Handling Forgiveness for More Operation Efficiency
Protect your process, product, profits, and people from inadvertent filter damage. Megalam EnerGuard filters offer outstanding robustness to reduce the risk of airborne particulate leakage from tears, punctures, and rips in the filter media which is common from mishandling during installation and validation.
Typically 5-30% of glass fiber media filters are replaced at installation due to inappropriate handling. Megalam EnerGuard will not only eliminate the expense of replacement filters and reduce the need for spares, but most importantly will reduce cleanroom downtime while waiting for re-installation and re-validation.
The life sciences industry faces tough demands in achieving clean air. Be careful choosing an air filter. The filter must protect your products from particulate contamination, and must prevent undesirable materials from entering into your process.
When you see the ProSafe symbol, you know the filter has been made with qualified materials that offer the maximum process safety. There is full material and process traceability and control.
Camfil invests heavily in research and development because it is a very important part of our future and the future of air filtration. With numerous labs and testing capabilities around the world, Camfil is able to analyze performance requirements and use the information to develop the best filtration system for your needs.
Camfil has created a new HEPA filter lifetime test grid, to assist the Megalam EnerGuard development effort and provide the most scientifically advanced HEPA filter analysis in the world. Camfil can control the airflow, particulate< contamination concentration, temperature, humidity, scan test and filter challenge simulations, as well as most other variables affecting a filter’s service life.
Camfil’s HEPA Filter Lifetime Test Grid, Riverdale, New Jersey.
The Grid monitors all key performance characteristics of the test filters, nine of which can be simultaneously tested not for hours or days but weeks and months, to develop a scientifically derived predictive model.