disinfection implementing tools management

Disinfection Management

Implementing Tools

for Optimising

Disinfection

Management Implications from the Research Programs of the Cooperative Research Centre for Water Quality and Treatment

The Cooperative Research Centre for Water Quality and Treatment

Fact Sheet Objective

This document summarises research undertaken by the Cooperative Research Centre (CRC) for Water QualityandTreatmentintheareaofdisinfectionmanagement.Theobjectiveoftheresearchistodevelopbothunderstandingand tools, which the water industry can apply to achieve safe and efficient disinfection in their own distribution systems. This knowledge is designed to help water supply authorities effectively meet the requirements of the Frameworkfor Management of Drinking Water Quality (WQMF), now contained within the revised Australian Drinking WaterGuidelines(ADWG).

The information contained within this document is structured into a series of fact sheets and case studies, whicharereferencedtothevarioussteps in implementationof theWQMF.Thefactsheetsgive informationonthedesignandapplicationofarangeofnewtoolsformanagementofdisinfection,whilethecasestudiesprovideexamplesofapplicationofthetoolsinrealsystems.TherangeoftoolsdevelopedintheCentre’sresearchprogramsiscurrentlybeingconsolidatedintoacommonsoftwareplatformforusebyindustryparties.

Fact Sheet Contents

ThesefactsheetsarederivedfromthefollowingCRCforWaterQualityandTreatmentresearchprojects:

Consolidationofmanagementtoolsfordistributionsystems

Optimisationofchlorineresidualinadistributionsystem(Melbourne)

Modelling Biofilms and Interventions

Consolidation of Modelling Tools In Distribution Systems

Developmentoftoolsforimproveddisinfectioncontrolwithindistributionsystems

Applicationofhazardanalysisandcriticalcontrolpointsfordistributionsystemprotection

Table OF COnTenTS

Disinfection Management Overview Page 1

FS1 The Disinfection Management Tool and How to Apply It Page 4

FS2 Prediction of Disinfection By-Product Formation in Bulk Water Page 8

FS3 Tools to Optimise Disinfection Across Water Treatment and the Distribution System Page 14

FS4 Applying Disinfection Residual Control Tools (DrCT®) Page 16

CS1 Validation of the Disinfection Management Tool on the Seville Chloraminated Zone Page 18

CS2 Optimisation of Disinfection Dosing using DMTool in North Richmond Distribution System 0

CS3 Disinfectant Demand Sensor Case Study Outcomes Page 22

CS4 Instrument Evaluation Results Page 25

CS5 Development of ANN-based Models for Disinfection Residuals Page 28

Appendix 1 Disinfection Management for Service Reservoirs (Tanks) Page 30

Acknowledgements Page 32

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Disinfection Management Overview

Tools for Improved Disinfection Management in Distribution Systems

Disinfection is the final stage in the multi-barrier water treatment process and protects customers from microbiological pathogens. The development of tools to aid the management of disinfection in water distribution systems has been identified as a high priority by water utilities throughout Australia. This need has arisen from the increasingly stringent demands placed on water utility managers to better manage their systems, as highlighted by the Framework for Management of Drinking Water Quality (WQMF) incorporated within the 2004 Australian Drinking Water Guidelines (ADWG). More than ever before, managers are now faced with the challenge of balancing compliance with respect to disinfectant residuals and coliform counts at the customer tap; while minimising formation of disinfection by-product (DBP) and taste or odour complaints.

The CRC for Water Quality and Treatment has developed a suite of tools that can be used to improve the management of disinfection residuals within water distribution systems. The suite comprises design, planning and operational tools that will allow water utility managers to consider the effects of the many factors that contribute to the performance of disinfection systems. Examples of the latter include dynamic, non-linear system demands, seasonal or storm-event related variation in raw water quality characteristics, such as the concentration of natural organic matter (NOM); and the cleaning of pipes or tanks.

Process-Based Tools

Process-based tools to predict chlorine or chloramine decay in water distribution systems are described in Fact Sheet 1. The approach developed incorporates an improved disinfection decay model, which separates two key processes on an objective basis:

• Bulk decay, where disinfectant is consumed by reactions with NOM in the treated water; and

• wall decay, where disinfectant is consumed by reactions with biofilms, corrosion products and sediments that line pipes.

Coupled to an existing hydraulic model of a water distribution system, the improved decay models allow managers to more accurately simulate the behaviour of disinfection residuals, including the impact of actions such as pipe cleaning or rechlorination. The tools also allow management teams to undertake scenario modelling to assess the impact that operational changes may have on their system and/or conduct an analysis to predict and evaluate the performance of modified or newly designed systems.

These activities can assist in the evaluation of risks (exposure to hazards) and preventive measures, both of which are part of the Framework for Management of Drinking Water Quality. Fact Sheet 2 describes the application of disinfection modelling tools (DMTools) to the evaluation of risks and preventive measures associated with chlorine decay and disinfection by-product formation. Case Studies 1 and 2 describe the application of the process-based tools to a chloraminated and chlorinated system respectively. It is also feasible to use these tools as predictors in on-line control systems, provided the flows are available (or can be inferred) from a SCADA system.

In some systems, the NOM in the source water can vary faster than the time it takes to conduct decay tests required to determine whether dosing needs to be changed to match the change in disinfectant demand. Alternative methods to determine this demand more rapidly are discussed in Fact Sheet 3.

Data-Based Tools

In a different research approach described in Fact Sheet 4, data based tools are being developed to assist water supply managers improve control of disinfection in distribution systems. This toolkit consists of a number of tools which aim to predict disinfectant residual concentration and provide advice on optimal doses to achieve target residual concentrations at key network locations. The following tools can be used selectively to match the needs and available resources and are known as the Disinfection Toolkit (DrCT®).

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Surrogate disinfectant demand sensor

A prototype sensor to enable rapid surrogate chlorine or chloramine demand assessment of water prior to disinfection is being developed. The sensor will use a parameter indicative of water quality variation to estimate chemical dosing. The approach is particularly useful for water supplies requiring only disinfection and with rapidly changing source water quality.

On-line disinfectant residual analysers

Reliable and cost-effective instruments suitable for long-term use in distribution system applications are often needed. This component evaluates and recommends cost effective commercially available on-line sensors to measure disinfectant residuals at distribution system locations.

Data transmission

Communications hardware to transmit data from sensors in remote locations to a central SCADA system are being investigated.

Artificial Neural Network (ANN)-based forecasting tool

Data-driven modelling techniques to forecast disinfection residuals at critical control locations are being developed. A tool to analyse the massive amount of SCADA data available and advise on optimum disinfectant doses or provide early warning of compliance failures is in development.

A guidance manual is being developed to enable water supply managers to assemble a system for their distribution system.

Current Status

This three year project is due for completion over the next six months. Most of the components mentioned, such as disinfectant demand sensor, on-line instrument evaluation, data communication options, are largely completed, except for one or two case studies (field trials) which are still in progress and will be completed shortly.

The development of ANN-based water quality models is the focus of PhD research, which is due for completion in late 2006. The outcome of this component will be a reliable framework for development of disinfection residual forecasting models and SCADA integration.

Case Studies

This project includes a 24-month sampling program for water samples provided by the industry partners collected from various water supplies as part of the disinfectant demand sensor development – Case Study 3.

An evaluation of a complete range of commercially available on-line instruments provides a good comparison of the performance including accuracy, drift etc – Case Study 4. This information not only helps water managers to understand the performance limitation of each instrument but also the decision of selecting the best instrument for the particularly application.

Two site specific case studies are included in this project to evaluate the tools developed from the project. Myponga, SA and Woronora, NSW are the two sites for chlorine and chloramine systems, respectively – Case Study 3.

Case Study 5 describes the application of artificial neural network (ANN) models to the management of disinfectant residuals.

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Utilisation

A guidance manual will be produced at the end of the project. Dissemination and packaging the tool box approach for disinfection management will be undertaken in conjunction with other Centre projects. Targeted awareness sessions for industry partners on project outcomes and establishment of networks of champions and user groups will be undertaken in 2006.

The toolkit developed from this project will allow water utilises to ‘pick and choose’ the right tools for their needs. The technical know how will be covered in the guidance manual.

Main Projects on Disinfection Theme

Consolidation of management tools for distribution systems

Optimisation of chlorine residual in a distribution system (Melbourne)



Development of tools for improved disinfection control within distribution systems

Application of hazard analysis and critical control points for distribution system protection

PhD on reservoir modelling

More Information

Chow CWK, Fitzgerald F and Holmes M (2004) The Impact of Natural Organic Matter on Disinfection Demand - A Tool to Improve Disinfection Control. Enviro 04, AWA, March, Sydney.

Fitzgerald F, Chow CWK and Holmes M (2004) Link between Organic Character and Disinfection - Australian Experience. NOM research: Innovations and Applications for Drinking Water Treatment, March, Victor Harbor, Australia (Poster).

May RJ, Maier HR, Dandy GC and JB Nixon (2004) General Regression Neural Networks for Modeling Disinfection Residual within Water Distribution Systems. Proceedings of the ASCE 6th Annual Symposium on Drinking Water Distribution Systems Analysis, June, Salt Lake City.

Fitzgerald F, Chow C and Holmes H (2005) Development of a Disinfectant Demand Sensor – An Attempt to Predict Disinfectant Demand. Ozwater Convention, AWA, May, Brisbane.

Holmes M, Chow C, Dandy G, May R, Fitzgerald F, Maier H, Badalyan A and Nixon J (2005) DrCT®: Developing Tools for Improved Disinfection Control within Water Distribution Systems. WEF/AWWA/IWA Disinfection Specialty Conference, February,Mesa, Arizona, USA.

NHMRC (2004) Australian Drinking Water Guidelines. http://www.nhmrc.gov.au

FS 1

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Overview

The Disinfection Management Tool (DMTool) is a process-based model that may be used to establish dosing and booster disinfection levels for chlorine and chloramines; predict free and total chlorine levels; predict trihalomethanes (THMs) and haloacetic acids (HAAs); and improve overall understanding and operations relating to disinfection in distribution systems.

