water” case studies using s::can on-line monitoring system

Research Report

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The Cooperative Research Centre (CRC) for Water Quality and Treatment operated for 13 years as Australia’s national drinking water research centre. It was established and supported under the Australian Government’s Cooperative Research Centres Program. The CRC for Water Quality and Treatment officially ended in October 2008, and has been succeeded by Water Quality Research Australia Limited (WQRA), a company funded by the Australian water industry. WQRA will undertake collaborative research of national application on drinking water quality, recycled water and relevant areas of wastewater management. The research in this document was conducted during the term of the CRC for Water Quality and Treatment and the final report completed under the auspices of WQRA.

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Case Studies Using S::CAN On-line Monitoring System

Research Report 75

CRC FOR WATER QUALITY AND TREATMENT – RESEARCH REPORT 75

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Darwin River Dam (Source Water Monitoring)

The S::CAN Spectro::lyser was installed at the off-take tower to monitor the untreated (prior to chlorination) raw water quality of the Darwin River Dam through the 2006 wet season (December to February). During the wet season, poor quality water is expected to enter the supply. The probe was in place from 11/1/06 to 21/2/06, and turbidity readings were taken by an on-line turbidity meter every 13 hours 34 minutes throughout that period. Figure 5.3 shows the setup of S::CAN. (a) (b) (c)

Figure 5.3 a) Off-takes tower, b) on-line turbidity meter and c) S::CAN setup.

Palmerston Tank

The S::CAN unit was installed at Palmerston elevated tank. The tank is located approximately 8 km down-stream of the secondary chlorination facility at McMinns. A Google Earth image is shown in Figure 5.4. It is the first major service reservoir following chlorination. The aim was to check whether there was any significant variation in organic content of the water following chlorination. The probe was in place from 21/4/2006 to 22/5/2006 and the chlorine meter readings at the site were taken every 10 hours and 26 minutes.


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Palmerston Tank



Christopher Chow, Rolando Fabris and Mike Dixon

Australian Water Quality Centre, South Australia

Research Report 75

CASE STUDIES USING S::CAN ON-LINE MONITORING SYSTEM

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DISCLAIMER The Cooperative Research Centre for Water Quality and Treatment officially ended October 2008, and has been succeeded by Water Quality Research Australia Limited (WQRA), a company funded by the Australian water industry. WQRA and individual contributors are not responsible for the outcomes of any actions taken on the basis of information in this research report, nor for any errors and omissions. WQRA 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 research report. This research report 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. © Water Quality Research Australia Limited 2008 Location: WQRA Head Office Level 3, 250 Victoria Square, Adelaide SA 5000 Postal Address: GPO BOX 1751, Adelaide SA 5001 For more information about WQRA visit the website www.wqra.com.au Case Studies Using S::CAN On-line Monitoring System Research Report 75 ISBN 18766 16253


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FOREWORD

With the recent introduction of the “Framework for Management of Drinking Water Quality” approach, a number of organisations are preparing to adopt the “Framework” guidelines. The Framework was developed to guide the design of a structured and systematic approach for the management of drinking water quality from catchment to consumer, to assure the safety and reliability of potable supplies. This approach encourages water authorities to rigorously manage their entire water supply system and develop preventive strategies to ensure the control of hazards entering the system and if necessary the removal of hazards using various treatment options. Monitoring is a large component in this approach, and it is particularly important to select the right methodology and equipment for the task. UV-Vis spectrometry is becoming a popular tool for water operators to optimise treatment processes and manage water quality in water supply systems. The potentially wide application of the on-line monitoring, S::CAN Spectro::lyser has been recognised by the drinking water industry and a number of systems-specific applications/investigations were suggested by several industry participants. This project aims to demonstrate the benefits of on-line water quality monitoring for a range of applications at sites selected by the participating industry partners. The CRCWQT co-ordinated and assisted the case study and facilitated technology transfer activities (factsheets, presentations and final report) towards the end of the project to ensure knowledge sharing among industry partners. The information received from the site specific case studies of this project can directly benefit our partners by providing experience in on-line monitoring and the interpretation of new water quality data useful for the planning of future monitoring activities. Partners participating without a specific case study were benefited indirectly via sharing of the knowledge gained from the project. The research team would like to thank, Rob Dexter, Alan Neethling, Luke Sutherland-Stacey (DCM Process Control), Fiona Fitzgerald, Paul Hackney, Vince Sweet, Kevin Adam, Mary Drikas, Alex Keegan, Chris Saint (SA Water / AWQC), Dammika Vitanage, Corinna Doolan, Phil Duker, Carl Deinnger, Peter Cresta, Heri Bustamante, Michael Storey, Adam Lovell (Sydney Water), Keith Craig, Collin Storey (Veolia Water), Kevin Xanthis, Rino Trolio, Luke Zappia, Alan Maus, Gary Ash, Ralph Henderson, Richard Walker, Keith Cadee (Water Corporation, WA), Alex Donald, Amy Dysart, Darryl Day (Power and Water, NT), Mike Holmes, Craig Heidenreich, Neil Crossing, Stephanie Rinck-Pfeiffer (United Water International), Dharma Pharmabalan, Colin Young (Central Highlands Water), Asoka Jayaratne (Yarra Valley Water), Robert Considine, Caroline Hussey (Melbourne Water), Therese Flapper, Christobel Ferguson (Ecowise), Peter Mosse (Gippsland Water), Robert Kagi (Curtin University of Technology), Roger O’Halloran (CSIRO), Dennis Mulcahy, Greg Gnos, Lise Cruveiller, Leon Li (University of South Australia), Skadi Motzko, Edgar Schicker (Georg-Simon-Ohm-Fachhochschule Nürnber, Germany) and finally Gregory Korshin (University of Washington, US).


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Research Report Title: Case Studies Using S::CAN On-line Monitoring System Research Officers: Rolando Fabris, Mike Dixon Project Leader: Christopher Chow Research Nodes: Australian Water Quality Centre, South Australia CRC for Water Quality and Treatment Project No. 2.0.2.3.2.4 – Case Studies Using S::CAN On-line Monitoring System


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EXECUTIVE SUMMARY

On-line water quality monitoring has gathered a lot of momentum in the potable water industry recently, particular the use of UV-Visible spectrometry instruments. These have the advantage of no moving parts and reagent free operation which is welcomed by the industry. The S::CAN Spectro::lyser is one of these and with its versatile monitoring applications has attracted much interest. The use of UV absorbance as a surrogate measure, e.g. of nitrate, turbidity, colour, total organic carbon (TOC) and dissolved organic carbon (DOC), is well established. Further development of surrogate parameters extended to chlorine demand and disinfection by-product (DBP) formation prediction which are useful ones for water operators. The project evaluated both the robustness of the S::CAN hardware and the in-built surrogate parameter determination algorithms. The project was delivered in a different format: instead of laboratory evaluation, most of the project work involved field trials via case studies supported by our project partners. During the course of this project, two major and five minor case studies were completed. Case studies covered a full range of applications including source water quality monitoring, treatment process optimisation, distribution system assessment and development of the monitoring of new surrogate parameters such as real time monitoring / prediction of disinfection by-product formation and chlorine demand - control of chlorine dosing. Full spectrum UV absorbance measurement provides absolute concentration information for a number of parameters, such as UV254 and colour, and calculated equivalents for others such as TOC, DOC, nitrate, turbidity and total suspended solids. For parameters requiring sample filtration through 0.45 micron filter in standard methods, the in-built algorithm mathematically removed the optical effects of solids. The evaluated results matched well with laboratory standard measurements. Four evaluated parameters were evaluated in the source water monitoring case study (Case Study 1). Nitrate was determined accurately without the need of local calibration. Turbidity determined by S::CAN was comparable with the laboratory turbidity meter readings after local calibration. Colour and DOC were not completely evaluated due to the stable water quality during the evaluation period. However, the results indicated the suitability of using them as an early warning system to detect colour change in water. The water industry has spent quite a lot of effort in developing techniques for rapid assessment of chlorine demand. This is particularly important for utilities requiring to manage systems with reactive waters. In this project, several case studies were related to evaluating the concept of using UV absorbance measurement to determine chlorine demand. The previous case study in the Myponga (SA) system successfully demonstrated the link between UV254 and chlorine demand with promising results in terms of linking chlorine residuals in the distribution system with UV254 prior to chlorination at the plant. In the Woronora case study (Case Study 2), the feasibility of using UV254 for chlorine dosing control was further demonstrated. This disinfection control component was further extended into other case studies in this project. Results obtained in this trial were very promising and the team is hoping to work closer with the operators to transform the analytical results into some form of control parameter that can be used to adjust the chlorine dosing system. The use of UV absorbance to predict DBPs was also progressed in the right direction with promising preliminary results with the help from Prof. Gregory Korshin, University of Washington. The use of differential spectroscopy to predict DBP formation has been well documented in the literature and implementing this technique in a field trail was supported by some preliminary data obtained. The concept of real time DBP formation monitoring would be ideal for water quality managers. In this project, an attempt at using water quality change between two locations as a parameter to assess distribution system performance was partially successful. Further investigations will be required here.


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TABLE OF CONTENTS

Foreword ................................................................................................................................................. 3 Executive Summary ............................................................................................................................... 5 Table of contents ................................................................................................................................... 6 Table of figures ...................................................................................................................................... 7 List of tables ........................................................................................................................................... 8 1 Introduction ......................................................................................................................................... 9

1.1 Mannum (SA) source water monitoring case study ....................................................................... 9 1.2 Woronora (Sydney Water) treatment process control and water distribution network management case study. ..................................................................................................................... 9 1.3 Darwin disinfection management and network management case study (minor)........................ 10 1.4 Tinworth Tank (Central Highlands Water) network management case study (minor) ................. 10 1.5 Bolivar WWTP (United Water) recycling water pre-treatment optimisation for membrane filtration pilot plant case study (minor) .............................................................................................................. 10 1.6 York Re-dosing Station (WA) chloramination optimisation using UV-Vis spectroscopy case study (minor) ....................................................................................................................................... 11 1.7 Preliminary assessment of disinfection by-product prediction using UV spectroscopy ............... 11

2 On-line Monitoring and S::CAN Evaluation .................................................................................... 12 2.1 Conclusion .................................................................................................................................... 18

3 Mannum (SA) Source Water Monitoring Case Study .................................................................... 19 3.1 Introduction ................................................................................................................................... 19

4 Woronora (Sydney Water) Treatment Process Control and Water Distribution Network Management Case Study. .................................................................................................................... 39

4.1 Introduction ................................................................................................................................... 39 4.2 Experimental ................................................................................................................................. 39 4.3 Results and Discussion ................................................................................................................ 40 4.4 Conclusions .................................................................................................................................. 45

5 Darwin (PAW) Source Water, Disinfection and Network Management Case Study ................... 46 5.1 Introduction ................................................................................................................................... 46 5.2 Experimental ................................................................................................................................. 46 5.3 Results and Discussion ................................................................................................................ 49

6 Central Highlands Water Network Management Case Study ....................................................... 54 6.1 Introduction ................................................................................................................................... 54 6.2 Experimental ................................................................................................................................. 55 6.3 Results and Discussion ................................................................................................................ 55

7 York Re-dosing Station (WA) Chloramination Optimisation Using UV-Vis Spectroscopy Case Study ..................................................................................................................................................... 59

7.1 Introduction ................................................................................................................................... 59 7.2 Summary ...................................................................................................................................... 63

8 Future works ...................................................................................................................................... 64 References ............................................................................................................................................ 65 Appendix 1 ............................................................................................................................................ 67

Real Time Monitoring of Disinfection By-Products Using Differential UV Absorption Spectroscopy, AWA Water Journal 35(3) 83-87 Refereed Paper. ............................................................................. 67

Appendix 2: ........................................................................................................................................... 72 On Line Monitoring of Disinfection By-Products Formation using Differential Absorbance Spectroscopy: An Outline of Principles and Results .......................................................................... 72


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TABLE OF FIGURES

Figure 1.1 Schematic of the Tinworth Tank network management case study ................................... 10 Figure 1.2 Schematic of the Bolivar WWTP pilot plant case study. ..................................................... 11 Figure 2.1 S::CAN Spectro::lyser™ (from S::CAN manual, ver 4.0, 2003). ......................................... 13 Figure 2.2 CON::STAT unit. ................................................................................................................. 13 Figure 2.3 Correlation between DOC and UV254 for both raw and treated waters. .............................. 14 Figure 2.4 Comparison of analytical results between laboratory standard procedures and S::CAN

Spectro::lyser™ (a) UV254, (b) colour and (c) DOC. Sample filtration was included in the laboratory standard methods while S::CAN Spectro::lyser™ measurement was performed without filtration. ......................................................................................................................................... 15

