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Building a quality monitoringsystem for the production of

drinking waterThesis

Allard Naber

[email protected]

Supervisors:

ing. Natasja ’t Hart Atos Nederland B.V.

prof. dr. ir. Marco Aiello University of Groningen

prof. dr. Alex Telea University of Groningen

Date:

01/01/2013

Summary

After the attacks of September 11, 2001 and the publication of the 2020 Strategy by the Euro-

pean Union (EU), monitoring of water quality to reduce public health threats and to improve

drinking water treatment has gained much attention. The latter goal is the main subject of

this thesis. We perform a feasibility study to the development of a monitoring system for the

drinking water treatment process. The system is based on the control paradigm from which

the monitoring part is discussed in detail.

Existing regulations are used to compile a water quality parameter list, used to find appli-

cable sensors. Much sensors already exist and the EU initiatives have led to a number of

projects that develop new sensors or improve existing ones. Design decisions related to the

implementation of sensors are discussed, but definitive choices should be made in a design

phase for such a system.

To analyze sensor data we can use knowledge-driven or data-driven quality models. Know-

ledge-driven models use their physical knowledge to simulate a process. Few models are

available, their performance is limited and development seems to have stalled. Data-driven

models simulate processes purely based on data they saw in the past. A few of these models

exist, but they are fragmented and not enough historical quality data is available to suffi-

ciently train these models. An intermediate modeling step can be realized by using soft sen-

sors. These sensors are pieces of software which combine parameters into new parameters

that can be used by the quality model. This now popular topic in process modeling has no

ready-to-use implementations yet.

We investigate the key requirements to which a control system should adhere, what hard-

ware is necessary and how accurate it should be. Furthermore we indicate several aspects

in which key decisions for the architecture of the system should be made and per aspect we

discuss the several alternatives that are available. A direct contribution of this research is the

overview of commercially available water quality sensors or the lack thereof.

i

Contents

Summary i

Contents iii

List of Figures vi

List of Tables vii

1 Introduction 1

1.1 Water quality monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Foundations of the project . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Measure water quality . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.2 Analyze quality data . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.3 Adapt purification process . . . . . . . . . . . . . . . . . . . . 4

1.2.4 Visualize quality data . . . . . . . . . . . . . . . . . . . . . . . 4

1.3 Drinking water backgrounds . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.1 Water Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.2 Drinking water in The Netherlands . . . . . . . . . . . . . . . 5

1.3.3 Treatment process . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.5 Structure of this report . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2 State of the art 9

2.1 Platforms, consortiums and projects . . . . . . . . . . . . . . . . . . . 9

iii

iv Contents

2.1.1 WssTP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.1.2 ACQUEAU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.1.3 TECHN’EAU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.1.4 Project SAWA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Subjects for Early Warning Systems . . . . . . . . . . . . . . . . . . . . 12

2.3 Quality monitoring initiatives . . . . . . . . . . . . . . . . . . . . . . . 13

2.4 Water quality modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.5 In this project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3 Use Cases and Requirements Analysis 17

3.1 Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.2 Functional Requirements . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2.1 Sensor network requirements . . . . . . . . . . . . . . . . . . 21

3.2.2 Data analysis requirements . . . . . . . . . . . . . . . . . . . . 21

3.3 Requirements discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4 Water quality measurements 25

4.1 Parameter and sensor selection . . . . . . . . . . . . . . . . . . . . . . 25

4.1.1 Water quality parameters . . . . . . . . . . . . . . . . . . . . . 26

4.1.2 Contaminants . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.1.3 Available sensors . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.1.4 Response times and sampling rate . . . . . . . . . . . . . . . . 28

4.1.5 Smart sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.2 Sensor placement and location discovery . . . . . . . . . . . . . . . . 29

4.2.1 Placement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.2.2 Location discovery . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.3 Sensor connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.3.1 Wired sensor connection . . . . . . . . . . . . . . . . . . . . . 32

4.3.2 Wireless sensor connection . . . . . . . . . . . . . . . . . . . . 33

4.3.3 Data transfer method . . . . . . . . . . . . . . . . . . . . . . . 35

5 Quality data analysis 37

5.1 Data input, storage and processing . . . . . . . . . . . . . . . . . . . . 37

5.2 Soft sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.2.1 Combined values . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.2.2 Spatial relation between values . . . . . . . . . . . . . . . . . 39

5.3 Quality data prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Contents v

5.3.1 Knowledge-driven prediction models . . . . . . . . . . . . . . 40

5.3.2 Data-driven prediction models . . . . . . . . . . . . . . . . . 44

6 Evaluation 49

6.1 Water quality measurements . . . . . . . . . . . . . . . . . . . . . . . . 49

6.2 Quality data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.3 Requirement coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

7 Conclusion 53

7.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

7.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Bibliography 59

A Water quality parameters and their legal ranges 65

B Available sensors 69

List of Figures

1.1 De Leeuw’s control paradigm. . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Activity diagram for a water purification control system. . . . . . . . . . 3

1.3 Schematic view of the water cycle. . . . . . . . . . . . . . . . . . . . . . . 5

1.4 Water companies’ supply areas in The Netherlands. . . . . . . . . . . . . 6

3.1 Use case diagram for a water purification control system. . . . . . . . . . 18

4.1 Consequences of small deviations in location estimation. . . . . . . . . . 31

4.2 Sensor network layout with sensor stations. . . . . . . . . . . . . . . . . . 32

5.1 Linear and non-linear approach to modeling the relationship between

water quality and physical watershed parameters. . . . . . . . . . . . . . 40

5.2 SimOx calculated data compared to experimental results. . . . . . . . . . 45

5.3 Model prediction results for the inverse process model for softening. . . 47

vi

List of Tables

1.1 Typical water treatment process steps. . . . . . . . . . . . . . . . . . . . . 7

3.1 Use case: Measure actual intake water quality. . . . . . . . . . . . . . . . 19

3.2 Use case: Calculate expected clean water quality. . . . . . . . . . . . . . . 19

3.3 Use case: Adapt water purification process. . . . . . . . . . . . . . . . . . 20

3.4 Use case: Visualize water quality. . . . . . . . . . . . . . . . . . . . . . . . 20

3.5 Functional requirement prioritization overview. . . . . . . . . . . . . . . 24

4.1 Average location estimation error using various methods. . . . . . . . . . 31

4.2 Common network protocols and their properties. . . . . . . . . . . . . . 33

5.1 The parameters used in the (inverse) softening process model. . . . . . 47

6.1 Requirements traceability matrix. . . . . . . . . . . . . . . . . . . . . . . . 52

A.1 Legal ranges for water quality parameters. . . . . . . . . . . . . . . . . . . 66

B.1 Overview of available sensors. . . . . . . . . . . . . . . . . . . . . . . . . . 70

vii

CHAPTER

1Introduction

Sensor use and monitoring have recently become hot topics in water management.

Water companies, as well as wastewater treatment companies, more and more re-

alize that significant improvements can be reached using modern techniques to

gain more insight in the process of cleaning water. Continuous knowledge about

the quality of the raw or partially treated water can be employed to adapt the treat-

ment process and reduce costs by using less chemicals when possible or optimizing

the use of filters. Also, companies can prove that they satisfy the legal conditions

for clean water and are proactively monitoring this to guarantee the quality of their

product in the future.

The increased popularity of the subject is partially credited to governments. Na-

tional governments limit the freedom of water companies by requiring a price fix

for a certain amount of time, forcing them to save money elsewhere in the process.

Furthermore, the European Parliament established Directive 2000/60/EC [WFD00]

in 2000, which stimulates “the community” to take action in the field of water poli-

cies in order to reach a more sustainable environment. The American Water Works

Association (AWWA) has already predicted water treatment plants will be fully au-

tomated by the year 2050 [WOSR07].

1

2 Introduction

1.1 Water quality monitoring

This project focuses on the monitoring part of a control system for a drinking water

treatment plant. The research is limited to water quality and only elaborates on

measuring and analyzing the quality parameters. The final system should be able

to regulate the purification process in order to produce clean drinking water, even

when the intake water is of lower quality. This feature is considered in the global

architecture description, but is omitted in the detailed discussion to limit the scope

of this research.

Systems for asset management and monitoring and control of the water distribu-

tion network can be placed in the same context, but are outside the scope of this

project.

1.2 Foundations of the project

The global architecture of the proposed monitoring system is based on a complete

control system in which only the monitoring part is elaborated on. In [Lee74] De

Leeuw suggests a paradigm for successful control systems in general. This paradigm

can easily be adapted to a digital system and is clearly applicable to the situation

of water purification. The Adapters in the original paradigm are omitted both for

simplicity and because adapters are a very common pattern in software systems.

The simplified control paradigm is shown in Figure 1.1.

The paradigm is then converted into an activity diagram, Figure 1.2, in which the

activities for water purification are mentioned explicitly. Note that the visualization

activity is not a part of the original control paradigm, but is an essential part of the

control system we consider. For each activity we indicate whether the purification

system or the control system is responsible. Four activities are performed by the

control system, to which we refer by their numbers:

1. Measure water quality, described extensively in Chapter 4.

2. Analyze quality data, described extensively in Chapter 5.

3. Adapt purification process.

4. Visualize quality data.

1.2. Foundations of the project 3

Subject

Control system

Environment

Observer ofinput

Prognosisand model

Steering signalselector

Controlled system

Figure 1.1An adapted version of DeLeeuw’s control paradigm.

Purification system

Control system

Take inraw water

Measurewater quality

Analyze quality data

Adaptpurification process

Purify water

[purification still in progress]

Storeclean water

[purification is done]

Visualizequality data

1 2 3

4

Figure 1.2Activity diagram for a wa-ter purification control sys-tem, based on Figure 1.1.

1.2.1 Measure water quality

For measuring the water quality we evaluate existing options for a sensor network.

This will cover an overview of existing sensors, parameter and sensor selection,

connectivity options, sensor placement and location discovery. For these subjects

we discuss several options and suggest a solution where possible.

1.2.2 Analyze quality data

After measurements the raw quality data is analyzed for several purposes. The pro-

cessed data can be used to predict the quality of the purified water. Eventual adap-

tations in the process will be based on this data. Process controllers and managers

can interpret this data or the data can be visualized before presentation to these or

other stakeholders.

We shortly consider data input and storage, but focus on quality data prediction

mostly. There are several approaches available for predicting the quality of the wa-

ter that results from the purification process.

4 Introduction

1.2.3 Adapt purification process

To extend the monitoring system into a control system, the system should be able

to adapt the process. By modifying certain parameters or certain steps in the pro-

cess, the resulting product can reach a more or less constant quality. The result of

the data analysis serves as input to the modifications. In this project adapting the

process is not considered.

1.2.4 Visualize quality data

Process controllers and their managers should be able to read and interpret the

state of the purification system and the expected state of the resulting product. By

visualization parameters and predictions for future values of these parameters can

be communicated to the responsible employees. Based on the visualization one

can decide to apply some adaptations to the process manually. In this project visu-

alizing the data is not considered.

To limit the scope of this research the first two activities are described in detail, the

last two are not.

1.3 Drinking water backgrounds

In this section we first outline the context of drinking water treatment within the

water cycle, how it is organized in the The Netherlands and then we explain the gen-

eral approach to drinking water treatment. The Netherlands, with its water boards

dating back from 13th century, has a unique approach to water management. The

tasks are divided over many of separate instances. The instances that are the most

important to drinking water will be introduced here. Then we illustrate how the

drinking water treatment process is typically set up. It serves as a basis of knowl-

edge, as the process will be different on every site, but in this report we do not go

into detail about the several individual process configurations.

1.3.1 Water Cycle

The treatment of drinking water is part of the water cycle, as can be seen in Fig-

ure 1.3. Surface water evaporates and will return to the surface as rain. It enters

the ground or other surface waters, which are used as drinking water sources. Af-

ter treatment it is distributed to customers and it will leave them as sewage wa-

1.3. Drinking water backgrounds 5

2 Condensation As the water vapour rises into the air, it gradually cools and collects together to form tiny droplets of water through a process called condensation. It is this process that causes your bathroom mirror to steam up when you have a bath or shower.

1 Evaporation Water is heated by the sun and evaporates to become water vapour. As it’s light, it floats into the air – just like steam rising from a kettle.

On average we supply more than one thousand litres of water a week to each of our 8.8m drinking water customers. We get this water from rivers and underground stores and turn it into high-quality drinking water that we send to your home, work and school for you to use.

Once you have used the water, we recycle it safely back to the environment.

Providing you with water and taking it away again once you have used it, forms part of a larger process called the water cycle.