Applications of the DMTool include prediction and analysis of residual disinfectant levels throughout a distribution system, as well as levels of disinfection by-products (THM’s and HAA’s). The model can account for biofilms, optimise disinfectant dosing operations, compensate for temperature effects, locate secondary chlorinators and reduce customer complaints.

Conceptual Approach

Accurate prediction of total disinfectant decay requires separation of its components:

• bulk decay –where disinfecant reacts with the natural organic matter (NOM) in the bulk water; and

• wall decay – where disinfectant reacts with the pipe wall (its material and attached corrosion products, sediment and biofilms). This is highly variable depending on pipe material and may contribute to total decay at a factor many times that of bulk decay.

The DMTool measures the ‘bulk decay’ through laboratory decay testing and characterisation of the decay through a set of coefficients that remain invariable through the system.

The ‘wall decay’ is estimated via the difference between total and bulk decay as illustrated in Figure 1 below and is accounted for in the model via the ‘equivalent diameter’ concept. A pipe of physical diameter ‘d’ has an equivalent diameter of ‘d’ when the decay rate due to the pipe wall is equal to the decay rate of bulk water passing through it.

Estimation of disinfection by-products, in particular THM’s and HAAs are estimated by measurement of their formation in the disinfectant decay tests. This is defined by a coefficient as is bulk decay.

distance (km)

Bulk prediction (from DMTool)

Measurement in system

chlorine(mg/L)

Combined prediction (fromDSMtool)

Reacted with bulk

Reacted with wall

Figure 1. Chlorine decay due to bulk and wall effects

FS� The Disinfection Management Tool and How to Apply It

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FS 1

Limitations

The current model has limitations with:

• Zones with more than one water source

• Run times for large models or zones

• Costs for deriving water property files (WPF) for different water sources.

Implementation

Model Inputs

To run a simulation the DMTool requires:

• An hydraulic model – in EPANET with some level of calibration. Many commercial packages have a direct export to EPANET 2.0 that performs this task.

• A WPF – that defines the reaction between chlorine and the organics within the water.

The WPF is obtained from laboratory disinfectant decay analysis of water samples taken immediately prior to the disinfection point of the selected zone. The laboratory data is processed via a parameter estimation package such as AQUASIM that defines concentrations and reaction rates of fast and slow reacting organic matter under various temperatures and chlorine/chloramine dose levels. THM and/or HAA formation is also analysed and defined in the WPF.

Field Sampling

Samples need to be taken from select tap sites throughout the case study area for calibration purposes, providing good geographical and pipe size representation. Record the following:

• free chlorine

• total chlorine

• THMs and/or HAAs

• water temperature

• time of sampling.

Calibrating the Disinfection Model

• collection of field calibration sample set

• simulation of hydraulic flows to represent those on calibration sample day

• running of the Disinfection model with WPF

• application of pipe reactivity factors to calibrate model outputs to field data

• collection of field validation sample set to verify model.

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Figure: Disinfection Model Display

Future Direction

The model is being integrated with other water quality models onto a common EPANET

platform. This will include automated file conversion functionality that converts the user’s

hydraulic model from their commercial hydraulic package into EPANET format.

More Information

Clement J, Powell J, Brandt M, Casey R, Holt D, Grayman W and LeChevallier M (2004)

Predictive Models for Water Quality in Distribution Systems, AwwaRF Report 91023F,

Denver Co.

Rossman LA (2000) EPANET 2 Users Manual, Environmental Protection Agency,

Cincinnati Ohio.

Kastl G and Fisher I (1997) Predicting and maintaining drinking water quality in distribution

systems. Water 24(5): 35-38.

Kastl G, Fisher I, Jegatheesan V, Chandy J and Clarkson K (2002) Prediction of chlorine

decay and THM formation in bulk drinking water systems from laboratory data. Proceedings

of the IWA 3rd World Water Congress, April, Melbourne, (CD-ROM).

Fisher I and Kastl G (1996) Numerical modelling of water quality in distribution systems.

Proceedings First AWWA WaterTECH Conference, Sydney.

Kastl G, Fisher I, and Jegatheesan V (1999) Evaluation of chlorine decay kinetics

expressions for drinking water distribution system modelling. Journal of Water SRT –Aqua

48(6), 219-226.

Kastl G, Fisher I and Chen P (2002) A tool for accurate simulation of chlorine and THM

concentration profiles in drinking water distribution systems from laboratory data.

Proceedings of IWA Specialty Conference on Management of Productivity at Water Utilities,

June, Praha.

Figure 2. Disinfection Model Display

Future Direction

The model is being integrated with other water quality models onto a common EPANET platform. This will include automated file conversion functionality that converts the user’s hydraulic model from their commercial hydraulic package into EPANET format.

More Information

Clement J, Powell J, Brandt M, Casey R, Holt D, Grayman W and LeChevallier M (2004) Predictive Models for Water Quality in Distribution Systems, AwwaRF Report 91023F, Denver Co.

Rossman LA (2000) EPANET 2 Users Manual, Environmental Protection Agency, Cincinnati Ohio.

Kastl G and Fisher I (1997) Predicting and maintaining drinking water quality in distribution systems. Water ��(5): 35-38.

Kastl G, Fisher I, Jegatheesan V, Chandy J and Clarkson K (2002) Prediction of chlorine decay and THM formation in bulk drinking water systems from laboratory data. Proceedings of the IWA 3rd World Water Congress, April, Melbourne, (CD-ROM).

Fisher I and Kastl G (1996) Numerical modelling of water quality in distribution systems. Proceedings First AWWA WaterTECH Conference, Sydney.

Kastl G, Fisher I, and Jegatheesan V (1999) Evaluation of chlorine decay kinetics expressions for drinking water distribution system modelling. Journal of Water SRT –Aqua �8(6), 219-226.

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Kastl G, Fisher I and Chen P (2002) A tool for accurate simulation of chlorine and THM concentration profiles in drinking water distribution systems from laboratory data. Proceedings of IWA Specialty Conference on Management of Productivity at Water Utilities, June, Praha.

Sathasivan A, Fisher I, and Kastl G (2005) A simple method for measuring microbiologically assisted chloramine decay in drinking water. Environmental Science and Technology �9(14), pp 5407-5413.

Contact PersonGeorge KastlSydney WaterPh: (02) 9350 6793Email: [email protected]

FS 2

Overview

Disinfection by-products (DBPs) are formed after any disinfectant is added to a water supply, as a result of the disinfectant reacting chemically with the natural organic matter (NOM) remaining in the water after treatment. However, only the DBPs associated with chlorine and chlorine dioxide have concentrations set in the 2004 Australian Drinking Water Guidelines (ADWG). As chlorine dioxide is not commonly used in Australia, this overview considers only the DBPs associated with chlorination. These are trihalomethanes (THMs) and haloacetic acids (HAAs).

There have been numerous studies of THM and HAA formation potentials and the factors that influence them. The formation potential is the concentration formed in a water sample over a specific period following an initial chlorine dose. The dose is high compared with those normally used in distribution systems, to ensure there is chlorine remaining at the end of the test. Consequently, DBP formation potentials are usually higher than the level of DBPs formed in a distribution system and cannot be used as a basis for prediction.

More recently, the simulated distribution system (SDS) test has been introduced to provide a more realistic indicator. It is identical to the formation potential test, except that the initial dose is that typically used in the distribution system of interest. If the test duration is greater than the maximum travel time in the real system, and the DBP concentration formed is less than the relevant number in ADWG, then the real system should comply.

If either of these conditions is not met, then no conclusion regarding compliance can be made for the system as a whole. Instead, the actual levels of DBPs formed are needed at all points in the system, under the normal operating conditions; ie a general prediction tool for these DBPs is required.

Conceptual Approach

The CRC for Water Quality and Treatment has developed a prediction tool for both of these DBP groups and incorporated it within the disinfection modelling tool (DMTool) software. It is based on the principle that the amount of each DBP group produced over any period is a (small) percentage of the amount of chlorine that has reacted with the NOM present. This percentage (or yield) varies with the type of water, because the type of NOM varies. Consequently, it needs to be determined for each water, in the laboratory.

Figure 1 shows how the THM yield (defined as mg/L of THM formed per mg of chlorine reacted) is constant, in a single water at constant temperature over a wide range of chlorine reacted. All other waters tested have shown similar strong relationships. Figure 2 shows the same relationship for water samples subjected to different levels of treatment. The percentage is the same – it is merely the time taken to consume the same amount of chlorine that varies.

For HAAs, the yield varies with the initial level of NOM, as well as the amount of chlorine reacted.Consequently, the yield needs to be determined for different levels of NOM, if this varies with season.

For HAAs, the yield varies with the initial level of NOM, as well as the amount of

chlorine reacted. Consequently, the yield needs to be determined for different levels

of NOM, if this varies with season.

Figure 1. Linear relationship between THMs formed and chlorine consumed

Figure 2. Linear relation ship between THMs formed and chlorine consumed

after different degrees of treatment at North Richmond (NSW)

Implementation

Concentrations of THMs have been successfully predicted with the DMtool in two

distribution systems having very different water sources, after determining the THM

yield per mg chlorine reacted (as in Figures 1 and 2). The systems were

• North Richmond (NSW), which is supplied with treated water abstracted from

the river downstream of Warragamba Dam; and

• Mirrabooka high-level, which is supplied with a mixture of treated groundwater

and artesian water.

BOLGANUP

y = 40.426x

R2 = 0.9822

0

200

400

600

800

1000

1200

0.00 5.00 10.00 15.00 20.00 25.00 30.00

Total Chlorine Utilized (mg/L

TT

HM

(g

/L)

T. THMs

Linear (T. THMs)

0

20

40

60

80

100

0 1 2 3

T_Cl used [mg/L]

TT

HM

[m

cg

/L]

Raw

DAF

DMF

GAC


FS � Prediction of Disinfection By-Product Formation in Bulk Water and Use of Tools for Hazard Evaluation

Total Chlorine Utillised (mg/L)

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For HAAs, the yield varies with the initial level of NOM, as well as the amount of

chlorine reacted. Consequently, the yield needs to be determined for different levels

of NOM, if this varies with season.