Figure 2.5 Comparison of real time chorine demand monitoring using on-line UV absorbance measurement and chlorine demand determined in a laboratory using conventional method. Pink line: on-line chlorine demand prediction by S::CAN Spectro::lyser. ○: Laboratory chlorine demand measurement from grab samples. ♦: Determined chlorine demand based on chlorine residual at a location (Almond Grove Road – location number 1607) downstream from the Myponga WTP with travel time correction. ............................................................................................................. 16

Figure 2.6 Relationship between UV254 and THMfp from water samples collected for a range of water quality. ............................................................................................................................................ 17

Figure 3.1 S::CAN installation in Mannum Pump Station No.1. ........................................................... 19 Figure 3.2 The single-sample autosampler installed to capture event samples. a) the cabinet to house

the autosampler and b) autosampler inside the cabinet.. .............................................................. 20 Figure 3.3 a) CON::STAT unit installed inside the pump station for real time display of selected water

quality parameters. b) Laptop computer available to view and process data on site. ................... 20 Figure 3.4 Comparison of S::CAN calculated nitrate concentration with laboratory grab sample results

(pink dots) over a 15-month evaluation. ........................................................................................ 22 Figure 3.5 Comparison of S::CAN calculated turbidity with laboratory grab sample results (pink dots)

over a 15-month evaluation. .......................................................................................................... 23 Figure 3.6 Comparison of S::CAN calculated colour with laboratory grab sample results (pink dots)

over a 15-month evaluation. .......................................................................................................... 24 Figure 3.7 Comparison of S::CAN calculated DOC concentration with laboratory grab sample results

(pink dots) over a 15-month evaluation. ........................................................................................ 25 Figure 3.8 a) Correlation between UV254 with laboratory 24 hr chlorine demand (pink dots) over a 3-

month study. b) S::CAN calculated 24 hr chlorine demand with laboratory 24 hr chlorine demand (pink dots) over a 3-month study. .................................................................................................. 26

Figure 3.9 Petrol Contamination Raw Spectrum: ................................................................................. 27 Figure 3.10 Petrol contamination after solids compensation: .............................................................. 27 Figure 3.11 Petrol Contamination after solids compensation and comparison to baseline spectra: ... 28 Figure 3.12 Zoomed spectra with different view angles ....................................................................... 28 Figure 3.13 2D Plots of petrol and biodiesel spectra: (peak shifted between samples) ...................... 29 Figure 3.14 Spectra of laboratory simulation of petrol addition to Mannum water. .............................. 31 Figure 3.15 Spectra of laboratory simulation of bio-diesel addition to Mannum water. ....................... 32 Figure 3.16 Spectra of laboratory simulation of a sewage spill using nitrate spikes to Mannum water.

....................................................................................................................................................... 34 Figure 3.17 Spectra of laboratory simulation of algal bloom in Mannum water. .................................. 36 Figure 3.18 a) Algal cell count between September 06 and July 07 and b) spectral data before and

after the events. ............................................................................................................................. 37 Figure 3.19 The link between Coliforms and nitrate ............................................................................ 37 Figure 3.20 The link between turbidity and Cryptosporidium ............................................................... 38 Figure 4.1 On-line monitoring of (a) UV254 and (b) DOC for both raw and treated water over a period

of time (1 month). The DOC data presented is only for illustration purposes................................ 41 Figure 4.2 Illustration of the advantage of using UV/Vis-spectrometer for feed-forward control of the

chlorine dosing system at the WFP. .............................................................................................. 42


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Figure 4.3 UV absorption spectrum of chloramine dosed Woronora Water in the laboratory. ............ 42 Figure 4.4 Chromatograms (HPSEC scans) of water samples collected along the Woronora

distribution system. ........................................................................................................................ 43 Figure 4.5 On-line data of UV254 and total chlorine at the Menai outlet. ♦ Marks the times for chlorine

tablet dosing and the UV/Vis-spectrometer signal between 18/11/05 to 24/11/05 was removed due to an unidentified problem. ..................................................................................................... 43

Figure 4.6 The molecular weight profile of a series of samples collected from the Menai outlet between 2/11/05 to 28/11/05. Results are shown only for five samples. ..................................... 44

Figure 5.1 Key locations of this case study: Darwin River Dam, Palmerston, Tiwi and Darwin city and the first and second chlorine dosing stations. ................................................................................ 47

Figure 5.2 Illustration of the experimental plan and set up of the case study. ..................................... 47 Figure 5.3 a) Off-takes tower, b) on-line turbidity meter and c) S::CAN set up. .................................. 48 Figure 5.4 Google Earth image of the Palmerston Tank. ..................................................................... 48 Figure 5.5 Turbidity comparison between on-line turbidity meter and S::CAN in Darwin River Dam. . 49 Figure 5.6 A side by side comparison of S::CAN turbidity and a Zullig Cosmos 25 on-line turbidity

meter. ............................................................................................................................................. 50 Figure 5.7 Darwin River Dam UV254 monitoring. .................................................................................. 50 Figure 5.8 UV254 monitoring at the Palmerston Tank ......................................................................... 51 Figure 5.9 UV254monitoring at the Tiwi Pump Station ........................................................................ 51 Figure 5.10 a) UV254 monitoring at the three locations and b) chlorine residuals at Palmerston and

Tiwi Pump Station .......................................................................................................................... 52 Figure 6.1 a) Google Earth image of the Central Highlands Water Tinworth Ave Tank and b)

schematic of the S::CAN spectrometers set up. ............................................................................ 54 Figure 6.2 Water flow and inlet and outlet, turbidity, UV254 and DOC. ............................................... 56 Figure 6.3 A summary of the grab sampling program and BRP results. ............................................. 57 Figure 6.4 HPSEC molecular weight profiles, organised by a) inlet and b) outlet. .............................. 57 Figure 6.5 HPSEC results, organised by sampling date. ..................................................................... 58 Figure 7.1 Scheme of the Goldfields and Agricultural Water Supply (GAWS) owned by Water

Corporation. York re-dosing station is surrounded by the red ellipse. ........................................... 59 Figure 7.2 Scheme of the installations at York. ................................................................................... 60 Figure 7.3 Results obtained at York on the 22nd and 23rd May 2007 ................................................... 60 Figure 7.4 Sampling lines arriving at the pump station. The travel time of water through the sampling

lines is around 30-40s. The temperature of water varied by 0.1°C from the beginning to the end of the sampling lines. ......................................................................................................................... 61

Figure 7.5 a) set up of the S::CAN probes. The water coming from the sampling lines overfills the PVC tubes. The flow is around 1L per minute. b) The air compressor is activated every 40min and flushes the two probes at the same time. c) The two S::CAN probes operating at York. ...... 61

Figure 7.6 a) DOC, b) Turbidity, c) UV254 and d) Nitrate monitoring data over the period of 23/5/07 to 1/6/07. ............................................................................................................................................ 62

Figure 7.7 UV254 monitoring data between 30/5/07 and 31/5/07. Both before and after chlorination data are shown. ............................................................................................................................. 62

LIST OF TABLES

Table 3.1 Petrol Experimental Data (file name: 2008-02-14_21-29-42) .............................................. 30 Table 3.2 Bio-Diesel Experimental Data (2008-02-17_21-08-00) ........................................................ 32 Table 3.3 Nitrate experimental data (2008-02-19_01-11-00) ............................................................... 33 Table 3.4 Algae Experimental Data (2008-02-26_01-30-00) ............................................................... 35 Table 3.5 Algae/Turbidity Experimental Data (2008-02-28_23-32-00) ................................................ 35 Table 4.1 Set up locations and duration of the two UV/Vis-spectrometers used in this study. ............ 40


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1 INTRODUCTION

Water quality monitoring is an important part of water quality management and UV-Visible spectrometry is becoming a popular tool for water operators to optimise treatment processes and manage water quality in water supply systems. Based on a 24-month laboratory study using water samples collected from various water supplies, the CRC 2501 disinfection residual control tools project identified UV254 absorbance measurement as one possible surrogate parameter for rapid assessment of chlorine demand. Preliminary research work by DCM Process Control using an on-line full spectrum UV-Vis spectrometer suggests that on-line UV254 augmented by or replaced by multiple other frequencies may provide a more accurate and reliable means of feed-forward control of chlorine dosing. With the support of DCM Process Control, a successful on-line monitoring field trial for real-time chlorine demand monitoring at the Myponga (SA) treatment plant was conducted using the S::CAN UV-Vis Spectroanalyser (S::CAN). Following the technology transfer of project 2501 outcomes, many water utilities and other potential industry partners have expressed their interest of repeating similar field trials at their sites. In addition, the potentially wide utility of S::CAN on-line monitoring has been recognised by the drinking water industry and a number of system specific applications/investigations have been suggested by several industry participants. As a result, DCM Process Control was approached by a number of CRCWQT partners to support various field trails via the loaning of an instrument (DCM Process Control had previously loaned project 2501 an S::CAN for short evaluation). DCM Process Control proposed that it would be beneficial for CRCWQT to be involved in the case studies. The project activity was supported by the loan of two S::CANs to conduct field trials. The project aimed to demonstrate the benefits of on-line water quality monitoring for a range of applications at sites selected by the participating industry partners. The CRCWQT co-ordinated and assisted the case study and facilitated technology transfer activities (factsheets, presentations and final report) towards the end of the project to ensure knowledge sharing among industry partners. Four categories of research activity were identified: 1. source water monitoring; 2. treatment process control and optimisation; 3. disinfection management; and 4. water distribution network management. In this report, each case study that was supported by our project partners covering the listed categories will be described. In this project, we completed two major case studies together with several smaller ones. 1.1 Mannum (SA) source water monitoring case study Storm water enters the River Murray very close to the pipeline offtake with consequent water quality risks. There are also other potential hazards upstream of the offtake. It was therefore decided to provide a facility to continuously monitor the quality of water drawn at the Mannum No.1 pumping station inlet to alert SA Water of significant decreases in water quality. This would allow cessation of pumping until the water quality returned to acceptable levels. A 26-week weekly sampling program was used to validate/calibrate the system against laboratory measurement to produce a local profile for nitrate, DOC, colour, turbidity and chlorine demand. In addition, the alarm function was evaluated against hydrocarbon (petrol and diesel), sewage spill and algal bloom contamination warning. The aim was to develop an early warning system for water incidents. 1.2 Woronora (Sydney Water) treatment process control and water

distribution network management case study. One main focus in chloraminated systems is to minimise nitrification. The practice adopted by Sydney Water is to optimise the total chlorine setpoint at the water filtration plant and maintain a Cl2:NH3 ratio of 4:1 in the distribution system by locking up the free ammonia using secondary disinfection in the form of (calcium hypochlorite) tablet dosing at key storage reservoirs. This case study monitored water quality changes in the Woronora Distribution System in order to investigate the benefit of using an on-line water quality monitoring system as part of the disinfection management process.


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Two S::CAN on-line UV absorbance monitoring units were used at some key locations to monitor changes in water quality. A smart monitoring program was set up to allow the use of only two units to study an entire arm of the distribution system. The studies included treatment plant performance evaluation, impact of disinfection on water quality change, water character change in both the trunk main and downstream distribution network. In addition to the on-line water quality assessment, this investigation included an extensive study of organic character change in several key sampling locations along the distribution system using two advanced organic characterisation techniques, high performance size exclusion chromatography (HPSEC) and bacterial regrowth potential (BRP), together with standard water quality parameters such as UV254, DOC etc. 1.3 Darwin disinfection management and network management case

study (minor) This case study involved the rapid assessment of chlorine demand direction. Seasonal variation of water quality has a large impact on chlorine dosing control. The case study conducted was a monitoring exercise in three locations, Darwin River Dam (source), Palmerston Tank (middle of the distribution system) and Tiwi Tank (close to the end of the system) to assess water quality variation. Follow up projects will be recommended based on the results obtained. 1.4 Tinworth Tank (Central Highlands Water) network management case

study (minor) The effect of tank cleaning on water quality improvement was assessed. Two S::CANs were used together with advanced organic analyses, BRP and HPSEC, performed in the laboratory. This information can be used to develop a monitoring tool to allow better planning for the cleaning program.

