Ortiz-Grisales, P.; Patiño-Murillo, J.; Duque-Grisales, E. Comparative Study of Computational Models for Reducing Air Pollution through the Generation of Negative Ions. Sustainability2021, 13, 7197.
Ortiz-Grisales, P.; Patiño-Murillo, J.; Duque-Grisales, E. Comparative Study of Computational Models for Reducing Air Pollution through the Generation of Negative Ions. Sustainability 2021, 13, 7197.
Ortiz-Grisales, P.; Patiño-Murillo, J.; Duque-Grisales, E. Comparative Study of Computational Models for Reducing Air Pollution through the Generation of Negative Ions. Sustainability2021, 13, 7197.
Ortiz-Grisales, P.; Patiño-Murillo, J.; Duque-Grisales, E. Comparative Study of Computational Models for Reducing Air Pollution through the Generation of Negative Ions. Sustainability 2021, 13, 7197.
Abstract
Today, air quality is one of the global concerns that governments are facing. One of the main air pollutants is the particulate matter (PM) that affects human health. This article presents the modeling of a purification system by means of negative air ions (NAIs) for air pollutant removal, using computational intelligence methods. The system uses a high voltage booster output to ionize air molecules from stainless steel electrodes; its particle-capturing efficiency reaches up to 97%. With two devices (5 x 2 x 2.5 cm), 2 trillion negative ions are produced per second, and the particulate matter (PM 2.5) can be reduced from 999 to 0 mg / m3 in a period of approximately 5 to 7 minutes (in a 40 x 40 x 40 cm acrylic chamber). This negative ion generator is a viable and sustainable alternative to reduce polluting emissions, with beneficial effects on human health.
Keywords
Environmental pollution; air purification; negative ion generators; particulate matter.
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.