Figure 2. Linear relation ship between THMs formed and chlorine consumed

after different degrees of treatment at North Richmond (NSW)

Implementation

Concentrations of THMs have been successfully predicted with the DMtool in two

distribution systems having very different water sources, after determining the THM

yield per mg chlorine reacted (as in Figures 1 and 2). The systems were

• North Richmond (NSW), which is supplied with treated water abstracted from

the river downstream of Warragamba Dam; and

• Mirrabooka high-level, which is supplied with a mixture of treated groundwater

and artesian water.

BOLGANUP

y = 40.426x

R2 = 0.9822

0

200

400

600

800

1000

1200

0.00 5.00 10.00 15.00 20.00 25.00 30.00

Total Chlorine Utilized (mg/L

TT

HM

(g

/L)

T. THMs

Linear (T. THMs)

0

20

40

60

80

100

0 1 2 3

T_Cl used [mg/L]

TT

HM

[m

cg

/L]

Raw

DAF

DMF

GAC

Figure 2. Linear relation ship between THMs formed and chlorine consumed after different degrees of treatment at North Richmond (NSW)

Implementation

Concentrations of THMs have been successfully predicted with the DMTool in two distribution systems having very different water sources, after determining the THM yield per mg chlorine reacted (as in Figures 1 and 2). The systems were:

• North Richmond (NSW), which is supplied with treated water abstracted from the river downstream of Warragamba Dam; and

• Mirrabooka high-level, which is supplied with a mixture of treated groundwater and artesian water.

Use of DMTool for Hazard Evaluation

According to the Framework for Drinking Water Quality Management (NHMRC 2004), there are two major hazards for customers, related to disinfection:

• Micro-organisms (including health risks from pathogens) and

• Disinfection by-products (DBPs).

A major problem for the risk assessment proposed in the Framework is the determination of exposure of the customer population to a hazard, because this often varies considerably in time and space. The process-based tools described in Fact Sheet 1 can be used for this purpose because they predict the concentration of disinfectant and chlorinated DBPs at all points in the distribution system over the time period simulated. These tools can also be used to plan and design the preventive measures recommended in the Framework, so that the requirements of ADWG related to disinfection are met.

Evaluation of Exposure to a Hazard

The software incorporating these tools (DMTool) has been modified to compute the frequency with which the predicted concentrations of disinfectant or DBPs fall within specified ranges, at nominated sites, over the period simulated. Localities within the distribution system with roughly comparable concentrations of DBPs can be identified and attention focussed on areas where either disinfectant residuals or DBPs are at unacceptable levels.

In a recent study of the North Richmond (NSW) system, the DMTool was used in this fashion to evaluate the risk of exceeding the aesthetic guideline for chlorine, where this was of greater concern than the low level of DBPs formed (see Case Study 2).

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The same approach cannot be taken to assess exposure to microbiological agents, because their occurrence is not readily described in process terms; those of most interest occur at very low levels and/or sporadically. Instead, different disinfectant concentrations are considered to correspond to different levels of risk to such agents.

In the study of North Richmond system (Figure 3), a level of 0.2mg/L free chlorine or more was considered to constitute a negligible microbial risk. The DMTool was then used to evaluate exposure to microbiological risk at specified locations as frequencies of exceeding this concentration.

Figure 3. Representation of North Richmond delivery system in DMTool.

Evaluation of preventive measures

Once the DMTool has been set up to evaluate existing exposure levels, it can be readily reapplied to evaluate a wide range of potential preventive measures to decrease this exposure. These include:

• Initial and booster disinfectant dosing

• Reservoir detention management and improved mixing

• Re-zoning and smart valve operation

• Mains cleaning

• Change disinfectant (eg chlorine to chloramine)

• Improved NOM removal at the treatment plant upstream

In particular, the evaluation can directly examine performance at locations where critical limits are imposed as part of a critical control point.

Figure 3. Representation of North Richmond delivery system in DMtool.

Evaluation of preventive measures

Once DMtool has been set up to evaluate existing exposure levels, it can be readily

reapplied to evaluate a wide range of potential preventive measures to decrease this

exposure. These include:

• Initial and booster disinfectant dosing

• Reservoir detention management and improved mixing

• Rezoning and smart valve operation

• Mains cleaning

• Change disinfectant (e.g. chlorine to chloramine)

• Improved NOM removal at the treatment plant upstream

In particular, the evaluation can directly examine performance at locations where

critical limits are imposed as part of a critical control point.

Selection of initial disinfectant dose

Figure 4 illustrates the general procedure for finding the initial disinfectant dose

required at location A to achieve a minimum residual of 0.2mg/L at all locations in a

simple distribution system. The decay prediction over time, for a trial value of initial

dose, is shown on the left hand graph as a function of travel time. The profile over

distance is shown on the right, where a significant loss of chlorine occurs in a service

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Selection of initial disinfectant dose

Figure 4 illustrates the general procedure for finding the initial disinfectant dose required at location A to achieve a minimum residual of 0.2mg/L at all locations in a simple distribution system. The decay prediction over time, for a trial value of initial dose, is shown on the left hand graph as a function of travel time. The profile over distance is shown on the right, where a significant loss of chlorine occurs in a service reservoir. The trial value needs to be increased successively in the prediction tool until the predicted value at D is raised to 0.2mg/L.

travel time (hr)

Tank

chlorine (mg/L)

0A

distance (km)

chlorine (mg/L)

0

Last houseCl

dosed

B C D

A B C D

min 0.2

??

Figure 4. Initial dose to meet minimum disinfectant concentration in system

The initial dose that satisfied this requirement (to control microbial risk) can then be checked, to determine whether the predicted associated DBP formation complies with the DBP guidelines. The compliance of the predicted disinfectant concentrations at the first customers’ taps can be similarly checked.

The same procedure can be applied to a complex distribution network, for which a prediction tool (such as DMTool) has been set up. If the initial dose that meets the microbial risk requirement fails to meet either the DBP or the aesthetic guidelines, then the more complex and costly preventive measures listed above may be needed. A framework for considering these measures is presented in Figure 5.

Selection of locations and doses for rechlorination stations

The ‘parallel reaction’ model incorporated in the DMTool has been proven to accurately represent the slower chlorine decay that occurs subsequent to rechlorination. Representation of booster chlorination has also been improved in the DMTool, compared with that offered in EPANET2. Setpoints for each chlorinator can be set for dosing into the reservoir or into its outlet. The mass of chlorine consumed is accumulated during the period simulated, so that the total chlorine consumed in the system can be considered as one of the performance criteria.

In the study of the North Richmond system (Figure 3), setpoints for the initial chlorine dose and five booster chlorination stations at tanks downstream were determined (by re-running the DMTool with many trial combinations), so that free chlorine concentrations at 17 sampling sites were maintained between 0.2 and 0.6mg/L. The setpoints needed to vary considerably over the year, because source water temperature varied from 10-30°C.

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Goals not met by initial dose of chlorine

Rechlorination plants meet goals?

Chloramine provides sufficient Ct?

Preoxidation provides sufficient Ct? Chloramine meets goals?

Nitrification controllable?

Process meets goals

No

No

Yes

Yes

No

Yes

Yes

Yes

Need NOM removal to meet goals

No

No

Goals not met by initial dose of chlorine

Figure 5. Preventive measures if initial dose does not meet downstream requirements

The difficulty of finding the system-wide optimal doses for multiple chlorinators

increases exponentially with the number of chlorinators. To find such solutions

efficiently requires the prediction tool to be embedded within a systematic

optimisation procedure.

Change to another disinfectant

If rechlorination still fails to meet DBP and/or aesthetic guidelines throughout a

system, changing to chloramination is generally the next simplest and cheapest

option. This does not generate either THMs or HAAs (if chloramine is formed before

being dosed). Although (mono)chloramine lasts longer in the system, it may not

provide sufficient power (Ct) to satisfy primary disinfection requirements. This may

be overcome by dosing chlorine first and adding ammonia downstream after sufficient

Ct has accumulated. Then no further production of THMs and HAAs occurs.

A major problem associated with chloramination in Australia is the large acceleration

of monochloramine decay that can occur due to microbial activity, including

nitrification. Sathasivan et al (2005) have recently developed a method to measure

this microbial contribution to total chloramine decay and found that it can be over ten

times that due to the ‘chemical’ decay (eg. by reaction with NOM). It is therefore

essential to develop a microbial decay control strategy before changing from

chlorination to chloramination.

Further NOM removal at the treatment plant

If changing to chloramination fails to meet the requirements for disinfection or is

otherwise unacceptable, then further removal of NOM at the treatment plant may be a

viable alternative. (see Fact Sheet 3).

More information


Figure 5. Preventive measures if initial dose does not meet downstream requirements.

The difficulty of finding the system-wide optimal doses for multiple chlorinators increases exponentially with the number of chlorinators. To find such solutions efficiently requires the prediction tool to be embedded within a systematic optimisation procedure.

Change to another disinfectant

If rechlorination still fails to meet DBP and/or aesthetic guidelines throughout a system, changing to chloramination is generally the next simplest and cheapest option. This does not generate either THMs or HAAs (if chloramine is formed before being dosed). Although (mono)chloramine lasts longer in the system, it may not provide sufficient power (Ct) to satisfy primary disinfection requirements. This may be overcome by dosing chlorine first and adding ammonia downstream after sufficient Ct has accumulated. Then no further production of THMs and HAAs occurs.

A major problem associated with chloramination in Australia is the large acceleration of monochloramine decay that can occur due to microbial activity, including nitrification. Sathasivan et al (2005) have recently developed a method to measure this microbial contribution to total chloramine decay and found that it can be over ten times that due to the ‘chemical’ decay (eg. by reaction with NOM). It is therefore essential to develop a microbial decay control strategy before changing from chlorination to chloramination.

