Peter Chalk Centre

University of Exeter

Stocker Road

Exeter

EX4 4QD

Tel: +44 (0)1392 263637

E-mail: CCWI2019@exeter.ac.uk 

17th International Computing & Control for the Water Industry Conference

1st - 4th September 2019
University of Exeter, UK
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7C Water quality modelling

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Collaborative

Joby Boxall

Chair:

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Development of discolouration prediction tool �Aquarellus�

Joost Van Summeren

Presenter:

Authors:

Joost Van Summeren, Mirjam Blokker and Mark Morley

A tool has been developed to predict discoloration risk associated with particle settling, bed-load transport, and resuspension in drinking water distribution systems. The capabilities and first results of the tool will be presented, along with future steps to improve performance and refine input parameters using field measurements and lab experiments.

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Preparing a real-time sensor network for water quality monitoring in a real-life distribution systems

Joost Van Summeren

Presenter:

Authors:

Joost Van Summeren and Dirk Vries

To determine the source and mixing ratios of different water types with continuous electrical conductivity measurements, we formulated an approach aimed at application of a real-life network case. This can be regarded as a first step towards a better understanding of the distribution of water quality and a validation method to check the network configuration. The current presentation focusses on preparing and installing the sensors in a real-life network, and determining the sensor location based on a hydraulic analysis.

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Simulation of Tanks behaviour in Quality Models of Water Supply Systems

Marta Hervás Carot

Presenter:

Authors:

Marta Hervás Carot, Fernando Martínez Alzamora and Pilar Conejos Fuertes

When developing a water quality model in EPANET 2.0, there must be taken into account that the tanks are the components that most affect water age, because water is stored in this devices a certain amount of time before exiting again to the network. The two main aspects that determine the evolution of water age inside a tank are the mixing model, the connection of the tank to the system and the tank regime. In this way, the objective of this study is to analyse how these variables affect water age, and to check the results obtained with experimental results. In terms of water age, it has been analysed that the factors that most affect the evolution of water quality are the rate of the reserve and daily injected volume, the tank operation (pumping and demand regimes) and the tank connection to the system. It is well known that the analysis of the evolution of water age inside the tanks is crucial to guarantee the correct quality of the water supplied to the system. Thus, in this study there have been developed two graphs that show the maximum water age for single pipe connection and independent pipe connections to the system, depending on the rate of reserve vs daily injected volume, and on the tank operation. The results achieved are very significant and must be considered when designing, operating and modelling water tanks.

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Modelling chlorine decay in supply systems using an improved model for wall decay

Laura Monteiro

Presenter:

Authors:

Laura Monteiro, Joana Carneiro and Dídia Covas

Maintaining effective chlorine concentrations in drinking water systems is a major challenge for water utilities. Chlorine decay modelling can be a powerfull tool for chlorination control, if accurate models are achieved. However, computing chlorine wall decay is still inchoate. Recently, a new model for wall decay (EXPBIO), combining the effect of biofilm activity moderated by chlorine concentration and mass transport limitation, was developed. The aim of this paper is to assess the use of the recently developed EXPBIO wall decay model in full-scale systems modelling, regarding enhanced accuracy, ease of implementation, parameter estimation and computational effort, comparatively to the classical first-order model. A sensitivity analysis on the model’ parameters is presented. Results show that the improved accuracy of the EXPBIO model is only marginal when tested in a full-scale system modelling, in comparison with the first order model. Such improved accuracy is due to the increased number of adjustable parameters. EXPBIO model, though more complex, may not be a suitable model for routine water quality modelling.

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