Success Story: Revolutionising recycling with AI

NCC presenting the success story

UK-NCC

Technical/scientific Challenge:

Recycling waste is one of the easiest ways to reduce the use of limited resources and curb climate
change, but often the materials that end up on the conveyors of the materials recovery facilities  (MRFs) are not what recyclers want and many contaminants such as containers soiled with food waste have to be removed by hand. It’s estimated that the world generates three billion tonnes of domestic waste each year, but less than 10 per cent of it is recycled.

Business impact:

In mid 2022 the  project began two months of lab tests to integrate the software with the robotic hardware, and then a three-month trial of the prototype system at Glasgow City Council’s recycling centre. Several large European recycling companies are interested in Xiaoyan’s product.

The system is designed to be sustainable, flexible, affordable, scalable and future proof and the  technology can help recycling companies recoup their investment within two years and double their profit within three or four years’ time.

Industrial organisations Involved:

Danu Robotics is an Edinburgh based start-up specialising in AI solutions that protect the environment. The company’s founder and CEO Xiaoyan Ma, completed an MSc in HPC with Data Science at EPCC.

Solution:

The Danu Robotics solution is based on machine learning software that can visually identify recyclable and non-recyclable material and remove any items that should not be there. Before deploying the robot picking hardware, the company had to build up a waste image database to help the system identify contaminants. Now that the initial system training is complete, Danu Robotics is working on the software which will direct the robotic sorting system to remove contaminants from a moving conveyor belt as efficiently and effectively as possible. For this part of the programme, the company called in EPCC for support. EPCC initially worked with Xiaoyan to outline the system’s architecture and this led to another project to train the AI part of the system to identify recyclable and contaminant items. EPCC’s Cirrus supercomputer was employed to help process the data and train the software.

Benefits: