Using artificial intelligence to improve Energy-from-Waste plant performance

SUEZ in the UK, in collaboration with the Group Digital team, deployed a first-of-a-kind AI-computer vision solution to detect non-conforming waste, which can cause blockages and unplanned downtime in energy-from-waste (EfW) plants. The solution is actively running at two sites in West London, which manage the residual waste from 6 London boroughs.
Our mission

Avoid non-conforming waste to ensure a continuous activity in energy from waste plants

Non-conforming waste can cause blockages in energy-from-waste incinerators and lead to unplanned shutdowns, costing a single plant up to 100,000€ per shutdown due to lost production capacity.  Moreover, the blockages caused by non-compliant items must be removed, which presents a health and safety risk and often requires repair to restart normal plant operations.  

As part of its waste recovery activity, SUEZ has set up an industrial sorting process: waste is sent to a transfer station where it is monitored remotely by operators, via screens, before being sent to a plant where it will be incinerated to produce electricity. This process requires the constant focus of the operators, who must observe up to 8 screens simultaneously to detect non-compliant items in the continuous waste stream. These are typically bulky objects, such as mattresses, metal pipes, supermarket shopping trolleys or rubbish bins.
Our solution

Using artificial intelligence as an extra set of eyes

An application of artificial intelligence (AI) called computer vision can provide operators with more accurate monitoring tools and thus limit the risk of unplanned shutdowns.  The AI-enabled solution acts as an extra set of eyes, raising alerts when non-conforming items are detected and “augmenting” the operator in their day-to-day responsibilities.

The solution was developed in a three-part collaboration between the Group Digital team, SUEZ in the UK, and a startup partner called Deepomatic.  A Proof of Concept (POC) was first completed in 2019 as part of the SUEZ Digital HUB Acceleration Program, and the solution was later industrialized and deployed with the support of the Group Data Office in 2020.  

AI Project (short version)

Credit: SUEZ UK

Our results

Increased detection of non-compliance objects helps plants’ blockages decline

The solution analyses images from surveillance cameras and alerts operators to the potential presence of non-compliant objects in the waste stream. For each detection, the operator accesses images of the non-conforming item, processes an alert, and can then take necessary actions to remove the item safely.  

In 4 months of operation, the system detected 11 non-conforming items that were missed by the operator and could have caused blockages.  Since deploying the solution, the EfW plant experienced a sharp decline in shutdowns, and the Group is looking to scale up the solution to other EfW sites in the UK, France, and Belgium.

+11

objects detected and removed that may have caused shutdowns

The SUEZ Digital team, in collaboration with R&R UK operational teams, built and deployed a unique artificial intelligence solution that has been integrated into the daily operations of our sites in West London. In just six months of operation, the system has helped operators detect many non-conforming items, and we've seen a significant reduction in plant shutdowns over the period.

John Scanlon , CEO, SUEZ R&R UK

In addition to “augmenting” operators on the ground, the solution brings a wealth of information about the waste-flows (volumes, nature, frequency of non-conforming waste) and allows the site management to make better data-driven decisions about their operations.

Meriem Riadi , Group Chief Digital Officer, SUEZ

Contact

To learn more about the project or discuss your idea for digital acceleration

Send us an email: andrew.collier@suez.com