一个分析模型,用于预测典型室内环境中空气净化器的质量负荷,并根据标准化过滤器负荷测试估算服务周期

Stefan Schumacher, Christof Asbach
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引用次数: 0

摘要

对于室内空气净化器,尤其是使用驻极体过滤器的净化器,众所周知,由于过滤器的颗粒负载,洁净空气输送率(CADR)会随着时间的推移而大幅降低。GB/T 18801 等标准测试用于确定导致初始 CADR 降低 50%(累积洁净度)的测试气溶胶颗粒的质量,但这种方法无法得出在典型的室内环境中何时会降低 CADR 的结论。不过,一个好的估计值将有助于制造商给出合理的建议,说明在多长时间内有必要对空气净化器进行维修。因此,我们开发了一个分析模型,其中包括典型室内环境中最相关的参数,并假设了 CADR 随时间变化的不同衰减过程。我们的研究表明,可以得出一致的服务间隔估算值,该值仅略微取决于模型的确切选择。不过,我们也发现了室内或室外辐射源主导情况下的明显差异。我们将新模型与 GB/T 18801 的模型进行了比较,结果表明,对于给定的参数集,该标准高估了服务间隔约 30%。最后,我们提出了一种仅通过一个加载和一个卸载步骤来估算服务间隔的方法,并为该模型的进一步应用提供了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An analytical model to predict the mass loading of air cleaners in typical indoor environments and to estimate the service interval from standardized filter loading tests
For indoor air cleaners, especially those using electret filters, it is known that the clean air delivery rate (CADR) can strongly decrease over time due to loading of the filters with particles. Standardized tests like in GB/T 18801 are used to determine the mass of test aerosol particles leading to a reduction of the initial CADR by 50 % (cumulative clean mass), but this method does not allow to draw conclusions on when this reduction is reached in a typical indoor environment. However, a good estimate would help manufactures to give reasonable recommendations in which intervals a service of the air cleaner becomes necessary. Therefore, we developed an analytical model including the most relevant parameters of a typical indoor environment and assumed different courses for the time-dependent decay of the CADR. We show that consistent estimates for the service interval can be derived, which do only slightly depend on the exact choice of the model. However, we partially find pronounced differences between scenarios dominated by either indoor or outdoor sources. We compare the new model to the model of GB/T 18801 and show that the standard overestimates the service interval for a given set of parameters by about 30 %. We finally propose a method for estimating the service interval from only one loading and one discharging step and give perspectives for further applications of the model.
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