AI mercury scanner for waste separation
ML-powered scanner identifying mercury in e-waste monitors for safer separation processes, eliminating worker safety risks.
Confidential (Waste Management)
A waste management facility needed to identify mercury-containing components in discarded monitors and screens before manual disassembly. Mercury exposure poses severe health risks to workers, and existing detection methods were unreliable.
Older monitors and flat-panel displays often contain mercury in their backlighting. Without reliable detection, workers risked exposure during the separation process. Existing methods relied on visual identification or manufacturer databases that were incomplete. The facility needed a fast, accurate scanning solution that could be integrated into the existing waste processing line.
What SevenLab built
SevenLab developed an ML-powered scanning system that identifies mercury-containing components using advanced sensor data and image analysis, flagging hazardous items before they reach the manual separation stage.
Mercury detection ML
Machine learning model trained on sensor signatures to identify mercury-containing components.
Inline scanning
Integrated into the existing processing line for real-time detection without slowing throughput.
Automated routing
Flagged items automatically diverted to specialised hazardous materials handling.
Safety reporting
Complete audit trail of detected hazardous items for regulatory compliance.
Measurable business impact
Worker safety risk from mercury exposure was eliminated entirely. The scanner processes items at line speed, maintaining throughput while ensuring every hazardous component is caught and properly handled.
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