Use of near-infrared spectroscopy on predicting wastewater constituents to facilitate the operation of a membrane bioreactor
Sang Yeob Kim (TU Delft - Applied Sciences, IHE Delft Institute for Water Education)
Josip Ćurko (University of Zagreb)
Jasenka Gajdoš Kljusurić (University of Zagreb)
Marin Matošić (University of Zagreb)
Vlado Crnek (University of Zagreb)
Carlos M. López-Vázquez (IHE Delft Institute for Water Education)
Hector A. Garcia (IHE Delft Institute for Water Education)
Damir Brdjanović (TU Delft - Applied Sciences, IHE Delft Institute for Water Education)
Davor Valinger (University of Zagreb)
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Abstract
The use of near-infrared (NIR) spectroscopy in wastewater treatment has continuously expanded. As an alternative to conventional analytical methods for monitoring constituents in wastewater treatment processes, the use of NIR spectroscopy is considered to be cost-effective and less time-consuming. NIR spectroscopy does not distort the measured sample in any way as no prior treatment is required, making it a waste-free technique. On the negative side, one has to be very well versed with chemometric techniques to interpret the results. In this study, filtered and centrifuged wastewater and sludge samples from a lab-scale membrane bioreactor (MBR) were analysed. Two analytical methods (conventional and NIR spectroscopy) were used to determine and compare major wastewater constituents. Particular attention was paid to soluble microbial products (SMPs) and extracellular polymeric substances (EPSs) known to promote membrane fouling. The parameters measured by NIR spectroscopy were analysed and processed with partial least squares regression (PLSR) and artificial neural networks (ANN) models to assess whether the evaluated wastewater constituents can be monitored by NIR spectroscopy. Very good results were obtained with PLSR models, except for the determination of SMP, making the model qualitative rather than quantitative for their monitoring. ANN showed better performance in terms of correlation of NIR spectra with all measured parameters, resulting in correlation coefficients higher than 0.97 for training, testing, and validation in most cases. Based on the results of this research, the combination of NIR spectra and chemometric modelling offers advantages over conventional analytical methods.
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