Kaeser Compressors Sigma Air Manager 4.0 blower controller

The Sigma Air Manager 4.0 offers intelligent and straightforward blower control for blower station automation, delivering energy savings while making the station ready for Industry 4.0 applications.

The SAM 4.0 acts as the central mastermind of a blower station and efficiently controls the individual machines while adjusting the flow capacity to match flow rate demand. All it needs from the process control system is the desired differential pressure value — the controller then ensures optimal control of all blowers. All applications are supplied by one central blower station with a master controller that analyses operating data in seconds and simulates various scenarios in order to select the most efficient solution.

Components within the station are optimally matched and controlled to meet the user’s specific needs. Real-time process monitoring provides valuable operating data, which is forwarded for detailed evaluation. The analysed results help to predict potential faults as early as possible and prevent them by taking timely action.

The product offers communication in 30 languages while the easy-to-operate 12″ colour touchscreen display shows at a glance whether the station is operating in the ‘green zone’ for optimal energy performance. It’s easy to display and analyse a wealth of data, such as operating status, pressure curve, flow rate and power, as well as maintenance and any fault messages. This can be done not only at the machine itself, but also conveniently from a PC at the office or elsewhere.

The product makes it possible to initiate predictive maintenance before a fault occurs, thereby avoiding downtime and consequential damage. The sensors and Sigma Control 2 controller integrated into the machine collect process data, which is immediately forwarded to the SAM 4.0. Specialised software is then used to transfer the information to the Kaeser Data Center for real-time analysis. The data centre performs central monitoring and processing of operating messages. Required predictive maintenance measures are then initiated as needed based on this information.