From the beginning
Parameters change regularly during a component cleaning process. That is what makes it so complex.
Even the initial states of the components may be very different: the type and degree of soiling or throughput can vary. With each washing cycle, bath soiling increases, while the concentration of the cleaning agent decreases and the filter bags fill up. At the end, however, the cleaning result must be identical.
Smart Cleaning is a “thinking” cleaning system. System states can be presented in a clearly structured way. The system controls itself.
Smart Cleaning makes BvL cleaning systems “intelligent”. The various Smart Cleaning apps show any changes immediately. Sub-processes are self-regulating. These intelligent processes no longer require any user intervention.
Intelligent is also ecological!
Smart Cleaning also means ensuring environmentally friendly cleaning processes. Reductions in energy and water consumption have a very positive effect on life cycle assessments for BvL cleaning systems. Exhaust air management lowers the heat that is output by a continuous system, while drying control reduces the energy input into the drying process.
- Reliable quality and reliable processes
- High plant uptime
- Time- and cost-efficient
- Predictive maintenance
- Energy savings
- Simple operation
Smart Cleaning solutions
Intelligent cleaning solutions improve the system hardware and the state of the cleaning bath, as well as the component position and workpiece identification. This ensures your cleaning system is fit for the future.
BvL cleaning solutions
with Libelle sensor system
With the Libelle sensor system, BvL has been a pioneer in the area of sensor-based process reliability for many years. The objective is to make the cleaning process simpler and easier to monitor. Libelle sensors allow better monitoring of components, system and media to achieve more reliability in the cleaning process.
Self-learning cleaning systems
The smart technology used in BvL cleaning systems is always being improved. Very soon now, these algorithm-controlled systems will not only be capable of detecting problems but will also be able to learn from them.
To ensure stable and efficient cleaning processes, an artificial intelligence unit analyses the product feed state plus washing, system and process parameters, and discharge conditions. This enables the system to react independently to changes and unknown states, and calculate the target discharge condition.
Thanks to their continuous monitoring and adaptation of process parameters, self-learning cleaning systems maintain a higher-quality cleaning process for longer while requiring less resource input and fewer personnel.