Further NOM removal at the treatment plant

If changing to chloramination fails to meet the requirements for disinfection or is otherwise unacceptable, then further removal of NOM at the treatment plant may be a viable alternative. (see Fact Sheet 3).

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More information


Fisher I, Kastl G, Sathasivan A, Chen P, van Leeuwen J, Daly R and Holmes M (2004) Tuning the enhanced coagulation process to obtain best chlorine and THM profiles in the distribution system. Water Science and Technology: Water Supply �(4), 235-243.

Kastl G, Fisher I, Sathasivan A, Chen P, and van Leeuwen J (2003) Modelling water quality from source water to tap by integration of process models. Proceedings of MODSIM 2003, International Congress on Modelling and Simulation, Townsville, Queensland, Australia, July. CD-ROM.

Kastl G, Fisher I, Jegatheesan V, Chandy Jand Clarkson K (2002) Prediction of chlorine decay and THM formation in bulk drinking water systems from laboratory data. Proceedings of the IWA 3rd World Water Congress, April, Melbourne, CD-ROM.


Fisher I, Kastl G, Chen P and Sathasivan A (in preparation) Final Report Consolidation of management tools for distribution systems.

Fisher I, Kastl G, Sathasivan A and Chen P (2003) Modelling chlorinated by-products in drinking water distribution systems – formation and the impact of treatment. Proceedings of AWA Conference on Chemicals of Concern in Water June, Sydney. CD-ROM.

Sathasivan A, Fisher I and Kastl G (2005) A simple method for measuring microbiologically assisted chloramine decay in drinking water. Environmental Science and Technology, 39(14), 540-7-5413.

Contact PersonDr Ian FisherSydney WaterPh: (02) 9334 0938Email: [email protected]

FS 3

Page ��

FS� Tools to Optimise Disinfection Across Water Treatment and the Distribution System

Overview

In some distribution systems, it may not be possible to meet the requirements of the Australian Drinking Water Guidelines (ADWG) related to disinfection with chlorine or chloramine alone, even with booster dosing at intermediate locations. Additional removal of natural organic matter (NOM) at the treatment plant may provide the solution by decreasing the bulk decay rate of disinfectant. It is also possible that some combination of NOM removal with disinfectant dosing may meet ADWG requirements at lower cost than dosing alone. This fact sheet describes how tools developed in the CRC for Water Quality and Treatment (and elsewhere) can be applied to determine the best combination.

FS3 Tools to optimise disinfection across water treatment and the

distribution system

Overview

In some distribution systems, it may not be possible to meet the requirements of the

Australian Drinking Water guidelines (ADWG) related to disinfection with chlorine

or chloramine alone, even with booster dosing at intermediate locations. Additional

removal of natural organic matter (NOM) at the treatment plant may provide the

solution by decreasing the bulk decay rate of disinfectant. It is also possible that some

combination of NOM removal with disinfectant dosing may meet ADWG

requirements at lower cost than dosing alone. This fact sheet describes how tools

developed in the CRC for Water Quality and Treatment (and elsewhere) can be

applied to determine the best combination.

Figure 1. The water treatment – distribution system and interventions

related to improving disinfection

Figure 1 provides a general description of the processes which can control water

quality at customers’ taps. To predict the effects of these processes, the following

modelling tools can be used:

System to be optimised

Water treatment

2. Pre-oxidation of leftover DOC

to further reduce chlorine decay

rates and THM formation

Distribution system

Reservoirs: bulk chlorine decay With re-chlorination?

Pipes: bulk chlorine decay,

biofilm/sediment chlorine decay and

reaction with wall material (if unlined iron)

1. DOC removal by enhanced

coagulation* at low pH (4-6) to

reduce chlorine decay rates and

THM formation

Consumer taps

Required chlorine concentration

0.2-0.6mg/L,

THMs < 0.1mg/L

Raw water

*Processes other than enhanced coagulation

could be used, but no linkage with impact in

the distribution system has been established

Figure 1. The water treatment – distribution system and interventions related to improving disinfection

Figure 1 provides a general description of the processes which can control water quality at customers’ taps. To predict the effects of these processes, the following modelling tools can be used:

• Estimation of chlorine decay parameters (Fact Sheet 1 and 2)

• Removal of dissolved organic carbon (DOC) by enhanced coagulation (CRC Project Modelling coagulation to maximise removal of organic matter)

• Effect of DOC removal on chlorine decay parameters ( Fisher et al, 2004)

• Effect of pre-oxidation on chlorine decay parameters (Fisher et al, 2004)

• Chlorine decay and THM formation in pipes (Fact Sheets 1 and 2)

• Chlorine decay and THM formation in reservoirs (Appendix 1)

FS 3

Page ��

A tool to predict the degree of NOM removal from a particular water, for any combination of pH and coagulant dose, has been developed in the Centre’s Water Treatment Technology Program (CRC Project Modelling coagulation to maximise removal of organic matter). This tool has been linked to the disinfection modelling tool, so that the impact of different degrees of NOM removal can be evaluated in terms of actual chlorine decay and disinfection by-product (DBP) formation at customers’ taps.

In principle, this approach enables simulation of any configuration of treatment plant – distribution system receiving any given raw water. In practice, it is not common to start with a new source water and have complete freedom to design new water treatment plant, pipes and reservoirs. However, the concept is quite flexible and can be used in solving these typical tasks:

• Design of a completely new treatment - distribution system, given raw water and demand distribution. A hydraulic model is a prerequisite for applying the tools listed above and each alternative scenario must also be costed before the best design can be identified.

• Upgrade of a treatment plant and the impact of including enhanced coagulation on water quality at the tap.

• Introduction of pre-oxidation and quantification of its impact at the tap.

• Quantitative predictions of impact of various chlorine dosing regimes at water treatment plant (WTP).

• Quantitative predictions of impact of various chlorine set points at re-chlorination stations (Case Study 2).

• Predictions of impact of changed hydraulic conditions such as changes in flow, zoning, size of reservoir or pipe diameter on tap water quality.

To implement these studies, the following system parameters need to be determined (there are a number of hydraulic limitations which are taken into account by the hydraulic model and are not discussed here):

• Selection of coagulant (Fe3+ or Al3+)

• Coagulant dose and pH

• Selection of pre-oxidant and its dose

• Chlorination dose

• Pipe material, pipe diameter and eventually pipe route (in planned systems)

• Reservoir number, size and location (in planned systems)

• Re-chlorination stations and set point concentrations

This toolkit outputs water quality parameters throughout the distribution system for each selected scenario of NOM removal. If each scenario is costed, then the most acceptable combination of operational parameters, water quality and cost can be determined.

More information

Kastl G, Fisher I, Jegatheesan V, Chandy J and Clarkson K (2002) Prediction of chlorine decay and THM formation in bulk drinking water systems from laboratory data. Proceedings of the IWA 3rd World Water Congress, April, Melbourne (CD-ROM).


Kastl G, Sathasivan A, Fisher I and van Leeuwen J (2004) Modeling DOC removal by enhanced coagulation. Journal of American Water Works Association 9�(2), 79-89.

Page ��

FS 4

Overview

A number of database tools have been developed aimed at improving the control of secondary disinfection. In contrast to the process-based tools described fact sheets 1 and 3, these tools utilise data collected from an operating system. Collectively known as DrCT, the tools include:

• a surrogate disinfectant demand sensor

• recommendations for the selection of on-line disinfectant residual monitors

• options for data transmission

• arrificial neural network (ANN)-based software models to predict disinfectant residual in advance and to control secondary disinfection.

Package : DrCT Toolkits

1) Surrogate Disinfectant Demand Sensor

For chlorinated systems, on-line UV monitoring of water quality prior to chlorination can provide useful information that can be used to optimise the applied chlorine dose. This will be particularly useful for water supplies with disinfection as the only required treatment.

For chloraminated system, two techniques can be used to predicted chloramine demand.

• Technique 1 uses a short-term demand to predict long-term demand.

• Technique 2 uses chloramine demand assessment at an elevated temperature to predict long-term chloramine demand.

In addition, the experimental work on other novel approaches based on water quality parameters is still in progress. Results will be discussed as soon as the experimental work is completed.

2) Recommendation of On-line Disinfectant Residual Monitors

A full report titled Comparative Study of On-Line Free Chlorine, Total Chlorine/ Monochloramine and Ammonia Monitors will be issued. This contains valuable information such as, response time, linearity, coefficient of variation, coefficient of detection, repeatability, lowest detectable change, limit of quantification, short-term drift, interferences, and memory effect will be available for operators to make the correct choice of a monitor for their specific applications.

3) Options for Data Transmission

A CRC report titled Options for Data Communication from Remote Sensors to Central Personal Computer in Distribution System is being produced and will be distributed. It contains information which may help end users to determine the correct choice of the method for transmission of field information to a remote computer.

This report discusses various options for data communication between field water quality sensors installed at two locations along water distribution network and provides details of their advantages and disadvantages when used in specific locations. The correct choice of a data communication system demands a careful analysis of the various aspects of water distribution system such as topology of the distribution pipe network, type of on-line monitors, distances from proposed monitoring stations and central computer and proximity to residential or industrial areas.

One of the possible choices is a mobile modem and Magpie-2 data acquisition software which were successfully applied for remote communication.

FS� Applying Disinfection Residual Control Tools (DrCT®)

Page ��

FS 4

4) ANN Model

A data-driven model can be developed, which utilises the massive amount of valuable real-time information available via on-line monitoring and SCADA systems. The model can analyse this data to forecast trends in key water quality parameters within the water distribution system, including disinfection residuals.

The framework for the development of an ANN-based model comprises the following steps:

• Data Collection

• Input Selection

• Data Mining

• ANN Calibration (or Training)

• ANN Validation

Implementation of this framework requires data management and data analysis software to be integrated with existing SCADA. Calibrated models can potentially be used to determine optimal set-points for local controllers.