Evaporation

Condensation

Water useTreatingsewageCompleting

the cycle

Rivers

Clouds Rain

Watertreatment

works

Waterdistribution

1

2 3 4

5

6

78

910

The water cycle

Page 1 of 3

Figure 1.3Schematic view of the wa-ter cycle, from [Wat11].

ter. To maintain the cycle and provide access to drinking water to more and more

people, continuous attempts to optimize the treatment processes are made. Opti-

mized processes ensure that the environment is spared as much as possible —by

limiting the amount of chemicals used in the process for example— and that more

diverse water sources can be used. This is among others stimulated by Directive

2000/60/EC [WFD00] to which we referred earlier.

1.3.2 Drinking water in The Netherlands

In The Netherlands there are ten drinking water companies. They all have their own

distribution areas as illustrated in Figure 1.4. These companies are responsible for

the intake, treatment and distribution of drinking water.

Water quality is monitored by the water companies themselves. Values that are out-

side allowable ranges are reported to the Dutch National Institute of Public Health

and the Environment (RIVM). Since 1997 Vewin, the association for drinking wa-

ter companies in The Netherlands, publishes a benchmark each three years about

the performance of the companies. Drinking water quality is considered, but also

service, environment, finance and efficiency. The results are based on laboratory

measurements and customer interviews. This is a voluntary benchmark in which

all the companies participate [GvB09]. The RIVM publishes water quality results

from the companies on a yearly basis. Vewin and RIVM have developed new guide-

lines for the benchmark in 2004. One of the changes was the calculation of the

Water Quality Index (WQI), a single parameter to describe the water quality, as is

described in [VTM04]. In the past there have been norm overruns, but these did

6 Introduction

Figure 1.4Water companies’ supplyareas in The Netherlands,from [GvB09].

N.V. Waterbedrijf Groningen

N.V. Waterleidingmaatschappij Drenthe

N.V. PWN Waterleidingbedrijf Noord-Holland

Vitens N.V.

Stichting Waternet

Dunea

Oasen N.V.

Evides Drinkwater B.V.

Brabant Water N.V.

N.V. Waterleiding Maatschappij Limburg

not cause health risks. Where possible the situation was investigated and a solu-

tion was implemented.

Sewerage and sewage water treatment are not among the responsibilities of the

drinking water companies. Sewerage is managed by the municipalities and sewage

water treatment by the several water boards. Only Stichting Waternet takes care

of both tasks, but internally these are again two separate companies. All drinking

water companies are public companies.

1.3.3 Treatment process

The treatment process from intake to drinkable water consists of several steps. The

configuration of the process may vary based on the properties and the general qual-

ity of intake water at a specific site. Some steps are not necessary in some locations,

but are in other locations. The configuration for each single step can also vary per

site. For example, there are several types of filtration, rapid or slow, or with com-

pletely different types of filters. Explaining these differences is beyond the scope of

this project. The most used steps for the purification of drinking water are listed

and explained in Table 1.1.

1.4. Contribution 7

Process Explanation

Coagulation Adding a reagent (called coagulant) to destabilize particlesfrom dissolved solids.

Flocculation Destabilized particles agglomerate into flocs, a processthat is improved by adding a reagent (called flocculant).

Sedimentation Heavy particles and the just formed flocs settle to the bot-tom and clear water continues to the next step.

Filtration Water passes through filters of sand, charcoal or othertypes of filters to remove the smallest particles.

Disinfection By ozonation (adding O3) bacteria or microorganisms arekilled. Sometimes chlorination (adding Cl2) is applied, butthis is prohibited in The Netherlands.

Softening Adding NaOH (sodium hydroxide) or Ca(OH)2 (lime) to re-duce water hardness.

Table 1.1Typical water treat-ment processsteps [EPA09, HOSR12].

1.4 Contribution

This report, commissioned by Atos, describes a feasibility study towards the im-

plementation of a monitoring system —and a control system in the future— for

guarding the water quality in a water purification process. Currently, quality mon-

itoring at the water treatment plant is done mainly by sending water samples to a

laboratory that then reports on the results. For 8 million customers, around half a

million tests are performed per year [Wat11]. This takes time and in the case of an

emergency it can happen that contaminants end up in the drinking water that is

distributed to clients. More and more on-line sensors for measuring specific con-

taminants or other water quality parameters are being developed. These sensors

are used to measure individual quality parameters and are read and interpreted by

process controllers. In the future integration of these sensors into an automated or

partly automated control system for water quality monitoring is expected be feasi-

ble. Atos has implemented several control systems in other production processes

and is interested in helping drinking water companies to build such a system.

We investigate the requirements to which a control system should adhere, what

hardware is necessary and how accurate it should be. Furthermore we indicate

several aspects in which key decisions for the architecture of the system should be

made and per aspect we discuss the several alternatives that are available. A direct

8 Introduction

contribution of this research is the overview of commercially available water quality

sensors or the lack thereof.

We conclude with an overview of future work: essential problems that are required

to be solved before a system can be built, but also optimizations that are of later

interest.

1.5 Structure of this report

Before going into detail in the different subjects of the control system, we identify

the current state of the art in sensor usage and water quality monitoring in Chap-

ter 2.

We define the purpose of the system, its use cases and requirements in Chapter 3.

Two of the four key aspects —quality measurement and data analysis— are then

discussed in Chapters 4 and 5 respectively. The results and their coverage of the

subject is evaluated in Chapter 6 after which Chapter 7 concludes this report by

summarizing the results and indicating what work should still be done before a

quality control system for water purification becomes reality.

CHAPTER

2State of the art

Purifying water to make it drinkable is done by humans for centuries. Simple and

advanced processes have been developed through the years or did already exist

in nature. Using on-line sensor measurements and modeling and quantifying the

influence of the treatment process on the quality of water, however, are recent fields

of research with space for investigation and great progress.

In this chapter we list several consortiums, projects and other initiatives that have

been started lately. Then we outline the availability of literature in the fields of

quality monitoring and quality analysis. We do not limit our overview to drinking

water related publications, much of the available information is related to surface

waters or distribution networks.

2.1 Platforms, consortiums and projects

Since the creation of the Water Framework Directive, Directive 2000/60/EC [WFD00],

several platforms and projects were initiated in order to improve research to water

in its broadest sense. The following overview indicates the context in which this

research can be placed and it outlines that realizing a monitoring system can con-

tribute to lowering the use of chemicals and energy consumption and move for-

ward to a more sustainable water supply.

9

10 State of the art

2.1.1 WssTP

The Water supply and sanitation Technology Platform (WssTP) was initiated by the

European Commission in 2004 to stimulate “collaborative, innovative, visionary

and integrated research and technology development strategy” for the European

water sector [HB10a]. The main driver is to meet the objectives of the 2020 Strat-

egy, developed by the European Union (EU). Since then many projects have started

under this label, of which one is the TECHN’EAU project, described in Section 2.1.3.

2.1.2 ACQUEAU

ACQUEAU is the first EUREKA Cluster dedicated to environmental and water re-

lated technologies, founded in 2010. EUREKA is a network established in 1985 by a

Conference of Ministers from 17 European countries. It focuses on market oriented

research and development and is complementary to WssTP, which in turn hosts

ACQUEAU. It is an initiative supported by more than 20 countries and 40 compa-

nies across Europe [ACQ10]. It has published Blue Books on Vision and Organiza-

tion and on Technology Road Mapping [HB10a, HB10b]. Its fifth Call for Projects

was recently opened, no results of earlier projects are known yet.

2.1.3 TECHN’EAU

Technology Enabled Universal Access to Safe Water (TECHN’EAU) is an integrated

project funded by the European Commission and supported by WssTP. The goal of

this project is to “challenge the ability of traditional systems and technology solu-

tions for drinking water supply to cope with present and future global threats and

opportunities” [HRS+11]. It has identified research gaps in four major areas:

FRAMEWORK FOR EVALUATING RISKS, PERFORMANCE, SUSTAINABILITY AND COSTS

Among others tries to indicate energy consumption and carbon footprint on newly

developed processes and indicates studies are needed to prioritise pollutants to be

considered in drinking water.

WATER QUALITY MONITORING

Indicates there is need for improving existing sensors (with respect to sensitivity,

response time, etc.) and for more appropriate on-line monitoring tools for the in-

take water and the treatment process.

2.1. Platforms, consortiums and projects 11

WATER TREATMENT PROCESSES

An approach to better exploit current knowledge by creating a database with tech-

nologies for pollutant removal and research to new or improved treatment steps.

MISCELLANEOUS ISSUES, ADDRESSING OTHER PARTS OF THE WATER SUPPLY CHAIN

More general issues were covered, like resource management, interaction between

water treatment and the distribution network and smart billing to consumers.

Initiatives to fill these gaps are taken. As an example: the currently available water

treatment process models were reviewed and a new model is being developed [RD06b,

MRB+08].

2.1.4 Project SAWA

Project Sensors And WAter (SAWA), a project in the Northern Netherlands, was ini-

tiated in 2010 and lasts three years. It originates from the Sensor Universe project,

a broader sensor project in The Netherlands by companies, governments and re-

search institutes. The project started with the opening of the SenTec testing center

where newly developed sensors can be tested simultaneously [SAW12]. The project

consists of three sub-projects:

THE DRENTSCHE AA

Drinking water from the Groningen water company is produced from surface water

in the river Drentsche Aa. In this project an on-line quality monitoring system for

the intake water is developed. Pollution should be detected within two hours, after

which the water intake will halt. Five sensors, of which two already existed, are

tested and improved. While testing in practice an actual contamination event was

immediately noticed by 2 of the sensors.

DISTRIBUTION NETWORK

The second project develops intelligent and inexpensive sensors to monitor water

quality in the distribution network in order to reduce the need for manual sam-

pling and to ensure a good water quality at the consumers’ taps. Three companies

provided sensors for this project and have tested them in the SenTec lab. In 2011

one sensor was actually measuring the water quality in the network of the Drenthe

water supply company and will be tested in 2012 in the Groningen network.

12 State of the art

AFTERGROWTH

The Aftergrowth project tries to indicate the risk of bacteria aftergrowth in the dis-

tribution network by using sensors that quickly detect and quantify regrowth. Three

companies are developing sensors for this project and all these sensors were tested

in the SenTec lab successfully. Tests in practice are now going to be performed.

2.2 Subjects for Early Warning Systems

Especially after the attacks of September 11, 2001 interest in water quality mon-

itoring systems is growing. A country is vulnerable through its water supply and

deliberate contamination of drinking water is seen as a potential threat. Extensive

research is done to prepare the development of Early Warning Systems for contam-

ination events.

Several agencies have reported about their specific research to the use and imple-

mentation of Early Warning Systems and they identify much common issues [AGL+05].

Subjects that arise from these reports that are also related to this project are:

Sensor testing For a specific application appropriate sensors should be selected.

Vendors generally supply fact sheets on their sensors and three testing and evalu-

ation programs exist: the U.S. Environmental Protection Agency (EPA) has an En-

vironmental Technology Verification program with voluntary vendor participation

and (under preparation) a Technology Testing and Evaluation Program with invol-

untary vendor participation. The Edgewood Chemical Biological Center (ECBC)

verifies Chem-Bio Agent sensors.

Sensor placement The reports indicate that sensor placement is an important

aspect with regards to planning, analysis and maintenance. The specific guidelines

for sensor placement in contamination warning systems are not completely appli-

cable to water treatment plants and therefore not indicated here. In Section 4.2 this

subject is considered in more detail.

Hydraulic and contaminant transport modeling For water-flow and contaminant-

transport modeling widely accepted pipeline network models are available: EPANET,

WaterCAD and PipelineNET. Building the model is time-consuming, but after this

phase the spread of contaminants over time can be identified. This functionality

2.3. Quality monitoring initiatives 13

is also important in a water treatment plant as process models should simulate the

incoming concentration of a certain contaminant to simulate its treatment process.

Early Warning Systems as described in the previously mentioned reports did not ex-

ist at the time of publication (2005), but the Dutch drinking water company Vitens

nowadays performs measurements in the distribution network to monitor the re-

fractive index of the water in several locations within its distribution network. The

sensors used here are Optiqua EventLab sensors, linked to an event detection sys-

tem [Gra12]. The refractive index appears to be an indicator for most contami-

nants. Using such surrogate parameters for contaminants is an approach that was

suggested in [AGL+05].

2.3 Quality monitoring initiatives

Several publications describe initiatives to developing monitoring systems for vari-

ous purposes. In this section we mention publications closely related to the subject

of this thesis, to surface water monitoring and to distribution network monitoring.

We conclude with a publication that defines a research strategy on water quality

monitoring systems in general.

One publication that focuses on the monitoring and quality analysis of drinking

water is [AFF+03], which proposes research to develop statistical quality models

—called data-driven models in this thesis— by using available quality and environ-

mental data, to examine different sensor-deployment plans based on hypothetical

data and to identify sensitivity requirements for sensors. This proposal does not

seem to be followed up by actual research or a publication about it, but it does pro-

vide interesting guidelines which can be helpful to fill in the open areas identified

in this thesis.

