Classification of buildings mold threat using electronic nose
Materiały konferencyjne
MNiSW
15
WOS
| Status: | |
| Autorzy: | Łagód Grzegorz, Suchorab Zbigniew, Guz Łukasz, Sobczuk Henryk |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2017 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Arkusze wydawnicze: | 0,5 |
| Język: | angielski |
| Numer czasopisma: | 1 |
| Wolumen/Tom: | 1866 |
| Numer artykułu: | 030002 |
| Strony: | 1 - 5 |
| Web of Science® Times Cited: | 3 |
| Scopus® Cytowania: | 5 |
| Bazy: | Web of Science | Scopus | AIP |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 22nd International Meeting of Thermophysics (Thermophysics) / 4th Meeting of the Energy and Responsibility (EnRe) 2017 |
| Skrócona nazwa konferencji: | 22nd THERMOPHYSICS 2017 and 4th EnRe 2017 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 12 września 2017 do 14 września 2017 |
| Miasto konferencji: | Terchova |
| Państwo konferencji: | SŁOWACJA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| Mold is considered to be one of the most important features of Sick Building Syndrome and is an important problem in current building industry. In many cases it is caused by the rising moisture of building envelopes surface and exaggerated humidity of indoor air. Concerning historical buildings it is mostly caused by outdated raising techniques among that is absence of horizontal isolation against moisture and hygroscopic materials applied for construction. Recent buildings also suffer problem of mold risk which is caused in many cases by hermetization leading to improper performance of gravitational ventilation systems that make suitable conditions for mold development. Basing on our research there is proposed a method of buildings mold threat classification using electronic nose, based on a gas sensors array which consists of MOS sensors (metal oxide semiconductor). Used device is frequently applied for air quality assessment in environmental engineering branches. Presented results show the interpretation of e-nose readouts of indoor air sampled in rooms threatened with mold development in comparison with clean reference rooms and synthetic air. Obtained multivariate data were processed, visualized and classified using a PCA (Principal Component Analysis) and ANN (Artificial Neural Network) methods. Described investigation confirmed that electronic nose – gas sensors array supported with data processing enables to classify air samples taken from different rooms affected with mold. |
