Real-time diagnostic tool for hygienic and energy parameters of air handling components


  • Christian Friebe Institut für Luft- und Kältetechnik gGmbH
  • Thomas Oppelt Institut für Luft- und Kältetechnik gGmbH



liquid sorption, humidity recovery,, enthalpy recovery, HVAC, textile heat exchangers, hygiene, energy efficiency, evaporative cooling, closed-loop-system


Material and surface properties in air conditioning systems are subject to change, e.g. due to aging, wear or fouling. This is why regular inspections, maintenance or even cleaning measures are mandatory. In the course of quality assurance and the striving for sustainability and energy efficiency, heat- and mass-transferring air conditioning components particularly require permanent metrological monitoring. This goes beyond the conventional data recording in air conditioning components. With the aim of parameter-guided maintenance management, this means a comprehensive monitoring of function and properties based on design and process data. An extensive exchange of information throughout the entire value chain and life cycle (Industry 4.0) also is essential. ILK Dresden has developed a measurement-based diagnostic tool especially for a new type of membrane heat and mass transfer unit for air humidification and sorptive dehumidification. The aim was to process real-time measurement data, considering long-term data, and to apply it for statistical evaluations of changing material parameters, for target-performance comparisons and for risk assessments (e.g. mould growth). The central element of the tool was programmed in Python and is a script for the direct and simultaneous iterative calculation of the ideal reference process and the real characteristic values of the heat and mass transfer process in the membrane heat exchanger. In addition to the thermodynamic calculation, the mould growth risk can be determined with this algorithm. The basis for assessing the mould growth risk is based on a known algorithm comprising different thermodynamic parameters. This method in addition with a detection of special events (load curves, maintenance, faults, etc.), provided by machine learning algorithms is the basis for a process optimisation, quality assurance and sustainable system operation. This presentation will illustrate the technical parameters and the function of the diagnostic tool. Furthermore, the results of the initial test phase under laboratory conditions will be presented as well as an overview of further development steps and potential areas of application.




How to Cite

Friebe, C., & Oppelt, T. . (2022). Real-time diagnostic tool for hygienic and energy parameters of air handling components . CLIMA 2022 Conference.

Conference Proceedings Volume