Paper
6 May 2024 The comprehensive evaluation of air purifier performance based on the entropy weight-TOPSIS model
Xilong Li, Qicheng Xu, Lin Zhang, Enyuan Cui
Author Affiliations +
Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 1310703 (2024) https://doi.org/10.1117/12.3029207
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China
Abstract
With the development of modern society and the acceleration of urbanization, people are increasingly paying attention to indoor air quality issues. Indoor air pollutants pose a threat to human health, especially significantly affecting the respiratory and immune systems. Currently, the quality of air purifiers on the market varies widely, requiring an effective evaluation system. To study the purification performance of various air purifiers, this research collected and selected ten representative air purifiers and six indicators. The entropy weighting method was used to calculate the weights of operational indicators for some air purifiers on the market. The TOPSIS evaluation model was applied to assess the purification capabilities of each air purifier. The results showed that the applicable area of the air purifier had the greatest impact on its purification ability, while the price had the least influence. This study provides a basis for selecting suitable air purifiers and is of significant practical importance in promoting the sustainable development of air purifiers. It enhances the understanding of air purifiers' performance and facilitates their practical application, ultimately contributing to better indoor air quality and human health.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Xilong Li, Qicheng Xu, Lin Zhang, and Enyuan Cui "The comprehensive evaluation of air purifier performance based on the entropy weight-TOPSIS model", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 1310703 (6 May 2024); https://doi.org/10.1117/12.3029207
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KEYWORDS
Performance modeling

Air quality

Data modeling

Manufacturing

Power consumption

Energy efficiency

Decision making

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