of space better targeted the area. The collected data regarding feeling levels will be used in a machine learning model to predict the needed division of the space in the bin.

Citizens - More space for quality use instead of messy waste areas
Municipalities - Smartere city, more livable city, better use of public spaces, possible savings regarding waste handling
Utilities - More optimal routes and frequencies for picking up waste, reduced cost of operation, possible reduced carbon footprint