Greyparrot shares the fascinating data insights from sorting facilities being uncovered by AI waste analytics systems.
In recent years, global materials recovery facilities (MRFs) have adopted automated monitoring systems to gain deeper insights into their expanding waste streams. Where manual sampling once provided less than 1% visibility, AI now offers data on the remaining 99%.
Operators can access detailed insights into waste classification, mass, food-grade status, financial value, brand, and potential emissions for the waste objects they process.
While the resulting deluge of detailed waste data can be overwhelming, it provides valuable insights that can improve facility performance – if facility staff can turn that data into action.
Facility operators have turned to AI waste analytics systems to help them translate millions of data points a day into higher yields, purer products, and profitable facilities that run at maximum capacity.
Here’s what systems like Greyparrot Analyser have revealed at real-world facilities, and how their operators have responded:
-
Reducing avoidable losses
The average MRF residue line is around 26% recyclable plastic, and 37% recyclable paper and cardboard. Facility operators are using AI to uncover hidden value in residue lines, and are taking action based on the insights gained.
The insight: 93% of residue material could have been recycled
The action:
With a detailed breakdown of residue material composition, facility managers can diagnose the valuable resources they lose most often.
There’s often plenty to choose from – Greyparrot Analyser data has shown that in some facilities, up to 93% of residue material is recoverable.
Operators have used this information to adapt processes and measure the impact, as well as make a clear business case for investment in upgrades.
The insight: £1.6 million worth of recyclable plastics lost each year
The action:
Generally, residue losses increase as operators increase their facility’s throughput, with machinery and manual sorters struggling to keep up. In one high-volume PRF, our data revealed £1.6 million worth of valuable plastics being lost to residue ines.
Real-time insights into residue composition revealed when high throughputs were causing avoidable losses, allowing the operator to adjust processing speeds to achieve an optimal balance.
Removing guesswork helped align the team on the right actions based on clear data, and demonstrated the impact of operational changes.
The insight: Balanced infeed blends reduce losses by 18%
The action:
Consistent infeed blends result in fewer resources lost to residue: in another PRF, we saw a balanced infeed blend reduce valuable material loss by 18%.
Operators use live and historical infeed and residue line data to adjust their infeed “recipes”, measure the results, and reduce future losses by cutting costs associated with landfilling and incineration.
-
Meeting purity targets
Waste stream composition changes on a minute-to-minute basis, and the proportion of target material (in other words, the stream’s purity), shifts just as often.
Quick adaptation to these changes improves sorting efficiency and profitability. The faster operators can adapt to those changes, the more efficient their sorting — and the higher their profits:
The insight: Purity fell 4% below contractually agreed product quality
The action:
After measuring product lines with Greyparrot Analyser, one European MRF revealed they were delivering 95% product purity instead of the agreed 99%.
By proactively addressing the disparity, they prevented a £19k loss per 40 tonnes of aluminium and maintained valuable contracts with their customers.
The insight: £47k at risk from a single dip in purity
The action:
When an alert from Greyparrot Analyser indicated a purity drop, a European MRF stopped the plant running, fixed a machinery issue and had the plant back up and running in 35 minutes.
This swift action prevented further contamination and saved £47k by avoiding the need to reprocess 40 tons of material.
-
Maximising facility capacity and machinery performance
Mechanical failures and poor process control are some of the most common hurdles to full production capacity.
Typically, those challenges lead to blockages or stoppages that result in facility downtime – and ultimately, less revenue.
Waste data can be a critical asset for facility managers and process engineers who are trying to maximise their facility’s capacity.
The insight: Surges in specific materials lead to blockages
The action:
Continuous waste data enables facility managers to visualise and diagnose downtime in detail. Facility staff can directly compare historical trends in material composition and throughput volume to downtime logs, identifying the scenarios that most commonly result in stoppages.
Composition data in one facility revealed that the volume of paper often spiked before a blockage. Operators used that insight to isolate paper sorting machinery for inspection and reduce throughput rates on their fibre belt when live infeed data revealed a spike in paper.
The insight: Sorting machine miscalibration leads to £240K in annual losses
The action:
Pre- and post-sort composition data provides insight into machinery performance. That visibility enables operators to react faster when infrastructure malfunctions, proactively maintain equipment — or determine whether machinery is the right solution in the first place.
After installing Analyser units before and after newly purchased sorting machinery, a US-based MRF found that it caused purity to drop from 93% to 80%. If left unchecked, the £800 daily loss caused by that purity dip would have cost the facility £240K over the course of a year.
Instead, the facility’s manager used the data to build a clear business case for investing in machinery re-calibration.
The insight: Underutilised belts reveal untapped capacity
In other cases, there’s more productivity on offer than current machinery can support. Maximising plant capacity can be a case of ensuring that belts are being used to their full potential as often as possible.
One MRF operator discovered that many of their belts were empty or carrying very low volumes of material more frequently than they realised.
Historical belt availability data allowed them to determine that they could increase their processing capacity by 5,000 tonnes a year. That insight supported a £2.5 million investment plan for facility upgrades.
The future of waste management
As waste regulations tighten and waste streams become increasingly complex, AI offers a powerful solution for the waste management industry: by leveraging data-driven insights, MRFs can optimise operations, reduce environmental impact, and drive profitability.
The future holds the promise of a circular economy where data-driven waste management will be a key enabler in achieving this vision.
Take the next step in your career with CIWM membership, find out more about the benefits today.
