The first system for solid waste management took shape in late 18th-century London, when waste workers began to manually collect and recycle discarded material in the city’s dust yards.
Facilities grew alongside swelling waste flows, and recycling innovators applied machinery like collections vehicles and conveyor belts to process more material. Only in the last few decades have tools like optical sorters, mechanical balers, and air jet separators introduced automation to materials recovery facilities (MRFs), helping them keep pace with the rising tide of waste.
Today, a spike in global consumption means we’re throwing away more recyclable material than current manual or mechanical systems can effectively recover: by 2050, we’ll be producing 3.8 billion tonnes of municipal solid waste a year.
How big is the waste data gap?
Technology has streamlined everything from sorting to baling, but until now, facility operators have relied on manual counting to gather waste data. The average recovery facility measures just 1% of the waste it receives, but no two waste streams are the same. To add to that challenge, MRFs are expected to process more mixed materials each year.
Plastic alone now accounts for 350 million tonnes of waste material annually, yet only 15% of it is recovered due to a lack of visibility and automation. As the saying goes, "You can't manage what you don't measure."
How does AI waste analytics unlock real-time data on waste material?
AI waste analytics systems like Greyparrot Analyzer combine computer vision with automated data analysis, delivering real-time data on waste flows at scale.
These systems significantly enhance visibility into waste streams, automatically gathering and analyzing waste composition at a rate 62 times faster than manual counting. Additionally, retrofits can be completed within 1-2 hours, without needing new, costly infrastructure.
The magnitude of AI's impact is clear: our systems analysed over 25 billion waste objects in 2023 alone, a number that increases exponentially with each new Analyzer deployment.
How does AI waste analytics give rise to waste intelligence?
If we think of AI waste analytics as the tool responsible for collecting and organising vast amounts of waste data, then the concept of waste intelligence naturally emerges as the outcome of adopting data-driven strategies.
By systematically analysing this data and using advanced algorithms and data processing, waste intelligence becomes a valuable asset, providing useful insights and turning raw data into practical knowledge and recommendations.
💡 Waste intelligence is the actionable insight gathered from detailed waste data, used to minimise waste and optimise resource use.
In other words, waste intelligence is the process of translating waste data into tangible action. That action translates into resource efficiency across the entire value chain.
By leveraging waste intelligence, operators can adjust their operations to respond to changes in material composition. This empowers them to extract more value from waste streams while reducing landfill waste. For instance:
- Operators can find the optimal balance between the amount of waste processed and the recoverable material lost to inefficient sorting.
- They can source higher-quality material and tailor sorting processes to specific suppliers.
- They can proactively maintain hardware based on historical downtime trends, among other strategies.
Across 50+ facilities in over 20 countries, plant managers are using waste intelligence insights to enhance the profitability of their operations. For example, at Re-Gen Waste’s flagship MRF, Greyparrot Analyzer data ensures that fiber output consistently meets quality specifications. Here's an example of how waste intelligence is applied.
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Optimising sustainable packaging production with waste intelligence
Brands and retailers make significant investments in tracking consumption, sales trends, and logistics to enhance production processes. However, when it comes to the end-of-life stage for their products, the absence of actionable information on waste flows creates a "dark abyss" of post-consumption insight.
MRFs, such as Re-Gen, provide a crucial platform for analysing post-consumption materials at an industrial scale, enabling informed action based on resulting insights.
Similarly, producers and regulators face challenges due to the absence of comprehensive waste data, hindering the formulation of effective circular economy policies.
Waste intelligence presents an opportunity to unlock the hidden value within global waste streams:
- Brands grappling with regulations like extended producer responsibility (EPR) and plastics taxes need to innovate to develop more sustainable products. Post-consumption insight identifies areas for improvement to remain compliant.
- Product designers contending with technically recyclable materials that end up in landfills can employ waste intelligence to pinpoint under-recovered products, informing design enhancements and improving packaging recyclability.
- Regulators can use waste intelligence to assess compliance, target problematic materials, and evaluate policy effectiveness to craft impactful regulation.
Collaboration through waste intelligence
To take action on waste, producers and regulators will need to work closely with the facilities that are processing it. The waste sector is now at the heart of a collaborative value chain relying on data to use resources as efficiently as possible, and minimise their impact on the environment.
Many innovative recovery facilities are already using waste intelligence insights to improve their operations - with measurable results. In the process, they’re also gathering insights on post-consumption material that, over time, is poised to transform the entire value chain.
The unprecedented insights that waste intelligence unlocks about the materials we throw away marks an exciting turning point in our relationship with resource recovery.
Learn more about waste intelligence in our guide to AI for recovery facilities
