VISUALIZING PROCESS INFORMATION AND
THE HEALTH STATUS OF WASTEWATER
TREATMENT PLANTS
A Case Study of the ESPRIT-Project WaterCIME
Peter Dannenmann
Deutsches Forschungszentrum für Künstliche Intelligenz GmbH
P.O. Box 2080
67608 Kaiserslautern, Germany*
peter.dannenmann@dfki.uni-kl.de
Hans Hagen
University of Kaiserslautern
P.O. Box 3049
67653 Kaiserslautern, Germany
hagen@informatik.uni-kl.de
Abstract
In this case study we present an approach to support the operators of wastewater treatment plants by supervising the health status of the plant with an online diagnosis software and by visualizing the process data and the diagnosis
results. In addition to measuring the available on-line data, we simulate the
behavior of the biological processes in order to retrieve data on-line that
normally only can be determined by analyzing water samples in the laboratory.
Based on the automated analysis of these data we provide to the operator a
view of the states of the biological processes within his plant. This information
allows an early reaction in the case of deviations from an optimal process
behavior. Thus the plant can always be operated in an optimal state so that we
can be sure that the treated water always meets the environmental standards
that are required.
Keywords:
information visualization, process control, simulation, diagnosis
*
Peter Dannenmann conducted this work while staying with Astrium GmbH,
P.O. Box 28 61 56, 28361 Bremen, Germany
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1.
Introduction
Process supervision is an important topic in wastewater treatment. Downtimes of a plant are expensive and in this domain they even may cause
environmental problems. Anyway, most of the current supervision systems
restrict to displaying only large numbers of sensor-values which require an
experienced operator to determine the current status of the cleaning process.
In case of an error normally there are lots of messages about limit-violations
in some process-parameters that only give little hint to the actual failure.
What is even more important, many of the process-parameters are not even
on-line available.
Here it is an important task to supply the appropriate information about
the process-state to the operator and visualize it in a way that covers the vital
information and is easy to understand.
In this paper we propose an approach to support the plant-operator by
visualizing the health status and information about the biological processes
within the plant in a clearer way than it is currently done by just displaying
raw measurement values. For this purpose, the plant’s “Supervision, Control
and Data Acquisition System” (SCADA System) is linked to a decisionsupport system based on dynamic simulation of the processes within the
plant. This permits to acquire deeper information about the process-state
from the simulation model than we can get only from on-line measurements.
With the support of such a knowledge-based system that uses on-line measurements as well as simulated values of the cleaning-process, deviations
from an optimal process state can be detected early. The decision-supportsystem then will provide suggestions for re-establishing an optimal processstate. The information about the current state of the cleaning process as well
as suggestions for re-establishing an optimal cleaning process are visualized
to the operator. These suggestions can be verified by simulating the future
development of the cleaning-process.
The work described in this case study has been performed in the scope of
a research project sponsored by the European Union within the “European
Strategic Programme for Research and development in Information
Technologies” (ESPRIT). In this project, named WaterCIME (ESPRIT
Project 8399), a European consortium of eight companies developed new
methods to improve water management information systems by developing
an open software framework combining advanced software tools. One of our
tasks within this project was the integration of advanced technologies for
supporting the operation of wastewater treatment plants into this framework.
Visualizing Process Information
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In the next chapter we will describe the simulation- and diagnosiscomponents that are the basis for the decision-support system. Here we
introduce a representation for the biological processes within wastewater
treatment plants in the form of simulation-models. The graphical
representation of these models visualizes the structure of the underlying
biological processes much more adequately than it is done today in a set of
differential equations. Afterwards we describe in chapter three the methods
how the information from this decision-support system is visualized to the
plant operator. In chapter four we discuss the experiences we made during a
test installation of the system.
We conclude our paper with the lessons learned from the use of this
decision-support system in operating a wastewater treatment plant. A short
outlook on future trends will close our discussions.
2.
Simulation- and Diagnosis Components of the
Decision-Support System
2.1
A Simulation Model for Representing Complex
Biological Processes in Wastewater Treatment
Modern wastewater treatment plants are designed to eliminate several
pollutants from the incoming water. Besides removing organic substances an
important task is the removal of nitrogen and phosphorous. In modern plants
this is done in a nitrification / denitrification process for organic components
and nitrogen-compounds and by chemical and biological phosphorouselimination.
The current standard for describing these processes is set by the International Association on Water Quality (IAWQ). Currently most simulation
is based on the “Activated Sludge Model No. 1” and the „Activated Sludge
Model No. 2“ (ASM1 and ASM2) (Henze et. al. 87, Henze et. al. 95) that
describe the biological processes as a set of eight differential equations
(equal to eight processes covered by the model) with thirteen parameters (for
thirteen substances in the wastewater that are affected).
