Chloramine demand estimation using surrogate chemical and microbiological parameters

Sina Moradi; Sanly Liu; Christopher W K Chow; John van Leeuwen; David Cook; Mary Drikas; Rose Amal

doi:10.1016/j.jes.2017.01.010

Chloramine demand estimation using surrogate chemical and microbiological parameters

J Environ Sci (China). 2017 Jul:57:1-7. doi: 10.1016/j.jes.2017.01.010. Epub 2017 Jan 29.

Authors

Sina Moradi¹, Sanly Liu¹, Christopher W K Chow², John van Leeuwen³, David Cook⁴, Mary Drikas⁴, Rose Amal⁵

Affiliations

¹ School of Chemical Engineering, The University of New South Wales, Sydney, NSW 2052, Australia.
² Australian Water Quality Centre, SA Water Corporation, Adelaide, SA 5100, Australia; Centre for Water Management and Reuse, School of Natural and Built Environments, University of South Australia, SA 5095, Australia.
³ Centre for Water Management and Reuse, School of Natural and Built Environments, University of South Australia, SA 5095, Australia.
⁴ Australian Water Quality Centre, SA Water Corporation, Adelaide, SA 5100, Australia.
⁵ School of Chemical Engineering, The University of New South Wales, Sydney, NSW 2052, Australia. Electronic address: r.amal@unsw.edu.au.

PMID: 28647228
DOI: 10.1016/j.jes.2017.01.010

Abstract

A model is developed to enable estimation of chloramine demand in full scale drinking water supplies based on chemical and microbiological factors that affect chloramine decay rate via nonlinear regression analysis method. The model is based on organic character (specific ultraviolet absorbance (SUVA)) of the water samples and a laboratory measure of the microbiological (F_m) decay of chloramine. The applicability of the model for estimation of chloramine residual (and hence chloramine demand) was tested on several waters from different water treatment plants in Australia through statistical test analysis between the experimental and predicted data. Results showed that the model was able to simulate and estimate chloramine demand at various times in real drinking water systems. To elucidate the loss of chloramine over the wide variation of water quality used in this study, the model incorporates both the fast and slow chloramine decay pathways. The significance of estimated fast and slow decay rate constants as the kinetic parameters of the model for three water sources in Australia was discussed. It was found that with the same water source, the kinetic parameters remain the same. This modelling approach has the potential to be used by water treatment operators as a decision support tool in order to manage chloramine disinfection.

Keywords: Chloramine demand; Drinking water treatment plants; Modelling.

MeSH terms

Australia
Chloramines / chemistry*
Drinking Water / chemistry
Kinetics
Models, Chemical*
Water Purification / methods
Water Supply

Substances

Chloramines
Drinking Water