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An Innovative Air Purification Method and Neural Network Algorithm Applied to Urban Streets

An Innovative Air Purification Method and Neural Network Algorithm Applied to Urban Streets

Meryeme Boumahdi, Chaker El Amrani, Siegfried Denys
Copyright: © 2019 |Volume: 10 |Issue: 4 |Pages: 19
ISSN: 1947-3176|EISSN: 1947-3184|EISBN13: 9781522571995|DOI: 10.4018/IJERTCS.2019100101
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MLA

Boumahdi, Meryeme, et al. "An Innovative Air Purification Method and Neural Network Algorithm Applied to Urban Streets." IJERTCS vol.10, no.4 2019: pp.1-19. http://doi.org/10.4018/IJERTCS.2019100101

APA

Boumahdi, M., El Amrani, C., & Denys, S. (2019). An Innovative Air Purification Method and Neural Network Algorithm Applied to Urban Streets. International Journal of Embedded and Real-Time Communication Systems (IJERTCS), 10(4), 1-19. http://doi.org/10.4018/IJERTCS.2019100101

Chicago

Boumahdi, Meryeme, Chaker El Amrani, and Siegfried Denys. "An Innovative Air Purification Method and Neural Network Algorithm Applied to Urban Streets," International Journal of Embedded and Real-Time Communication Systems (IJERTCS) 10, no.4: 1-19. http://doi.org/10.4018/IJERTCS.2019100101

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Abstract

In the present work, multiphysics modeling was used to investigate the feasibility of a photocatalysis-based outdoor air purifying solution that could be used in high polluted streets, especially street canyons. The article focuses on the use of a semi-active photocatalysis in the surfaces of the street as a solution to remove anthropogenic pollutants from the air. The solution is based on lamellae arranged horizontally on the wall of the street, coated with a photocatalyst (TiO2), lightened with UV light, with a dimension of 8 cm × 48 cm × 1 m. Fans were used in the system to create airflow. A high purification percentage was obtained. An artificial neural network (ANN) was used to predict the optimal purification method based on previous simulations, to design purification strategies considering the energy cost. The ANN was used to forecast the amount of purified with a feed-forward neural network and a backpropagation algorithm to train the model.

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