Figure 1.1 Schematic of the Tinworth Tank network management case study The project plan included 2 weeks monitoring prior to tank cleaning followed by another 2 weeks monitoring afterwards. 1.5 Bolivar WWTP (United Water) recycled water pre-treatment

optimisation for membrane filtration pilot plant case study (minor) This project was part of a Veolia Water project to assess the impact of water quality on membrane performance for recycled water application. Due to the variation of the inlet water quality, an on-line water quality system is a useful monitoring tool. The aim of this case study was to identify the impact of water quality on membrane performance. A pilot plant consisting of one micro-filtration (MF) unit and one reverse osmosis (RO) filtration unit was installed at the United Water Bolivar Waste Water Treatment Plant. The feed water was treated effluent pumped to the pilot plant. The feed was then passed first through the MF and then to a filter tank and sludge filter, before going through Stage 1 and Stage 2 of the RO process to complete the treatment. The case study was set up to use grab samples in parallel to on-line data and monitoring of transmembrane pressure of the membrane to assess pre-treatment on microfiltration membrane performance. Several pre-treatment processes, including PACl dosing, were planned as part of the

Storage Reservoir

inlet outlet

S::CAN S::CAN


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evaluation. In addition, this work can be extended to look at WQ after microfiltration to assess the fouling potential of the permeate on RO membranes.

Figure 1.2 Schematic of the Bolivar WWTP pilot plant case study. Unfortunately, this case study was ceased with only the on-line monitoring results available due to confidentiality issues. This case study will not be reported in this report. 1.6 York Re-dosing Station (WA) chloramination optimisation using UV-

Vis spectroscopy case study (minor) This case study was part of the CRCWQT project 2317 ammonia analyser evaluation case study. An ammonia analyser was developed for chloramination control and from earlier work, it indicated that chloramine can be detected by UV absorbance. In addition, the full wavelength spectrum information may be used to develop control parameters to optimise the chloramination process. Two S::CAN units were set up in the York re-dosing station, before and after chlorination, to monitor water quality. 1.7 Preliminary assessment of disinfection by-product prediction using

UV spectroscopy Prof. Gregory Korshin, of the University of Washington, spent a sabbatical in Australia with the CRCWQT between September 2007 and November 2007. He provided a lot of valuable information on disinfection by-product measurement using differential UV spectroscopy. The use of S::CAN for on-line monitoring of disinfection by-product formation is a useful tool for operators to manage systems with historically high disinfection by-product levels. A trial was conducted in the Quinninup system (WA) as part of another major case study. The full study involved four S::CAN units (raw, pre and post GAC and post chlorination) installed in this system. The two key locations of interest for this case study were pre and post chlorination for DBP prediction and based on the differential signal, trihalomethanes (THMs) and haloacetic acids (HAAs) can be monitored. Some laboratory calibration was conducted to confirm the predictability. However, during the field trial, it was identified that the start / stop operation of the system was not ideal for testing proof of concept using differential spectroscopy for disinfection by-product formation. This concept will be trialled later in SA and WA. Prof. Korshin published a paper in Water (AWA) to describe the differential spectroscopy technique and a presented a seminar to describe this work. Both are attached in the Appendices of this report.

CMF RO

Feed water

pretreatment

Bolivar Treated Effluent

S::CAN S::CAN


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2 ON-LINE MONITORING AND S::CAN EVALUATION

Recent advances in analytical instrument development have contributed to improvements in the process of producing and ensuring good quality drinking water. UV absorbance measurement is a simple analytical method that has been widely adopted by the water industry. Current examples of UV absorbance measurement can be found in the areas of source water monitoring, process optimisation and determination of surrogate water quality parameters in treated water. To perform UV/Vis absorption measurements, a UV/Vis spectrophotometer is required. Conventional laboratory methods require a sample to be manually placed in the spectrophotometer using a cuvette and UV or visible light at a certain wavelength, or range of wavelengths, is transmitted through the sample (a quartz cuvette is required for measurement in the UV region). The spectrophotometer measures how much of the light is absorbed by the sample and the absorbance of the sample is determined based on the intensity of light. There are several types of laboratory UV/Vis spectrophotometers available, such as single beam and double beam design, with the use of different types being dependent upon the application. Natural organic matter (NOM) is one of the most important influences in drinking water treatment and supply. NOM is usually represented by the measurement of total or dissolved organic carbon concentration. The impact of NOM on various treatment processes is based upon both concentration and character (Owen et al. 1995). A number of techniques are available to characterise organics and UV absorbance is certainly one of the easiest techniques available. UV absorbance has been reported as a surrogate parameter for the concentration of NOM (Edzwald et al. 1985; Wang and Hsieh 2001) and has also been used as one of the parameters to predict coagulant dose for process optimisation (van Leeuwen et al. 2001a&b; van Leeuwen et al. 2005). In addition, UV absorbance tends to give a measure of unsaturated bonds that are potential sites with which chlorine can react. Hence, the UV absorbance of NOM is related to its chlorine demand (Powell et al. 2000; Chow et al. 2004; Fitzgerald et al. 2005). It is also reported that UV absorbance measurement can provide an indication of overall disinfection by-product formation after chlorination (Korshin et al. 1997a&b; Li et al. 1998). The recent developments in instrument portability, have resulted in a number of reliable on-line / in situ monitoring instruments which are now commercially available. The S::CAN Spectro::lyser™ hardware is unique in that rather than bringing the sample to the unit, the unit is submersible and can go directly into the water. It comprises a robust but sensitive double beam UV/Vis-spectrometer (200.nm to 750.nm) with optical pathlength in a selectable range of 0.05 to 10 cm (Figure 2.1). The instrument was designed based upon the principle of the photodiode array (PDA) spectrophotometer which has no moving parts and the added advantage of reagent free operation. Together with its full stand-alone capability (integrated data logger and battery), this instrument is ideal for on-line / in situ monitoring applications. The operational software uses chemometric algorithms running in the onboard minicomputer which improves the functionality of the instrument. S::CAN consists of three components: the sending, measuring and receiving units, which can also be easily identified from the outside as three different parts of the instrument. The central element of the sending unit is a source of light – a xenon flashlamp. It is supported by an optical system to guide the beam of light and an electronic control system to operate the lamp. In the measuring section the xenon light passes through the space between the two measuring windows which is filled with the measuring medium. A second light beam within the probe – called the compensation beam - is guided across an internal comparison chamber. It allows for the identification of disturbances in the measuring process which it automatically compensates for, i.e. the S::CAN spectrometer probe is a dual-beam measuring instrument. The length of the light path is adapted to specific applications. The receiving unit is located on the main cable side of the instrument, and it consists of two major components: the detector and the operating electronics. An optical system focuses the measuring and compensation beams on the entrance port of the detector. The light received by the detector is split up into its wavelengths by an optical grid and guided to the 256 fixed photodiodes, so there is no need for sensitive moving components here. The operating electronics take full control of the measuring process and of various processing steps to edit and check the measuring signal. Thus, intelligent algorithms in the measuring instrument itself contribute to excellent long-term stability.


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Figure 2.1 S::CAN Spectro::lyser™ (from S::CAN manual, ver 4.0, 2003). S::CAN turbidity compensation was developed to take into account the impact of solids on spectral measurements of dissolved parameters such as nitrate or UV254, depending on their concentration and particle size. Various pollutants in the µg/L range can be detected. Although UV absorbance measurement does not provide data on absolute concentrations it has proved to be a useful alarming tool to provide warning of any sudden charges of water quality. In addition, application-specific global calibrations make it possible to interpret the fingerprint in an optimum way. Various important water quality parameters, such as total organic carbon (TOC), dissolved organic carbon (DOC), trihalomethanes (THM) / disinfection by-product (DBP) formation and nitrate, can be estimated (Langergraber et al. 2002). These algorithms are based on on-site experience gained by S::CAN in comparable applications, i.e. real media properties, not artificial standard solutions created in a laboratory. Local calibration based on real media properties enables adjustment to laboratory results available in each specific case. Automatic cleaning is a hydraulic – pneumatic process which reliably prevents window soiling as this may distort readings. In this way, measurements without drift can even be obtained from sewage-treatment plant influents for weeks without maintenance.

Figure 2.2 CON::STAT unit. The CON::STAT (Figure 2.2) has been designed as an input and operating computer to run the Spectro::lyser. It has all the features required to continuously run all S::CAN spectrometer probes on-line and link them up with external control systems. Due to the scope of functions (e.g. integration of external sensors), it can even be expanded to become a complete measuring station. When connected with the probes, the CON::STAT ensures communication with the appliances and the


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analysis, visualisation, transmission and storage of the data measured. Connection to the S::CAN spectrometer probe is ensured via the connecting cable supplied, which only needs to be plugged into the exterior waterproof socket. Most of the literature reporting S::CAN data is in the wastewater area (Langergraber et al. 2004a; Rieger et al. 2004; Hofstaedter et al. 2003; Langergraber et al. 2002; Fleischmann et al. 2002). However, it is anticipated to have similar utility in the drinking water industry, as continuous monitoring can provide much useful information to help understand the characteristics of the complete water system. The information that the S::CAN can provide may aid a number of areas such as control and optimisation of water treatment processes, regulating action due to sudden changes of water quality and implementation of an alarming system for protection against any kind of “abnormal” water composition or contamination (Langergraber et al. 2004b; Perfler et al. 2002). S::CAN Evaluation UVabs as a surrogate measurement of DOC – the advantages of using multi-wavelength measurement UV254 has been identified as a useful surrogate measure for DOC although it predominantly represents only the aromatic NOM character. UV254 is becoming a popular parameter for water treatment operators to assess treatment performance and also as one of the parameters for feed forward prediction of coagulant dose (van Leeuwen et al. 2005). This technique only requires very simple instrumentation and can be performed by the operators at the treatment plant. However, care must be taken when using UV254 as a DOC surrogate, particularly when it is to be used for control purposes. Under these conditions, the apparent correlations of DOC to UV254 must be verified at times of compositional variances. Good correlations usually occur only in compositionally stable waters. Water samples were chosen to represent the wide variation in water quality found in Australia and included both surface and ground waters. A laboratory trial was conducted using standard laboratory procedures for UV254, colour, DOC and trihalomethane formation potential (THMfp) measurements (Bennett and Drikas, 1993; APHA et al., 1998). For the standard methods, sample filtration through 0.45 micron filter was performed. The comparison used the S::CAN Spectro::lyser in the on-line / in-situ mode without prior sample filtration, as would be the case in real-time monitoring. Figure 2.3 indicates that raw water has higher UV absorbance (UV254) per unit DOC than treated water. Good prediction can only be obtained for well characterised waters.

Figure 2.3 Correlation between DOC and UV254 for both raw and treated waters.

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The use of multiple wavelength measurement together with partial least squares (PLS) correlation for quantitative calibration is well documented in the literature (Haaland and Thomas, 1988; Thomas and Gallot, 1990, Langergraber et al., 2003). The analytical results (Figure 2.4) obtained from standard laboratory methods and the S::CAN Spectro::lyser™ correlated very well (UV254: R2 = 0.98; Colour: R2 = 0.96; DOC: R2 = 0.84). a) b)

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Figure 2.4 Comparison of analytical results between laboratory standard procedures and S::CAN Spectro::lyser™ (a) UV254, (b) colour and (c) DOC. Sample filtration was included in the laboratory standard methods while S::CAN Spectro::lyser™ measurement was performed without filtration. It should be noted that very fine particulates (< 0.45 micron filter) can contribute to the UV254 and colour readings of a water sample obtained using standard optical laboratory instruments. S::CAN has a mathematical function to compensate particle related optical effects giving it a distinct advantage over conventional optical devices when used for applications when fine particulates are present, such as in waters of the lower River Murray (SA). The advantage of using multiple wavelength measurements PLS calibration (S::CAN) can be seen in Figure 2c, where the predicted DOC values closely match those of actual values for both raw and treated waters.

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Prediction of chlorine demand Conventional methods used to determine bulk water chlorine demand and residual kinetics are contact time dependent and often require several days to complete in order to relate these to the actual water retention times in distribution systems. Water supplies, having variable water quality, may have a variable chlorine demand. This requires substantial changes to be made to the applied disinfectant dose if disinfection residuals are to be maintained within required ranges throughout the distribution system. This presents a challenge to operators when attempting to control secondary disinfection as feedback loops are usually of the order of several days, making the process highly reactive. Based on a 24-month laboratory study using water samples ranging in water quality, UV absorbance measurement was identified as the best surrogate parameter for rapid chlorine demand assessment. A linear relationship was determined between chlorine demand and UV254 absorbance from which prediction can be made with an accuracy of better than ± 1 mg/L chlorine demand (Fitzgerald et al., 2006). A two week field trial using the S::CAN Spectro::lyser was conducted at the Myponga (SA) WTP. The S::CAN Spectro::lyser was installed at the filtered water tap (prior to chlorination) at the WTP laboratory. A chlorine demand prediction algorithm based on previous laboratory data was used to determine the real time chlorine demand of the filtered water. During the two week field trial, 19 grab samples were taken for laboratory chlorine demand measurement. In addition, field chlorine residuals from the routine sampling program at a downstream location (Almond Grove Road – location number 1607) were used to evaluate the concept of a “chlorine demand sensor” as a real time control of chlorine dosing. In terms of practicality, chlorine demand can also be estimated based on the difference between the chlorine set-point at the WTP and the residual at the downstream locations provided the water travel time is known. A particularly useful feature of the S::CAN Spectro::lyser is the on-line / in-situ monitoring capability. During the 2-week on-line monitoring trial conducted at the Myponga WTP, the S::CAN Spectro::lyser performed well, with incorporation of specifically developed software. On-line chlorine demand prediction based on UV absorbance measurement was able to be displayed in real time on the instrument (Figure 2.5). The chlorine demand predictions matched well with the laboratory measurements made of grab samples collected during the period. Note that grab sampling did not occur during events of major changes in chlorine demand as detected by the S::CAN Spectro::lyser caused by plant shutdowns. This indicates that a sampling program can be refined using a S::CAN Spectro::lyser to enable samples to be taken when water quality changes or events occur rather than on a time, flow or random basis.