In [GBR+04] the current application of real-time remote monitoring of ocean water

quality is reviewed. It handles the advancements in sensor, telemetry and comput-

ing technologies and provides an overview of projects in which sensors measure

water quality in several different ways. It also mentions sensor manufacturers and

project websites. [ARE+09] designs a quality monitoring system for the Chesapeake

Bay, a heavy polluted watershed in the United States. In this design several multi-

parameter sensors are considered in terms of maintainability, operational quality

14 State of the art

and reliability. Several communication technologies are compared. In this context

long-range communication is important, so GSM or GPRS is proposed, for short

range radio frequency transmission would be suggested (Bluetooth, etc). Commu-

nication technologies will also be considered in this thesis, but in the context of a

treatment plant other criteria apply. A decision for a fixed or a moving sensor con-

cludes the design. In this context it is suggested to start with one or more moving

sensors until the testing phase is complete and then use fixed positions.

Monitoring the quality of water flowing through distribution networks has gained

more attention in the scientific community than quality monitoring inside the treat-

ment plant. This is explained by the fact that the distribution networks are more

vulnerable to intentional contamination or regrowth of bacteria. Two publications

that outline the area of distribution network monitoring are [HZM+07, MXX+11].

These publications identify critical quality parameters that can indicate problems

with the distributed water and the design of a system that can monitor those pa-

rameters.

We conclude this overview with a publication that outlines a research strategy on

water quality monitoring systems in general [GV08]. In defines improvements to

the depth of review of current on-line monitoring projects, consistency of testing

and data handling. It proposes to link sensor development to water quality regu-

lations and to establish a community to exchange knowledge on the development

and application of sensors. These guidelines can be used to improve every water

quality monitoring system, including the one proposed in this thesis.

2.4 Water quality modeling

Research on water quality modeling, or simulating processes in water, is widely

available, but most models consider surface water in rivers or the influence of agri-

cultural processes on ground water. Just a few models for drinking water treatment

are available. These models are reviewed in an effort to build a European plat-

form to enhance drinking water quality monitoring, an initiative from the Euro-

pean TECHN’EAU project. Within that project a new water quality model for the

drinking water treatment processes is developed, based on the experiences with

the already existing models [RD06b, MRB+08].

2.5. In this project 15

Statistical models (or data-driven prediction models) for simulating single processes

in water treatment were developed in Canada. These models are mainly used for

training new process controller and for process analysis, additional to bench-scale

experiments. In both cases the models support the process but are not directly used

to monitor or influence the process [BZS+01].

2.5 In this project

Based on the findings from the previous sections, we list the currently available

sensors for the monitoring system and indicate which sensors are necessary to be

developed before a control system becomes reality in Chapter 4. This chapter also

indicates guidelines for sensor placement and the design decisions that should be

taken for this subject. Sensor testing and hydraulic modeling is not further dis-

cussed in this report, as this goes beyond the scope of this project.

The state of the art in water quality modeling is further detailed in Chapter 5 where

we review all the models that were mentioned before. It is clear that the water qual-

ity modeling for drinking water treatment is a field in which a lot of research still

needs to be done. Models are scarcely available, not complete or not integrated

and the performance in actual treatment plants is often unknown.

CHAPTER

3Use Cases and Requirements

Analysis

To concretize the needs for a monitoring system, we start with a Use Case Analysis.

This analysis results in an overview of tasks that the system should be capable of.

Based on these use cases a Requirements Analysis is performed. The result of this

analysis is a detailed overview of all functional requirements that the system should

meet.

3.1 Use Cases

Based on the activity diagram in Figure 1.2 we define four use cases. The use cases

are numbered according to the numbering of the activity diagram. Two of the use

cases are performed continuously and, as illustrated in Figure 3.1, these are ini-

tiated by the system itself: Measure actual intake water quality and Calculate ex-

pected clean water quality. Based on the data that results from the latter, the sys-

tem can initiate the use case Adapt water purification process, but that use case can

also be initiated by the Process Controller who can always override the decisions of

the system. This is illustrated by the two actors that are related to this use case. The

fourth use case, Visualize water quality, covers the visualization of the measured

and the predicted quality data for the Process Controller as well as his manager, so

they are both an actor for this use case.

The use case of measuring the intake water quality for the purification process,

UC1, is listed in Table 3.1. The measurements will be performed by several sensors

17

18 Use Cases and Requirements Analysis

Figure 3.1Use case diagram for awater purification controlsystem.

ProcessController

Manager

Visualizewater quality

Adapt waterpurification process

Control system

Measure actualintake water quality

«system»

Calculate expectedclean water quality

«includes»

«includes»

and their outputs are persisted by the system for further use in the process and for

historical reference. This use case is initiated by the system itself and runs contin-

uously.

The measured values are used to predict the water quality after the treatment pro-

cess has completed. The analysis and prediction use case, UC2, is listed in Table 3.2.

Like UC1, the system is also running this use case continuously. As external con-

ditions, like weather conditions, can influence the treatment process applicable

parameters are first retrieved from an external data provider, which should also

provide value forecasts if available.

The system can contain ‘soft sensors’, an advanced type of software sensors which

can enhance the modeling process and make it easier at the same time. More in-

formation about the soft sensors can be found in Section 5.2 (page 38). With the

measured and external values the system calculates values for soft sensors and then

predicts the quality of the treated water, assuming the treatment process does not

change.

If the predicted quality of the treated water is outside the allowable quality ranges,

the process should be adapted as is described in UC3, listed in Table 3.3. Based on

the calculated values and the available process models, the system calculates a new

configuration for the purification process. When necessary, a Process Controller

can always adapt the process otherwise according to his own judgement.

3.1. Use Cases 19

In order to provide information about the current quality of the water inside the

process and the expected quality of the clean water, the system should visualize

the quality parameters that were acquired previously, UC4. As listed in Table 3.4 the

system will perform predefined visualizations steps on the data and then it shows

the resulting visualization to the user who requested it, which can be the Process

Controller or the Manager.

In the current stadium only the success scenarios are considered. When actually

designing the proposed control system alternative execution paths should also be

considered.

UC1: Measure actual intake water qualityGoal: System reads the quality of the intake water at the cur-

rent moment.Primary actor: SystemPre-conditions: –Post-conditions: System has actual and historical quality data available.Basic flow:

1. System reads values from several sensors in differ-ent locations in water intake.

2. System persists quality and location data.Repeat from step 1.

Frequency: Continuous

Table 3.1Use case: Measure actualintake water quality.

UC2: Calculate expected clean water qualityGoal: System calculates expected quality of the clean water.Primary actor: SystemPre-conditions: System has intake water quality data available.Post-conditions: System has predicted quality data for clean water, based

on the current intake water quality, available.Basic flow:

1. System uses measured values from UC1.2. System performs calculations to get values for soft

sensors.3. System retrieves external data like weather in-

formation and forecasts, or laboratory measure-ments.

4. System uses water quality prediction models tocalculate expected clean water quality.

5. System persists calculated quality data.Repeat from step 1.

Frequency: Continuous

Table 3.2Use case: Calculate ex-pected clean water quality.


Table 3.3Use case: Adapt water pu-rification process.

UC3: Adapt water purification processGoal: Adapt the purification process to produce water of opti-

mal quality.Primary actor: System or Process ControllerPre-conditions: System has predicted quality data for clean water avail-

able.Post-conditions: Purification process is adapted as specified.Basic flow:

1. System finds expected quality values for clean wa-ter that are substandard.

2. System prepares a new configuration of the purifi-cation process.

3. System requests a change in the purificationprocess.

*a. Process Controller can override the configurationof the purification process at any time.

Frequency: When necessary

Table 3.4Use case: Visualize waterquality.

UC4: Visualize water qualityGoal: The system provides an easily interpretable visualization

of quality data.Primary actor: Process ControllerPre-conditions: System has quality data available (measured as well as

predicted).Post-conditions: Process Controller (or Manager) reads detailed quality

information.Basic flow:

1. Process Controller requests a visualization of mea-sured or calculated quality data.

2. System performs visualization steps on the data.3. System presents the resulting visualizations to

Process Controller.

Frequency: Upon request / continuous

3.2 Functional Requirements

From the use cases we can derive more detailed Functional Requirements (FRs).

The FRs listed here will be used as a starting point for the next two chapters. To

limit the scope of this project, the use cases elaborated on are UC1 and UC2. The

latter two are part of future work, but these cover more generic challenges.

In Chapter 6 (on page 52) a requirements traceability matrix shows which require-

ments are covered in the next chapters and whether they are realizable using the

current state of the art.

3.2. Functional Requirements 21

3.2.1 Sensor network requirements

To realize UC1 we derive FRs for each step in the use case. The feasibility and avail-

able options to meet these requirements are discussed in Chapter 4, page 25.

SYSTEM READS VALUES FROM SEVERAL SENSORS IN DIFFERENT LOCATIONS IN WA-

TER INTAKE

This step requires that selected sensors are installed in several locations in the

pipeline and these sensors provide information about the water quality they ob-

serve.

FR1: Perform continuous on-line measurements on selected parameters.

One technology-based requirement should be considered. Most sensors nowadays

are ‘smart sensors’, but there are still a lot of sensors that do not directly return a

measured value, like a temperature in ◦C. The value they return should yet be in-

terpreted, like an electrical current. The translation into an actual parameter value

should be done by our proposed system, as explained in Section 4.1.5, page 28.

FR2: Translate sensor measurements into actual parameter values.

The location of a measurement should be stored, as this is an important piece of

metadata. Knowing the difference between values before and after a specific treat-

ment step is crucial.

FR3: Record location data for each parameter value.

SYSTEM PERSISTS QUALITY AND LOCATION DATA

The measurements of the sensors should be persisted in the central control sys-

tem. In order to realize this, there must be functionality in place to transfer the

values there.

FR4: Transfer parameter values from the sensors to the control system.

FR5: Persist quality data for later use.

3.2.2 Data analysis requirements

For calculating the expected clean water quality, UC2, we again derive the FRs per

step in the use case flow. The feasibility and available options to meet these re-

quirements are discussed in Chapter 5, page 37.


SYSTEM USES MEASURED VALUES FROM UC1

As we will see in Chapter 4, a control system will contain several sensors from dif-

ferent brands and for different parameters. They all have different response times

and therefore the input of parameters will take place with irregular intervals.

FR6: Handle a mix of continuous signals and discrete signals with varying frequen-

cies.

SYSTEM PERFORMS CALCULATIONS TO GET VALUES FOR SOFT SENSORS

As explained in the use case description, the soft sensor is a sensor built in software,

which calculates its output value based on historical values, multiple sensor values

or other data. The system should support the calculation of the soft sensor output.

FR7: Calculate soft sensor output based on several other parameters.

SYSTEM RETRIEVES EXTERNAL DATA LIKE WEATHER INFORMATION AND FORECASTS,

OR LABORATORY MEASUREMENTS

The water quality of influent water is influenced by a lot of circumstances. The

weather or any other knowledge that is available can possibly be used by the pre-

diction model to make more accurate predictions on the optimal treatment for the

water or the quality of the treated water. Furthermore, in the following years labo-

ratory monitoring will still be performed because not all parameters can be mea-

sured and it is obligatory for quality reporting. This additional information should

be available in the system as it can enhance model predictions.

FR8: Use external data to observe the state of the system or the environment in

which the system operates.

FR9: Get predictions for external data from external systems, when applicable.

SYSTEM USES WATER QUALITY PREDICTION MODELS TO CALCULATE EXPECTED CLEAN

WATER QUALITY

One of the main objectives of the analysis part of the system is to predict the quality

of the effluent water, knowing the quality of the influent water. Several process

models can be used to simulate the treatment process and indicate the resulting

water quality in advance.

FR10: Predict effluent water quality on the short term.

3.3. Requirements discussion 23

SYSTEM PERSISTS CALCULATED QUALITY DATA

The calculated quality data is already in the control system and does not need to

be transferred to another system, contrary to the values that are measured by the

sensors. The calculated water quality parameters should be persisted, similar to

the intake water quality parameters, so FR5 is also required for this step.

3.3 Requirements discussion

To conclude this Requirements Analysis we prioritize the requirements that we

have defined now. Each requirement will be prioritized Essential, Conditional or

Optional (decreasing order of importance). The resulting prioritization is listed in

Table 3.5.

The first five requirements (FR1 – FR5) are related to UC1. As this project is a feasi-

bility study and the use cases and requirements were defined with that in mind, one

would expect to (almost) only find requirements that are prioritized Essential. For

these requirements this is the case. These requirements are all essential to deliver

a working sensor data acquisition system.

The requirements for the second part of the system, UC2, are more difficult to pri-

oritize. Due to different process models that can be used we cannot exactly indicate

which parameters are needed. FR6 is one essential requirement, as we know by the

list of sensors and their characteristics that the incoming data will be based on dif-

ferent types of measurements, different intervals, etc.

