AI USE CASE
Recycling Stream Contamination Detection
Automatically identify contaminants in recycling streams to improve sorting quality and reduce downstream processing costs.
What it is
Computer vision models trained on waste stream imagery detect non-recyclable contaminants in real time on conveyor belts, triggering alerts or automated diverters. Facilities typically see contamination rates drop by 20–40%, reducing material rejection costs and improving the purity of outbound recyclate. Integration with sorting line PLCs enables closed-loop control with minimal human intervention. Early pilots commonly report 15–25% reduction in manual sorting labour hours within the first quarter of operation.
Data you need
Labelled image datasets of waste stream frames showing both acceptable materials and contaminants, captured from existing or newly installed line cameras.
Required systems
- erp
- none
Why it works
- Capture diverse, well-labelled image data across seasonal waste composition shifts before model training begins.
- Install consistent, calibrated lighting on conveyor lines to reduce visual noise and maintain inference accuracy.
- Involve line operators early in the pilot to build trust and define clear escalation protocols for edge cases.
- Establish a model monitoring and retraining cadence (at least quarterly) to handle evolving waste stream compositions.
How this goes wrong
- Insufficient or poorly labelled training images lead to high false-positive rates that disrupt line throughput.
- Variability in lighting conditions on the sorting line degrades model accuracy over time without regular retraining.
- Integration with legacy PLC or conveyor control systems proves more complex than anticipated, delaying go-live.
- Operational staff distrust automated decisions and override system alerts, negating efficiency gains.
When NOT to do this
Do not deploy this system if your facility processes fewer than 5 tonnes per hour — at low volumes, manual inspection remains more cost-effective than the camera infrastructure and model maintenance overhead.
Vendors to consider
Sources
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