This investigation demonstrates the capability of a bench-scale sequencing batch reactor (SBR) to biodegrade an inhibitory substrate at a high loading rate. A SBR loading rate of 3.12 kg phenol.m3 d-1 (2.1 g COD.g-1 MLVSS d-1) with a COD removal efficiency of 97% at a SRT of 4 days and a HRT of 10 hours was achieved; this rate was not reached before. The SBR was operated at 4 hours cycle, including 3 hours react phase. The synthetic wastewater of 1300 mg/L phenol was the sole carbon source. Oxygen uptake rates (OUR) were monitored in-situ at various stages of the SBR. The oxygen mass transfer coefficient, KLa, of 12.6 h-1 was derived from respirometry. Use of respirometry in SBR aided the tracking of the soluble substrate through OUR.
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, r 2 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 wasPage ii evaluated in terms of the aerodynamics of the airflow inside the tunnel, and the gas recovery efficiency rate, in order to further improve the performance of the wind tunnelThe 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 od...
This investigation looked at the influence of high phenol concentrations (1000-1500 mg/l) on the growth yield of phenol degrading organisms in batch culture. The yield coefficient varied from 0.16 to 0.27. These values are considerably lower than those determined by others at lower phenol substrate concentrations. Although the conversion efficiency to biomass was low, the removal of phenol in terms of COD in the batch cultures was high (93.6% average). Present results did not show a relationship between yield and specific growth rate over the range 1000-1500 mg/l phenol. More work is required over a wider range of substrate concentration. With increasing phenol concentrations, the specific growth rate declined, consistent with Haldane inhibition kinetics.
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