When considering a monitoring system, showing the actual sensor data is a large

part of the functionality, and FR10, predicting the results of the observed process,

would be a conditional requirement. In our context we are actually planning to

build a complete control system, which makes it an essential requirement. Opti-

mizing the observed process does require the system to know the expected water

quality when nothing changes, as this prediction forms the base of the optimization

step.

Two requirements for the use of external data, FR8 and FR9, were defined. As ex-

plained earlier it is good to have the external data available in the system as the pro-

cess model possibly needs it. However, the control system could perform without


having external data available, though the quality of predictions would be lower.

Therefore, FR8 is seen as a conditional requirement. FR9 handles the predictions

for external data. This requirement arose from the analogy between internal and

external data; both need to be acquired and for both predictions should be made.

This is partly true, but having predictions for external data is of less importance, as

it can help to predict future treatment steps but not necessarily improve the quality

of predictions for internal data. This requirement is considered optional.

The last requirement considered, FR7, is the requirement handling the calculation

of soft sensor output. As explained it is a software sensor that can ease implemen-

tation of switching of process models. It is a popular topic currently, but without

supporting soft sensors the system is also able to work, therefore this is an optional

requirement.

Table 3.5Functional requirement pri-oritization overview.

Requirement Priority

FR1 Perform continuous on-line measure-ments on selected parameters.

Essential

FR2 Translate sensor measurements into ac-tual parameter values.

Essential

FR3 Record location data for each parametervalue.

Essential

FR4 Transfer parameter values from the sen-sors to the control system.

Essential

FR5 Persist quality data for later use. Essential

FR6 Handle a mix of continuous signals anddiscrete signals with varying frequencies.

Essential

FR7 Calculate soft sensor output based onseveral other parameters.

Optional

FR8 Use external data to observe the state ofthe system or the environment in whichthe system operates.

Conditional

FR9 Get predictions for external data from ex-ternal systems, when applicable.

Optional

FR10 Predict effluent water quality on the shortterm.

ConditionalEssential

(to monitor)(to control)

CHAPTER

4Water quality measurements

By means of sensors a control system is aware of the status of its subject. The sys-

tem acquires the status —in the form of parameters— and uses the gathered infor-

mation to perform its task. This chapter considers various aspects of implementing

the sensors, starting with an overview of parameters that are needed for full control

on the water purification process and parameters that can be measured on-line.

Based on these results a selection of commercially available sensors is composed.

We then continue with the placement of the sensors and the way the system discov-

ers the location of all the sensors. This chapter concludes by discussing the network

options to enable the sensors to communicate with the system.

4.1 Parameter and sensor selection

The choice of parameters for the control system depends on three criteria:

• What parameters are representative to the purification process? [ASC04]

• What sensors exist? (What can we measure?)

• What do regulations expect us to measure?

By using these three criteria we fulfill FR1 as much as possible. It is not possible to

completely hold to this requirement. For some parameters no (suitable) sensors do

exist as follows from Section 4.1.3. For these parameters, like bicarbonate or coli

bacteria, additional laboratory work can determine the actual concentrations.

25

26 Water quality measurements

Parameter selection closely relates to the Data Analysis part, as that part depends

on what parameters we are able to select here. This is nicely illustrated by the re-

lation between FR1 from the measurement use case and FR8 from the analysis use

case, used to insert the results from laboratory work into the system.

Several governments have strict regulations on the quality of drinking water. These

regulations are expressed in allowable ranges for the concentration of certain con-

taminants or other parameters. These regulations are used as a starting point for

parameter selection. The resulting parameter set serves two purposes:

• Indicate levels of contamination as input to the decision maker who can ad-

just the purification process to optimize water quality.

• Raise an alarm if certain levels of contamination are measured, types of con-

taminations which cannot be acted upon are found or if parameters indicate

there is a problem (indicative or surrogate parameters).

To decide on the final list of parameters, we need more information about the

purification process. In Chapter 5 we outline how water quality can be modeled.

Based on these models a list of parameters can be compiled. Additionally, a water

treatment expert is needed to indicate what parameters are absolutely necessary to

ensure ‘safe’ water results from the purification process.

4.1.1 Water quality parameters

The government of The Netherlands has defined a set of parameters in the Drink-

waterregeling. For each of these parameters, separated into four groups, a legal

range was specified [VTM04], see Table A.1 (page 66). These parameters cover con-

taminant levels as well as other parameters like temperature and acidity.

The Saturation Index (SI) is the parameter which describes the general state of the

water and how it influences the water infrastructure. As process controllers are

particularly interested in this value, this value should be available in the monitoring

system. This value is calculated with the following quality parameters [Raf99]:

pH Acidity

TDS Total Dissolved Solids (conductivity) in mg/L

Ca2+ Calcium concentration in mg/L

4.1. Parameter and sensor selection 27

HCO3− Bicarbonate concentration in mg/L

T Temperature in ◦C

All of these parameters can be measured with existing sensors, except for the Bicar-

bonate concentration. Several institutes (Wetsus, US-EPA) are researching ways to

develop such a sensor, but no sensor is available yet.

4.1.2 Contaminants

The parameter list shall not be static. Due to several circumstances the mix of con-

taminants that end up in the water will vary. Several institutions keep lists of con-

taminants that are expected to be found in surface or ground water and can impose

health issues [ASC04]. The EPA for example has a list of contaminants along with

maximum allowable concentrations in drinking water. Biological contaminants,

which can be used by terrorists to sabotage drinking water facilities, are listed by

the Centers for Disease Control and Prevention (CDC) and in the Bad Bug Book on

pathogens and biotoxins by the U.S. Food and Drug Administration (FDA).

These lists can be found at the following URLs respectively (October 2012):

EPA: http://www.epa.gov/safewater/mcl.html#mcls

CDC: http://www.bt.cdc.gov/agent/agentlist.asp

FDA: http://www.fda.gov/Food/FoodSafety/FoodborneIllness

Empirical studies show relations between certain contaminants and surrogate mea-

sures. Based on these studies, the following parameters [ASC04] should be consid-

ered to be measured:

• Flow or pressure

• Temperature

• pH

• Conductivity

• Residual chlorine

• Turbidity

• Total Organic Carbon (TOC)

• Oxidation Reduction Potential (ORP)

• Ammonia, chloride, and nitrate

http://www.epa.gov/safewater/mcl.html#mcls

http://www.bt.cdc.gov/agent/agentlist.asp

http://www.fda.gov/Food/FoodSafety/FoodborneIllness


• Toxic materials

• Radiation (alpha, beta, and gamma)

4.1.3 Available sensors

Before making a definitive selection of parameters, we will provide an overview of

available sensors, based on the parameter list from the Drinkwaterregeling, as re-

ferred to in Section 4.1.1. The most important requirement for an individual sensor

is that it is an on-line sensor: a sensor that can be placed in the production pipeline

and export the measured values.

The parameters and corresponding sensors are listed in Table B.1 (page 70). For

each sensor we will list characteristics like response time, accuracy, etc.

4.1.4 Response times and sampling rate

In the ideal world, every parameter would be measured in real-time. However,

most of the aforementioned sensors need time before the result of a measurement

is available. This time varies between instant, a few seconds or a few minutes and it

makes it impossible to build a true real-time control system for water purification.

There will always be delays between the measurement and the real water quality.

Having good models to predict the water quality can take this problem away. Only

in the case of exceptional values, possibly emergency cases, the delay will be noti-

cable.

It is possible to have a high sampling rate while using sensors with high response

times by adding similar sensors, which then make measurements turn by turn. The

added value of this approach is expected to be low because the values are already

outdated and the price of adding more sensors than necessary is high. However,

this approach can be useful while developing a quality model, in order to train or

validate the model with higher resolution data.

4.1.5 Smart sensors

Simple analog sensors measure a value, for example temperature, and return the

measured value by means of an electrical property. The system connected to the

sensor then has to interpret the property to deduce the actual measured value.

Smart sensors adopt this responsibility from the system and have built-in electron-

4.2. Sensor placement and location discovery 29

ics to interpret the property and output the actual parameter value [Lin06]. By tak-

ing away the task of interpreting electrical properties from the control system, it is

easier to switch to another type of sensor.

Nowadays most of the sensors are smart sensors having connections that can be

read by almost any consumer computer. However, if we need to use a less smart

sensor, we should be able to interpret its value. This ability corresponds to FR2.

Translating a sensor output value into an actual parameter value is a trivial process

that should be described by the sensor’s manufacturer.

4.2 Sensor placement and location discovery

Because sensors are expensive and consume energy, the network they form should

be optimized in a way that the lowest number of sensors provides accurate data on

the water quality inside the purification process. Additionally, the location of the

sensors should be known to the system to be able to interpret the data it provides.

Decisions made for location discovery are limiting the freedom in the network or-

ganization, as it can prevent using a sensor in multiple locations.

4.2.1 Placement

Sensors should be placed in suitable locations in the water system. The American

Society of Civil Engineers (ASCE) outlines requirements and guidelines for the se-

lection of locations for a sensor platform [ASC04]. The requirements applicable to

a drinking water purification system are:

• Accessible to authorized personnel.

• Enough space available to house the sensor.

• Power supply available, if necessary.

• Good hydraulic conditions (i.e. low turbulence).

The guidelines provided by ASCE focus on deliberate contamination which is not

the main driver for the system outlined in this report. The guidelines can be trans-

lated to guidelines that focus on a monitoring system for water purification, which

is also capable of finding contaminations that were added deliberately. According

to these guidelines optimal locations should be determined with regards to:


• Potential areas or entry points of contamination. In case of a water purifica-

tion system this should be reversed. Where are certain contaminations ex-

pected to be removed and how can that expectation be validated?

• Contaminant transport time and concentration. Due to the transport inside

the pipeline the concentration of a contaminant will change with its location.

Also, the response times for sensors are relatively high. To prevent a contami-

nant from entering the distribution system, the sensor should be placed early

in the pipeline, so once the value is known necessary actions can be taken on

the other side of the pipeline.

The remaining guidelines refer to large distribution networks and are not applica-

ble to an on-site monitoring and control system.

4.2.2 Location discovery

One of the most important pieces of context data for each sensor is its location, in-

dicated by FR3. The system should know whether the water measured by a specific

sensor is raw water, or already has undergone a (partial) treatment. The location of

a sensor can be manually entered or it can be detected automatically. Having auto-

matic location discovery enables the system to use mobile sensors. In this section

we will elaborate on the possibilities and the corresponding challenges as well as

the pros and cons for each alternative.

FIXED MANUAL LOCATION

The advantage of placing a sensor on a fixed location and entering the location

manually is that the location of the sensor within the pipeline is guaranteed to be

correct.

VARIABLE AUTOMATIC LOCATION

The main advantage of using automatic location discovery for sensors is mobility.

A sensor can follow the water stream and see how the water quality changes or it

can be used in different locations without manually providing the exact location

every time. The downside is that location discovery systems are not perfect. The

average accuracy is relatively low, as shown in Table 4.1. Varying environmental

circumstances can cause changing accuracy for Wi-Fi location discovery. A change

in relative humidity can lower the accuracy with 40%, while people walking around

4.3. Sensor connectivity 31

Method Error

Wi-Fi 3m

Global Positioning System (GPS) 15m

Differential GPS (DGPS) 5m

Wide-area Augmentation System (WAAS) < 3m

Table 4.1Average location estimationerror using various meth-ods [CCC+05, Gar12].

(a) Straight-forward pipeline: effect is minimal.

×Pipeline

× = actual location= estimated location

(b) Complex pipeline: problem.

×

Figure 4.1Consequences of smalldeviations in location esti-mation.

can lower accuracy with 85% [CCC+05]. Location discovery will be completely im-

possible for underground parts of the pipeline.

Additionally, the production pipeline is not a straight line, so the geographical lo-

cation alone is meaningless as this tells nothing about the order in which the water

flows through the sensors. To overcome this problem the system should have a map

of the pipeline so it can map geographical locations onto places in the pipeline. A

small deviation can have large effects. Figure 4.1(a) shows the situation where a

small deviation in the location discovery is resolvable, but in Figure 4.1(b) it is not.

Location discovery in the second case leads to wrong results: the sensor is assumed

to be in the upper pipe of the system, while it actually is situated in the lower pipe.

Hence, measurements from this sensor will be useless as the actual context is com-

pletely different than the observed context.

4.3 Sensor connectivity

Several possible ways of connecting the sensors to the control system are avail-

able and widely used already. Connecting the sensors directly corresponds to FR4.

Which connection type is most suitable depends on the amount of data that is

transferred, the location of the sensors and the size of the plant that should be cov-


Figure 4.2Sensor network layoutwith sensor stations.

