Machine learning for filter pollution control
Modern buildings usually have an air-tight envelope. Therefore mechanical ventilation is very often necessary. A crucial part of the system is the filter, which allows creating an atmosphere that is free of dust, aerosols, and pollen. As organic material accumulates on the filter surface, the risk o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2020-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2020/20/matecconf_cryogen2020_03002.pdf |
| _version_ | 1819035547555856384 |
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| author | Krause Ralph Oppelt Thomas Friebe Christian Döge Sabine Herzog Ralf |
| author_facet | Krause Ralph Oppelt Thomas Friebe Christian Döge Sabine Herzog Ralf |
| author_sort | Krause Ralph |
| collection | DOAJ |
| description | Modern buildings usually have an air-tight envelope. Therefore mechanical ventilation is very often necessary. A crucial part of the system is the filter, which allows creating an atmosphere that 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.
The method was implemented in both a test rig and the HVAC system supplying different laboratories with fresh air in order to aggregate data for different abnormal and normal operation conditions. Subsequent considerations focuses on the test-rig measurements. The machine learning algorithm was trained successfully to detect anomalies of the filter behavior.
Finally, the change intervals of the filter may be adapted to the real degree of pollution without the requirement for visual observation in order to provide best air conditions. This algorithm is part of a general strategy for machine-learning processes for HVAC systems. |
| first_indexed | 2024-12-21T07:51:22Z |
| format | Article |
| id | doaj.art-751e156fba974933a0d4b10654c75e37 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-21T07:51:22Z |
| publishDate | 2020-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-751e156fba974933a0d4b10654c75e372022-12-21T19:11:05ZengEDP SciencesMATEC Web of Conferences2261-236X2020-01-013240300210.1051/matecconf/202032403002matecconf_cryogen2020_03002Machine learning for filter pollution controlKrause Ralph0Oppelt Thomas1Friebe Christian2Döge Sabine3Herzog Ralf4Institute of Air Handling and RefrigerationInstitute of Air Handling and RefrigerationInstitute of Air Handling and RefrigerationInstitute of Air Handling and RefrigerationInstitute of Air Handling and RefrigerationModern buildings usually have an air-tight envelope. Therefore mechanical ventilation is very often necessary. A crucial part of the system is the filter, which allows creating an atmosphere that 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. The method was implemented in both a test rig and the HVAC system supplying different laboratories with fresh air in order to aggregate data for different abnormal and normal operation conditions. Subsequent considerations focuses on the test-rig measurements. The machine learning algorithm was trained successfully to detect anomalies of the filter behavior. Finally, the change intervals of the filter may be adapted to the real degree of pollution without the requirement for visual observation in order to provide best air conditions. This algorithm is part of a general strategy for machine-learning processes for HVAC systems.https://www.matec-conferences.org/articles/matecconf/pdf/2020/20/matecconf_cryogen2020_03002.pdffilterpollutionmachine learninghvachygieneenergy efficiency |
| spellingShingle | Krause Ralph Oppelt Thomas Friebe Christian Döge Sabine Herzog Ralf Machine learning for filter pollution control MATEC Web of Conferences filter pollution machine learning hvac hygiene energy efficiency |
| title | Machine learning for filter pollution control |
| title_full | Machine learning for filter pollution control |
| title_fullStr | Machine learning for filter pollution control |
| title_full_unstemmed | Machine learning for filter pollution control |
| title_short | Machine learning for filter pollution control |
| title_sort | machine learning for filter pollution control |
| topic | filter pollution machine learning hvac hygiene energy efficiency |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2020/20/matecconf_cryogen2020_03002.pdf |
| work_keys_str_mv | AT krauseralph machinelearningforfilterpollutioncontrol AT oppeltthomas machinelearningforfilterpollutioncontrol AT friebechristian machinelearningforfilterpollutioncontrol AT dogesabine machinelearningforfilterpollutioncontrol AT herzogralf machinelearningforfilterpollutioncontrol |