KEYWORDS: Performance modeling, Air quality, Power consumption, Manufacturing, Data modeling, Energy efficiency, Matrices, Engineering, Design, Decision making
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.
This study utilizes numerical simulation methods to explore the influence of air purifier placement on the distribution of indoor pollutants. With the continuous acceleration of urbanization, indoor air quality has been receiving increasing attention. As a commonly used indoor air treatment device, the placement of air purifiers may significantly affect the dispersion and removal of pollutants. In this research, a numerical model is established based on fluid mechanics principles and differential equations to simulate indoor airflow and pollutant transport under different placement scenarios. Quantitative analysis of air purifier efficacy in different positions is conducted using finite difference and Jacobi iteration methods. The study reveals the correlation between placement positions and indoor pollutant concentration distribution, providing practical recommendations for optimizing air purifier placement strategies.
In order to classify glass samples with known chemical composition, this paper adopts K-Means clustering analysis algorithm. By distinguishing the main element content of the two kinds of glass, we have made a subclassification under the major category. The high potassium glass is divided into high silica low potassium oxide group and low silica high potassium oxide group. Lead barium glass can be divided into low barium oxide low phosphorus pentoxide group and high barium oxide high phosphorus pentoxide group. Therefore, after the content of various elements of various glass is obtained, the grey correlation analysis model is used to make the data of each chemical component as the parent sequence and the other chemical components as the sub-sequence to solve the grey correlation coefficient of pair chemical components successively, so as to further analyze the correlation between their chemical components. Therefore, after the completion of the subclassification, it is helpful for archaeologists to distinguish the glass cultural relics after weathering. With the analysis of grey relational degree, the composition relationship of ancient glass relics can be further explored.
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