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Spectrophotometric online detection of drinking water disinfectant: a machine learning approach

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dc.contributor.authorHossain, S.
dc.contributor.authorChow, C.W.K.
dc.contributor.authorHewa, G.A.
dc.contributor.authorCook, D.
dc.contributor.authorHarris, M.
dc.date.issued2020
dc.description.abstractThe 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.
dc.identifier.citationSensors, 2020; 20(22, article no. 6671):1-29
dc.identifier.doi10.3390/s20226671
dc.identifier.issn1424-8220
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/11541.2/145370
dc.language.isoen
dc.publisherMDPI AG
dc.relation.fundingUniversity of South Australia
dc.relation.fundingSouth Australian Water Corporation through Water Research Australia 4535-17
dc.rightsCopyright 2020 The Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/)
dc.source.urihttps://doi.org/10.3390/s20226671
dc.subjectchloramine
dc.subjectmachine learning
dc.subjectonline detection
dc.subjectspectral compensation
dc.subjectsupport vector regression
dc.subjectUV-Vis absorbance signatures
dc.titleSpectrophotometric online detection of drinking water disinfectant: a machine learning approach
dc.typeJournal article
pubs.publication-statusPublished
ror.fileinfo12207863610001831 13207863600001831 v2.pdf
ror.mmsid9916460799001831

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