Control System

Sensorstation 1

Sensorstation 2

Sensorstation 3

Sensors

Wi-Fi

ered. The starting point of this orientation is that we want to form a local network

to which the control system and the sensors are connected, directly or indirectly. A

local network uses proven technology and is flexible and scalable.

4.3.1 Wired sensor connection

Most sensors support other connection types than a Local Area Network (LAN) con-

nection: RS232, USB, SDI-12 or an electrical current are common connection types

among sensors, see also Appendix B on page 69. Using these connections the sen-

sors cannot be linked to the network directly. Therefore the suggested solutions is

to place so-called sensor stations in the treatment plant. These stations are com-

puters to which the sensors are connected directly which allows them to collect

values from multiple sensors and transfer them to the central system. This results

in a situation as illustrated in Figure 4.2, where the available sensor connections

are used to connect to the sensor stations and these connect to the control system

through the local network by LAN or Wireless LAN (WLAN). A WLAN, or Wi-Fi, con-

nection provides convenient ways of connecting to the network in places that are

hard to reach with cables or to cross long distances, limited by its characteristics as

shown in Table 4.2.


Protocol Speed (Mb/s) Distance (m) Authentication

Ethernet/LAN 100/1,000 100 Physical

WLAN/Wi-Fi 54 150 AES-128

Bluetooth 1 10 – 100 SSP

ZigBee 0.25 70 AES-128

GSM/GPRS 2 N/A EAP-SIM (external network)

Table 4.2Common network pro-tocols and their proper-ties [CDK05, Don12]

4.3.2 Wireless sensor connection

With the increasing popularity of mobile communications, it is to be expected that

more sensors will also support wireless connections in the near future. There al-

ready are sensors that come equipped with General Packet Radio Service (GPRS)

connection capabilities. This is especially useful for sensors that are used outside

treatment plants, in distribution networks for example.

Several network standards from the Institute of Electrical and Electronics Engineers

(IEEE) are listed in [CDK05]. The commonly known methods from this list will

be considered. An additional option for networking is the cellular protocol GPRS,

based on Global System for Mobile Communications (GSM). The networking stan-

dards are listed in Table 4.2. For connecting sensor to the sensor stations, we will

consider the advantages and disadvantages of Bluetooth, ZigBee and GSM/GPRS.

In this comparison security is also considered, as the sensor network is part of a

critical system.

BLUETOOTH

The Bluetooth protocol was designed to link mobile devices without the need of

wires. It has a relatively low power consumption, achieved by using adaptive signal

strength based on the communication distance, among others. The maximum dis-

tance reached by Bluethooth signals ranges from 10 m with a 1 mW device to 100 m

with a 100 mW device [CDK05]. A Bluetooth Piconet, a small network in which

communication takes place, can consist of a maximum of 8 devices of which one is

designated to be the master node. Multiple Piconets can be linked into a Scatternet,

but this increases the complexity when considering addressing the right nodes.

Older versions were shown to be unsafe, but Bluetooth Security Mode 4, which is

also referred to in the table, offers reliable security [Don12].


ZIGBEE

A protocol that was specifically designed for sensor data transfer is ZigBee. It achieves

ultra low power consumption by sleeping most of the time and by the use of lower

frequencies, also resulting in lower data transfer rates. This side-effect is fine for

sensors, providing only a small amount of data. Simple sensors that used ZigBee

have shown years of battery life, hardly influenced by the radio signals it sent. Zig-

Bee supports more network topologies and larger networks than Bluetooth which

makes it more scalable [Bak05].

When configured correctly ZigBee uses similar security mechanisms as Wi-Fi, lead-

ing to the same level of security [Don12].

GSM/GPRS

The GSM/GPRS network is widely available and especially useful for communica-

tion over long distances. It uses the network of an external provider who charges

for data usage and availability depends on the network coverage and the uptime of

the provider. However, no maintenance costs for the network are made and it does

not require to set up a new network.

The older security profile (A5/1) for GSM was cracked, but the newer A5/3 profile

is considered safe [Gol10]. However, data is sent over the network from an external

provider, so their policies can influence the reliability of the network.

COMPARISON

Based on the comparison made above we can conclude that for connecting sen-

sors on-site GSM/GPRS is less suitable, as data transfer is charged and the long

distances for which the protocol was designed are not used, while it does consume

the energy to potentially cross that distance.

For the other two protocols, ZigBee is preferred above Bluetooth. Especially the

low power consumption and the scalability of the network are the key advantages

which would make ZigBee the preferred choice.

An important consideration is the use of wireless technologies for the parts of the

production pipeline that are underground. The transfer of radio signals is mostly


blocked by the ground, so communication will be only possible if sender and re-

ceiver are near each other and not separated by soil.

4.3.3 Data transfer method

After determining the right connection type or types, a protocol for data transfer

must be considered. The first question raised here is whether communication hap-

pens through a pull or push based protocol. With the kind of sensors in mind (dif-

ferent response times) it would be most logical to push measurements from the

sensor to the control system. The sensor or sensor station knows when a new mea-

surement is available and at that time it takes care of sending that measurement

to the control system. Some sensors might require a pull-based reading from the

connected client. In these cases the client should pull the measurement from the

sensor at specified time intervals and then send it to the control system using the

push-protocol that was selected.

In general cases there is one major downside to a push based protocol: the server

should keep track of all clients that are connected and send out updates to all of

them, leading to a higher load on the server side. In the case of a sensor that is con-

nected to a control system, the only client is the control system, so this downside

does not apply.

CHAPTER

5Quality data analysis

The Analysis component forms the core of the control system and translates data

into information. It interprets and registers the sensor values and predicts what

the quality of the treated water will be. Based on the historical data and the pre-

dictions the system is able to optimize the treatment process. In this chapter we

consider design decisions for data input and storage shortly, we discuss the use of

‘soft sensors’ and we look into several water treatment process models.

5.1 Data input, storage and processing

Incoming sensor data should be persisted, then analyzed. Persisting data coming

from the network, corresponding to FR5, is a not a trivial task per se, but a lot of

applications have proven that simple and advanced methods for data storage ex-

ist. At the current stage of this project we will not further detail the decisions that

should be taken for this functionality. In the design phase additional requirements

and solutions can be indicated for this specific task.

One important remark for processing the sensor data is that this data enters the

system at random time intervals, varying per parameter. We have sensors with re-

sponse times between 0 and 120 s (or even 30 min), so measurements 120 s from

the past can still come in. Values should be persisted with the correct timestamp,

indicated by FR6. Soft sensor and model calculations should be highly dynamical

as historical values can be inserted while calculations for the corresponding time-

37

38 Quality data analysis

stamp have already been made. If we have 10 parameters, it is possible that all 9

variables for time t − x are known, while the last parameter is only known at time

t . All calculations for time range [t −x, t ) have been performed already and should

thus be updated.

The data resulting from the analysis may be more complex than the raw sensor val-

ues. Simple values for parameters can be predicted, but multi-dimensional values

can also be part of an analysis. The output of the analysis depends on how the

analysis works exactly, which model is used, et cetera. A good solution for this is

not trivial, and it is clear that upon changing the model the storage scheme may

also need to be updated.

5.2 Soft sensors

An upcoming topic in sensor usage is the so-called soft sensor. This sensor is a vir-

tual one which can combine several hardware sensor values, or even external data,

to come up with new values which are not or cannot be measured. The use is two-

fold, which is explained in the following two sections. Keeping the soft sensors out-

side the process model makes it easier to switch process models, while still being

able to use the relationships or sub-models as defined by soft sensors. Supporting

soft sensors is specified by FR7.

Soft sensors are not to be mistaken with smart sensors, described in Section 4.1.5,

which are actually ‘enriched’ hardware sensors.

5.2.1 Combined values

Combined values may be more descriptive for the water quality than one single pa-

rameter. A common water quality parameter is the Langelier Saturation Index (SI)

which indicates whether the water is scale-forming or corrosive and thus whether

it will affect the distribution system. Since 2001, this index is part of the Dutch

drinking water resolution [VtB05].

Process controllers consider the SI an important parameter, so it should be avail-

able for visualization. It can also be useful to include it in the model calculations.

It is a combined parameter using values for temperature, acidity, conductivity and

the concentrations of bicarbonate and calcium. A soft sensor can be implemented

5.2. Soft sensors 39

to combine these values according to the common definitions into a single param-

eter value that can be processed by the quality model.

5.2.2 Spatial relation between values

Sensors provide quite exact measurements on the quality of water at the location

where the sensor is. However, as sensors are expensive and much parameters are

measured, we want to use the least possible sensors and still provide an accurate

overview of the quality of the water in the whole system. To reconstruct values on

locations where no sensor is available, we can model the relationship between the

water quality at a measured location and an unmeasured location. If the model is

accurate enough, we are still able to show the quality of the water in unmeasured

locations.

Recent research has assessed relationships between quality parameters and physi-

cal watershed attributes in an unmonitored stream [GBS12]. Input data consisted

of 16 quality parameters, measured between 1991 and 2008 in 44 stations on a

monthly basis. These values were averaged per quarter and per year using four

different statistical estimates: mean, geometric mean, trimmed mean and the me-

dian. The data was then compared to data collected between March 2007 and

March 2010 on 11 other sites. 14 Parameters were available in both parameter sets

and these have been used. The goal of this research was to classify a new unmon-

itored shed into a cluster with sheds that have similar water quality, based on the

physical attributes of the shed.

To build the classifier to classify stations in the reference data set a linear and a non-

linear approach were taken, see also Figure 5.1. The pipeline is the same for either

approach: after applying the statistical estimate the input data is normalized, then

variable reduction is applied. The stations are clustered into groups of stations with

similar water quality. Based on the clustering a classification method is developed

to find the relationship between cluster membership and physical characteristics

of the watershed, which can be used to classify a new watershed, even though it is

not monitored.

The referenced research is an example of what can be reached by using soft sensors.

Soft sensors can also be used to perform filtering or interpolation of parameters, or

use an Artificial Neural Network (ANN) to make more advanced predictions.


Figure 5.1Linear and non-linearapproach to modelingthe relationship betweenwater quality and physicalwatershed parameters,from [GBS12].

1. Box-Cox Normalization2. Softmax Standardization

A) Principal Component Analysis

B) k-Means Clustering of Principal Components

C) Linear Discriminant Analysiswith physical watershed variables

Softmax Standardization

A) Self Organizing Maps (SOM)

B) k-Means Clustering of SOM

C) Support Vector Machinewith physical watershed variables

Data Transformation

Variable Reduction

Cluster Modeling

Classification

Linear Non-Linear

5.3 Quality data prediction

Several models are available for predicting quality parameters after the treatment

process has (partly) completed, FR10. These can globally be grouped in two cate-

gories: knowledge-driven and data-driven models [SSS11]. Knowledge-driven mod-

els use the physical knowledge of the observed subject. In the case of water quality,

such a model defines for instance the response of a contaminant on a specific step

in the purification process. Data-driven models predict values of the water after

specific steps in the process only by means of the values it has seen in the past in-

stead of understanding the underlying process. These models generally have of a

training phase in which historical data is entered and the model is formed. After

training, the model is capable of predicting values. If the control system also mea-

sures quality parameters at the outlet, feedback from these sensors can be used to

employ the self-learning capabilities of the model.

5.3.1 Knowledge-driven prediction models

A few water quality models that focus on drinking water purification exist. In this

section we provide an overview of these models and indicate their main goals and

assess their performance, where possible.

The advantage of knowledge-driven models over data-driven models is that it can

be adapted to other situations more easily [RD06b]. Knowledge-driven models

provide calibration options to adapt to site-specific conditions, while data-driven

models should be re-trained from the beginning or a reasonable amount of time is

5.3. Quality data prediction 41

needed to adapt to the new situation by the self-learning capabilities of the model.

The first six models are reviewed by [RD06b]: Metrex, OTTER, Stimela, TAPWAT,

WatPro and WTP Model. This review is made in an effort to build a European plat-

form for modeling of drinking water treatment processes [MRB+08] by TECHN’EAU.

Also a new simulation model is developed: SimOx, concluding this overview.

5.3.1.1 METREX

This model was developed at the University of Duisburg, Germany, in 2002 [RD06b].

Purpose and goal The emphasis of the model is on particle removal and ozona-

tion, which is not a complete purification process. It simulates the whole plant as

well as its individual processes and it is designed to simulate the operation but also

to function as support while designing individual process steps.

Performance in practice The model was not tested on a site, so its performance

is unknown. Furthermore it is not being developed actively anymore.

5.3.1.2 OTTER

This model was developed by the Water Research Centre in Swindon, United King-

dom, starting in 1996 with the oldest module dating back from the 1980s [RD06b].

Purpose and goal The purpose of the model exactly matches what we are trying

to achieve. It simulates the whole plant and its individual processes and is typically

used to optimize the purification process.