Future Directions

A guidance manual will be produced that will assist water utilities to tailor DrCT for their distribution system. It will explain how DrCT can be used to meet many of the requirements of the Framework for Management of Drinking Water Quality as described in the Australian Drinking Water Guidelines.

The case studies in this project provided the necessary information for the project team to fine-tune the procedure in the operational environment and prepare for technology transfer activities.

Other activities include disseminating and packaging the DrCT approach for disinfection management in conjunction with other Centre projects. Targeted awareness sessions will be conducted for industry partners on project outcomes and to establish networks of champions and user groups.

Contact PersonDr. Chris ChowAustralian Water Quality Centre, South AustraliaPh: (08) 8259 0281Email: [email protected]

Page �8

CS 1

Overview

The objective of this study was to trial the Disinfection Management Tool (DMTool) on the Seville zone in the Yarra Valley Water business district, with the intention of developing a water quality dosing and optimisation strategy to reduce customer complaints.

Seville was selected as it:

• had taste and odour problems

• is a chloraminated zone

• has a single source of supply

• is a relatively small zone.

CS1 Validation of the Disinfection Management Tool on the Seville

Chloraminated Zone

Overview

The objective of this study was to trial the Disinfection Management Tool (DMTool)

on the Seville zone in the Yarra Valley Water business district, with the intention of

developing a water quality dosing and optimisation strategy to reduce customer

complaints.

Seville was selected as it:

• had taste and odour problems

• is a chloraminated zone

• has a single source of supply

• is a relatively small zone.

Seville distribution zone in the DM Tool layout.

Model Conversion

A converted EPANET 2.0 model with coarse hydraulic calibration was utilised using

telemetry data. Conversion from H2O Map to EPANET was achieved through the

direct export function within H2O Map. Slight alteration was required for the pressure

reducing valve (PRV) logic controls. The system is gravity fed from a tank to the west

of the zone which is pump fed from Silvan. One PRV exists toward the centre of the

zone.

Decay Characterisation

Natural organic matter and chlorine decay properties are defined by the water

property file (WPF). Raw water was taken from near the inlet to the Seville

chloramination plant near Silvan Reservoir. Water samples were dosed with

chloramine under laboratory conditions at 0.8, 1.3 & 1.8 mg Cl/L. Concentration was

logged over time at both 10ºC and 24ºC each to represent the full range of

temperatures and doses experienced in a year.

Seville distribution zone in the DMTool layout.

Model Conversion

A converted EPANET 2.0 model with coarse hydraulic calibration was utilised using telemetry data. Conversion from H2O Map to EPANET was achieved through the direct export function within H2O Map. Slight alteration was required for the pressure reducing valve (PRV) logic controls. The system is gravity fed from a tank to the west of the zone which is pump fed from Silvan. One PRV exists toward the centre of the zone.


Natural organic matter and chlorine decay properties are defined by the water property file (WPF). Raw water was taken from near the inlet to the Seville chloramination plant near Silvan Reservoir. Water samples were dosed with chloramine under laboratory conditions at 0.8, 1.3 & 1.8 mg Cl/L. Concentration was logged over time at both 10ºC and 24ºC each to represent the full range of temperatures and doses experienced in a year.

The Solver function in Microsoft Excel was utilised to fit a curve to the laboratory data for the different doses and concentrations. The exponential model provided a good approximation of the lab tested chloramine decay. E/R and k values were estimated.

Negligible THMs were detected due to the zone being chloraminated.

CS� Validation of the Disinfection Management Tool on the Seville Chloraminated Zone

Page �9

CS 1

Calibration

Field Sampling

Eighteen sites were selected to represent water quality throughout the entire zone on mains of varying sizes. Total and free chlorine, water temperature and the time of sampling were all recorded to calibrate the model.

Model Calibration

The model was run and calibrated to an R2 of 0.96 by altering pipe factors representing wall decay to match data collected from the field (12 and 13 January 2005).

Validation

The model’s hydraulic flows were adjusted to represent the sample day. An initial validation run at the same sample sites as the calibration field data gave an R2 of 0.88 (24 and 25 January 2005).

A second validation run used 15 different sample sites around the system (1 and 2 February 2005). These results are depicted below:

The Solver function in Microsoft Excel was utilised to fit a curve to the laboratory

data for the different doses and concentrations. The exponential model provided a

good approximation of the lab tested chloramine decay. E/R and k values were

estimated.

( )( )( )

++

=

2027320273

20

tt

RE

tekk

Negligible THMs were detected due to the zone being chloraminated.

Calibration

Field Sampling

Eighteen sites were selected to represent water quality throughout the entire zone on

mains of varying sizes. Total and free chlorine, water temperature and the time of

sampling were all recorded to calibrate the model.

Model Calibration

The model was run and calibrated to an R2 of 0.96 by altering pipe factors

representing wall decay to match data collected from the field (12 and 13 January

2005).

Validation

The model’s hydraulic flows were adjusted to represent the sample day. An initial

validation run at the same sample sites as the calibration field data gave an R2 of 0.88

(24 and 25 January 2005).

A second validation run used 15 different sample sites around the system (1 and 2

February 2005). These results are depicted below:

R2 = 0.842

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Actual Total Cl (mg/L)

Pre

dic

ted

To

tal

Cl

(mg

/L)

Issues

Limitations with the case study methodology:

• Hydraulic calibration of model

• Validation good however the effects of nitrification (and other chloramine

process) were not considered

• Mathematical formula estimating chloramine decay may not be optimal

Issues

Limitations with the case study methodology:

• Hydraulic calibration of model

• Validation good however the effects of nitrification (and other chloramine process) were not considered

• Mathematical formula estimating chloramine decay may not be optimal

• Cannot apply temperature and wall decay factors for groups of pipes at once.

Summary

The DMTool provided good accuracy in the Seville chloraminated zone with coarse hydraulic calibration.

Page �0

CS 2

Overview

The objective of this study was to trial the Disinfection Management Tool (DMTool) to determine the chlorine dosing necessary to achieve acceptable free chlorine concentrations (0.2-0.6mg/L) at consumers’ taps at all times.

This is a practical problem faced by many water utilities. Initial chlorine and re-chlorination doses have to be determined for all conditions encountered. Chlorine doses are likely to be different for different water quality, water temperature and system flows. Use of the DMTool enables managers to test various doses and to select one, which is regarded as the most suitable.

Model Conversion

A network model was constructed within EPANET for the entire system covering all mains down to 100mm (Figure 1). As the system has a long residence time (300h), water needs to be re-chlorinated and there are five re-chlorination stations at selected reservoirs. The DMTool was used to compute the chlorine concentration profile across the system over time, for any combination of flow (demand), temperature and chlorine doses.

CS2 Optimisation of Disinfection Dosing using DMTool in North

Richmond Distribution System

Overview

The objective of this study was to trial the Disinfection Management Tool (DM Tool)

to determine the chlorine dosing necessary to achieve acceptable free chlorine

concentrations (0.2-0.6mg/L) at consumers’ taps at all times.

This is a practical problem faced by many water utilities. Initial chlorine and re-

chlorination doses have to be determined for all conditions encountered. Chlorine

doses are likely to be different for different water quality, water temperature and

system flows. Use of DMTool enables managers to test various doses and to select

one, which is regarded as the most suitable.

Model Conversion

A network model was constructed within EPANET for the entire system covering all

mains down to 100mm (Figure 1). As the system has a long residence time (300h),

water needs to be re-chlorinated and there are five re-chlorination stations at selected

reservoirs. DMtool was used to compute the chlorine concentration profile across the

system over time, for any combination of flow (demand), temperature and chlorine

doses.

Figure 1. Network model of North Richmond distribution system


The DMtool was developed by incorporating accurate chlorine decay kinetics into the

EPANET 2.0 drinking water network package. Decay parameters were derived on the

basis of laboratory chlorine experiments with treated water from North Richmond

plant. These experiments were evaluated using AQUASIM modelling software to

derive the coefficients in the DMtool chlorine decay model (see Fact Sheet 1). THM

decay was also analysed and coefficients derived.

Calibration

Chlorine analysers were installed at South Windsor and two other strategic locations

along the Pitt Town pipeline. Chlorine profiles from these analysers were used to

determine the coefficient characterising the effect of the pipe wall on chlorine decay.

Figure 1. Network model of North Richmond distribution system


The DMTool was developed by incorporating accurate chlorine decay kinetics into the EPANET 2.0 drinking water network package. Decay parameters were derived on the basis of laboratory chlorine experiments with treated water from the North Richmond plant. These experiments were evaluated using AQUASIM modelling software to derive the coefficients in the DMTool chlorine decay model (see Fact Sheet 1). THM decay was also analysed and coefficients derived.

Calibration

Chlorine analysers were installed at South Windsor and two other strategic locations along the Pitt Town pipeline. Chlorine profiles from these analysers were used to determine the coefficient characterising the effect of the pipe wall on chlorine decay. This coefficient is the only one that cannot be derived in the laboratory, but needs to be determined from field data. The wall effect has a larger impact in small pipes, as the volume to surface ratio increases.

CS� Optimisation of Disinfection Dosing using DMTool in North Richmond Distribution System

Page ��

CS 2

Optimisation

The model was run for various conditions and suitable initial and re-chlorination set points selected for different seasons (temperatures 10-30°C). The set points were tabulated for implementation by operators as monthly changes. Before implementation, 67% of consumers were exposed to chlorine concentration above 0.6mg/L.

This coefficient is the only one that cannot be derived in the laboratory, but needs to

be determined from field data. The wall effect has a larger impact in small pipes, as

the volume to surface ratio increases.

Optimisation

The model was run for various conditions and suitable initial and re-chlorination set

points selected for different seasons (temperatures 10-30°C). The set points were

tabulated for implementation by operators as monthly changes. Before

implementation, 67% of consumers were exposed to chlorine concentration above

0.6mg/L.