Figure 2.5 Comparison of real time chorine demand monitoring using on-line UV absorbance measurement and chlorine demand determined in a laboratory using conventional method. Pink line: on-line chlorine demand prediction by S::CAN Spectro::lyser. ○: Laboratory chlorine demand measurement from grab samples. ♦: Determined chlorine demand based on chlorine residual at a location (Almond Grove Road – location number 1607) downstream from the Myponga WTP with travel time correction.

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Furthermore, the “chlorine demand sensor” concept was studied using chlorine residual measurement at a location (Almond Grove Road) downstream of the WTP. The feasibility of using UV absorbance measurement (prior to chlorination) to estimate chlorine demand of the water as a way to control chlorine dosing and manage the chlorine residual in the distribution system was assessed. The chlorine demand (field data) shown in Figure 2.5 was estimated based on the difference between the chlorine set-point at the WTP and the chlorine residual at the Almond Grove Road sampling point after travel time correction (travel time is approximately 2 days). The results also matched well with the real time chlorine demand measurements using the S::CAN Spectro::lyser. Although this study didn’t provide a full demonstration of using on-line UV spectrophotometer for chlorine dosing control, the results obtained so far indicate feasibility of the concept. Prediction of THM formation potential In a similar way to DOC and chlorine demand, a generally linear relationship was found between UV254 and THM formation, based on laboratory analyses of grab samples (Figure 2.6). A more detailed comparison between laboratory determined and S::CAN Spectro::lyser determined THMfp results can be found in Mosse (2003).

Figure 2.6 Relationship between UV254 and THMfp from water samples collected for a range of water quality. The capability for operators to use simple tools such as single wavelength or full UV/Vis spectrum measurements to predict THM formation offers a number of advantages when managing distribution system performance. As previously discussed, care should be taken when using UV254 (single wavelength) only for prediction and measurement using a full spectrum (multiple wavelength) detection instrument such as the S::CAN Spectro::lyser is recommended in order to lower the risk of significant errors. Regular cross checking of S::CAN Spectro::lyser output with actual laboratory analysis both during stable water conditions and during times of water quality changes is required in order to validate the acquired on-line data.

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2.1 Conclusion For water utilities required to manage a dynamic water distribution system, the application of UV absorbance measurement enables rapid assessment of water quality that is convenient and technically easily achievable. Using single wavelength (254 nm) measurement as a surrogate for the relevant parameters can only provide limited accuracy of prediction and care must be taken to ensure reliable measurement. The use of a multiple wavelength instrument with PLS calibration such as the S::CAN Spectro::lyser, can improve the accuracy and reliability of measurement. In addition, the option of using it in an on-line / in-situ mode provides an extra dimension and makes it a potentially helpful tool in managing water treatment operation and distribution system performance through automated control. The S::CAN Spectro::lyser is a useful tool that can be used on-line at a critical control point prior to disinfection as part of a HACCP process to provide early warning of process upset / water quality deterioration.


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3 MANNUM (SA) SOURCE WATER MONITORING CASE STUDY 3.1 Introduction South Australia depends heavily on water flowing down the River Murray to meet its drinking water requirements. Water from the River Murray is supplied around the State through five major pipelines. The 60 km long Mannum-Adelaide Pipeline is one of two major pipelines supplying raw water to Adelaide, a city of over 1 million residents. Storm water enters the River Murray very close to the pipeline offtake with consequent water quality risks. In addition there are other potential hazards upstream of the offtake. It was therefore decided to provide a facility to continuously monitor the quality of water drawn at the Mannum No.1 pumping station inlet to alert SA Water of significant decreases in water quality. This would allow cessation of pumping until the water quality returned to acceptable levels. If this proved successful, then consideration could be given to installing similar systems in other river, reservoir and water treatment plant locations. A S::CAN Spectro::lyser was purchased under the SA Water capital project C6199. The concept of the project was to evaluate the suitability of implementing an early warning system as part of a decision support system for pumping control based on the incoming water quality. This could provide a better water quality management strategy and reduce potential risks by preventing unwanted contaminants entering into the Mannum-Adelaide pipeline. A full year evaluation program was set up as part of the capital project to evaluate the accuracy of the key parameters, nitrate, turbidity, colour, DOC determined by the S::CAN unit. This section is separated into four parts, 1) installation at Mannum, 2) calibration and validation of the S::CAN system, 3) laboratory simulation to set up the alarm functions for petrol, diesel and sewage spills, plus attempts to set up algal bloom warning and 4) preliminary results to link S::CAN on-line water quality parameters with water incidents such as those involving coliforms, E. Coli and Cryptosporidium. Installation at the Mannum Pump Station The S::CAN unit was installed at the inlet of the Mannum-Adelaide pipeline offtake and attached to the end of a stainless steel rod. The installation kept the probe under water with the sensing window exposed to the water. Air cleaning (compressed air) was controlled by the interfacing unit installed in the jetty (Figure 3.1).

Figure 3.1 S::CAN installation in Mannum Pump Station No.1.

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A single-sample autosampler was included in the installation to collect grab samples (Figure 3.2). This was set up as part of this pilot trail to capture event samples. The trigger can be programmed by changes in key parameters, colour, turbidity, DOC and/or by abnormal changes in the UV-Vis spectrum generated by the S::CAN.

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Figure 3.2 The single-sample autosampler installed to capture event samples. a) the cabinet to house the autosampler and b) autosampler inside the cabinet.. The CON::STAT was installed back into the pump station and connected to a laptop computer (Figure 3.3). Sampling rate was set at 30 min which equated to 312 days of data storage on the CON::STAT unit. Newer electronics have vastly more storage capability and can store data at 1 minute sampling intervals for more than 1 year.

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Figure 3.3 a) CON::STAT unit installed inside the pump station for real time display of selected water quality parameters. b) Laptop computer available to view and process data on site. The system is currently configured to “calculate” three parameters, DOC, colour and turbidity and these are displayed on the CON::STAT.


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Calibration and validation of the S::CAN system The full spectrum UV absorbance measurement of the S::CAN system provides concentration information for a number of parameters, such as UV254, and calculated equivalents for others such as colour, TOC, DOC, nitrate, turbidity, total suspended solids and particle size. A 15 month study period between Sept 06 and Dec 07 was set up to assess the performance and accuracy of the system. During this period, a special sampling program was set up in conjunction with the routine sampling program at the sample location 20025 (Mannum - River Murray at intake depth - sample pump) to make possible evaluation of the key parameters, nitrate, turbidity, colour, DOC and chlorine demand, via the laboratory using standard methods, to compare with the S::CAN calculated equivalents measured on-site. The S::CAN calculated equivalents are based on the analytical information obtained from the spectrum, which is considered as a surrogate measurement. The laboratory analytical data was also used to produce a local calibration profile for the system and this study also provided information to determine the most suitable maintenance strategy. Due to extremely stable water quality during the study period, additional laboratory simulations of turbidity spike, algal boom, petrol, diesel and sewage spills were conducted to obtain data to set up the appropriate alarming function for the events mentioned. The study provided information to SA Water about the effectiveness and usefulness of this instrument, and enabled decisions to be made about how to use this (and any future) instrument. Evaluation of standard S::CAN calculated parameters Five derived parameters, nitrate, turbidity, colour, DOC and chlorine demand (developed from another CRC research project), were evaluated against laboratory grab samples. The on-line data was processed using the ANAPro software first without local calibration then re-processed after local calibration using the analytical results of the laboratory grab sample in the beginning of the 15-month study. The 15-month results were compared by plotting a time series graph of each parameter with laboratory grab sample results superimposed. The 15-month period was separated into five graphs with each one covering a 3-month period. Nitrate was well correlated with the laboratory results even without the need of local calibration (Figure 3.4). Turbidity required local calibration but after calibration it correlated well with laboratory grab samples (Figure 3.5). Colour measurement was reasonably well correlated; however, from our experience in colour measurement it is not a highly` sensitivity method and together with the short pathlength (0.5 cm) of the S::CAN unit which was selected to accommodate the signal of the UV region has further reduced the sensitivity of the measurement. Despite this, the S::CAN hardware performed well even with a shorter pathlength as compared with the laboratory standard method using a 5 cm cell. During the study period the water quality improved with lower colour and greater stability due to the effect of the drought. While this stability is good from a water quality point of view, it is not ideal for instrument evaluation (comparison) situations. Figure 3.6 was plotted on a larger scale which covers the usual range of colour in the river. It may be concluded that colour measurement was reasonably accurate. For the S::CAN system to determine the correct DOC concentration of a particular water, local calibration is essential. DOC in Figure 3.7 indicated regular calibration might be required. The on-line data was processed using the DOC laboratory grab samples from the beginning of the study and the first 3 months correlated very well with the laboratory results, however then it deviated from the laboratory samples later in the study. This deviation could also be caused by change in organic character during the study period as the laboratory DOC results were very similar over the period. The previously developed chorine demand parameter was also evaluated in this study. Figure 3.8 shows that UV254 correlated well with the laboratory 24 hrs chlorine demand measurement. This indicated that it is possible to use UV254 as an indicative parameter to manage chlorine dosing adjustment in response to water quality change. The custom-built chlorine demand algorithm did reasonably well to predict chlorine demand in real time.


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Figure 3.4 Comparison of S::CAN calculated nitrate concentration with laboratory grab sample results (pink dots) over a 15-month evaluation.

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Figure 3.5 Comparison of S::CAN calculated turbidity with laboratory grab sample results (pink dots) over a 15-month evaluation.

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Figure 3.6 Comparison of S::CAN calculated colour with laboratory grab sample results (pink dots) over a 15-month evaluation.

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Figure 3.7 Comparison of S::CAN calculated DOC concentration with laboratory grab sample results (pink dots) over a 15-month evaluation.

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a)

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Figure 3.8 a) Correlation between UV254 with laboratory 24 hr chlorine demand (pink dots) over a 3-month study. b) S::CAN calculated 24 hr chlorine demand with laboratory 24 hr chlorine demand (pink dots) over a 3-month study. Set up of River Alarm functions for Mannum Apart from using the S::CAN alarm system to provide warning of unusual changes of water quality parameters such as turbidity, colour, DOC etc, there is another purpose for setting up an alarm system at Mannum. Concerns exist over the local water activities, such as houseboat waste discharge and township storm-water discharge, in the area adjacent to the offtake. To assess these events, a laboratory simulation was set up and various contaminants were spiked into a sample of Mannum water. Approximately 40 Litres of Mannum Inlet Water was collected. 9 Litres of this water was used in each of the petrol, bio-diesel, nitrate and algae experiments. The S::CAN Spectro::lyserTM was programmed to record results every 1 minute. A stirrer was used in all of the experiments to ensure a maximum level of mixing was achieved between the substrates and the Mannum water. The stirrer was run at 250 rpm. A 100 – 1000 µL micropipette was used to dose the 0.1 mL volumes of petrol, bio-diesel, nitrate and algae. For larger dosing volumes, appropriate measuring cylinders were used. Spectrum characteristics of petrol and bio-diesel in Milli-Q water. An initial experiment was conducted to characterise the spectra of petrol and bio-diesel in Milli-Q which was later used to compare with Mannum water. The S::CAN was placed in a bucket of tap water (with stirring) and petrol was spiked into it. Data acquisition was set to 1 min intervals and every 5 mins a spike of diesel was added. It was possible to visually identify signals from the aromatic hydrocarbons in the time-resolved UV/VIS spectrum (Figure 3.9).

