Britador novo Kinglink PCX0910 PX series Super-Fineness Energy-Saving Crusher

• Introduction of PX series Super-Fineness Energy-Saving Crusher

PX series Super-Fineness Energy-Saving Crusher was developed and improved technology from Germany and China by KINGLINK and the overall design has been further optimized.

This series feature of rational multi crushing chambers and novel double adjusting mechanism which can ensure 85% of the discharged materials less than 3mm and the uniformity of the product fineness can be controlled. Easy access for maintenance has made the hammers and screen bars replaced much easier.

• Advantage and application of PX Series Super-Fineness Energy-Saving Crusher

All the wearing parts are made of new type of high chromium alloy materials with multiple elements so as to ensure the abrasive property and impact-resisting property of hammers and liner plates.

This series of crushers also feature of stable operation, low dust and less power consumption which have prestigious advantages when comparing with conventional crushers. The PX series have been widely used for crushing mid-hard materials such as limestone, cement clinker ,iron ore, shale, dolomite ,gypsum, pyrites and mineral slag in chemical construction, mining, coal processing, sand making, refractory industries etc

• Features of PX Series Super-Fineness Energy-Saving Crusher

1. Super-fineness crushing capability: The new particle size adjusting mechanism guarantees 85% of the particles controlled within 3mm

2. High-efficient and energy-saving: 40% power consumption reduced at equivalent output

3. Extra long life: hammer heads are made of new type of high quality alloy materials with multiple elements and can be turned for use thus to prolong the service life by 4 times

4. Great crushing ratio: incorporating primary

secondary crushing for clinkers

secondary and tertiary crushing for limestone into one step

5. Easy Maintenance: Double inspection door is convenient for quick and changing hammer heads

Please contact us for parameters of different models.