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Open AccessArticle

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

1
Scarce Resources and Circular Economy (ScaRCE), UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
2
Future Industries Institute, University of South Australia, Mawson Lakes, SA 5095, Australia
3
Water Science Laboratory, South Australian Water Corporation, Adelaide, SA 5000, Australia
4
Operations & Water Quality, TRILITY, Adelaide, SA 5000, Australia
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(22), 6671; https://doi.org/10.3390/s20226671
Received: 19 October 2020 / Revised: 12 November 2020 / Accepted: 18 November 2020 / Published: 21 November 2020
(This article belongs to the Section Intelligent Sensors)
The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L−1. View Full-Text
Keywords: chloramine; machine learning; online detection; spectral compensation; support vector regression; UV-Vis absorbance signatures chloramine; machine learning; online detection; spectral compensation; support vector regression; UV-Vis absorbance signatures
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Hossain, S.; Chow, C.W.; Hewa, G.A.; Cook, D.; Harris, M. Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach. Sensors 2020, 20, 6671.

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