Performance in practice Several studies have been done to the functioning of

OTTER. The studies were mostly successful, but also show that relatively much data

is needed to successfully calibrate the model. Once calibrated this model could be

a serious candidate to be used in the control system.

5.3.1.3 STIMELA

This model was developed at the TU Delft, The Netherlands [RD06b].

Purpose and goal Stimela is an environment in which different water treatment

processes can be modeled dynamically. Is especially used by academia and for pro-


totyping. It needs a process model and calibration parameters for each step in the

process and outputs the quality of the outlet water, but also additional information

about the state of the filters that are used during the process, etc. Its planned use is

in training new process controllers.

Performance in practice Still being developed actively. The model was used in

a Dutch drinking water company to train new process controllers, by linking the

the model to the process automation system that is already in use at the plant. The

Stimela model, together with the EPANET hydraulic model were validated in this

plant [WOSR07]. The experiment led to the Dutch WaterSPOT project in which

a more generic simulator, company specific simulators and model based process

optimization are researched.

5.3.1.4 TAPWAT

This model was developed by the RIVM [RD06b, VGR+01].

Purpose and goal The TAPWAT (Tool for the Analysis of the Production of drink-

ing WATer) was developed to predict the quality of drinking water given the quality

of raw water, to estimate whether microorganisms or disinfection by-products oc-

cur in the water and to advise on new production plants. Uses percentage-removal

based and empirical models and even incorporates stochastic modeling for certain

treatment steps.

Performance in practice Performance is unknown. The plan of action presented

in [VGR+01] is suggested to be followed to make it suitable to assess public health

risks, implying it is not ready for this function at this moment.

5.3.1.5 WATPRO

This model was developed by Hydromantis Inc., Hamilton, Canada [RD06b].

Purpose and goal The WatPro (WATer PROductivity) models was developed with

a focus on disinfection and formation of disinfection by-products. Other aspects of

the water treatment process are supported but are less significant in the scope of

the model.


Performance in practice According to [RD06b] this model is “a poor example of

the state of the art in water treatment”. This statement follows from the fact that all

processes apart from disinfection and the formation of disinfection by-products are

modeled by specifying a percentage removal —independent of inlet water quality

or flow— or a predefined outlet turbidity value.

5.3.1.6 WTP MODEL

This model was developed by the EPA [RD06b].

Purpose and goal The Water Treatment Plant (WTP) Model was developed to

support the Disinfectant/Disinfection By-products (D/DBP) Rule. It focuses on av-

erage performance values for the removal of natural organic matter and the forma-

tion of disinfection by-products.

Performance in practice Because of the design focused on average performance

values, this model is less suitable for use on a specific site. It does not support site-

specific calibrations.

5.3.1.7 SIMOX

This model was developed by Anjou Recherche/ENSCR (France) [MRB+08].

Purpose and goal This model results from a collaboration between several par-

ties in the TECHN’EAU project to enhance drinking water treatment modeling. OT-

TER, Stimela and Metrex functioned as starting points for the development of this

model [RD06a]. It was built as a model focused on chlorination and ozonation,

because other available models focus on the whole process and then their lack of

precision for single processes lowers the quality of predictions for the outlet param-

eters.

The three-stage model is based on the idea that for oxidation few measurements

are available and they are often measured at the outlet of a process. The majority

of oxidation simulators assume the initial state (inlet) is known and it needs this

information to calculate the outlet state. SimOx enables input of measurements at

both locations of the process (inlet and outlet). In order to do so it runs in three

stages:


1. Based on the outlet values it tries to reconstruct a likely inlet value for the

parameter based on statistical analysis or an ANN.

2. It iteratively tries to determine the actual inlet value by solving the processes

that are related to the oxidant profile.

3. With the reconstructed inlet values the traditional simulator approach can

be used to simulate the process.

Based on the large data requirements for other models to calibrate, this model tried

to simplify the calibration procedure by only requiring the parameters that are not

easy to predict and to guide the implementer by suggesting the easiest test methods

to find the required values.

Performance in practice Following up on the introduction of SimOx, one of the

authors researched micropollutant removal and evaluated the SimOx simulations

alongside [Man08]. Chlorination and degradation of atrazine (a herbicide), carba-

mazepine (a drug, Tegretol) and desethylatrazine (atrazine metabolite) were simu-

lated by SimOx. To acquire real life values for selected parameters the process was

performed manually, measuring specified values at predefined times. The mea-

sured values are then compared to the calculated values, both drawn in Figure 5.2.

In these experiments the first measurement is made after 30 seconds, so rapid

changes after injecting an oxidant are not observed.

The predictions are quite accurate although it can be seen that in Figure 5.2(b)

the atrazine degradation is slightly overestimated, resulting in a higher atrazine

concentration than calculated, and in Figure 5.2(d) the pCBA (para-chlorobenzoic

acid) concentration is highly overestimated. One remark is that for the results shown

in Figure 5.2(a), 9 parameters were adjusted afterwards to fit the experimental data.

These parameters were reaction rate constants (8) and the initial concentration of

Natural Organic Matter (NOM) defined as a fraction of Total Organic Carbon (TOC).

Complementary experiments are suggested, but the current state of the model and

its performance in practice is unknown.

5.3.2 Data-driven prediction models

Research on data-driven prediction models is largely available, as these models are

not specific to a single field of research. However, every model has its own char-


(a) Chlorination

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Experimental CHCl3Experimental CHCl2BrExperimental CHClBr2

Simulated Cl2 Experimental Cl2

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Simulated pCBASimulated bromates Experimental bromates

Experimental pCBA

Simulated O3 Experimental O3

Figure 5.2SimOx calculated datacompared to experimentalresults, from [Man08].

acteristics that can make it suitable for a specific problem. To determine to which

degree the model is suitable, field-specific research is required. Unfortunately not

much research has been done on predicting water quality.

One research that is of high interest for this project was performed at two water

treatment plants in Edmonton (Canada). These plants functioned as development

sites for several ANNs to model water quality, water demand and several purifi-

cation processes. Rossdale WTP and E.L. Smith WTP are situated near the North

Saskatchewan River and use its water as intake water. An overview of the devel-

oped models and their results when tested on unseen data is given in [BZS+01]. The

quality-related models are summarized below to show the performance of these

models.

5.3.2.1 INTAKE WATER QUALITY PREDICTION

The intake water color prediction model was developed in 1995 and predicts the

color 24 hours in advance, based on eleven parameters. These parameters con-

sist of historical values of the intake water, current values for the intake water and


environmental parameters like temperature and spring or summer indexes.

Data from 1990 – 1993 were used for developing the model and data from 1994 for

testing. The mean absolute error for predictions was 0.62 True Color Units (TCU),

with R2 = 0.94. For reference: 5 TCU is detectable by humans in large basins like

swimming pools, 15 TCU in a glass of water [HCPP02].

5.3.2.2 COLOR REMOVAL

The first process model predicts color removal by coagulation. Color is used as a

surrogate measure for organic matter.

The model was developed in 1998 using 12 parameters from three years of full-scale

water-treatment data. When tested on unseen data, it showed a mean absolute er-

ror of< 0.32 TCU, which is less than the accuracy of the instrument used to measure

the color.

5.3.2.3 SOFTENING

For the softening process two models were developed: a process model predicting

the water hardness after treatment, but also an inverse process model to predict the

necessary lime dose to realize a specified hardness. Inverse process models are of

specific interest for this project as they closely relate to process optimization, which

should be performed by the complete control system.

The models were developed in 1997, based on eight months of daily average data.

Both models use the parameters listed in Table 5.1.

The results of the tests are shown graphically in Figure 5.3. The model predicted

effluent hardness, measured as CaCO3 concentration, with a mean absolute error

of 2.7 mg/L with R2 = 0.84. The inverse model predicted the necessary lime dose

with a mean absolute error of 2.0 mg/L with R2 = 0.95.

5.3.2.4 TURBIDITY REMOVAL

For the turbidity removal process also a process model and an inverse process model

were developed. The process model predicts the effluent turbidity while the in-

verse model predicts the necessary alum dose to get to a specified effluent turbid-


Parameter Unit

Raw water temperature ◦C

Raw water pH pH

Raw water total hardness mg/L as CaCO3

Raw water alkalinity mg/L

Plant flow rate ML/d

Alum dose mg/L

Softening clarifier effluent pH pH

Lime dose mg/L (process model)Effluent total hardness mg/L as CaCO3 (inverse model)

Table 5.1The parameters used inthe softening process model(or inverse process model),from [BZS+01].

Figure 5.3Model prediction resultsfor the inverse processmodel for softening,from [BZS+01].

ity. These models were developed in 1998, based on three years of daily average

data. Both processes use the same eleven parameters.

Tests have show the effluent turbidity could be predicted with a mean absolute er-

ror of< 0.77 Nephelometric Turbidity Units (NTU), while the inverse process model

predicted the necessary alum dose with a mean absolute error of < 1.8 mg/L.

5.3.2.5 FILTRATION

The last model handles the filtration process. It predicts the filter effluent particle

counts. The model was developed in 1999 with one year of full-scale data from

E.L. Smith WTP. Tests showed a mean absolute error of 2.3 particle counts/mL with


R2 = 0.79. However, the test was performed on only 17 data points. Because particle

counters were installed in 1998, no more data was available then.

All these models are currently in use at the two water treatment plants for process

analysis in addition to bench-scale tests. The models support the process, but the

process is still controlled manually.

CHAPTER

6Evaluation

In the previous two chapters the main subjects of this report are discussed: measur-

ing water quality and analyzing the results. The considerations, findings, remarks

and suggestions from these chapters are evaluated in this chapter, leading to an in-

dication of the feasibility for each subject. The feasibilites are then related to the

Functional Requirements (FRs) that were defined in Chapter 3.

6.1 Water quality measurements

The list of selected parameters to be measured in the treatment process is not de-

termined exactly. Important parameters are indicated using the Saturation Index

(SI) and the Dutch legal ranges that apply to the quality of drinkable water. The re-

sulting list forms the basis of the list of available sensors which is used to show the

deficiencies in sensor availability. The fact that not all parameters can be measured

using a sensor leads to a ‘partly feasible’ classification of FR1. When more suitable

process models are available the final list can be made and for future work can be

identified.

The use of smart sensors is discussed in Section 4.1.5. These sensors provide a

parameter value directly, while others provide an electrical current that should be

translated to a parameter value. The specification for this translation should be

provided by the sensor’s manufacturer and is a trivial process, so supporting this

translation is feasible (FR2).

49

50 Evaluation

Guidelines for sensor placement are identified in short, but the exact placement

should be determined per site, based on the requirements of the process model

that is going to be used, on advice by process controllers and on on-site tests. Op-

tions for automatic location discovery for the individual sensors are available, but

they are too inaccurate to be useful for this purpose. Additionally, even if the lo-

cation discovery is more accurate it is hard to translate the physical location of the

sensor into a place in the pipeline of the treatment process. As most sensors move

seldom, probably only while testing or implementing sensors on a new site, fixed

locations which are manually entered are advised. Both cases would satisfy FR3, so

this requirement is considered feasible.

For the connectivity of sensors to the control system a basic solution is suggested,

assuming to use wired sensors and link them to sensor stations on the plant. These

links use the connection types the sensors provide. The sensor stations are linked

to the network to which the control system is also linked. Connection to this net-

work can be realized through LAN or WLAN. For future wireless sensors a preferred

protocol, ZigBee, is indicated, because of its low energy consumption and high scal-

ability. The proposed data transfer protocol would be a push-based protocol. Any

choice satisfies FR4.

Summary For measuring water quality only FR1 is not completely feasible, be-

cause the lack of sensors for certain parameters. The available sensors however,

are one of the most important parts in measuring the quality as the performance

of the rest of the system depends on the data it retrieves from the sensors. Seri-

ous efforts should be made to improve the availability of sensors to make a reliable

control system.

6.2 Quality data analysis

From the model reviews in Section 5.3, both knowledge- and data-driven, it can be

concluded that a lot of developments are necessary before reliable and complete

process models for drinking water treatment exist. Most of the discussed models

are not being developed anymore, or the current state of the model is unknown.

The knowledge-driven model SimOx looks like the most promising model, but even

on this model no recent literature (after 2009) was found. The data-driven models

are promising, but currently these are separate small models for which an integra-

6.2. Quality data analysis 51

tion project should be initiated. To summarize: to a certain extent predictions can

be made but the field of process models, corresponding to FR10, needs a lot of re-

search before it can be used in production systems.

A commonly mentioned problem is the amount of historical data. The available

data often is insufficient to calibrate a knowledge-driven model, let alone to train

a data-driven model. The increasing use of real-time monitoring should provide

more data in the future to overcome this problem.