Additionally the sludge-thickening process is also described in a similar
way. The sludge in the wastewater that has passed the biological stage settles
down in “settling tanks”. Most of that sludge is re-fed into the biological
stage and only a small percentage is removed from that circulation and taken
to the surplus-sludge treatment (Benefield, Molz 84). In order to get a
realistic model of the sludge-circulation in the whole cleaning process a
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model for the settling of the sludge is needed. In contrast to the biological
processes described above, in the settling tanks only the physical processes
of the sinking sludge are of interest. In the literature there are several models
for these processes. They start with models of ideal settlers that completely
separate solid and soluble fractions (SIMBA 95, Otterpohl 95) but can also
be quite complicated ones that subdivide the settling tanks in several layers
with different sludge concentrations and that describe transport-processes of
the several fractions between these layers (Härtel 90, Takacs et. al. 91).
Since the presentation of the differential equations describing these
processes to the plant-operators is not useful, the information contained has
to be presented in a more intuitive manner. Therefore we developed an
ASM1 representation (Lunde 96) in the simulation tool CSS (Columbus
Simulation System) (CGS-Project 95), that has been developed at Astrium.
In addition to that model of the biological part of the plant we also modelled
the settling tanks. These are the components that are vital to the cleaning
process, so we restricted to them.
The graphical editor of the simulator allows modeling the processes in a
very clear manner. It supports a structural decomposition of the system by its
ability to model it hierarchically. The differential equations are hidden in
function blocks which now build up a structural representation of the sewage
plant instead of a functional one.
The simulation-model is decomposed according to the different stations
the water passes during the purification-process (Figure 1). In every block
the decomposition of the wastewater into the fractions of solid and soluble
substances in the water is computed, so that at every point in the simulationmodel a characterization of the wastewater is available that can be visualized
to the operator or can be used within the diagnosis component.
The computation of the characterization of the wastewater is done by a
set of blocks that represent the biological processes taking place within a
particular stage of the sewage plant (Figure 2). In addition to the possibility
to get an overview of the health-status of the whole cleaning-process (see
chapter three) here the user can get detailed information about the single
process parameters that are either measured on-line or simulated (as shown
in figure 2).
With such a structured approach the processes taking place within the
sewage plant can be displayed more clearly than in their original form and
they are easier to understand to the operators.
Visualizing Process Information
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Figure 1. Simulation model of the sewage plant
Figure 2. Simulation model of the circulation tanks
2.2
The Diagnosis Component in the Architecture of the
Decision Support System
The decision support system (Abels 95) integrates the simulation component described above with the diagnosis software CONNEX-II (CONNection-matrix EXpert system), developed at Astrium (Sielaff 92). This
architecture is used for supervising the cleaning-processes in the sewage
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plant. It is the results of the diagnosis process that are visualized to the plantoperator to support him keeping the cleaning processes in an optimal state.
Figure 3 describes the architecture of the decision-support system: The
acquisition of the on-line measurements is performed by a SCADA-system
that is coupled to the sewage plant’s data bus. This system also serves as a
database for the measured values and additionally performs some
preprocessing where it is necessary (e.g. when the depending modules need
average values of some sensors instead of the actual ones).
The simulation-system is fed with the on-line measurements and controlinputs of the plant. Here one simulation-model is run in parallel to the
cleaning process. This model provides via the SCADA-system those data to
the diagnosis-component that else could only be gathered by laboratorymeasurements.
Database
Process data
DataPreprocessing
DiagnosisModule
(CONNEX-II)
Control
DiagnosisUserInterface
Modelvisualisation
& Control
Modellvisualisation
& Control
Diagnosismodel
Simulationmodel
Diagnosisknowledgebase
Figure 3. Architecture of the decision support system
A second model within the simulation-system is used for simulations into
the future. If the diagnosis-component detects a non-optimal process state,
this model can be used to verify the suggested recovery-strategies. Based on
a given state of the on-line simulation, the user can simulate the effects of
different control-strategies on the future behavior of the cleaning-process.
Here he can check the results of the proposed actions either in the
simulation-model directly or the diagnosis-component can perform these
checks automatically and visualize the results to the operator.
All on-line and simulated sensor values are fed from the SCADA-system
into the diagnosis-component. With a real-time inference engine this module
detects deviations from the optimal process-state, informs the user and
suggests actions to improve the quality of the cleaning-process.
Visualizing Process Information
3.
