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.
This paper studies the prediction and performance comparison based on BP, PSO-BP and SA-BP models. Taking real estate as an example, because of the complex and nonlinear relationship between its influencing factors, the accuracy of traditional prediction methods is low. Therefore, this paper proposes an innovative prediction model based on SA-BP neural network, and verifies its effectiveness through 110 sets of real estate transaction data in Shenyang from 2021 to 2022. The experimental results show that the prediction accuracy of PSO-BP and SA-BP models is better than that of BP model, and SA-BP model performs best. This study fills the research gap in related fields and provides valuable reference for market decision-making such as real estate.
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