{"title":"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","authors":"Stefan Schumacher, Christof Asbach","doi":"10.1016/j.indenv.2024.100054","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":100665,"journal":{"name":"Indoor Environments","volume":"1 4","pages":"Article 100054"},"PeriodicalIF":0.0000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"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\",\"authors\":\"Stefan Schumacher, Christof Asbach\",\"doi\":\"10.1016/j.indenv.2024.100054\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":100665,\"journal\":{\"name\":\"Indoor Environments\",\"volume\":\"1 4\",\"pages\":\"Article 100054\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Indoor Environments\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2950362024000511\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Indoor Environments","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2950362024000511","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.