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Figure 3.9 Petrol Contamination Raw Spectrum: It is possible to isolate these signals by subtracting the contribution due to particulates (Figure 3.10)

Figure 3.10 Petrol contamination after solids compensation: Further enhancement is possible by subtracting the spectrum of the uncontaminated water (- - - line in above plot):

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Abs (m-1)


28

Figure 3.11 Petrol Contamination after solids compensation and comparison to baseline spectra: The uniform increase of hydrocarbon contamination is then quite clear at two distinct peaks (Figures 3.11, 3.12 and 3.13):

Figure 3.12 Zoomed spectra with different view angles

Increase in Peak Height with time (addition of petrol)

Abs (m-1)

Abs (m-1)

Abs (m-1)


29

a) Petrol

b) Diesel

Figure 3.13 2D Plots of petrol and biodiesel spectra: (peak shifted between samples)

Abs (m-1)

Abs (m-1)


30

Simulation of petrol, bio-diesel, sewage spits and algal bloom Petrol spill simulation: Approximately 100 mL of petrol was collected for use in this experiment. 0.1 mLs of petrol was spiked into 9 Litres of Mannum water every 30 minutes. The S::CAN was programmed to record the results in 1 minute increments. In all, 1.4 mL of petrol was added to the Mannum water over a period of approximately seven and a half hours (Table 3.1). Table 3.1 Petrol Experimental Data (file name: 2008-02-14_21-29-42)

Petrol Experiment (15/02/08) Time Dose (mL) Total Petrol Added (mL)

21:30 - 22:00 0 0 22:01 - 22:31 0.1 0.1 22:32 - 23:02 0.1 0.2 23:03 - 23:33 0.1 0.3 23:34 - 00:04 0.1 0.4 00:05 - 00:35 0.1 0.5 00:36 - 01:06 0.1 0.6 01:07 - 01:37 0.1 0.7 01:38 - 02:07 0.1 0.8 02:08 - 02:37 0.1 0.9 02:38 - 03:07 0.1 1 03:08 - 03:37 0.1 1.1 03:38 - 04:07 0.1 1.2 04:08 - 04:37 0.1 1.3 04:38 - 05:07 0.1 1.4


31

Figure 3.14 Spectra of laboratory simulation of petrol addition to Mannum water. Bio-Diesel spill simulation: Approximately 100 mL of bio-diesel was collected for use in this experiment. 0.1 mL of bio-diesel was spiked into 9 Litres of Mannum water every 30 minutes. The S::CAN was programmed to record the results in 1 minute increments. In all, 1.4 mL of bio-diesel was added to the Mannum water over a period of approximately seven and a half hours (Table 3.2).

Addition of 0.1 mls petrol

Abs (m-1)


32

Table 3.2 Bio-Diesel Experimental Data (2008-02-17_21-08-00)

Bio-Diesel Experiment (18/02/08) Time Dose (mL) Total Biodiesel Added (mL)

21:30:21 - 21:59 0 0 22:00 - 22:29 0.1 0.1 22:30 - 22:59 0.1 0.2 23:00 - 23:29 0.1 0.3 23:30 - 23:59 0.1 0.4 00:00 - 00:29 0.1 0.5 00:30 - 00:59 0.1 0.6 01:00 - 01:29 0.1 0.7 01:30 - 01:59 0.1 0.8 02:00 - 02:29 0.1 0.9 02:30 - 02:59 0.1 1 03:00 - 03:29 0.1 1.1 03:38 - 04:07 0.1 1.2 04:08 - 04:37 0.1 1.3 04:38 - 05:07 0.1 1.4

Figure 3.15 Spectra of laboratory simulation of bio-diesel addition to Mannum water.

Abs (m-1)


33

From the fingerprint of petrol and bio-diesel, the alarm function was generated using the procedure as follows: 1) A new time series of the concentration of simulated contamination of Petrol/Diesel (c(t)) was generated. This might be mostly zero with peak shapes or a sinusoidal pattern or more probably both c(t)=sin(t) or c(t)= [ 0 0 0 0 0 0 0 0 0 1 1.5 2 1 0 0 0 0 0] where the 1 1.5 2 1 bit is a spike of contamination and 0 0 0 0 is no contamination. 2) A time-resolved spectrum which would correspond to the simulated concentration of Petrol/Diesel (fpc(t,lambda)) was generated, where fpc(t=1,lambda)=fpc(lambda)c(t=1) etc. 3) This time-resolved spectrum is then added to actual measured uncontaminated data from Mannum (fp(t,lambda)). Because the spectral data is linear with concentration we now have a new time-resolved spectrum of clean water + known contamination/simulated contamination of Mannum water spectrum = fpc(t,lambda) + fp(t,lambda) 4) Then starting from the S::CAN factory alarm calibrations an alarm was tuned to return the original simulated contamination time series from the simulated contamination spectrum. The new calibration “global calibration” has been uploaded to the Mannum CON::STAT. Sewage spill (nitrate) simulation: A 1000 mg/L nitrate stock solution was prepared from KNO3. This stock solution was added to 9 Litres of Mannum water, in 15 minute increments, in order to increase the overall nitrate concentration from 0 mg/L to 50 mg/L. Results were recorded every minute (Table 3.3). Table 3.3 Nitrate experimental data (2008-02-19_01-11-00)

Nitrate Experiment (19/02/08)

Time Dose (mL) Total Nitrate Added (mL)

Conc. (mg/L)

01:11 01:25 0 0 0 01:26 - 01:40 4.5 4.5 0.5 01:41 - 01:55 4.5 9 1 01:56 - 02:10 18 27 3 02:11 - 02:25 18 45 5 02:26 - 02:40 45 90 10 02:41 - 02:55 45 135 15 02:56 - 03:10 45 180 20 03:11 - 03:25 270 450 50


34

Figure 3.16 Spectra of laboratory simulation of a sewage spill using nitrate spikes to Mannum water. This simulation was completed earlier than the main evaluation described in the previous section. The fact that the derived nitrate parameter in the S::CAN system was very accurate meant it was not necessary to use a unique simulation to set up the alarm function. Algal bloom simulation: A 1,800,000 cells/mL stock Microcystis (blue green algae) solution was used as the algae component in the experiment. The stock solution was added to 9 Litres of Mannum water, in 10 minute increments, in order to increase the overall algae concentration from 0 cells/mL to 50,000 cells/mL (previous algal bloom – 15,000 cells/mL). Results were recorded every minute (Table 3.4). This experiment was later repeated with an increased turbidity in the Mannum water. To 9 Litres of Mannum water, powdered Kaolin, was added until the turbidity of the water read between 65 – 70 NTU. The algae component was then added in an identical manner to the first algae experiment (Table 3.5).

Abs (m-1)

Abs (m-1)


35

Table 3.4 Algae Experimental Data (2008-02-26_01-30-00)

Algae Experiment (26/02/08)

Time Conc (cells/mL) Algae Added (mL) 03:01:00 - 03:10:00 0 0 03:11:00 - 03:20:00 5000 75 03:21:00 - 03:30:00 10000 75 03:31:00 - 03:40:00 15000 75 03:41:00 - 03:50:00 20000 75 03:51:00 - 04:00:00 30000 150 04:01:00 - 04:10:00 40000 150 04:11:00 - 04:20:00 50000 150

Table 3.5 Algae/Turbidity Experimental Data (2008-02-28_23-32-00)

Algae/Turbidity Experiment (29/02/08)

Time Conc (cells/mL) Algae Added (mL) 02:32:00 - 02:41:00 0 0 02:42:00 - 02:51:00 5000 75 02:52:00 - 03:02:00 10000 75 03:03:00 - 03:12:00 15000 75 03:13:00 - 03:22:00 20000 75 03:23:00 - 03:32:00 30000 150 03:33:00 - 03:42:00 40000 150 03:43:00 - 03:52:00 50000 150


36

Figure 3.17 Spectra of laboratory simulation of algal bloom in Mannum water. The step-wise addition of algal cells contributed to a signal increase in the 200 nm region. This could be the signals of culture medium of the algae. The algal stock was in the culture medium. Additional data processing has confirmed a week signal increase in the 400 nm region which could be caused by the algal pigment. Further data processing is required for confirmation. From the observations so far it may be difficult to set up an algal bloom alarm functions based on absorption of algal pigments. In additional to the measure of algal cells by the pigments, an attempt to use the full spectral information by comparing the spectrum before and after the algal bloom events has shown promising results (Figures 3.18a and 3.18b).

Abs (m-1)

Abs (m-1)


37

a)

0

4000

8000

12000

16000

Sep-06 Nov-06 Jan-07 Mar-07 May-07 Jul-07

Cel

l Cou

nt (c

ells

/mL)

Pre-event(a) Post-event

(c)

Event(b)

b)

0

5

10

15

20

200 300 400 500 600 700

Wavelength (nm)

Abs

(m-1

)

(b-a)

(b-c)

Figure 3.18 a) Algal cell count between September 06 and July 07 and b) spectral data before and after the events. Preliminary results to link S::CAN on-line water quality parameters with water incidents

Figure 3.19 The link between Coliforms and nitrate

0

0.5

1

1/10/06 29/10/06 26/11/06 24/12/06

S::C

AN

Nitr

ate

(mg/

L)

010000200003000040000

Col

iform

s (c

ells

\100

mL)


38

Figure 3.20 The link between turbidity and Cryptosporidium These plots indicate it may be possible to use nitrate as surrogate for Coliforms and turbidity for Cryptosporidium. If so, S::CAN can be used to provide early warning of water incidents. This is all the work achievable within the capital project. Further work will be required to link water incidents with existing data; in addition, special surrogate parameters need to be developed based on a number of parameters that would improve the accuracy of the warning system.

0

20

40

60

1/01/07 29/01/07 26/02/07 26/03/07

S::C

AN

Tur

bidi

ty(F

TU)

0

5

10

15

Cry

pto

(cel

ls/1

0L)


39

4 WORONORA (SYDNEY WATER) TREATMENT PROCESS CONTROL AND WATER DISTRIBUTION NETWORK MANAGEMENT CASE STUDY.

4.1 Introduction The goal of Water Quality Managers is to maintain a safe water supply that is palatable to customers. Maintaining a disinfectant residual throughout the distribution system is an effective way of ensuring the aesthetic and bacteriological quality of the water at the customer tap. Managing disinfection residual in distribution systems can be a very challenging task. Chlorine is one of the most widely used disinfectants in drinking water treatment. The benefits of chlorine disinfection are well known and include low cost, a broad range of effectiveness and the added advantage of remaining active within the system for a considerable length of time. There are also a number of disadvantages in using free chlorine, such as reaction with natural organic matter (NOM) and formation of disinfection by-products (DBPs) that make managing chlorine residuals within a distribution system more difficult. The use of alternative disinfection process such as chloramination, has proven to be beneficial in some situations, such as reduction in trihalomethane (THM) levels. Chloramination is the addition of ammonia and chlorine compounds separately into a pipe or tank (usually anhydrous ammonia and hypochlorous acid). Chloramination requires significantly more careful control of water chemistry than chlorination. Chlorine reacts with ammonia to form three inorganic chloramine species as shown in equations 1, 2, and 3. Monochloramine is the preferred disinfectant species, being more biocidal while having minimal taste and odour. HOCl + NH3 → NH2 Cl (monochloramine) + H2O (1) HOCl + NH2 Cl → NH Cl2 (dichloramine) + H2O (2) HOCl + NH Cl2 → NH Cl3 (trichloramine) + H2O (3) The formation of these competing reactions is highly dependent upon pH, the chlorine:ammonia ratio (Cl2:NH3 expressed as NH3) and to a lesser extent temperature and contact time. Ideal conditions for monochloramine formation are those in which the pH range is between 8.0 to 8.5 with a Cl2:NH3 ratio of 4:1. By targeting these conditions, it is possible to ensure that monochloramine is the predominant species and so minimise the risk of water quality issues. Chloramination was first successfully applied in 1926 in the US for taste and odour control, and became increasingly popular. Sydney’s water supplies were initially (1940) chlorinated as disinfection process and it was not until the early 1960s that the first trial of chloramination commenced. Currently, Sydney Water has 7 chloraminated and 6 chlorinated water systems. One main focus in chloraminated systems is to minimise nitrification and the practice adopted by Sydney Water is to optimise the total chlorine setpoint at the water filtration plant and maintain a Cl2:NH3 ratio of 4:1 in the distribution system by locking up the free ammonia using secondary disinfection in the form of tablet dosing (calcium hypochlorite) at key storage reservoirs. This chapter describes a case study of monitoring water quality changes in the Woronora Distribution System in order to investigate the benefit of using an on-line water quality monitoring system as part of the disinfection management process. 4.2 Experimental The S::CAN Spectro::lyser selected in this project comprises of a robust but sensitive submersible double beam full spectrum UV/Vis-spectrometer with on-board data logging facility. It has no moving parts and has the advantage of reagent free operation which suits well for drinking water applications. It can provide direct readings of UV absorbance (abs) the same as the laboratory equipment and spectral data can be analysed using software to calculate several parameters (such as total organic carbon (TOC), dissolved organic carbon (DOC), nitrate, nitrite, turbidity, total suspended solids, and particle size).


