Process studies of odour emissions from effluent ponds using machine-based odour measurement

PhD Thesis

Sohn, Jae Ho. 2005. Process studies of odour emissions from effluent ponds using machine-based odour measurement. PhD Thesis Doctor of Philosophy. University of Southern Queensland.

Process studies of odour emissions from effluent ponds using machine-based odour measurement

TypePhD Thesis
AuthorSohn, Jae Ho
SupervisorSmith, Rod
Institution of OriginUniversity of Southern Queensland
Qualification NameDoctor of Philosophy
Number of Pages385

Odours caused by intensive piggery operations have become a major environmental issue in the piggery industry in Australia. Effluent ponds are the major source of odours in typical piggeries. It is assumed that the odour emissions from ponds are mainly driven by pond loading rate. However, there are few data to corroborate this concept. Allied to this is the need for a convenient and low cost method of odour measurement, which can be used as an alternative method for current olfactometry. The present odour measurement methods using olfactometry is time-consuming, expensive and often impractical because of its fundamental problem of using subjective human panels. In addition, one of the major problems in odour measurement lies in the air sampling method. Wind tunnels have been accepted as a preferred method for the sampling of odour from area sources. However, current wind tunnels do not consider meteorological factors, which directly affect the odour emission rates. A machine-based odour quantification method and a novel wind tunnel were developed and evaluated in this Ph D study. These methods were then used in a demonstration trial to investigate the effects of pond loading rate on odour emissions. The AromaScan A32S electronic nose, and an artificial neural network were used to develop the machine based odour quantification method. The sensor data analysed by the AromaScan were used to train an ANN, to correlate the responses to the actual odour concentration provided by a human olfactometry panel. Preprocessing techniques and different network architectures were evaluated through network simulation to find an optimal artificial neural network model. The simulation results showed that the two-layer back-propagation neural network can be trained to predict piggery odour concentrations correctly with a low mean squared error. The trained ANN was able to predict the odour concentration of nine unknown air samples with a value for the coefficient of correlation, r2 of 0.59. A novel wind tunnel was developed for odour sampling. The USQ wind tunnel was designed to have a capability to control wind speed and airflow rate. The tunnel was evaluated in terms of the aerodynamics of the airflow inside the tunnel, nd the gas recovery efficiency rate, in order to further improve the performance of the wind tunnel. The USQ wind tunnel showed that sample recovery efficiencies ranging from 61.7 to 106.8%, while the average result from the entire trial was 81.1%. The optimal sample recovery efficiency of the tunnel was observed to be 88.9% from statistical analysis. Consequently, it can be suggested that the tunnel will give estimates of the odour emission rate with significant level of precision. However, the tunnel needs to be calibrated to compensate for the error caused by different airflow rates and odour emission rates. In addition, the installation of a perforated baffle upstream of the sampling section was suggested to improve its performance. To investigate the relationship between the pond loading rate and odour emission rate, replicable experimental studies were conducted using a novel experimental facility and the machine based odour quantification method. The experimental facility consisted of reactor vessels to simulate the operation of effluent ponds and the USQ wind tunnel for odour sampling. A strong relationship between organic loading rate (OLR) and physical and chemical parameters was observed except pH and NH3-N. The pH was not affected by OLR due to the buffering capacity of piggery effluent. EC and COD were suggested as indicators to estimate the operating condition of the piggery effluent ponds because the regression results show that these two parameters can be predicted accurately by OLR. The time averaged odour emission rates from the reactor vessels showed a strong relationship with OLR. Consequently, it can be concluded that heavily loaded effluent ponds would produce more odours. The effect of hydraulic retention time (HRT) was examined. The HRT was increased from 30 days to 60 days, resulting in a significant decrease in odour emission rates from the reactor vessels. This decrease ranged from 59.1% to 54.9%, with an average of 57.1%. Therefore, it can be concluded that the increasing HRT will decrease the odour emission rate. This trial confirmed the value of the project methodology in obtaining unambiguous data on odour emission processes. However, more data are required for a wider range of OLR, HRT and other pertained variables before a usable model can be formulated.

Keywordseffluent ponds, odour, emissions, piggery, AromaScan, aerodynamics, gas
ANZSRC Field of Research 2020300207. Agricultural systems analysis and modelling
401102. Environmentally sustainable engineering
300101. Agricultural biotechnology diagnostics (incl. biosensors)
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