Optimum GU2 3Ph Dust Extractor
Product overview
About this machine.
The Optimum GU 2 3-Phase Extractor is a strong and durable, space saving cyclone extraction system designed for high efficiency dust and debris removal. Officially described by Optimum as a standalone extraction unit for OPTIMUM belt sanders (such as the BSM series) or external workshop machinery, this extractor features twin engine cyclone performance and an automatic start/stop switch, eliminating the need for manual activation when the connected machine powers on or off.
Optimum Maschinen is a respected German company known for manufacturing high-quality machine tools built to strict European standards. Since its founding in 2003, Optimum has become a trusted name in the metalworking industry, offering a wide range of equipment including metal lathes, milling machines, drilling machines, and grinding tools. Designed in Germany and engineered for precision, reliability, and durability, Optimum machines are used across workshops, training centres, and industrial environments throughout Europe and beyond. Their products are developed with a strong emphasis on innovation, safety, and compliance with European CE regulations, ensuring exceptional performance and long service life.
Features:
- Standalone cyclone extraction unit for Optimum belt sanders or other machines
- Twin motor cyclone system for efficient dust and chip removal
- Power supply 400 V, 3-phase
- Motor output 2.6 kW total (2 x 1.3 kW motors)
- Fan speed 29,000 rpm
- Maximum airflow 4.6 m3/min
- Dust collection approx. 15 L capacity
- Filter class L with 0.09 m2 filter surface
- Y-adapter for dual 60 mm hose connections
- EMC Class C2, compliant with DIN EN 55011
- Requires a 5 pole 400 V connection including neutral and earth
Safety features:
- Sound insulated housing for quieter operation
- Noise level approx. 89-92 dB(A)
- Automatic start/stop function with 2-3 second delay (on start and shutdown)
Note:
- Adaptors and hoses may be needed to suit specific industrial needs
Published data