AI Sorting Robots Raise PET Bottle Capture Up to 200%

Manual sorting had reached its practical limit

A 24/7 PET reclamation facility in Ohio relied on manual sorting as material volumes grew. Sorting shifts lasted 12 hours, the work was repetitive, and open positions were difficult to fill.

Experienced sorters could pick 30 to 40 bottles per minute, but sustaining that pace was difficult. At the same time, the operation needed consistently clean material to satisfy the rigorous quality demands associated with food-grade recycled PET.

The assignment was unusually exacting. The system had to distinguish clear PET bottles from green and opaque bottles within a mixed stream, recover more usable material, and fit an operation with no regular weekend downtime.

• Maintain purity despite uneven manual picking performance
• Increase PET recovery from an existing mixed-bottle stream
• Reduce dependence on difficult-to-staff 12-hour sorting shifts
• Install around the constraints of a continuously operating facility

Test the material, tune the system, train the plant

The documented project began with technology evaluation and trials using material from the facility itself. Those trials exposed application-specific needs before installation and informed adjustments to the equipment configuration.

Six AI-guided sorting robots were then installed during a carefully planned three-day period. Five were positioned in final sorting, while the sixth worked the residue line as a last opportunity to reclaim PET bottles that had passed earlier recovery points.

Training was built into the robot deployment and integration. Existing process technicians learned to support the robots alongside other sorting equipment, while operators and team leaders handled routine cleaning and periodic changes to suction cups and underlying screens.

The work continued after startup through weekly performance meetings and incremental tuning. That service cadence mattered because material streams change, and recognition settings must keep pace with the bottles and contaminants arriving on the belt.

• Run representative facility material through the proposed system
• Adjust recognition and picking behavior before installation
• Place robots at final-sort and residue recovery points
• Train existing technical and operating personnel
• Review performance regularly and refine recognition settings

Faster recovery came with cleaner output

The robots reached pick rates of up to 120 bottles per minute. PET capture rates increased by up to 200%, and the operation reported up to three times as many PET bottle picks from the material stream.

Purity improved alongside throughput. Across the different lines, the robots removed up to 90% of contamination on average while separating plastics more precisely by color.

Automation also brought consistency to a process area that had been persistently hard to staff. Team members were cross-trained, with some moving into more technical or higher-skilled plant roles, and the existing maintenance group supported the equipment without added specialist maintenance staff.

A repeatable buying framework for recycling operators

This is a documented industry example analyzed by Service Robot Co., not a Service Robot Co. client deployment. Its central lesson is practical: successful AI sorting starts with representative material testing, deliberate equipment placement, operator training, and a service plan that continues after go-live support.

Service Robot Co. applies that framework as a vendor neutral robot integrator for US businesses. We compare equipment across manufacturers, arrange robot financing for business through monthly payment programs or lease rental or sale, manage deployment and integration, train the team, and coordinate service through a nationwide US engineer network.

That structure gives recycling operators a single accountable partner across assessment, acquisition, installation, remote triage, on-site dispatch, and the robot maintenance service plan. A commercial robot demo or robot pilot program can validate the application before a broader fleet decision, while flexible financing can reduce dependence on upfront capital.