A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Data-Driven Models for Estimating Dust Loading Levels of ERV HEPA Filters
With increasing global concerns regarding indoor air quality (IAQ) and air pollution, concerns about regularly replacing ventilation devices, particularly high-efficiency particulate air (HEPA) filters, have increased. However, users cannot easily determine when to replace filters. This paper proposes models to estimate the dust loading levels of HEPA filters for an energy-recovery ventilation system that performs air purification. The models utilize filter pressure drops, the revolutions per minute (RPM) of supply fans, and rated airflow modes as variables for regression equations. The obtained results demonstrated that the filter dust loading level could be estimated once the filter pressure drops and RPM, and voltage for the rated airflow were input in the models, with a root mean square error of 5.1–12.9%. Despite current methods using fewer experimental datasets than the proposed models, our findings indicate that these models could be efficiently used in the development of filter replacement alarms to help users decide when to replace their filters.
Data-Driven Models for Estimating Dust Loading Levels of ERV HEPA Filters
With increasing global concerns regarding indoor air quality (IAQ) and air pollution, concerns about regularly replacing ventilation devices, particularly high-efficiency particulate air (HEPA) filters, have increased. However, users cannot easily determine when to replace filters. This paper proposes models to estimate the dust loading levels of HEPA filters for an energy-recovery ventilation system that performs air purification. The models utilize filter pressure drops, the revolutions per minute (RPM) of supply fans, and rated airflow modes as variables for regression equations. The obtained results demonstrated that the filter dust loading level could be estimated once the filter pressure drops and RPM, and voltage for the rated airflow were input in the models, with a root mean square error of 5.1–12.9%. Despite current methods using fewer experimental datasets than the proposed models, our findings indicate that these models could be efficiently used in the development of filter replacement alarms to help users decide when to replace their filters.
Data-Driven Models for Estimating Dust Loading Levels of ERV HEPA Filters
Seung-Hoon Park (author) / Jae-Hun Jo (author) / Eui-Jong Kim (author)
2021
Article (Journal)
Electronic Resource
Unknown
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