Assessment of Micro-Organism Growth Risk on Filters with Machine Learning

Abstract

Modern buildings usually have a practically air-tight envelope. Therefore, mechanical ventilation is very often necessary. A crucial part of the system is the filter which allows to create an atmosphere which is free of dust, aerosols, and pollen. As organic material accumulates on the filter surface, the risk of micro-organism growth rises. This may yield health issues especially for the occupants of buildings in humid regions. For this purpose, a test filter with electrodes has been designed which allowed to measure its electro-magnetic properties, such as resistance, capacitance, and impedance as an indicator for the micro-organism growth risk. After some preliminary tests, electrodes of stainless steel and the electrical capacitance have been selected due to their best durability and signal-to-noise-ratio. The test filter has been implemented in the HVAC system of the institute in order to aggregate data for different abnormal and normal operation data. A machine learning algorithm has been trained successfully to detect anomalies of the filter behaviour and therefore provided more insight than pressure drop measurement alone. Finally, the change intervals of the filter could be adapted to the real degree of pollution without the requirement for visual observation in order to provide best air conditions.