Free Cl samples during 06/02-06

0.00

10.00

20.00

30.00

40.00

50.00

60.00

Cl<0.20 0.20 =<Cl=< 0.60 0.60<Cl =< 1.00 Cl>1.00

Free Cl (mg/L)

% sample in 07/02-06/03% sample in 07/03-06/04

Figure 2. Frequency of chlorine concentrations before and after chlorine dose

selection

Figure 2 shows frequency of chlorine samples in various ranges before and after

selection of chorine doses with the help of model. It can be seen that the frequency in

high concentration ranges has diminished and that of the desired concentration range

(0.2-0.6mg/L) has increased. Greater improvement was expected on the basis of the

modelling predictions, but mixer breakdown and rezoning for maintenance at critical

times prevented this.

Even better results could be achieved if the set points were automatically adjusted

according to water temperature, rather than being set monthly.

Outcome

By changing of the chlorine set points according to the model results it was possible

to comply with chlorine aesthetic guideline of maximum average free chlorine

concentration of 0.6mg/L.

Measurement of THMs showed successful prediction of formation under dynamic

flow conditions at various temperatures, following different rechlorination strategies

in North Richmond.

Summary

DMTool was successfully utilised to solve a number of drinking water quality

problems, which include disinfection residual decay and by-product (such as THM

and HAA) formation.

Figure 2. Frequency of chlorine concentrations before and after chlorine dose selection

Figure 2 shows the frequency of chlorine samples in various ranges before and after selection of chorine doses with the help of the model. It can be seen that the frequency in high concentration ranges has diminished and that of the desired concentration range (0.2-0.6mg/L) has increased. Greater improvement was expected on the basis of the modelling predictions, but mixer breakdown and rezoning for maintenance at critical times prevented this.

Even better results could be achieved if the set points were automatically adjusted according to water temperature, rather than being set monthly.

Outcome

By changing the chlorine set points according to the model results it was possible to comply with the chlorine aesthetic guideline of maximum average free chlorine concentration of 0.6mg/L.

Measurement of THMs showed successful prediction of formation under dynamic flow conditions at various temperatures, following different rechlorination strategies in North Richmond.

Summary

DMTool was successfully utilised to solve a number of drinking water quality problems, which include disinfection residual decay and by-product (such as THM and HAA) formation.

Free Cl (mg/L)

% S

ampl

e

Page ��

CS 3

Overview

Why do we need a Disinfectant Demand Sensor?

• Conventional disinfectant decay methods require contact times of several days.

• In some case, water quality varies rapidly.

• A rapid on-line surrogate assessment of disinfectant decay characteristics can provide quick selection of the appropriate disinfectant dose.

Approaches for Rapid Assessment of Disinfectant Demand

• To use on-line water quality data to predict disinfectant dose.

• Short demand assessment to predict longer decay.

• Increase temperature to reduce the required measurement time.

Case Studies

A 24-month sampling program for water samples provided by the industry partners collected from various water supplies is used to develop and evaluate the above approaches.

In addition, two site-specific case studies, Myponga (SA) - chlorination and Woronora (NSW) - chloramination are selected to field test the disinfectant demand sensor.

Outcomes

1. Use Water Quality Parameters as a Surrogate

Based on a 24-month laboratory study using water samples supplied by our industry partners, UV absorbance measurement has been identified as a surrogate parameter for rapid chlorine demand assessment.

In addition, a suitable on-line UV monitoring instrument has been identified. A two-week on-line monitoring trial was conducted at the Myponga treatment plant. The S::CAN instrument performed well and together with the software specifically developed by DCM Process Control for this project, on-line chlorine demand prediction based on UV absorbance measurement can be displayed in real time on the instrument. The prediction showed considerable agreement with the conventional laboratory chlorine demand measurements using grab samples collected during the period of study.

Figure 1. Comparison of real time chorine demand monitoring using on-line UV absorbance measurement and chlorine demand determined in a laboratory using conventional method. •: Laboratory chlorine demand measurement from grab samples.

For chloramine demand prediction, similar laboratory assessment of surrogate parameters based on water quality measurements did not produce the desired result. Only a weak correlation was observed for all the parameters (DOC, UVabs, Colour) studied. This indicated that monitoring the bulk water quality parameters may not necessary be able to provide an accurate prediction of chloramine

CS� Disinfectant Demand Sensor Case Study Outcomes

demand, particularly used as a rapid assessment for dose adjustment to account for water quality change.

Preliminary work in a chlorinated system indicated that the measurement of organic character change (molecular weight distribution) between two points as surrogate for pipe conditions is a potential tool for operators to improve disinfection control, particularly in identifying problematic hotspots (rapid chlorine decay). The Woronora case study will be focussed on assessing the potential of using organic character change as a disinfection management tool.

Grab samples will be required for two separate locations in the distribution system for molecular weight determination (laboratory). In parallel, the S::CAN instrument will be evaluated as an alternative to laboratory molecular weight measurement. This has the potential to be developed into an on-line monitoring tool for operators to predict problematic hotspots where rapid decay of chloramine occurs.

2. Use of short demand assessment to predict longer decay (eg 1 or 6 hours demand to predict 3 to 7 days demand).

The result has also demonstrated that it is possible to determine the 3-day and 7-day bulk water disinfectant demand rapidly and accurately if the short term disinfectant demand (eg 3-hour) is known. This method was found to work extremely well for chlorine demand. Rapid determination of chloramine demand (1-day) was also possible.

3. Increase temperature to reduce the required measurement time

With a limited amount of data, the elevated temperature method gave the best results and can be applied to both the chlorine and chloramine demand predictions. This method seems to be independent of water quality but requires an individual temperature calibration curve for each water source. Further investigations into this method could see the development of an automated system for use in the field.

Summary

For water utilities required to manage disinfection in a dynamic system with reactive water, the disinfectant demand sensor concept can provide assistance. The use of water quality parameters and UV absorbance, to predict chorine demand is the most convenient and easily achievable option, particularly with the use of an on-line monitoring instrument. However, this option does not appear to be possible for chloramine demand prediction. Further study has been planned for alternatives prediction of chloramine decay.

In order to provide a rapid surrogate measurement for chloramine demand, both Options 2 and 3 would give better results, particularly the use of elevated temperature technique (Option 3). However, at this stage, both techniques require the use of laboratory facilities.

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CS 3

More Information

Chow CWK, Fitzgerald F and Holmes M (2004) The Impact of Natural Organic Matter on Disinfection Demand - A Tool to Improve Disinfection Control. Enviro 04, AWA, March, Sydney.

Fitzgerald F, Chow CWK and Holmes M, (2004) Link between Organic Character and Disinfection - Australian Experience. NOM research: Innovations and Applications for Drinking Water Treatment, March, Victor Harbor, Australia.

Fitzgerald F, Chow C and Holmes H (2005) Development of a Disinfectant Demand Sensor – An Attempt to Predict Disinfectant Demand. Ozwater Convention, AWA, May, Brisbane.

Holmes M, Chow C, Dandy G, May R, Fitzgerald F, Maier H, Badalyan A and Nixon J (2005) DrCT®: Developing Tools for Improved Disinfection Control within Water Distribution Systems. WEF/AWWA/IWA Disinfection Specialty Conference, February, Mesa, Arizona, USA.

Contact PersonDr Chris ChowAustralian Water Quality Centre, South AustraliaPh: (08) 8259 0281Email: [email protected]

CS� Instrument Evaluation Results

Overview

A key component of the CRC Project Development of Tools Improved Disinfection Control within Distribution Systems is to evaluate commercially available on-line sensors to measure disinfectant residuals at both treatment plant and distribution system locations. A number of analysers were loaned by instrument suppliers and were tested according to the two ISO standards, to evaluate several performance characteristics including:

• response time

• linearity

• coefficient of variation

• coefficient of detection

• repeatability

• lowest detectable change

• limit of quantification

• short-term drift

• interferences and

• memory effect.

Summary of Free Chlorine Analysers

Nine free chlorine analysers were evaluated in this trial. They used a range of measuring techniques including amperometric, colorimetric, and polarographic.

Some analysers used methods that were directly related to Standard Methods (APHA, 1998), while others used modified or even alternative techniques. This may lead to differences in results between laboratory methods and field conditions for chlorine analysers.

Response time is a very important parameter, which should be taken into account when a monitor is used in the control loop. Mean response times ranged from 42 to 202 seconds for tested monitors. Instruments having quick response times such as Wallas & Tiernan – Depolox 3 plus, ATi Model A 15/62 and Yokogawa Model FC400G-63*A/Z may be used for control applications. If, however, only monitoring is required, analysers with more sluggish response can be used.

Good linearity results were showed by Prominent – Dulcometer D1C, Yokogawa Model FC400G-63*A/Z and HACH CL17.

All analysers produced good repeatability results.

Low values of limit of quantification showed USF and Wallace & Tiernan – Depolox 3 plus. Yokogawa Model FC400G-63*A/Z, USF and Wallace & Tiernan – Depolox 3 plus have the best values of lowest detectable change.

Instruments were found to give variable responses during interference experiments.

The impact of sample pH ranging from pH 6.0 to 9.0 on analyser performance when presented with a 1.0mg/L free chlorine sample was found to be variable, the HACH CL17 showed the best performances.

Manganese and iron were also shown to impact the analysers performance, Prominent–Dulcometer D1C, Endress & Houser CCS141, and HACH CL17 being less affected by manganese concentration, and B & C Electronics CL 7685 and ATi Model A 15/62 being less affected by iron concentrations. This effect may become more pronounced during long term deployment in locations having high concentrations of iron or manganese.

Page ��

CS 4

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CS 4

Summary of Total Chlorine / Monochloramine Analysers

Six different analysers were tested during the total chlorine/monochloramine trial including one multiparameter analyser (SYSTEA–MicroMac) which measured monochloramine and total chlorine. The analysers that were evaluated employed a number of measuring methods including amperometric andpolarographic membrane as well as colorimetric.

The amperometric/polarographic systems, analysers ATi Model A 15/63 and Wallace & Tiernan - Depolox 3 plus required calibration using a reference measurement.

The calibration curve for the Applicon Alert 2004 analyser is factory calibrated. This analyser performs at regular intervals an automatic zero offset calibration by sampling distilled (chlorine free) water.