40

Table 4.1 Set up locations and duration of the two UV/Vis-spectrometers used in this study.

During the study, two S::CAN UV/Vis-spectrometers were used at some key locations to monitor changes in water quality. A smart monitoring program was set up to allow the use of only two units to study an entire arm of the distribution system (Table 4.1). The examples selected in this paper included: 1) identifying water quality variation at the distribution system entry point, 2) impact of chloramine residual on organic character change along the distribution system, 3) water character change in main trunk and further downstream in the distribution network. This investigation also included an extensive study of organic character change in several key sampling locations along the distribution system using advanced organic characterisation techniques, such as high performance size exclusion chromatography (HPSEC), together with standard water quality parameters such as UVabs at 254.nm (UV254), DOC etc. These techniques were applied to samples collected from

a) Woronora source water (plant inlet) b) Woronora Water Filtration Plant (WFP) outlet c) Section Valve 3 d) Menai reservoir inlet e) Menai reservoir outlet f) Illawong reservoir inlet g) Illawong reservoir outlet h) Customer taps close to the end of the network (Bond Place – Menai Zone and Bignell Street –

Illawong Zone) 4.3 Results and Discussion Benefits of On-line Water Quality Monitoring In recent years, considerable research effort has been expended to understand the impact of various forms of NOM on drinking water treatment processes. It is becoming a common practice for plant operators to perform regular UV254 measurement to assess treatment performance (van Leeuwen et al., 2005). UV/Vis absorbance measurement is a very simple but informative analytical technique, particularly when used in full spectrum mode (Langergraber et al., 2002). The Woronora system consists of the catchment of the Woronora River (area 75 km2), which feeds into the Woronora Dam. The raw water quality is in the category of low colour, low turbidity and low DOC. The Woronora WFP has a production capacity of 160 ML/d and can be readily upgraded to 220 ML/d. The WFP incorporates a high rate contact filtration process employing ferric chloride and polymeric coagulants and deep bed filters of coal and sand. It also includes a lime/CO2 buffering process to control corrosion, and a manganese removal process (Veolia Water, 2006).

ID Task Name Start Finish1 WTP Inlet Fri 2/09/05 Wed 28/09/052 WTP Outlet Wed 25/05/05 Wed 28/09/053 Menai Inlet Tue 1/11/05 Mon 14/11/054 Menai Outlet Fri 4/11/05 Mon 28/11/055 Bond Place Mon 14/11/05 Mon 21/11/056 Bignell St Mon 21/11/05 Mon 28/11/05

May Jun Jul Aug Sep Oct Nov


41

(a) (b)

Figure 4.1 On-line monitoring of (a) UV254 and (b) DOC for both raw and treated water over a period of time (1 month). The DOC data presented is only for illustration purposes. One of the major benefits of using on-line monitoring instrument is the continuity of water quality information that can capture events usually missed by grab sample monitoring program. In Figure 4.1, the UV254 and calculated DOC for both raw and treated water were presented. The DOC concentrations presented were not validated (no local calibration was performed at the time of monitoring) and may be different from the laboratory readings. Figure 4.1 shows a conventional way of presenting on-line water quality data (parameter against time). It can be used to pick up water quality change events and the DOC calculating function can provide water quality managers with a commonly used parameter to assess water quality. This demonstrates part of the benefits of using on-line instrumentation. Identifying Water Quality Variation at the Distribution System Entry Point One of the important aspects of using on-line water quality monitoring systems is the ability to obtain real-time water quality data to allow operators a quick assessment / adjustment without delay (no need to wait for the laboratory analytical report). This will provide operators with a useful tool for maintaining stable water quality and it is believed to be a good practice to reduce the risk of problems in the distribution system. With the capability of recording a full spectrum continuously (ie, 5 min per spectrum or faster), more useful analytical information can be generated. However, this has also prompted the need for a different way of presenting the information which is easily assessable and readable for operators. Several custom made softwares/procedures were introduced to process and visualise the spectral change over time. Although during an event the absorbance signal may vary and can be picked up by conventional single wavelength signal against time plot, the use of a derivative spectrum (a different mode of presenting the spectral information) can more easily distinguish changes. This is particularly useful for applications where hundreds of spectra are required to be inspected visually. The software included a fast screening step to display the derivative spectra to identify a period of interest then “zoom in” for further investigation which can include more detailed spectral analyses, such as 3-D, contour or colour map plots to identify relative changes in spectra against time and together with other available on-line water quality or operational data to identify the cause of event and determine possible corrective actions. Below is an example of identifying the cause of water quality change at the WFP by monitoring the treated water quality (WFP outlet). During the monitoring period a very small trough (reduction of UVabs signal / calculated DOC) was observed, likely not notifiable with normal operational monitoring. By inspecting the derivative spectra as described previously, a spectral shift was clearly identified (Figure 4.2). The derivative spectrum at the trough occurrence was a standout from the others. With the additional on-line data, it was noted that the WFP water flow was reduced during that period. The reduction of UV254 signal generally refers to the reduction of DOC / chlorine demand and the chlorine dosing system would automatically adjust the dose via a feedback control loop based on chlorine residual at the outlet.

2

3

4

5

01/09/2005 08/09/2005 15/09/2005 22/09/2005 29/09/2005

Abs

254

(m-1

)

Plant Inlet

Filtered Water

1

2

3

4

01/09/2005 08/09/2005 15/09/2005 22/09/2005 29/09/2005

Estim

ated

DO

C (m

g/L)

f

Plant Inlet

Filtered Water


42

Figure 4.2 Illustration of the advantage of using UV/Vis-spectrometer for feed-forward control of the chlorine dosing system at the WFP. From the chlorine dosing record, the chlorine dose was also automatically reduced; however, this showed the chlorine demand prediction algorithm detected the change 20 min earlier compared with the conventional feedback control. This demonstrated well the potential advantage of using the UV/Vis-spectrometer signal for feed-forward control of the chlorine dosing system. The response of the control can be improved over the current feedback control based on the chlorine residual at the storage tank. Impact of Chloramine Residual on Organic Character Change along the Distribution System Organic matter is one of the key water parameters related to disinfection and distribution system management. UV absorbance measurement is a good tool to monitor organics in water and in some cases both the concentration and character are important. One of the aims of this project is to monitor organic character change along the distribution system in order to identify issues and develop management strategies based on water quality change. The difficulty of using UV absorbance for distribution system investigation is the chloramine residual in the water as chloramine contributes to UV absorbance signal (Figure 4.3).

Figure 4.3 UV absorption spectrum of chloramine dosed Woronora Water in the laboratory.

0

500

1000

1500

2000

2500

3000

3500

4000

00:00 01:12 02:24 03:36 04:48 06:00 07:12 08:24 09:36 10:48 12:00

Time

Flow

(m3/

h)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

DO

C (m

g/l),

Cl

Deriverative Spectra

-0.1

-0.08

-0.06

-0.04

-0.02

0200 250 300 350 400

~20 min Lag

Refined chlorine demand indicator from the most sensitive wavelength - 255nm

Chlorine usage

Previously developed chlorine demand indicator

Plant flow

Calculated DOC

6/6/05

0

0.1

0.2

0.3

0.4

0.5

200 250 300 350 400Wavelength (nm)

Abs

orba

nce

(cm

-1) Woronora WaterWoronora Water + 2mg/L chloramineWoronora Water + 5 mg/L chloramine


43

In this part of the investigation, an advanced organic characterisation tool was used in conjunction with on-line UV absorbance measurement. HPSEC is a laboratory based analytical technique. It is a simple characterisation technique for NOM. It separates NOM constituents based on a differential permeation process, according to molecular weight (size). It is well understood that chlorination can alter organic character. This was demonstrated by the change of molecular weight distribution profile (chromatogram) after chlorination and chloramination above 5 mg/L as total chlorine (Chow et al., 2006). In Figure 4.4, the molecular weight profiles (500 to 2000 Da) from samples collected at key locations along the Woronora distribution system were very similar (almost identical). These results confirmed that chloramine concentrations below 2 mg/L as total chlorine have no impact on organic character (molecular weight profile).

Figure 4.4 Chromatograms (HPSEC scans) of water samples collected along the Woronora distribution system. Also in this study, we have discovered that extra analytical information can be obtained from HPSEC to study chloraminated distribution systems. There were several low molecular weight peaks present in the chromatogram which were believed to be related to chloramine rather than organic compounds. It also appeared that the size of these peaks aligns well with detention of the distribution system (larger peak in downstream location). Further investigation is under way.

Figure 4.5 On-line data of UV254 and total chlorine at the Menai outlet. ♦ Marks the times for chlorine tablet dosing and the UV/Vis-spectrometer signal between 18/11/05 to 24/11/05 was removed due to an unidentified problem.

0.0000

0.0005

0.0010

0.0015

10 100 1000 10000Apparent Molecular Weight (Da)

UV2

60nm

(cm

-1)

HWO2 29/9SV3 28/9Menai Inlet 28/9Menai Outlet 28/9Illawong Inlet 28/9Illawong Outlet 28/9

Organic

Unknown peaks

2

2.2

2.4

2.6

2.8

3

31/10/05 7/11/05 14/11/05 21/11/05 28/11/05 5/12/05

Abs

254

(m-1

)

0

0.5

1

1.5

2

Tota

l Chl

orin

e (m

g/L)

Tablet Dosing


44

By comparing the samples collected at different sampling locations, these “chloramine” peaks showed large daily variation in sampling locations with nitrification history. This may provide some useful information in disinfection management. More importantly, this finding also demonstrated that the commonly used UV254 will not be suitable to monitor organics in chloraminated systems, as the change in the UV absorbance signal may also be due to the change in chloramine concentration. In Figure 4.5, the plot of total chlorine against time indicated total chlorine increased after tablet dosing and the same trend was registered by the UV254 measurement. This result confirmed that the chloramine residual can impact UV254 measurement. This observation can also be considered as an additional application of UV254: to monitor chloramine residuals. During November 2005 a total of 22 samples from Menai tank outlet were analysed for molecular weight distribution using HPSEC. The molecular weight profile of the “organic” region (500 to 2000 Da) was almost identical, except that variation was observed in the low molecular weight region (Figure 4.6). This confirmed that part of the variation in 254 nm absorbance was caused by chloramine residual rather than organics. A new parameter “compensated 254 nm” based on using 265 nm also (chloramine shows its strongest signal in 265 nm) was developed to estimate the contribution of the “chloramine” component in UV254 (organic) measurement.

Figure 4.6 The molecular weight profile of a series of samples collected from the Menai outlet between 2/11/05 to 28/11/05. Results are shown only for five samples. Water character change in main trunk and further downstream in the distribution network. Water quality change between two locations can be used to assess the conditions of a tank, a section of a pipe, or a problematic zone. This information can be used for an early warning system and to set up corrective action plans. Two S::CAN UV/Vis-spectrometers were installed at two locations, upstream and downstream of the specific area of interest. The assessment was based on the signal difference between the two on-line units. The assessment procedure was similar to that previously mentioned. In addition, due to the requirement of comparing signals from two units, specific software was developed for averaging and subtraction. Several studies were conducted in the Menai zone. One of the UV/Vis-spectrometers was installed at the Menai tank outlet over a one month period and the second unit was located first at the Menai tank inlet, Bond Place (which is a customer tap located close to the Menai tank) and then at Bignell Street (a customer tap located in the Illawong zone – close to the end of the distribution system). Based on this set up, the Menai tank, section from Menai outlet to Bond Place and Menai outlet to Bignell Street were assessed. These results are not shown.

0.0000

0.0002

0.0004

0.0006

0.0008

0.0010

0.0012

10 100 1000 10000Apparent Molecular Weight (Da)

UV

260n

m(c

m-1

)

2-Nov10-Nov12-Nov19-Nov26-Nov


45

4.4 Conclusions Although this work did not demonstrate the real-time control of chlorine dosing at the WFP, it has certainly illustrated the feasibility of using on-line UV absorbance measurement for feed-forward control (in the first example). The second example demonstrated that tablet dosing (variation of chloramine concentration) at the Menai tank can impact on UV254 measurement for organic character. However, with the processing power of the software, the part of the UV254 signal contributed by chloramine can be compensated for. Due to the low chloramine concentration used in the Woronora system, organic character change along the distribution system was not observed. Thus, we did not demonstrate the usefulness of the compensated UV254 (organic) measurement in this study. It should be repeated in a distribution system which operates at higher chloramine concentration.


