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Building a quality monitoringsystem for the production of
drinking waterThesis
Allard Naber
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.
20 Use Cases and Requirements Analysis
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.
22 Use Cases and Requirements Analysis
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
24 Use Cases and Requirements Analysis
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
28 Water quality measurements
• 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:
30 Water quality measurements
• 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-
32 Water quality measurements
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.
4.3. Sensor connectivity 33
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].
34 Water quality measurements
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
4.3. Sensor connectivity 35
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.
40 Quality data analysis
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-
42 Quality data analysis
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.
5.3. Quality data prediction 43
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:
44 Quality data analysis
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-
5.3. Quality data prediction 45
(a) Chlorination
0
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Oxi
dant
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cent
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n (M
)
Simulated CHCl3 (mM)Simulated CHCl2Br (mM)Simulated CHClBr2 (mM)
Experimental CHCl3Experimental CHCl2BrExperimental CHClBr2
Simulated Cl2 Experimental Cl2
(b) Atrazine degradation
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(d) Desethylatrazine degradation
0
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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
46 Quality data analysis
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-
5.3. Quality data prediction 47
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
48 Quality data analysis
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
AquaScope, by Aqua Explorer is in testing phase in the SAWA Project [SAW12].
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
CEO Cogent Env. OVA 5000 . . . . . . varies . . . Current, LAN, RS232
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
continued on next page. . .
Availab
lesen
sors
71Table B.1: Overview of available sensors (continued from previous page).
Key Brand Type Cost Power Range Acc. R.t. Comm.
Dissolvable copper
CEO Cogent Env. OVA 5000 . . . . . . varies . . . Current, LAN, RS232
NIE Nico2000 ELIT 8227 $ 405 Batt., AC/DC 0.006 – 6,400 ppm ±3% 10s RS232, USB
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
NIE Nico2000 ELIT 8021 $ 405 Batt., AC/DC 0.3 – 6,200 ppm ±3% 10s RS232, USB
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
AquaScope, by Aqua Explorer is in testing phase in the SAWA Project [SAW12].
continued on next page. . .
72A
vailable
senso
rs
Table B.1: Overview of available sensors (continued from previous page).
Key Brand Type Cost Power Range Acc. R.t. Comm.
Ammonium
IST In-Situ Troll 9500 Prof.XP [9] $ 4.586 Batt., AC/DC 0.14 – 14,000 ppm ±10% 60s RS232, SDI-12
NIE Nico2000 ELIT 8051 $ 405 Batt., AC/DC 0.03 – 1,800 ppm ±3% 10s RS232, USB
NSW NexSens WQ-NH4 $ 499 USB 0.1 – 18,000 ppm . . . . . . USB
Coli bacteria In research at Wetsus [OLWE12].
Chloride
IST In-Situ Troll 9500 Prof.XP [9] $ 4.586 Batt., AC/DC 0.35 – 35,500 ppm ±15% 60s RS232, SDI-12
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].
continued on next page. . .
Availab
lesen
sors
73Table B.1: Overview of available sensors (continued from previous page).
Key Brand Type Cost Power Range Acc. R.t. Comm.
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
CEO Cogent Env. OVA 5000 . . . . . . varies . . . Current, LAN, RS232
TDS TraceDetect SafeGuard III [3] $ 35.000 AC/DC 1 – 100 ppb ±1 30min USB
continued on next page. . .
74A
vailable
senso
rs
Table B.1: Overview of available sensors (continued from previous page).
Key Brand Type Cost Power Range Acc. R.t. Comm.
Manganese
TDS TraceDetect SafeGuard II [3] $ 35.000 AC/DC 1 – 100 ppb ±1 30min USB
Sodium
NIE Nico2000 ELIT 8230 $ 405 Batt., AC/DC 0.05 – 2,300 ppm ±3% 10s RS232, USB
Sulphate
NIE Nico2000 ELIT 8081 $ 381 Batt., AC/DC 0.5 – 13,700 ppm ±3% 10s RS232, USB
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)
NIE Nico2000 ELIT 8041 $ 381 Batt., AC/DC 0.02 – 4,000 ppm ±3% 10s RS232, USB
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
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
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