Key points are not available for this paper at this time.
A gas sensor array consisting of eight metal oxide semiconductor (MOS) type gas sensors was evaluated for its ability for assessment of the selected wastewater parameters. Municipal wastewater was collected in a wastewater treatment plant (WWTP) in a primary sedimentation tank and was treated in a laboratory-scale sequential batch reactor (SBR). A comparison of the gas sensor array (electronic nose) response to the standard physical-chemical parameters of treated wastewater was performed. To analyze the measurement results, artificial neural networks were used. E-nose-gas sensors array and artificial neural networks proved to be a suitable method for the monitoring of treated wastewater quality. Neural networks used for data validation showed high correlation between the electronic nose readouts and: (I) chemical oxygen demand (COD) (r = 0.988); (II) total suspended solids (TSS) (r = 0.938); (III) turbidity (r = 0.940); (IV) pH (r = 0.554); (V) nitrogen compounds: N-NO3 (r = 0.958), N-NO2 (r = 0.869) and N-NH3 (r = 0.978); (VI) and volatile organic compounds (VOC) (r = 0.987). Good correlation of the abovementioned parameters are observed under stable treatment conditions in a laboratory batch reactor.
Guz et al. (Tue,) studied this question.