The paper presents and discusses the development and application of different odour monitoring models (OMMs) for the classification and quantification of odour emissions with Instrumental Odour Monitoring Systems (IOMSs). Feed-forward neural network and linear discriminant analysis were considered for the classification of different type of odours, while feed-forward neural network and partial least square were investigated for the odour quantification. The prediction accuracy of the models was examined by analyzing different data extracted from the sensors’ response curve (at rise, intermediate and peak period). The application has been carried out in a complex petroleum refinery plant. A total of 44 potential odour sources were monitored and grouped into 7 different classes.
Results highlight that the feed-forward neural network prevails in terms of high prediction capability having an architecture with three layers (input, hidden and output) of respectively 14-8-7 for odour classification, and of 14-8-1 for odour quantification, at =0.982 R2. Meanwhile, the most useful data were found using the peak period. The research contributes to the understanding of IOMS applications, providing data on refinery plant odour emissions and applicable mathematical models to ensure great data reliability. The study highlights the influence of the pattern recognition algorithms in the Odour Montoring Model (OMM) elaboration and suggests the utility of promoting the implementation of flexible and adaptable IOMS.