Artificial Intelligence in Waste Management – Dream or Reality?

New tech such as internet of things (IoT), Machine Learning, Robotics (collectively referred as Artificial Intelligence) have been used in many industries to bring innovative and more efficient solution to the problems. Could these technologies be applied to waste management sector as well? Why not? The below examples illustrate that the startups & companies are using these new technologies to usher in innovation in waste management sector as well.

1. Smart Bins


Smart bins use artificial intelligence (AI) to act as intelligent devices. Here, IoT sensors are used to monitor the level of the trash in the bin and notify/sound alarm through a mobile app when the bin is full. The associated app lets users know the location of the nearest available waste bin when a particular bin is about to be full. Thus, it prevents the bins from overflowing. Smart bins enable the waste management companies to optimize the waste collection timing, frequency, and the routes.

Computer vision (Using a machine learning technique called ‘Convolutional Neural Network (CNN)’) and other machine learning algorithms allow these sensors to distinguish between different types of garbage. A Poland based startup Bin-e has created an intelligent waste container that uses artificial intelligence to automatically separate the waste into different groups and additionally, compresses that waste. You can watch this smart bin in action in this 3 min video here – CLICK HERE

2. Smart Logistics an Austria based waste management company, works like ‘Uber’ for construction and demolition waste. The construction companies can easily ask for disposal of their construction waste through the mobile app of ‘’. As soon as the construction company books the disposal of C&D waste, the mobile app matches the order to the nearby drivers of regional waste disposal companies. Thus, the C&D waste is quickly and cheaply disposed of. This helps in traffic reduction and thereby reduces Co2 emissions.

Alba Group, a Germany based global recycling company, uses smart logistics to plan the complex logistics tasks and optimize routes. It has developed an algorithm which coordinates between centrally located dispatch executives and on-route drivers. Drivers receive their planned routes through the hand-held devices. This leads to fewer vehicles on the road and thereby saves time, fuel and reduces Co2 emissions. The smart logistics thus augments the subsequent waste recycling activities in contributing to the green economy. You can watch the 2 min video here on this link – CLICK HERE

3. Smart Sorting for Recycling


Here, AI is used to identify and analyze the objects on a conveyor belt. These objects, while moving on the conveyor belt, are scanned with cameras and analyzed by deep learning algorithms. Robotic arms then pull the items off the belt for further sorting and processing. AI-powered sensors are a vast improvement compared to traditional optic sensors. The AI powered sensors can detect items made from different materials as well as differences such as chemical contamination between items of the same basic material.

Alba Group, a German recycling company, in its packaging sorting plant in Leipzig, uses an automated sorting technique pioneered by TOMRA. TOMRA is a Norway based resource recovery company. TOMRA’s solution distinguishes between silicone cartridges and polyethylene packaging to sort them. The commonly available near-infrared separators (NIRs) are not capable of this sorting. In this solution, a sensor unit mounted above the sorting belt scans the incoming waste. A software processes the sensor data in real time. The robot arm then receives a command to grab the target object and carry it to the correct container. The system is capable of learning which is the essential characteristic of the machine learning technology. You can watch this technology in action in this 2 min video here – CLICK HERE

Greyparrot, uses computer vision software (a type of machine learning algorithm called CNN i.e.; Computational Neural Network) to increase transparency and automation in recycling. Using this technology, Greyparrot’s device can spot items on a conveyer belt faster and with better accuracy than a human. This device automatically identifies different types of waste, providing composition information and analytics to help companies improve the waste recycling process. You can watch a short 30 seconds video of this technology here – CLICK HERE



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