46

5 DARWIN (PAW) SOURCE WATER, DISINFECTION AND NETWORK MANAGEMENT CASE STUDY

5.1 Introduction The Darwin water supply consists of a 90% surface water supply from the Darwin River Dam and a 10% groundwater supply from McMinns borefield. The Darwin River Dam is a protected catchment, it supplies 38,000 ML (sufficient for 150,000 people) per year to the water supply system in Darwin and is the largest supply in NT. It is the third largest body of permanent fresh water in Australia. It contains 265,000 ML of water and covers 41 square kilometres with eight metres as the average depth. Darwin’s annual rainfall is highly variable and thus strongly affects the water levels in the Darwin River Dam. The level of water can drop 0.8 metre by abstraction for drinking water and 1.8 metres due to evaporation (Power and Water Corporation, 2006). The biannual change in hydraulics due to distinct wet and dry seasons affects water quality. In addition, the uneven detention times, excessive chlorine residuals, poor microbial compliance in aspects of the system, bore flooding during the wet season and high temperatures, are water quality issues in this supply. The Darwin water supply does not require treatment, only disinfection. Primary chlorination consists of gaseous chlorine injection at Darwin River Dam with secondary chlorination at McMinns, where the water is blended with the groundwater supply. Current disinfection practice aims for Giardia inactivation (Ct = 30min) at the protected catchment, then maintenance of a residual in the distribution system. Chlorination dosing is flow paced with residual trim for changes in chlorine demand. The dose rate aims to achieve 1.5 - 2.0 mg/L primary and 1.0 - 1.5 mg/L secondary chlorination. On-line Cl2 residual analysers are located at the primary dosing point, secondary dosing point and at the end of reticulation system - all connected to the Supervisory Control And Data Acquisition (SCADA) system. Dosing is controlled through daily checks with hand meters in the distribution system, aiming for 0.1 - <0.6 mg/L chlorine residual as measured at the tap. The age of water in the distribution system is approximately 2 - 4 days. The objectives of this case study were to develop strategies for better control of dosing subject to system (water quality variation due to seasonal variation) change, better control of residuals at the end points of the system and a better understanding of chlorine dosing control and organic matter in water. 5.2 Experimental Two components were set up in this case study: 1) to monitor water quality variation at the Darwin River Dam (turbidity & organics) and the impact of chlorination and 2) to study the impact of chlorination on organic change in the distribution system (Palmerston and Tiwi tanks). The Darwin River Dam section is focussed on source water quality monitoring and the last two sections, the Tiwi Pump Station and Palmerston Tank monitoring, were used to demonstrate the ‘chlorine demand concept’, which was introduced earlier. The key locations of this case study are illustrated in Figure 5.1.


47

Figure 5.1 Key locations of this case study: Darwin River Dam, Palmerston, Tiwi and Darwin city and the first and second chlorine dosing stations. An S::CAN Spectro::lyser was used in this case study. It was set up at three different locations during the study period (11/1/06 to 22/5/06). The three different locations were 1. Darwin River Dam, prior to chlorination (11/1/06 to 21/2/06); 2. Palmerston Tank (21/4/06 to 22/5/06); 3. Tiwi Pump Station (tank) (21/2/06 to 21/4/06). Figure 5.2 illustrates the relative location and period of monitoring at each location.

Figure 5.2 Illustration of the experimental plan and set up of the case study.

1st Cl22nd Cl2

PalmerstonTank

McMinns chlorine dosing2nd Disinfection

Tiwi Tank

Darwin City

8km11/01/2006 - 21/2/2006

21/04/2006 - 22/5/2006 21/02/2006

- 21/4/2006Turbidity

Cl2 residual monitored by chlorine analyser

Cl2 residual monitored by chlorine analyser

Chlorine dosing1st Disinfection


48


The S::CAN Spectro::lyser was installed at the off-take tower to monitor the untreated (prior to chlorination) raw water quality of the Darwin River Dam through the 2006 wet season (December to February). During the wet season, poor quality water was expected to enter the supply. The probe was in place from 11/1/06 to 21/2/06, and turbidity readings were taken by an on-line turbidity meter every 13 hours 34 minutes throughout that period. Figure 5.3 shows the set up of the S::CAN. (a)

(b) (c)

Figure 5.3 a) Off-takes tower, b) on-line turbidity meter and c) S::CAN set up.

Palmerston Tank

The S::CAN unit was installed at Palmerston elevated tank. The tank is located approximately 8 km downstream of the secondary chlorination facility at McMinns. A Google Earth image is shown in Figure 5.4. Palmerston Tank is the first major service reservoir following chlorination. The aim was to check whether there was any significant variation in organic content of the water following chlorination. The probe was in place from 21/4/2006 to 22/5/2006 and chlorine meter readings at the site were taken every 10 hours and 26 minutes.

Figure 5.4 Google Earth image of the Palmerston Tank.


49

Tiwi Pump Station (Darwin City)

The S::CAN unit was installed at a Power and Water facility at the extremity of the distribution system. Furthermore, there is an on-line chlorine analyser at this site and residual chlorine data was collected every 10 hours 26 minutes throughout the study period. Historically, this location has low chlorine residuals. The aim of monitoring this site was to identify changes in organic character and determine impacts on chlorine residuals. The S::CAN probe was in place from 21 February to 21 April 2006. 5.3 Results and Discussion Darwin River Dam (Source Water Monitoring)

The Darwin River Dam experiment was targeted on source water monitoring. Source water quality is very important for operators to maintain good and safe water supply. In this case, the surface water quality can be quite variable and monitoring and reaction to water quality changes has historically been a difficult task for operators.

Figure 5.5 Turbidity comparison between on-line turbidity meter and S::CAN in Darwin River Dam.

Figure 5.5 overlays turbidity plots from the S::CAN determined turbidity and the on-site on-line turbidity meter. The turbidity trends of these two curves are quite similar except that the S::CAN readings are several times higher. From a separate laboratory study, it was confirmed after appropriate calibration, that S::CAN can provide accurate turbidity measurement when compared with an on-line turbidity meter (Figure 5.6). Further investigation indicated that the initial (before 22/1/06) – turbidity readings were very similar. When the high turbidity water entered the off-takes, the S::CAN turbidity reading was several times higher. This “amplification” could be the result of inadequate cleaning of the sensor. It was initially believed that air cleaning may not be necessary for drinking water applications, however, from these results, it was established that an automatic air clean is required for unfiltered drinking water sources.

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Figure 5.6 A side by side comparison of S::CAN turbidity and a Zullig Cosmos 25 on-line turbidity meter.

Figure 5.7 shows the UV254 monitoring in Darwin River Dam water during the wet season. (December to February). By comparing UV254 results of a previous case study that relied on grab samples, it was confirmed that the readings were equivalent. However, the fact that the signal decreased when turbidity increased could be the result of inadequate cleaning.

Figure 5.7 Darwin River Dam UV254 monitoring. Palmerston Tank

Palmerston tank is in the middle of this water distribution system, and it is located 8 km after the secondary chlorine dosing station at McMinns. Monitoring the variation of organic matter at this site was the original purpose of this study. Figure 5.8 shows there is significant variation in organic content of the water in Palmerston Tank. Variation of UV254 between 3 to 5 (m-1) is equated to a 3 day chlorine demand of 2.3 mg/L to 3.2 mg/L respectively, based on the previously developed algorithm.

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Figure 5.8 UV254 monitoring at the Palmerston Tank Tiwi Pump Station

Tiwi Pump station is at the end of the Darwin water distribution system. The extremity of any water distribution system is a challenging location for operators to maintain good quality water. The UV254 readings were lower compared with Palmerston Tank and the Darwin River Dam (Figure 5.9).

Figure 5.9 UV254monitoring at the Tiwi Pump Station The observed drop in UV254 could be related to the chlorination process, as chlorine will react with organics and results in UV254 decrease.

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Data interpretation with all three locations taken into consideration The aim of this project was to establish a link between UV254 (prior to chlorination) and chlorine residual (downstream). Hence this case study was set up as a pre-trial to obtain water quality information for a later full trial. To achieve the aim mentioned, several elements of analytical data are required.

1. S::CAN at Darwin Dam, first chlorine dose, Cl2 residual before McMinns re-dosing station 2. S::CAN before McMinns, second chlorine dose, Cl2 residual at Palmerston 3. S::CAN at Palmerston, second chlorine dose or Cl2 residual at Palmerston, Cl2 residual at

Tiwi Tank a)

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Figure 5.10 a) UV254 monitoring at the three locations and b) chlorine residuals at Palmerston and Tiwi Pump Station

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53

Recommendations

• From this study (data summarised in Figure 5.10), we did not have the required water quality data (3 options listed) to fully characterise the behaviour of the distribution system.

• Some issues with running the system without the use of air cleaning have been identified. For future case studies, air cleaning should be included, especially in systems in which no clarification process is applied.

• For distribution system monitoring, more than one S::CAN will provide a lot more useful information.


54

6 CENTRAL HIGHLANDS WATER NETWORK MANAGEMENT CASE STUDY

6.1 Introduction The Central Highlands Water (CHW) Improvement of Water Quality – effect of reservoir cleaning – case study was mainly focused on application of S::CAN UV-vis spectrophotometers as a tool to manage facilities along the water distribution system. The Tinworth Avenue Tank was selected for this case study. This is situated in Mt. Clear just off Tinworth Avenue (VicRoads Country Street Directory ref. Map 572 E5). This tank has a 9 ML capacity with 300 mm inlet and 500 mm outlet with good flow rates. The normal inspection procedure comprised of periodic visual inspection and if a build up of sediment was evident, a cleaning operation was organised. In this case, water quality was measured between two locations (before and after the tank) to assess the conditions of the tank and attempt to use this information to establish the tank cleaning program. To facilitate this case study, a tank cleaning operation was schedule within the monitoring period to allow a comparison of water quality before and after the cleaning operation. However, during the case study, disinfection was switched from chloramination to chlorine in order to control nitrification and this complicated the case study. a)

b)

Figure 6.1 a) Google Earth image of the Central Highlands Water Tinworth Ave Tank and b) schematic of the S::CAN spectrometers set up.

Inlet Outlet

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55

6.2 Experimental Two S::CAN Spectro::lysersTM were installed at the inlet and outlet respectively of the Central Highlands Tinworth Ave Tank. The on-line monitoring period was from 18/9/06 to 24/10/06. The Tinworth Ave tank was cleaned on 2/10/06. During this study, an unexpected change in disinfection strategy from chloramination to chlorination (in terms of case study planning) was carried out by the operators at the treatment plant on 26/9/06. Tank flow data between 18/9/06 to 5/10/06 was provided by the operators. The flow velocity in litres per second was recorded every 32 minutes. The two S::CAN Spectro::lysersTM took readings every 10 minutes from the beginning until 29/9/06. The recording interval was changed to 2 minutes after that time. In this case study, a grab sampling program was used in conjunction with on-line monitoring for advanced organic characterisation analyses. The apparent molecular weight (AMW) of the UV absorbing NOM constituents was determined by HPSEC. HPSEC was conducted using a Waters Alliance 2690 separations module and 996 photodiode array detector (PDA) at 260 nm (Waters Corporation, USA). Phosphate buffer (0.02 M) with 0.1 M NaCl was flowed through a Shodex KW802.5 packed silica column (Showa Denko, Japan) at 1.0 mL/min. This column provides an effective separation range from approximately 50 to an exclusion limit of 50,000 Da AMW was derived by calibration with poly-styrene sulphonate (PSS) molecular weight standards of 35, 18, 8 and 4.6 kDa. The bacterial regrowth potential (BRP) analysis was performed as described by Withers et al. (1999). Samples were collected in glass bottles, stored at 4°C and analysed within 24 hours. All glassware was sterile and had been washed in sodium hydroxide solution. The water sample was filtered through pre-rinsed glass fibre filters to remove large particulate matter. All filters used were pre-rinsed with 1 L of Milli Q water to prevent DOC contamination from wetting agents and preservatives. An inorganic nutrient salt solution was then added to the water sample in the ratio 1:11, to ensure that assimilable organic carbon was the limiting nutrient. The sample was sterile filtered through pre-rinsed polycarbonate membranes (0.2 µm, Poretics, USA). A 250 mL aliquot of sample was placed in the 300 mL cuvette and inoculated with the bacteria which were washed off the polycarbonate membrane and resuspended in isotonic saline solution. The necessary volume of inoculum was determined by turbidity measurements. The ideal bacterial concentration was between 1 x 104 - 5 x 104 cells/mL. Each cuvette received the same volume of inoculum. The samples were then incubated at 20°C for a minimum of 72 hours with turbidity measurements taken every 30 minutes. Turbidity was measured at 12o forward scattering. A conversion factor calculated from over 150 standard acetate additions allows the BRP of a sample to be expressed as µg acetate carbon equivalents (ACE) per litre. For chlorinated samples, stoichiometric de-chlorination was performed using sodium thiosulphate prior to analysis. 6.3 Results and Discussion The main part of this case study was to compare water quality between the inlet and outlet of the tank. Three commonly used water quality parameters, turbidity, UV254 and DOC, were used for this comparison (Figure 6.2). The initial comparison indicated higher turbidity in the inlet than in the the outlet. This indicated that some particles were settled in the tank and contributed to better water quality at the outlet. However, when comparing UV254 and DOC readings, higher concentration of DOC were evident in the outlet than inlet. This indicated some organic matter was released from the tank. These results were contradictory. Further understanding about turbidity compensation of UV254 and DOC determination provided some indication that UV254 and DOC readings could be over compensated in this situation. This was supported by the fact that during the case study, the flow was increased in stages and three distinct data stages were observed based on the provided flow data (Figure 6.2). For ease of interpretation, three stages are marked in the Figure. At around 20/9/06, flow increased. This contributed to a short period of turbidity variation followed by a steady increase in turbidity in the water entering the tank (inlet). Further increase was observed in stage 3 (highest flow). On the outlet side, the turbidity varied initially but during the flow change at 20/9/06, it was relatively stable as compared with the increase in turbidity at the inlet. This indicated that more particles may be settled in the tank than previously anticipated. It can be considered that the inlet S::CAN was exposed to higher turbidity water and previous experience has shown that when air cleaning is not adequate, build up of deposits on the sensor window could impact on parameters requiring solids compensation determination, such as UV254 and


56

DOC. This could help explain the confounding results. In this situation, it was believed that the measurement at the outlet would provide reliable measurements of UV254 and DOC. Due to the issue mentioned above, the intended comparison of the water quality at the inlet and outlet was not possible. However, the findings led us to investigate the impact of flow on water quality through a tank. Based on the results presented in Figure 6.2, high system flow would contribute to poor water quality due to disturbance of the sediment. In this case, it is possible to optimise system flow against water quality using on-line monitoring.