The SYSTEA-MicroMac multiparameter total chlorine analyser uses a manual calibration referenced against a monochloramine calibration solution.

The evaluation has shown that both measuring techniques work reliably with a high level of accuracy.

However, differences in performance were found for the various analysers evaluated.

The Wallace & Tiernan – Depolox 3 plus performed well while the ATi Model A 15/63 and Prominent-Dulkometer-1 analysers failed totally in the end of the trial. They both indicated low value readings after the ammonia trial while they were exposed to water with a very low concentration of chlorine and a concentration of ammonia up to 1 mg/L. Even after exposing the analysers to a chlorine concentration around 1 mg/L for two days the readings did not settle and a calibration was not possible. One reason for this could be a blocked membrane or a malfunction on the electrodes or electrolyte solutions.

The Applicon Alert 2004 analyser performed well while multiparameter SYSTEA-MicroMac analysers, showed low precision and poor co-efficient of variation.

The study has shown two general differences which are related to the principle of the measuring method. Colorimetric systems generally have a longer response time than amperometric/polarographic instruments as they operate in a batch-wise mode. The cycle time of a colourimetric analyser is longer than the slowest response of any analyser using a membrane. If the application requires quick response then a membrane system is the better choice.

Membrane analysers (amperometric/polarographic) are not robust to changes to sample pH and this can give rise to readings that are out by as much as 50%. Colorimetric analysers are the best performers in situations where pH can vary.

Summary of Ammonia Analyser

The Applicon ADI 2018 analyser employs an ammonia selective electrode system. The Endress & Houser StamoLys CA 71 AM and SYSTEA MicroMac (multiparameter) ammonia analysers use a colorimetric technique based on the phenate method. All three analysers carry measurements in a continuous batch-wise analysis giving rise to long response times. All analysers generate chemical waste which must be disposed of.

All three ammonia analysers performed well during the trial. The multiparameter analyser showed in general less accuracy and precision than the other two analysers. This analyser also suffered from unstable calibration, as the signal (OD) varied during calibration. At least four sequential calibrations were required to determine an average OD value. The calibration curve for the Applikon analyser was factory calibrated and an automatic zero offset was performed on a measuring cycle with distilled (ammonia free) water. The Endress & Houser analyser was equipped with cleaning and calibration solution and performed automatic cleaning and calibration cycles.

The Endress & Hauser analyser performed very well for most of the performance characteristics evaluated. All reagents which needed to run the Endress & Houser analyser are premixed and distributed by Endress & Hauser. Reagents for the other two analysers must be obtained by the user.

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CS 4

The three ammonia analysers evaluated did not show any interference for changes in pH or conductivity. They were also not affected by restart after power failure or changes to sample flow.

The Applicon analyser measures free available ammonia while the Endress & Houser and SYSTEA analysers measure total ammonia. Total ammonia includes ammonia combined with chlorine. Evaluation protocol included the addition of ammonia to adjust the concentration supplied to the analysers in the absence of chlorine. Evaluation was also undertaken using a variable ratio of chlorine to ammonia. This trial clearly demonstrated that the Applikon analyser did not detect combined ammonia. This should be considered when choosing an analyser for an application. The Applikon analyser measures free ammonia and Endress & Houser and SYSTEA analysers measure total ammonia.

Utilisation

Instrument performance characteristics are a key consideration when selecting an instrument for a particular use. The ISO 15839 and ISO 8466-1 methodologies provide a rigorous protocol for evaluating online analysers and should be used to select the most appropriate analyser for an application.

More Information

Fitzgerald F, Buff J, Badalyan A, Holmes M (2005) Online Disinfectant Residual Analyser Evaluation. SA AWA Water Operators Conference, April, Hindmarsh, South Australia.

Buff J (2005) Laboratory Evaluation of On-Line Analysers to Measure Disinfectant Residual Concentration in Drinking Water. Diploma Thesis, FH Nürnberg and Australian Water Quality Centre, Adelaide, 187 pp.

Holmes M, Chow C, Dandy G, May R, Fitzgerald F, Maier H, Badalyan A and Nixon J (2005) DrCT®: Developing Tools for Improved Disinfection Control within Water Distribution Systems. WEF/AWWA/IWA Disinfection Specialty Conference, February, Mesa, Arizona, USA.

ISO standard 15839. (15.10.2003). Water Quality - Online Sensors/Analysing Equipment for Water - Specifications and Performance Tests. 1st edition.

ISO standard 8466-1 (01.03.1990). Water Quality - Calibration and Evaluation of Analytical Methods and Estimation of Performance Characteristics. Part 1: Statistical Evaluation of the Linear Calibration Function. 1st edition.


Overview

The objective of this study was to develop an artificial neural network-based model to describe the dynamic behaviour of free chlorine residuals within the Myponga water distribution systems, South Australia.

In this study, a model was developed that was capable of forecasting free chlorine residuals 24 hours in advance at a location 20 km downstream of the primary chlorine injection point at the outlet Myponga filtration plant.

Conceptual Approach

The framework for the development of the ANN model comprised the following steps:

• Data Collection

• Input Selection

• Data Mining

• ANN Calibration (or, Training)

• ANN Validation

Implementation of this framework utilised a combination of Microsoft Excel for data handling and storage, and custom software developed in C++ for data analysis and ANN calibration.

Data Collection

A database comprising six months of on-line data was compiled based on available measurements of flow, reservoir levels, free chlorine residual, pH, turbidity and temperature. Data was sourced at a resolution of 5-15 minutes from a combination of SCADA and data loggers coupled to sensors at the treatment plant and within the distribution system and further smoothed by averaging over 60 minute intervals.

Input Selection

A candidate set of more than 500 variables was created by considering a 48-hour historical time window of lagged measurements. Statistical analysis then reduced this to a set of ten selected model inputs including lagged downstream free chlorine residual, primary chlorine dose, water temperature, reservoir level and flow within the system.

Data Mining

A data mining technique was employed to extract training, cross-validation and validation data from the database. This process made the best possible of use of the collected data by ensuring that no bias was introduced into the calibration and validation of the model.

Calibration and Validation

A fast-calibrating fixed ANN architecture was optimised to yield a cross-validation R2 of 0.95 during and an R2 of 0.96 during validation, which is depicted in the figure over the page.

CS� Development of ANN-based Models for Disinfection Residual

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CS 5

0

0.4

0.8

1.2

1.6

2

0 0.4 0.8 1.2 1.6 2

Actual Free Chlorine Residual (mg/

Limitations

Known limitations of the ANN-based modelling approach include:

• an intensive on-line data requirement

• an inability to predict the effect of major operational changes and network design

modifications

Future Direction

The integration of ANN modelling techniques through the present case studies will

validate the concept. This approach is planned to be taken up by the consolidation of

modelling tools project for further integration into an overall tool box approach in

managing system performance and disinfection in distribution systems.

Contact Person

Dr Chris Chow

Australian Water Quality Centre, South Australia

Ph: (08) 8259 0281

Email: [email protected]

More Information

May RJ, Maier HR, Dandy GC and Nixon JB (2004) General Regression Neural

Networks for Modeling Disinfection Residuals within Water Distribution Systems,

Proceedings of ASCE 6th Annual Symposium on Drinking Water Distribution Systems

Analysis. Salt Lake City.

Limitations

Known limitations of the ANN-based modelling approach include:

• an intensive on-line data requirement

• an inability to predict the effect of major operational changes and network design modifications.

Future Direction

The integration of ANN modelling techniques through the present case studies will validate the concept. This approach is planned to be taken up by the consolidation of modelling tools project for further integration into an overall tool box approach in managing system performance and disinfection in distribution systems.


More Information

May RJ, Maier HR, Dandy GC and Nixon JB (2004) General Regression Neural Networks for Modeling Disinfection Residuals within Water Distribution Systems, Proceedings of ASCE 6th Annual Symposium on Drinking Water Distribution Systems Analysis, Salt Lake City, USA.

Page �9

CS 5

Fre

e C

hlor

ine

24-

hour

For

ecas

t (m

g/L)

Actual Free Chlorine Residual (mg/L)

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Issues

While travelling through a distribution system, water usually spends far more time in tanks than it does in pipes. Consequently, most of the time available for the decay of disinfectant is in tanks, where the bulk reaction dominates due to the large volume to surface ratio (compared with pipes). Tanks are also convenient sites at which additional (booster) dosing of chlorine or ammonia can be carried out. It is therefore critical to good disinfection that the processes occurring in tanks are well understood and accurately represented in predictive tools used to find improved management strategies.

Idealised mixing conditions in tanks

In pipes, water is kept well mixed by the turbulent diffusion generated by friction between the water and the pipe wall. In roofed tanks, velocities are generally much smaller, even during inflow periods, and the turbulent intensity decreases rapidly with distance away from the inflow. Consequently, the tank contents are often far from fully mixed. Reducing the retention time in a reservoir will generally improve mixing, whether achieved by reduction in working volume or increased throughput. Better mixing will improve control over disinfectant concentrations leaving the tank.

Full mixing is an idealised condition in which water always leaves the reservoir with an average age equal to the current retention time. It should be noted that it is usually the default condition in predictive tools (e.g. EPANET and its commercial derivatives), because it the simplest assumption conceptually and computationally. Two other idealised states that are simple to implement are complete short-circuiting, where the latest water entering the tank is withdrawn earliest (last in first out LIFO) and plug flow, where the water entering earliest is also withdrawn earliest (first in first out FIFO). These can be selected as options within EPANET, where it is considered to be appropriate (e.g. short-circuiting may be a more appropriate assumption than full mixing, when there is a common inlet-outlet main).

Fully mixed conditions have been shown to maintain disinfectant levels in tanks better than either short-circuiting or plug flow (Grayman et al. 2004).

Thermal stratification

There are other causes of incomplete mixing in tanks. If the inflow is at a different temperature than the tank contents, then thermal stratification (layering) occurs, because the warmer water is less dense. A vertically upward inflow will cause less stratification than the same inflow entering horizontally, because the upward jet entrains surrounding ambient water, potentially setting up tank-wide circulation.

