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

Abstract

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...

Utilização do electronic nose (e-nose) como ferramenta de análise rápida de alimentos

“…Esta técnica, apesar de ser padronizada dentro das indústrias, apresenta um elevado custo e subjetividade exacerbada. Deste modo, o NE propõe ser um grande avanço tecnológico na análise de odores (12).…”

Electronic Noses for Environmental Monitoring Applications

“…Another study aiming to characterize and monitor the quality of polluted waters was conducted by Sohn et al [ 116 ]. The work had the aim to use electronic noses for the characterization of pollutant waters in terms of volatile solids load, by correlating said parameter with the odor concentration of the air over the polluted water (headspace).…”

“…The electronic nose they used for the study turned out to be able to detect variations in the air odor concentration, and is therefore considered suitable as detector of possible anomalies downstream of odor abatement systems in wastewater treatment plants. A first study on a real abatement system was reported by Sohn et al [ 116 ]. More in detail, the authors investigated the possibility to monitor a biofiltrating system for the treatment of odorous air from a pig livestock, using a commercial electronic nose, Aroma Scan A32S, equipped with 32 CP sensors.…”

Odour Detection Methods: Olfactometry and Chemical Sensors

“…Sohn et al used an artificial neural network, trained by the data sets obtained with an electronic nose and dynamic dilution olfactometry, to predict the pig farm odour concentrations emanating from an effluent pond and to develop a confident, rapid, and cost-effective technique for odour measurement [ 168 ]; in addition they demonstrated the relationship between odour emission rates and pond loading rates on pig farm effluent ponds and the increased magnitude of emissions from a heavily loaded effluent pond [ 169 ].…”