The added value of soft sensors can not be determined without context. The se-

lected model, its implementation and its calibration form the context in which a

soft sensor can operate. However, a soft sensor can operate regardless of the se-

lected model, so once useful soft sensors are developed they can be used even after

switching to another process model. Designing a soft sensor that adds value is not

a straight-forward job, but once the sensor is designed and implemented, FR7 is

certainly feasible.

A topic that was not covered in Chapter 5 is the use of external data; data that is

gathered from external sources and can describe the quality of water (results of lab

experiments) or the environment (weather conditions and forecast). FR8 and FR9

describe the use of external data and the forecasts for these values. If the data is

available in an external system, transferring the data to the control system is proven

technology that occurs in a lot of other distributed systems. Without a detailed

analysis we can say that these requirements are feasible, with the remark that the

data should be available in another system.

Summary Water quality modeling has some serious limitations at this moment.

Process models are not used in water treatment processes directly and current re-

search to these or new models is hardly found. Some models are used for training

new process controllers, they should be tested for their performance in produc-

tion environments. Furthermore, several data-driven prediction models to sim-

ulate single process stepps were developed. Very good results could possibly be

achieved if they would have been integrated into a suite that could simulate the

whole treatment process. FR10 will therefore have the remark ‘needs research’.

52 Evaluation

6.3 Requirement coverage

The FRs are listed in Table 6.1. This table summarizes the results from the evalua-

tions in the previous sections and provides references to the sections in which each

subject is covered in detail. The Feasible column indicates whether the require-

ment can be realized using the currently existing technologies.

Table 6.1Requirements traceabilitymatrix.

Requirement Section Feasible?

FR1 Perform continuous on-line measure-ments on selected parameters.

4.1 Partly

FR2 Translate sensor measurements into ac-tual parameter values.

4.1.5 Yes

FR3 Record location data for each parametervalue.

4.2.2 Yes

FR4 Transfer parameter values from the sen-sors to the control system.

4.3 Yes

FR5 Persist quality data for later use. 5.1 Yes

FR6 Handle a mix of continuous signals anddiscrete signals with varying frequencies.

5.1 Yes

FR7 Calculate soft sensor output based onseveral other parameters.

5.2 Yes

FR8 Use external data to observe the state ofthe system or the environment in whichthe system operates.

– If available

FR9 Get predictions for external data from ex-ternal systems, when applicable.

– If available

FR10 Predict effluent water quality on the shortterm.

5.3 Needs research

CHAPTER

7Conclusion

This report originates from a literature study on the feasibility of implementing a

quality control system for the purification process of drinking water. It provides an

overview of the available techniques, hardware and software and can be used as

an entrance point into the literature on each subject. The proposed control system

consists of four subsystems as indicated by the control paradigm in Section 1.2:

measurement, analysis, process adaptation, visualization. To limit the scope of this

project we have considered the measurement and analysis subsystems.

7.1 Discussion

Based on the findings from the previous chapters we can conclude that building

the monitoring part of the control system is feasible. Most of the important param-

eters can be measured by sensors, but not all. We have indicated which important

parameters cannot be measured, but the consequences of missing these values is

unclear. The close-to-real-time measurements that are available can provide added

value by showing more details of the currently ongoing process than lab measure-

ments at longer intervals would. Furthermore, it provides much historical data,

which can be used to improve the following issue.

Quality modeling using water treatment process models is a serious challenge. There

are just a few models available and for these models it is not always clear whether

they are still being developed actively and how their actual performance is. SimOx

53

54 Conclusion

is the most recent knowledge-driven model and originates from the TECHN’EAU

project. This model looks promising, but also for this model development seems to

have stalled. Several data-driven models for single process steps are developed, but

they are not really integrated and it is hard to use them simulating the whole pro-

cess. The need of historical data could probably be fulfilled if more sensor based

monitoring systems come available.

7.2 Future work

As the scope was limited to the measurement and analysis part, it is clear that the

process adaptation and visualization parts (and the integration of these subsys-

tems) should further be researched. When an actual design phase starts an exten-

sive Use Cases and Requirements Analysis should be made and knowledge from a

drinking water production company should be employed.

We have seen that not all parameters could be measured using the currently avail-

able sensors. How this impacts the system, the quality of the predictions by the

models and the added value of the monitoring system for process controllers should

be researched. Then possibilities to develop the sensors for the most critical pa-

rameters could be investigated, probably together with projects like TECHN’EAU

or SAWA.

Regarding the models the most improvements can be achieved. For knowledge-

driven models further development of the SimOx model is suggested, as this model

has a solid base in different other models and has had relatively recent develop-

ment activity. An integration project for data-driven models, which are now mostly

suitable for single process steps, would be beneficial. If that is successful, the num-

ber of available process step models should be expanded. Closely related to select-

ing or developing a process model, a storage scheme for analysis results should be

designed.

Three other aspects that were not discussed in this report extensively but are very

important are security, energy consumption and economic feasibility. With secu-

rity being the motivation for reports on Early Warning Systems that use sensors,

such a system itself should be designed with security in mind. If the system is un-

safe, it can not keep the water safe. Safety aspects for sensor connection, system

7.2. Future work 55

access, calibrations, etc. should therefore be considered.

When implementing a control system the energy consumption at the treatment

plant will increase. The control systems requires power, the sensors consume en-

ergy, etc. On the other hand, optimizing the treatment process can decrease the

need for chemicals and maintenance or cleaning moments. Whether these advan-

tages and the increased safety regarding water quality justifies the extra power con-

sumption is subject to further experimentation and research.

Starting to use a control system requires high investments. As we have shown, sen-

sors are expensive and the control system itself was not yet developed. Placing

the sensors might require some changes in the production process or a rebuild of

a part of the plant. Further research can identify necessary investments in more

detail and indicate what savings should be realized in order to be economically at-

tractive. It should be clear that these money and time investments are not going to

be made by a single or a small number of water companies, so national or interna-

tional consortiums and governments should collaborate with water companies to

solve this challenge.

Acronyms

Acronyms

ANN Artificial Neural Network; 39, 44, 45

ASCE American Society of Civil Engineers; 29

AWWA American Water Works Association; 1

CDC Centers for Disease Control and Prevention; 27

CFU Colony Forming Units; 65–67

DGPS Differential GPS; 31

DO Dissolved Oxygen; 76

ECBC Edgewood Chemical Biological Center; 12

EPA U.S. Environmental Protection Agency; 12, 27, 43, 69, 77

EU European Union; i, 10

FDA U.S. Food and Drug Administration; 27

FR Functional Requirement; 20, 21, 49, 52

GPRS General Packet Radio Service; 14, 33, 34

GPS Global Positioning System; 31, 57

GSM Global System for Mobile Communications; 14, 33, 34

IEEE Institute of Electrical and Electronics Engineers; 33

LAN Local Area Network; 32, 33, 50, 58, 70, 71, 73

57

58 Glossary

NOM Natural Organic Matter; 44

NTU Nephelometric Turbidity Units; 47, 65, 67, 74

ORP Oxidation Reduction Potential; 27

Pt/Co Platinum-Cobalt Scale; 65, 67, 73

RIVM Dutch National Institute of Public Health and the Environment; 5, 42

SAWA Project Sensors And WAter; 11, 54, 70, 71

SI Saturation Index; 26, 38, 49, 72, 74

TCU True Color Units; 46

TECHN’EAU Technology Enabled Universal Access to Safe Water; 10, 14, 41, 43, 54

TOC Total Organic Carbon; 27, 44

WAAS Wide-area Augmentation System; 31

WLAN Wireless LAN; 32, 33, 50

WQI Water Quality Index; 5, 65

WssTP Water supply and sanitation Technology Platform; 10

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APPENDIX

AWater quality parameters and

their legal ranges

In Table A.1 the legal range of water quality parameters is outlined, as defined in

the Drinkwaterregeling [VTM04] for The Netherlands and in [SI00] for England.

One important difference is that the values in The Netherlands are measured in

the treatment works, while the English ranges are determined on the consumers’

taps.

The Dutch system defines a norm and a bandwidth for each parameter. If the mea-

sured value is within the bandwidth range, that parameter has an optimal value –

encoded as 0 in the WQI. If the value is outside the bandwidth, but below the norm

it is suboptimal but still legally accepted – encoded on a scale from 0 to 1 in the

WQI. The English system only defines a maximum value for the specified parame-

ters.

The special units used in this table are:

Colony Forming Units (CFU) an estimation of viable cells.

Nephelometric Turbidity Units (NTU) indication of turbidity caused by particles.

Platinum-Cobalt Scale (Pt/Co) a color scale to indicate pollution levels in water.

65

66 Water quality parameters and their legal ranges

The parameters are subdivided in four categories [GvB09]:

Acute health parameters Bacteria and viruses that directly threaten public health.

Non-acute health parameters Chemical substances that affect public health only

with lifelong exposure.

Operational parameters Parameters that are used during the treatment process

to ensure good operational management and drinking water quality.

Customer-oriented parameters Parameters that can be noticed by the customers.

Table A.1

Legal ranges for water quality parameters [VTM04, SI00].

Parameter Unit Norm (NL) Bandwidth (NL) Max (UK)

Acute health parameters

E. coli CFU/100 mL 0.31 0 0

Enterococci CFU/100 mL 0.31 0 0

Legionella CFU/L 100 0 N/A

Non-acute health parameters

Arsenic µg/L 10 0 10

Nickel µg/L 20 0 20

Boron mg/L 0.5 0 1.0

Bromate µg/L 5 0 10

1,2-Dichloroethane µg/L 3 0 3

Fluoride mg/L 1.1 0.2 1.5

Dissolvable copper mg/L 2 0−1 2

Nitrate mg/L 50 0 50

Nitrite mg/L 0.1 0 0.5 / 0.12

Polycyclic aromatic hydrocarbons (sum) µg/L 0.1 0 0.1

Pesticides µg/L 0.1 0 0.5

Tetra- and trichloroethane µg/L 10 0 10

Trihalomethanes (sum) µg/L 25 0 100

Operational parameters

continued on next page. . .

1In the Drinkwaterregeling, this value has a norm of 0. To prevent division by zero 0.3 was chosen.2This parameter has two limits: 0.5 mg/L in consumers’ taps, 0.1 mg/L in treatment works. Values

for the other parameters are measured at consumer’s taps.

Water quality parameters and their legal ranges 67

Table A.1: Legal ranges for water quality parameters [VTM04, SI00] (continued from previous page).

Parameter Unit Norm (NL) Bandwidth (NL) Max (UK)

Aeromonas CFU/100 mL 200 0 N/A

Ammonium µg/L 200 0 500

Coli bacteria CFU/100 mL 0.31 0 0

Chloride mg/L 150 0−75 250

Clostridium perfringens CFU/100 mL 0.31 0 0

Saturation index SI >−0,2 > 0 N/A

Temperature ◦C 25 5−15 N/A

Bicarbonate mg/L > 60 > 120 N/A

pH pH 7,0 < pH < 9,5 7,8−8,3 N/A

Customer-oriented parameters

Oxygen mg/L > 2 > 4 N/A

Aluminium µg/L 200 30 200

Hardness mmol/L 1 < H < 2,5 0−2,5 N/A

Color mg/L Pt/Co 20 0 20

Iron µg/L 200 0 200

Manganese µg/L 50 0 50

Sodium mg/L 150 0−75 200

Sulphate mg/L 150 0−75 250

Turbidity NTU 1 0 4

APPENDIX

BAvailable sensors

In Table B.1 we provide an overview of the currently available sensors and their re-

sponse times for each measurement. The table functions as a grid indicating which

parameters can be measured and by which sensors. The data is provided by the

manufacturers themselves. Where available, a reference to the EPA verification re-

port is added.

In the table we use the following abbreviations:

R.t. Response time.

Acc. Accuracy. Can be provided in a percentage or an absolute number (uses the

same unit as the range specification).

Comm. Communication, ways to receive data from the sensor.

Batt. Battery powered.

“. . . ” An unknown value.

In the Cost column, a price preceded by a number in brackets indicates how many

different parameters this sensor can meausure. The sections after the table provide

more information about the sensors and/or the manufacturers. Referred sources

have been retrieved in October 2012.

69

70A

vailable

senso

rs

Table B.1

Overview of available sensors.

Key Brand Type Cost Power Range Acc. R.t. Comm.

E. coli

AquaScope, by Aqua Explorer is in testing phase in the SAWA Project [SAW12].

In research at Wetsus [OLWE12, WHB12].

Enterococci


Legionella In research at Wetsus [OLWE12].

Arsenic

CEO Cogent Env. OVA 5000 . . . . . . varies . . . Current, LAN, RS232

TDS TraceDetect SafeGuard II [3] $ 35.000 AC/DC 1 – 100 ppb ±1 30min USB

Nickel


TDS TraceDetect SafeGuard III [3] $ 35.000 AC/DC 1 – 100 ppb ±1 30min USB

Boron No sensors were found.