CRC for Water Quality and Treatment research (Chuo et al. 2001) has shown that similar vertical stratification can be set up due to the solar radiation on roofed tanks, even in relatively mild summer conditions (Sydney) in small and large tanks. These effects will be correspondingly severe in more extreme climatic conditions.

Prediction tools for stratified tanks

The simplest representation of thermal stratification is the two-layer model, with exchange between the layers, which EPANET2 includes as an option.

CRC for Water Quality and Treatment research (Chuo et al. 2001) has applied a full three-dimensional computational fluid dynamics (CFD) model to two typical tanks in Sydney to accurately predict the stratified vertical temperature profiles resulting from the combination of a lower inflow temperature and the solar radiation on the roof in summer. The advantage of this tool (RMA10V) was its detailed representation of inflow geometry and flow, only in regions where it was needed for accuracy.

In a subsequent Sydney Water project, the same tool was used to first predict the temperature profiles throughout a much larger tank (as compared with detailed measured temperature data). The tool was then used to predict the improvement in mixing achieved by several different mixing devices (eg. propellers and upward jets), to find the most cost-effective option.

Appendix � Disinfection management for service reservoirs (tanks)

Page ��

Corresponding chloramine levels were also successfully predicted (compared with detailed in-reservoir data) after modifying an associated tool (RMA11), so that it represented both the chemical and microbial contributions to chloramine decay. The microbial contribution includes nitrification. A new method to distinguish these two distinct contributions (Sathasivan et al. 2005) was developed, so that different degrees of microbial activity and the influence of varying temperature and chloramine concentration down the profiles could be accounted for.

Measurement tool for microbial decay of chloramine (including nitrification)

The new method to measure the microbial contribution to chloramine decay has also been more generally applied as a diagnostic tool in both pipes and reservoirs in several distribution systems. It has also been used in winter/spring as an early warning of potentially severe chloramine loss in the following summer, so that early action can be taken to prevent it.

More information

Chuo PY, Ball JE and Fisher IH (2001) Modelling water quality in drinking water service reservoirs. Proceedings of 6th Conference on Hydraulics in Civil Engineering, IEAust, pp. 185-193.

Chuo PY, Ball JE and Fisher IH (2001) Thermal stratification in drinking water service reservoirs. Aust J. Water Resources �(2): 159-167.

Grayman W, Rossman L, Arnold C, Deininger R, Smith C, Smith J and Schnipke R (2000) Water Quality Modelling of Distribution Storage Facilities. AwwaRF, Denver, Co.

Grayman WM, Rossman LA, Deininger R, Smith C, Arnold C and Smith J (2004) Mixing and aging of water in distribution system storage facilities. Journal AWWA 9�(9), 70-80.

Sathasivan A, Fisher I and Kastl G (2005) A simple method for measuring microbiologically assisted chloramine decay in drinking water. Environmental Science and Technology �9(14): 5407-5413.

Page ��


Project Participants:George Kastl, Ian Fisher, George Kastl, Veeriah Jegatheesam, Mary Drikas.

Consolidation of management tools for distribution systems

Project Participants:Ian Fisher, George Kastl, Saatha Sathasivan, Tum Tam, Shaohua Ye, Laszlo Koska, Richard Walker, Graeme Dandy, Mike Holmes.

Optimisation of chlorine residual in a distribution system (Melbourne)

Project Participants:George Kastl, Ian Fisher, Arumugam Sathasivan, Mary Drikas, Robert Considine, Chris Chow.

Development of tools for improved disinfection control within distribution systems

Project Participants:Chris Chow, Fiona Fitzgerald, Mike Holmes, John Nixon, Graeme Dandy, Holger Maier, Robert May, Alex Badalyan, Terry Dermis, Sebastian Brunner, Philipp Kunkte, Paola Duartes Romeras, Joachim Buff, Karyn Jarvis, Karim Ghaoui, John Missikos, Ralf Mueller, Kathryn Clarkson, Alex Donald, Ken Turner, Steve Healy, Vince Sweet, David Smith, Richard Friend, Shane Hayden, Noel Miles, Robert Considine and Caroline Hussey, Dammika Vitanage, Corinna Doolan, Tony Venturino, Phil Duker, Richard Walker, Kevin Xanthis.

Application of hazard analysis and critical control points for distribution system protection

Project Participants:Dan Deere, Noel McCarthy, Leon Miles, Scott Plant, Melita Stevens, Corinna Doolan, David Cooper, Tony Venturino, Carl Deininger, Shariff Shockair.


Project Participants:Dharma Dharmabalan, Simon Pearce Higgins, Mark Bruno, Asoka Jayaratne, Greg Ryan, Ben Wilson, Chris Vigus, Conrad Dabrowski, Matthew Bains, Matthew MacDonald, Sean Wise, Sam Lamb.

Organisations Involved in these Projects

Sydney Water, Water Corporation, University of Adelaide, United Water, Central Highlands Water, Yarra Valley Water, South East Water, City West Water, Melbourne Water, Brisbane Water, SA Water, Power and Water Corporation, Gold Coast Water, Gippsland Water, Australian Water Quality Centre.

General Support in the collation and development of these fact sheets was provided by:Fiona Wellby, Tony Priestley, Dharma Dharmabalan and Mark Bruno.

Acknowledgements

Disclaimer

• The Cooperative Research Centre for Water Quality and Treatment and individual contributors are not responsible for the outcomes of any actions taken on the basis of information in this document, nor for any errors and omissions.

• The Cooperative Research Centre for Water Quality and Treatment and individual contributors disclaim all and any liability to any person in respect of anything, and the consequences

of anything, done or omitted to be done by a person in reliance upon the whole or any part of this document.

• The document does not purport to be a comprehensive statement and analysis of its subject matter, and if further expert advice is required, the services of a competent professional should be sought.

© CRC for Water Quality and Treatment �00�

Disinfection Management: Implementing Tools for Optimising Disinfection. Management implications from the Research Programs of the Cooperative Research Centre for Water Quality and Treatment.

ISBN �8��

In Australia, drinking water quality management is undertaken in the context of the Australian Drinking Water Guidelines Framework. In the table below the salient research findings are presented within the Framework to aid in their implementation by the Australian Water Industry.

aDWG Framework elements Key research findings and reference to Fact Sheet or Case Study No.

AssessmentoftheDrinkingWater Supply System

Water Supply System Analysis

FS1 – A disinfection management tool (DMTool), built on a process-based model, is available to accurately predict chlorine and chloramine decay throughout adistributionsystem,providedareasonablyaccuratehydraulicsystemmodelisalsoavailable.

CS1 – The DMTool has been verified by application to a chloraminated system.

CS2 – The DMTool has been verified by application to a chlorinated system.

Appendix 1 – Factors associated with applying the DMTool to disinfection managementinservicereservoirshavebeeninvestigated.

AssessmentofWaterQualityData

CS3 – A prototype sensor to enable rapid assessment of a surrogate for chlorine or chloraminedemandofwaterpriortodisinfectionisbeingdeveloped.

HazardIdentification andRiskAssessment

FS2 – The disinfection modelling tool has been extended to predict disinfection by-product formation and can be used to evaluate and compare the relative risks associated with chlorine decay and disinfection by-product formation.

PreventativeMeasuresforDrinkingWaterQualityManagement

PreventiveMeasuresandMultipleBarriers

FS2 – The DMTool can also be utilised to evaluate the relative effectiveness in riskreductionforarangeofpreventivemeasuresavailabletodistributionsystemoperators.

OperationalProceduresandProcessControl

OperationalProcedures

FS3 – Software tools to optimise disinfection across water treatment and the distributionsystemareavailable.

FS4 – A number of data based tools have been developed to assist distribution systemoperators.

CS5 – An artificial neural network (ANN)- based model to describe the dynamic behaviouroffreechlorineresidualswithinawaterdistributionsystemwasusedtoforecast free chlorine residuals 24 hours in advance at a location 20 km downstream oftheprimarychlorineinjectionpoint.

OperationalMonitoring

CS4 – A range of commercially available on-line sensors to measure disinfection residualsatvariousdistributionsystemlocationshavebeenevaluated.

ResearchandDevelopment

Investigative Studies and ResearchMonitoring

All researchprojectswithin theDistributionResearchProgramareconnectedtorelevantinternationalresearchthroughtheGlobalWaterResearchCoalition.

CRC for Water Quality and Treatment

Private Mail Bag 3

Salisbury SOUTH AUSTRALIA 5108

Tel: (08) 8259 0211

Fax: (08) 8259 0228

E-mail: [email protected]

Web: www.waterquality.crc.org.au

The Cooperative Research Centre (CRC) for Water Quality and Treatment is Australia’s national drinking water research centre. An unincorporated joint venture between 29 different organisations from the Australian water industry, major universities, CSIRO, and local and state governments, the CRC combines expertise in water quality and public health.

The CRC for Water Quality and Treatment is established and supported under the Federal Government’s Cooperative Research Centres Program.

The Cooperative Research Centre for Water

Quality and Treatment is an unincorporated

joint venture between:

• ACTEW Corporation

• Australian Water Quality Centre

• Australian Water Services Pty Ltd

• Brisbane City Council

• Centre for Appropriate Technology Inc

• City West Water Ltd

• CSIRO

• Curtin University of Technology

• Department of Human Services Victoria

• Griffith University

• Melbourne Water Corporation

• Monash University

• Orica Australia Pty Ltd

• Power and Water Corporation

• Queensland Health Pathology & Scientific

Services

• RMIT University

• South Australian Water Corporation

• South East Water Ltd

• Sydney Catchment Authority

• Sydney Water Corporation

• The University of Adelaide

• The University of New South Wales

• The University of Queensland

• United Water International Pty Ltd

• University of South Australia

• University of Technology, Sydney

• Water Corporation

• Water Services Association of Australia

• Yarra Valley Water Ltd

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