Fraunhofer Institute for Process Engineering and Packaging IVVHigh Protein = High Cleaning Effort?
More protein, more effort
In industrial dairy processing, processes such as pasteurization and ultra-high heating are essential for product safety and shelf life - at the same time, there is growing pressure to use energy and resources more efficiently.
A key problem here is fouling, i.e. the build-up of proteins, fats and minerals on the surfaces of the heat exchangers. They impair heat transfer, impede the flow and jeopardize process reliability. The result: frequent cleaning, long downtimes and high consumption of water, energy and cleaning agents.
High-protein products such as yoghurt drinks or mixed milk drinks, which are currently in high demand, pose a particular challenge. Their high protein content significantly increases the tendency to deposit, which makes process control more difficult, increases operating costs and increases the risk of microbiological contamination.
Smart prediction makes the difference
To tackle these challenges, the Fraunhofer IVV and the TU Braunschweig have joined forces in the "Fidelio" research project to develop an AI-based monitoring system that detects fouling in the heat exchanger during the heating process.
The new "CoControl-FouliQ" sensor uses real-time data from four clamp-on temperature sensors at the inlet and outlet of the heat exchanger, which is evaluated by a specially trained machine learning model. Temperature curves serve as an indicator for incipient deposits, making it possible to plan cleaning efficiently and in line with demand rather than at fixed intervals.
Ready for implementation
»CoControl-FouliQ« consists of temperature sensors, a compact control cabinet computer unit and a machine learning model for data evaluation and fouling prediction. Thanks to the clamp-on temperature sensors, the system can be integrated into existing systems with minimal effort. The innovative hardware solution enables use in the demanding production environments of dairies.
»CoControl-FouliQ« was tested for several weeks under real conditions in a dairy plant, where it was able to reliably predict fouling. The model demonstrated high stability and accuracy, particularly with longer data series.
On-demand cleaning significantly reduces cleaning cycles in the ultra-high-temperature process (UHT). The validation confirms »CoControl-FouliQ« as a solid basis for predictive maintenance and process optimization in milk heating. For high-protein products in particular, this can lead to less resource consumption, shorter downtimes and more stable processes.
Conclusion: Predictively detect fouling – increase safety and efficiency
With »CoControl-FouliQ« dairy plants benefit from AI-powered fouling detection — reliable and early in the heating process. By planning cleanings according to demand, the system not only helps to conserve resources and optimize plant availability, but also increases product safety by ensuring safe and consistent process control. »CoControl-FouliQ« can ensure more stable processes, fewer downtimes and more sustainable production, especially for demanding high-protein products.
Further project information
| Project duration | February 1, 2022 – August 31, 2024 |
| Project partner | TU Braunschweig |
| Project sponsor/Grant authority: | Deutsche Bundesstiftung Umwelt |