Bromate In research [BKM+02]

1,2-Dichloroethane No sensors were found.

Fluoride

NIE Nico2000 ELIT 8221 $ 381 Batt., AC/DC 0.06 – 1,900 ppm ±3% 10s RS232, USB

NSW NexSens WQ-FL $ 499 USB 0.2 ppm – saturation . . . . . . USB


Availab

lesen

sors

71Table B.1: Overview of available sensors (continued from previous page).


Dissolvable copper



TDS TraceDetect SafeGuard II/III [3] $ 35.000 AC/DC 1 – 100 ppb ±1 30min USB

Nitrate

IST In-Situ Troll 9500 Prof.XP [9] $ 4.586 Batt., AC/DC 0.14 – 14,000 ppm ±10% 60s RS232, SDI-12


NSW NexSens WQ-NO3 $ 499 USB 0.1 – 14,000 ppm . . . . . . USB

Nitrite

NIE Nico2000 ELIT 8021 $ 413 Batt., AC/DC 0.5 – 500 ppm ±3% 10s RS232, USB

Polycyclic aromatic hydrocarbons (sum) No sensors were found.

Tetra- and trichloroethane No sensors were found.

Trihalomethanes (sum)

MST MultiSensor MS2000 THM . . . AC/DC 1 – 1,000 ppb ±10 60s Current, USB, LAN

Aeromonas



72A

vailable

senso

rs

Table B.1: Overview of available sensors (continued from previous page).


Ammonium



NSW NexSens WQ-NH4 $ 499 USB 0.1 – 18,000 ppm . . . . . . USB

Coli bacteria In research at Wetsus [OLWE12].

Chloride


NIE Nico2000 ELIT 8261 $ 405 Batt., AC/DC 1 – 3,500 ppm ±3% 10s RS232, USB

NSW NexSens WQ-CL $ 499 USB 1.8 – 35,500 ppm . . . . . . USB

Clostridium perfringens No sensors were found.

Saturation Index (SI) Combination of several parameters, see Section 4.1.1 (page 26).

Temperature

GLW Global Water WQ101 $ 317 AC/DC −50 – 50◦C ±0.1 5s Current (4–20 mA)

IST In-Situ Troll 9500 Prof.XP [9] $ 4.586 Batt., AC/DC −5 – 50◦C ±0.1 30s RS232, SDI-12

NSW NexSens WQ-TEMP $ 199 USB −5 – 50◦C ±0.2 30s USB

YS6 YSI 6600 V2 . . . Batt., AC/DC −5 – 50◦C ±0.15 . . . RS232, SDI-12

Bicarbonate Patents available [Nie74].


Availab

lesen

sors

73Table B.1: Overview of available sensors (continued from previous page).


pH

GLW Global Water WQ201 $ 700 AC/DC 0 – 14 pH ±0.28 3s Current (4–20 mA)

IST In-Situ Troll 9500 Prof.XP [9] $ 4.586 Batt., AC/DC 0 – 12 pH ±0.1 15s RS232, SDI-12

NIE Nico2000 P11/P14 $ 75 Batt., AC/DC 0 – 14 pH . . . 10s RS232, USB

NSW NexSens WQ-PH $ 299 USB 0 – 14 pH . . . . . . USB

YS6 YSI 6600 V2 . . . Batt., AC/DC 0 – 14 pH ±0.2 . . . RS232, SDI-12

Oxygen

GLW Global Water WQ401 $ 849 AC/DC 0 – 8 mg/L ±0.5% 10s Current (4–20 mA)

IST In-Situ Troll 9500 Prof.XP [9] $ 4.586 Batt., AC/DC 0 – 8 mg/L ±0.1 45–60s RS232, SDI-12

NIE Nico2000 601 – 603 $ 232 Batt., AC/DC 0 – saturation ±3% 10s RS232, USB

NSW NexSens WQ-DO $ 349 USB 0 – 20 mg/L . . . 60s USB

YS6 YSI 6600 V2 . . . Batt., AC/DC 0 – 50 mg/L ±0.2 . . . RS232, SDI-12

Aluminium No sensors were found.

Hardness No sensors were found.

Color

KEM Kemtrak DCP007 . . . AC/DC 0 – 1,000 mg/L Pt/Co ±0.2 instant Current, USB

Iron


TDS TraceDetect SafeGuard III [3] $ 35.000 AC/DC 1 – 100 ppb ±1 30min USB


74A

vailable

senso

rs

Table B.1: Overview of available sensors (continued from previous page).


Manganese

TDS TraceDetect SafeGuard II [3] $ 35.000 AC/DC 1 – 100 ppb ±1 30min USB

Sodium


Sulphate


Turbidity

GLW Global Water TB500 $ 1.615 – 2.711 AC/DC 0 – 1,000 NTU ±0.02 5s Current (4–20 mA)

IST In-Situ Troll 9500 Prof.XP [9] $ 4.586 Batt., AC/DC 0 – 2,000 NTU ±2 instant RS232, SDI-12

KEM Kemtrak TC007 . . . AC/DC 0.01 – 50,000 NTU ±2% instant Current, USB

YS6 YSI 6600 V2 . . . Batt., AC/DC 0 – 1,000 NTU ±0.3 . . . RS232, SDI-12

Calcium (additional, required for SI)


NSW NexSens WQ-CA $ 499 USB 0.02 – 40,000 ppm . . . . . . USB

Conductivity (additional, required for SI)

IST In-Situ Troll 9500 Prof.XP [9] $ 4.586 Batt., AC/DC 5 – 20,000 µS/cm ±2 instant RS232, SDI-12

YS6 YSI 6600 V2 . . . Batt., AC/DC 0 – 100 µS/cm ±0.5% . . . RS232, SDI-12

Available sensors 75

CEO: Cogent Environmental

Cogent Environmental (UK) offers products formerly offered by Monitoring Tech-

nologies International (AU). The OVA 5000 is a monitoring system on itself, mea-

suring several types of heavy metals using Anodic Stripping Voltammetry in a max-

imum of 6 sample streams. Measurements happen real-time according to the prod-

uct description, but this is not mentioned in fact sheets. The detection limits vary

per metal, see the referred document.

Data sheet http://www.cogentenvironmental.co.uk/ova.php

Detection limits http://www.cogentenvironmental.co.uk/img/

monitoring/Online/Typical_Limits_of_Detection_

Portable_and_On-line_ASV_v02_May_2012.pdf

Website http://www.cogentenvironmental.co.uk

GLW: Global Water Quality Instrumentation

Global Water Instrumentation, Inc. (US) provides a whole range of water quality

instruments, as listed in the table. The TB500 Online Turbidity meter has a varying

price depending on two options: autocleaning of the sensor and the type of light

used – white or infrared. The catalog referred to below contains data sheets and a

price list.

Catalog http://www.globalw.com/catalog_wq.html

Website http://www.globalw.com

IST: In-Situ Troll 9500 Professional XP

A single sensor measuring multiple parameters. Sold for $4,586 or available for

rental for $ 125 per day, $ 400 a week or $ 1,100 a month. Response times vary

between instantateous (pressure, conductivity and turbidity) and 60 seconds.

Data sheet http://www.in-situ.com/force_download.php?file_id=397

Rental http://www.enviroequipment.com/rentals/

In-SituTROLL9500.html

Website http://www.in-situ.com

http://www.cogentenvironmental.co.uk/ova.php

http://www.cogentenvironmental.co.uk/img/monitoring/Online/Typical_Limits_of_Detection_Portable_and_On-line_ASV_v02_May_2012.pdf



http://www.cogentenvironmental.co.uk

http://www.globalw.com/catalog_wq.html

http://www.globalw.com

http://www.in-situ.com/force_download.php?file_id=397

http://www.enviroequipment.com/rentals/In-SituTROLL9500.html

http://www.enviroequipment.com/rentals/In-SituTROLL9500.html

http://www.in-situ.com

76 Available sensors

KED: Kemtrak DCP007

Kemtrak (SE) provides a range of photometric analzyers.

Data sheet http://www.kemtrak.com/pdf/

KemtrakHazenWaterColor.pdf

Data sheet http://www.kemtrak.com/pdf/

KemtrakTC007Turbidimeter.pdf

Website http://www.kemtrak.com

MST: MultiSensor MS2000 THM Monitor

MultiSensor Systems (UK) provides a Trihalomethanes monitor. Other monitor sys-

tems include Ammonia and Volatile Organic Compounds.

Data sheet http://www.multisensor.co.uk/UserFiles/file/

MS2000THMMonitorDatasheet.pdf

Website http://www.multisensor.co.uk

NIE: Nico2000 ELIT Series

A series of Ion-Selective Electrodes by Nico2000 (UK). Prices were converted with

rate £1 = $ 1.6012. Additional supplies are necessary for each parameter (electrode

heads, reference electrodes), they are included in the price that is listed in the table.

The interface to the computer is not included yet. Nico2000 provides three options

for such an interface: with 1/2, 4 or 8 electrode ports. Costs are respectively $ 768, $

1080 and $ 1441. Besides the electrodes the temperature, pH and Dissolved Oxygen

(DO) sensors can always be connected.

Measuring Sulphate concentration is a special case. This is measured by a reaction

of the water with Barium Chloride after which the removed amount of Barium is

measured. This is equivalent to the amount of Sulphate.

Data sheets http://www.nico2000.net/datasheets/

SpecificationTable.htm

Price list http://www.nico2000.net/Data/pricelist.htm

Accuracies http://www.nico2000.net/Book/Guide12.html

Website http://www.nico2000.net

http://www.kemtrak.com/pdf/Kemtrak Hazen Water Color.pdf

http://www.kemtrak.com/pdf/Kemtrak Hazen Water Color.pdf

http://www.kemtrak.com/pdf/Kemtrak TC007 Turbidimeter.pdf

http://www.kemtrak.com/pdf/Kemtrak TC007 Turbidimeter.pdf

http://www.kemtrak.com

http://www.multisensor.co.uk/UserFiles/file/MS2000 THM Monitor Datasheet.pdf

http://www.multisensor.co.uk/UserFiles/file/MS2000 THM Monitor Datasheet.pdf

http://www.multisensor.co.uk

http://www.nico2000.net/datasheets/Specification Table.htm

http://www.nico2000.net/datasheets/Specification Table.htm

http://www.nico2000.net/Data/pricelist.htm

http://www.nico2000.net/Book/Guide12.html

http://www.nico2000.net

Available sensors 77

NSW: NexSens Smart Sensor Series

NexSens (US) has a large product line with several smart sensors that can be con-

nected through USB. All single-parameter sensors are priced in a range of $ 299 –

$ 499, depending on the parameter it measures. This company also delivers other

accessories like sensor mounts, buoys and underwater cables.

Website http://www.nexsens.com

TDS: TraceDetect SafeGuard II and III

TraceDetect (US) offers two on-line metal trace detection systems: SafeGuard II

and III. SafeGuard II uses Anodic Stripping Voltammetry and measures Arsenic and

Copper & Manganese (total). SafeGuard III uses Adsorptive Stripping Voltammetry

and measures Nickel, Copper and Iron. More parameters are supported but are not

listed in the table.

EPA has tested the SafeGuard II and published the verification report, the price is

taken from this report. The price of SafeGuard III is not mentioned, but assumed

to be the same.

Data sheet http://www.speciation.net/Database/

Instruments/TraceDetect/SafeGuard-;i2562

Verification report http://www.epa.gov/etv/pubs/600etv06060.pdf

Operations manual ftp://tracedetect.com/SafeGuardII&IIIManuals/

Manuals/SafeGuardIIandIIIManualRev.2.0.2.pdf

Website http://www.tracedetect.com

YS6: YSI 6600 V2

A sonde measuring 5 parameters that are listed this table, manufactured by YSI

(originally from the US, now a world-wide company).

Data sheet http://www.ysi.com/media/pdfs/E52-6600V2.pdf

Verification report http://www.epa.gov/etv/pubs/01_vr_YSI_round2.pdf

Website http://www.ysi.com

http://www.nexsens.com

http://www.speciation.net/Database/Instruments/TraceDetect/SafeGuard-;i2562

http://www.speciation.net/Database/Instruments/TraceDetect/SafeGuard-;i2562

http://www.epa.gov/etv/pubs/600etv06060.pdf

ftp://tracedetect.com/SafeGuard II & III Manuals/Manuals/SafeGuard II and III Manual Rev. 2.0.2.pdf

ftp://tracedetect.com/SafeGuard II & III Manuals/Manuals/SafeGuard II and III Manual Rev. 2.0.2.pdf

http://www.tracedetect.com

http://www.ysi.com/media/pdfs/E52-6600V2.pdf

http://www.epa.gov/etv/pubs/01_vr_YSI_round2.pdf

http://www.ysi.com

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