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Amplifying Safety in Resource Recovery

The challenge

When it comes to delivering faster, more efficient sustainability solutions through resource recovery, EnviroWaste uncovered some challenges in their processing plants. They needed to enable a protected area where the general public could deliver their waste while in close proximity to the waste transfer professionals operating heavy machinery. To minimise the risks and maximise safety they turned to a high-tech solution.

Chris Aughton, CEO of EnviroNZ explains “Safety is a constant focus for us. We have strong controls to protect people on our sites but we’re always working on how we can take it to the next level. In the past year we’ve seen situations where members of the public have tried to retrieve something from a clearly signed and barrier protected no go area. Our on-site team constantly monitor for this type of situation but it got us thinking, could we use technology to make it easier for them?”

The solution

EnviroWaste, through their parent company, EnviroNZ began exploring the potential of AI and Multi Access Edge Computing, powered with Spark 5G to create a safer operating environment. Chris Aughton explains “We challenged Qrious to develop a cutting edge AI solution that could make our site safer by enabling us to anticipate and react immediately if someone bypasses our safety controls.” The pilot of this computer vision system helps solve a key business issue, identifying health and safety risks for EnviroWaste at its resource recovery centre and helping avert incidents.

Qrious, part of the Spark Business Group worked with EnviroNZ to develop a cutting edge AI hazard detection system to detect if people were in too close proximity to excavators working in the waste disposal area. Spark has taken a Qrious-developed AI-powered computer vision pilot that scans the operational areas and enhanced it with 5G connectivity and local edge computing (AWS Snowball Edge) to identify potential risks, triggering alerts.

The system incorporates computer vision and IoT enabled video cameras, to identify and track people and excavators within a specified detection zone, calculating the distances between them. The Qrious system then triggers alerts when a person is identified as being too close to the heavy machinery.  Data is transmitted over 5G connectivity to the cloud-based application hosted at the edge.  This has the benefit of allowing data to transfer faster, flagging hazards precisely and promptly.

Mark Beder, Technology Director for Spark explains that lower-latency data processing over a 5G network will increasingly deliver value to business applications involving machine learning, IoT, and video streaming.  

“We are looking for ways to bring the potential of 5G to life – demonstrating how faster throughput, lower latency and high levels of reliability can create tangible business outcomes. EnviroWaste is a fantastic example and we’re looking forward to identifying and testing further use cases.” 

The results

EnviroWaste installed the first of these automated MEC hazard detection systems to run as a pilot for six months. Aughton says that results were so successful that the business will extend the trial, exploring new variables and creating a robust, transferable system. “We’re hugely enthusiastic about the ability of this technology to alert our team as an incident is happening so we can instantly respond. We’re now looking to test it in different site situations.”  

EnviroWaste are one of the first New Zealand businesses to prove the value of Multi Access Edge Computing which minimises latency and the number of network hops required to connect from a business cloud-edge application to an end user’s or IoT device, in this case between their cameras and an AI detection app, then back to instantaneous alerts.  

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