Data-Driven Models for Estimating Dust Loading Levels of ERV HEPA Filters

Seung-Hoon Park, Jae-Hun Jo and Eui-Jong Kim
Additional contact information
Seung-Hoon Park: Department of Smart-City Engineering, INHA University, Incheon 22212, Korea
Jae-Hun Jo: Department of Architectural Engineering, INHA University, Incheon 22212, Korea
Eui-Jong Kim: Department of Architectural Engineering, INHA University, Incheon 22212, Korea

Sustainability, 2021, vol. 13, issue 24, 1-14

Abstract: 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.

Keywords: air purification; data-driven model; energy-recovery ventilation; HEPA filter; indoor air quality (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/24/13643/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/24/13643/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:24:p:13643-:d:699081

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().