Across material recovery facilities (MRFs) and plastics recycling operations, artificial intelligence is evolving beyond its early role in material identification. From incoming material composition to downstream residue losses, AI is being used to create a real-time, facility-wide picture of what is happening across the plant and to support more informed operational decisions.

As Matthew Steventon, head of business development at Greyparrot, explains, these systems are designed to track material at multiple points in the process, converting visual data into a running mass balance that establishes a central source of truth for the facility.

For operators managing variable feedstock and tight quality requirements, that visibility is becoming critical. But beyond delivering a snapshot of what is happening, AI is reshaping how that information is used.

Using AI to monitor plant-wide performance

In some MRFs, AI systems are now installed at multiple points across the process, capturing data on inbound material, sorted streams, and residue. The result is a system-wide view of performance that allows teams to track how material moves through the plant and where value is lost.

At Murphy Road Recycling, which processes up to 250,000 tons of recyclables annually throughout Connecticut and western Massachusetts, that visibility has reshaped how performance is monitored. The facility partnered with Greyparrot and Van Dyk Recycling Solutions to deploy multiple AI units throughout its system, establishing a centralized control environment.

"We installed 15 Greyparrot units throughout our facility, effectively transforming it into a real-time control centre," says Brian Popovich, senior financial analyst for operations at Murphy Road Recycling.

That shift reduces reliance on delayed feedback loops. Instead of waiting for feedback from downstream customers, plant operators can identify changes in purity or capture rates as they occur and respond immediately.

"If purity begins to swing, what we've built are dashboards and automated alerts that allow site management to intervene immediately," Popovich says. "We're not waiting three weeks, four weeks to get that feedback from a customer downstream."

How AI is guiding operational decisions

The ability to monitor performance in real time is only the first step. The more significant change is how facilities leverage that information to guide decisions across operations, maintenance, and capital planning.

At the KSI Recycling Facility in northern Holland, AI data has been used to identify subtle trends that would be difficult to detect through manual sampling. One example is the impact of equipment condition on recovery rates.

Using AI-generated data, operators identified a gradual increase in material entering the residue stream, which was ultimately linked to contamination buildup on optical equipment. By tracking that trend, they were able to refine their maintenance approach.

"When we clean this window, we see a jump in this line, and then we see an improvement of the quality and of the recovery," says KSI plant manager Foppe-Jan de Meer. "Now we know how much this line can decline before we have problems with our quality. So we have now adapted our cleaning scheme."

This shift from reactive to proactive maintenance illustrates how AI is used to anticipate issues rather than respond to them after performance has already declined.

In addition to the immediate feedback loop, historical data also supports longer-term planning. At Murphy Road Recycling, sustained trends in material volumes are used to inform investment decisions, helping ensure that capital is deployed where it will have the greatest impact.

"When we see sustained volumes of specific materials, it strengthens the business case for targeted upgrades, ensuring that when we are deploying capital, it delivers the strongest ROI," Popovich says.

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