A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Process studies of odour emissions from effluent ponds using machine-based odour measurement
AbstractReplicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.
Process studies of odour emissions from effluent ponds using machine-based odour measurement
AbstractReplicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.
Process studies of odour emissions from effluent ponds using machine-based odour measurement
Sohn, J.H. (author) / Smith, R.J. (author) / Yoong, E. (author)
Atmospheric Environment ; 40 ; 1230-1241
2005-10-14
12 pages
Article (Journal)
Electronic Resource
English
Odour , Electronic nose , Effluent pond , Olfactometry , Swine , Manure , Neural network
Odour control - Enveloping odour emissions: Discussing odour management at Reading WwTW
Online Contents | 2003
Odour emissions from an HVAC-system
| British Library Conference Proceedings | 2002
Odour emissions from an HVAC-system
| Online Contents | 2002