Figure 6.2 Water flow and inlet and outlet, turbidity, UV254 and DOC. Additional organic characterisation, including BRP and HPSEC, was included in this case study, a summary of sampling and result is presented in Figure 6.3.

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Figure 6.3 A summary of the grab sampling program and BRP results. The purpose of this case study was to use water quality change between two locations as a tool to assess the conditions of a pipe or tank. On-line monitoring using S::CAN was discussed before and in this case organic characterisation techniques were included. However, due to a change of disinfection strategy during the course of this case study, the results obtained were not appropriate for the purpose of comparison. The results are presented, however, just for the completeness of the study. a) b)

Figure 6.4 HPSEC molecular weight profiles, organised by a) inlet and b) outlet.

Inlet Outlet

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Grab Samples – Lab results28/9/06 Monochloramine: 1.8mg/L Monochloramine: 1.8 mg/L

BRP: 87µg/L BRP: 75 µg/L

2/10/06 Free Chlorine: 0.8 mg/L Monochloramine: 1.8 mg/L BRP: 87µg/L BRP: 99 µg/L

10/10/06Free Chlorine: 1.1 mg/L Free Chlorine: 0.5 mg/L BRP: 170 µg/L BRP: 168 µg/L

On-line Monitoring Period 18/9/06 to 24/10/06Change over from Chloramination to Chlorination 26/9/06 at the plantTank was clean (diver in) on 2/10/06

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Figure 6.5 HPSEC results, organised by sampling date.

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7 YORK RE-DOSING STATION (WA) CHLORAMINATION OPTIMISATION USING UV-VIS SPECTROSCOPY CASE STUDY

7.1 Introduction The York re-dosing station (Figure 7.1) is part of the Goldfields and Agricultural Water Supply. The main pipe goes from Mundaring Weir, where the drinking water treatment plant is located, across to Kalgoorlie, to supply water to the gold mines. There are several branches, or extensions, of this main pipe. York re-dosing station belongs to one of the extensions which is close (between 100 and 150 km) to the Mundaring Weir. At the re-dosing station, chlorine is dosed to top up the free ammonia incoming in the pipe. The case study was mainly for the trial of the CRCWQT project 2317: ammonia analyser. The analyser was set up at a sampling point along the distribution system just before the dosing of chlorine (see Figure 7.2). The aim was to determine the inlet free ammonia concentration to estimate the required chlorine dose. In parallel with this ammonia analyser trial, two S::CAN on-line UVabs spectrometers were set up, one before and one after the chlorination dosing point, in order to assess a new measurement idea of real-time monitoring of chlorine to ammonia ratio using UVabs technique.

Figure 7.1 Scheme of the Goldfields and Agricultural Water Supply (GAWS) owned by Water Corporation. York re-dosing station is surrounded by the red ellipse.


60

Figure 7.2 Scheme of the installations at York. Figure 7.3 shows the free ammonia concentration that was detected in the pipe by the ammonia analyser. It can be seen that the concentration is pretty stable and is around 0.6-0.7 mg/L of NH3. It is believed that the two sudden drops were outliers because there is only one point of measurement for each of these drops, unlike the other variations that contain several points of measurement. This must be due to an expected disturbance during measurement.

Figure 7.3 Results obtained at York on the 22nd and 23rd May 2007 Unfortunately, due to some unexpected problems during the trial, limited data was obtained. First, difficulty in adjusting the correct sample flow was encountered during the connection of the sampling line to the analyser from the main pipe. The pressure in the main pipe was around 20m of water head whereas the analyser requires 3 mL per minute of sample feed. A flexible pipe of 8 mm of diameter

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was connected from the main pipe down to the pump station (Figure 7.4), where the ammonia analyser and the two S::CAN units were set up (sheltered inside the pump station). A T-piece was used to allow a separate water sample line to feed the ammonia analyser and the S::CAN units. Only a small sample volume is required for the ammonia analyser and the rest of the water was used to feed the two S::CAN units (see Figure 7.5). It is important to use a pressure reduction valve to allow proper control of the water flow to deliver the right sample volume to feed the ammonia analyser, otherwise water wastage will result.

Figure 7.4 Sampling lines arriving at the pump station. The travel time of water through the sampling lines is around 30-40s. The temperature of water varied by 0.1°C from the beginning to the end of the sampling lines. S::CAN collects full spectrum data and the software provides the calculation of UV254, DOC, nitrate and turbidity. In this case, we were attempting to obtain additional analytical information from the data relating to a better understanding of the chloramination process. These simple parameters were first assessed to identify trends before the more powerful spectral analysis was performed. Both before and after chlorination data are shown in Figure 7.6. a)

b) c)

Figure 7.5 a) set up of the S::CAN probes. The water coming from the sampling lines overfills the PVC tubes. The flow is around 1L per minute. b) The air compressor is activated every 40min and flushes the two probes at the same time. c) The two S::CAN probes operating at York.


62

a) b)

c) d)

Figure 7.6 a) DOC, b) Turbidity, c) UV254 and d) Nitrate monitoring data over the period of 23/5/07 to 1/6/07. In Figure 7.6a, a large variation of DOC concentration between 24/5/07 and 28/5/07 was observed in the before chlorination data. Further inspection on the turbidity data in Figure 7.6b confirmed that there could be some problems with the measurements as it was not likely to have water of turbidity 100 NTU in the water supply. During this period, the after chlorination data also showed an upward trend for DOC and a downward trend for turbidity. This could be the result of the flow control problem reported earlier. The data collected after 28/5/07 is believed to be more reliable. A zoom in section (1 day) of UV254 before and after chlorination is shown in Figure 7.7. The pattern between the two traces could be used to develop a chlorine dosing control algorithm. At this stage, further interpretation requires full spectral analysis.

Figure 7.7 UV254 monitoring data between 30/5/07 and 31/5/07. Both before and after chlorination data are shown.

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In addition, in Figure 7.6d, the observation of diurnal variation of nitrate requires further investigation. Nitrate detection by S::CAN is a surrogate measurement using the wavelength at 220 nm. This parameter had been evaluated in the Mannum (SA) case study and it was confirmed as accurate. In this case the sensor detected an unidentified chemical compound. Further investigation is required. 7.2 Summary This case study was conducted in conjunction with the main study of evaluation of the CRCWQT project 2317: ammonia analyser. The concept of using UV-Vis as a tool to manage the chloramination process has not been fully evaluated. Unfortunately, the ammonia analyser failed during this trial and comparative data could not be used to assist the interpretation of spectral data extraction of analytical information as parameters to control the chloramination process.


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8 FUTURE WORK

Water Quality Managers throughout the world recognise the importance of maintaining good water quality at the customer tap. This often presents a significant challenge for water system operators and there is a continuous effort to develop better tools for this demanding task. This report presents some examples of the use of on-line water quality monitoring combined with data processing software as a tool to assist operators to maintain stable water quality. The case studies reported only represent a portion of potential applications using on-line monitoring tools in source water, treatment and distribution system management but highlight the need to understand the spectral response to the water character itself rather than relying on a single frequency as a surrogate. This extra spectral information can be used as a tool to achieve the goal of water quality improvement. Several projects currently in discussion will utilise the S::CAN set up at Mannum (SA). This requires setting up on-line instrumentation to monitor stormwater discharge to support a study to assess its impact on water quality at major offtakes. Mannum is one of the proposed study sites and the data from the S::CAN system will be used for the new project. From the Mannum (SA) project, we have learnt the capability of this on-line system and its comparability with laboratory results, obtained some data to set up the alarm system and some preliminary links between general water quality parameters such as nitrate and turbidity and water incidents (coliform and Cryptosporidium, respectively). Recent discussion with Water Quality & Integrated Management (SA Water) focused on the possibility of setting up a project to integrate the Mannum S::CAN on-line / real time signal into the water quality management system as an alarm system for water incidents. Several chlorine dosing control applications have been suggested by SA Water and other project partners. The chlorine residual control issue in the Eyre Peninsula region (SA) may be a good candidate for further testing the chlorine demand calculation and it may be possible to integrate an automatic chlorine adjustment system according to the water quality determined using S::CAN. One of the major benefits of using on-line monitoring instruments is the continuity of water quality information that permits capture of events usually missed by grab sample monitoring programs. This is particularly important for water utilities required to manage a dynamic system with reactive water. Using UV absorbance measurement as a rapid assessment of water quality is the most convenient and easily achievable option.


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APPENDIX 1

Real Time Monitoring of Disinfection By-Products Using Differential UV Absorption Spectroscopy, AWA Water Journal 35(3) 83-87 Refereed Paper.

Korshin GK, Chow CWK, Drikas M (2008)


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APPENDIX 2:

On Line Monitoring of Disinfection By-Products Formation using Differential Absorbance Spectroscopy: An Outline of Principles and Results

Prof. Gregory Korshin, University of Washington


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Research Report

Water Quality Research AustraliaMembership at December 2008

Water Quality Research Australia Limited GPO BOX 1751, Adelaide SA 5001

For more information about WQRA visit the website www.wqra.com.au

The Cooperative Research Centre (CRC) for Water Quality and Treatment operated for 13 years as Australia’s national drinking water research centre. It was established and supported under the Australian Government’s Cooperative Research Centres Program. The CRC for Water Quality and Treatment officially ended in October 2008, and has been succeeded by Water Quality Research Australia Limited (WQRA), a company funded by the Australian water industry. WQRA will undertake collaborative research of national application on drinking water quality, recycled water and relevant areas of wastewater management. The research in this document was conducted during the term of the CRC for Water Quality and Treatment and the final report completed under the auspices of WQRA.

Industry Members• Australian Water Association Ltd• Degrémont Pty Ltd• Barwon Region Water Corporation “Barwon

Water”• Central Highlands Water• City West Water Ltd• Coliban Region Water Corporation• Department of Human Services (Vic)• Goulburn Valley Regional Water Corporation

“Goulburn Valley Water”• Grampians Wimmera Mallee Water Corporation• Hunter Water Corporation• Melbourne Water Corporation• Power & Water Corporation• South East Water Limited• Sydney Catchment Authority• Sydney Water Corporation• United Water International Pty Ltd• Wannon Region Water Corporation• Water Corporation of WA• Yarra Valley Water Ltd• South Australian Water Corporation• Central Gippsland Regional Water Corporation

Research Members• Australian Water Quality Centre• Centre for Appropriate Technology• Curtin University of Technology• Flinders University• Griffith University• Monash University• RMIT University• University of Adelaide• University of NSW• The University of Queensland• University of South Australia• University of Technology, Sydney• University of Wollongong, Faculty of Engineering,• Victoria University

General Members• Cradle Coast Water• Department of Water (WA)• Esk Water Authority• Lower Murray Urban and Rural Water Corporation

“LMW”• NSW Water Solutions, Commerce• NSW Department of Health• Orica Australia Pty Ltd 75


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Palmerston Tank



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Palmerston Tank


water” case studies using s::can on-line monitoring system

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