Quantification of odours from piggery effluent ponds using an electronic nose and an artificial neural network
Article
Article Title | Quantification of odours from piggery effluent ponds using an electronic nose and an artificial neural network |
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ERA Journal ID | 3455 |
Article Category | Article |
Authors | Sohn, Jae Ho (Author), Smith, Rod (Author), Yoong, Ernest (Author), Leis, John W. (Author) and Galvin, Geordie (Author) |
Journal Title | Biosystems Engineering |
Journal Citation | 86 (4), pp. 399-410 |
Number of Pages | 12 |
Year | 2003 |
Publisher | Elsevier |
Place of Publication | London, England |
ISSN | 1537-5110 |
1537-5129 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.biosystemseng.2003.09.003 |
Abstract | An Artificial Neural Network (ANN) and an electronic nose, AromaScan, were used to predict the piggery odour concentrations emanating from an effluent pond and to develop a confident, rapid, and cost-effective technique for odour measurement. Odour samples from five different piggery effluent ponds were analysed using the AromaScan and dynamic dilution olfactometry. The resulting sensor data were used to train the artificial neural network to correlate the responses to the odour concentrations measured by olfactometry. |
Keywords | electronic nose; air flow-rate; emission; gas recovery efficiency; liquid effluent; odour; olfactometry |
ANZSRC Field of Research 2020 | 409902. Engineering instrumentation |
409901. Agricultural engineering | |
410404. Environmental management | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Department of Primary Industries and Fisheries, Queensland |
Department of Agricultural, Civil and Environmental Engineering | |
Faculty of Engineering and Surveying | |
Department of Primary Industries, Queensland |
https://research.usq.edu.au/item/9zyv3/quantification-of-odours-from-piggery-effluent-ponds-using-an-electronic-nose-and-an-artificial-neural-network
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