Artificial Neural Network in the Measurement of Environmental Odours by E-Nose

摘要

Odour measurement plays a crucial role in environmental odour management. Continuous odourmeasurement systems are promoted to keep the situation always under control, such as being able to adoptthe most suitable mitigation measures in real time to avoid odour complaints and impacts. Electronic Nose(eNose) represents currently the instrument of having the highest future developing potential to guaranteecontinuous odour measurements. To use an eNose, a training phase is however mandatory, which has thescope to create the Odour Monitoring Model (OMM) that is able to identify the presence of odour, the differentodour classes and the quantification of the odorous stimuly. Statistical or biological inspired measurementtechniques are applied to create the optimum OMM.The study presents and discusses the elaboration of an Artificial Neural Network (ANN) technique torecognize environmental odour with eNose. The proposed system was architected on a feed-forward neuralnetwork with Bayesian Regularization algorithm using Matlab R2017a software.The elaborated ANN was tested and validated using the seedOA eNose, realized by the SanitaryEnvironmental Engineering Division (SEED) of the Department of Civil Engineering of the University ofSalerno (Italy). Tests were carried out analyzing odour samples collected at a large Wastewater TreatmentPlant (WWTP). The comparison between the Odour Monitoring Model (OMM) elaborated through theproposed ANN system and the traditional statistical techniques, such as the Partial Least Square (PLS) andthe Linear Discriminant Analysis (LDA), is also discussed.Results shown the efficiency of the elaborated ANN to identify the different odour classes and predict theodour concentration in terms of OUm-3. The artificial neural network shows smaller Root Mean Squared Errors(RMSE) and greater coefficient of determination (R2) as compared to the traditional statistical methods. Themain advantages of neural networks are their adaptability in terms of learning, self-organization, training andnoise-tolerance.