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Visualizing the Process State to the Operator of a
Wastewater Treatment Plant
The main user-interface of the decision-support system is the diagnosis
user-interface (Figure 4). It has been integrated with the SCADA-system and
provides comfortable access to all related modules. Here the diagnosisprocess can be controlled and the health-status of the cleaning-process can
be visualized (Abels, Dannenmann 97a).
Figure 4. Main user interface of the decision support system for wastewater treatment plants
Besides access to the process-parameters (Figure 5), the analysis-results
of the diagnosis-component as well as suggestions for recovery-actions
(Figure 6) can be displayed here. However, since these data are mere text,
advanced visualization techniques are needed, to give the user a clearer
impression of the diagnosis system’s findings. This is done by integrating a
virtual environment into the user-interface. Within this environment, the socalled “System Explorer,” a virtual model of the wastewater treatment plant
is available in VRML format. All relevant components within this model are
linked to their counterparts within the simulation-model, which in turn
serves as a basis for the knowledgebase used for determining the healthstatus of the whole cleaning process. Thus we get a consistent representation
of the wastewater treatment plant as well in the simulation-model as in the
VR-model and the representation of the diagnosis-results.
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Figure 5. Displaying raw sensor values in a listbox
Figure 6. Displaying diagnosis-results in a list-box with corresponding explanation
In operating the plant, this allows the user now to get a more detailed
view of the processes in the plant: First a conventional user-interface is
available. The operator can display a list of sensors that are available within
the plant where he can check the sensor-values directly. In addition the
location of the sensors within the plant is shown in an overview-window.
This helps the operator in selecting the correct sensors if there are several
sensors of the same type distributed all over the plant. In combination with
the values retrieved from the simulation-model a good overview of the
current parameters of the cleaning-process is available to the plant operator.
For further support in plant operations, the diagnosis-component
permanently monitors all measured and simulated process-parameters and
performs a system-diagnosis on the cleaning-process. First, the results are
displayed within the main user-interface in a textual manner. If a deviation
from an optimal process-state or even a failure within a component is
detected, a message is displayed to the operator that states the possible
reason of the problem. In contrast to the conventional process-supervision
systems that only state limit violations at several sensors, this is a big
advantage to the operators. Additionally, an explanation text is available,
that can give a deeper explanation of the reasons of the failure and countermeasures suggested by the diagnosis-component (Figure 6). At the same
Visualizing Process Information
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time the component where the problem occurred is highlighted within the
virtual representation of the plant (Figure 7).
Figure 7. Highlighting a faulty component in a turbine of the process-air supply-system
This helps the operator to get a clearer view of what had happened and
which measures he can take to get the cleaning-process back to an optimal
state. If a technical problem has occurred, the operator gets important
information about the location of the faulty component within the plant and
can arrange the repair of that component quite quickly.
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Due to its modular architecture, the decision-support system can also be
ported to other wastewater treatment plants without much effort. In order to
install such a system on another plant, three tasks have to be performed: The
simulation model has to be adapted to the new plant, the VR model also has
to be adapted and the diagnosis knowledgebase has to be adjusted to the
process parameters within the new plant.
With the use of component libraries in a manner similar to (SIMBA 95),
the modelling effort for the simulation model restricts to selecting
components that reflect the architecture of the actual plant and connecting
and parametrising them. These parameters are known for every plant, so that
they just have to be entered into the model.
The virtual model of the plant can also be adapted to a new architecture
in the same manner. Also here a set of component- or subsystem-models is
available, that permits a quick and easy generation of a virtual model of a
wastewater treatment plant.
The third adaptation, the adjustment of the diagnosis knowledgebase is a
little different. Since we do symptom-based diagnosis, the symptoms have to
be adjusted to the new plant and the new simulation model. However, since
the structure of such a set of symptoms is the same for all sewage plants (the
processes within the plant are always the same), only the new parameters
that describe the symptoms have to be adjusted to the specific plant.
Additionally, the possible failure modes have to be linked to the new virtual
model in order to allow a proper visualization in the new environment. This
also can be done easily in an editor integrated into the supervision system to
give the model-developer an easy access to all modelled components.
4.
Experiences During the Test Installation of the
Decision-Support System
The modules contributed by Astrium Space Infrastructure and the
University of Kaiserslautern were tested within the WaterCIME project’s
test-environment at our project partner’s wastewater treatment plant (Abels,
Dannenmann 97b). For evaluating the modules, we used operational data
provided by the plant’s existing supervisory system. On this basis we ran the
modules in parallel to the existing control system of the plant. During the
tests the decision-support system showed its capabilities to provide clearer
information to the plant’s operators than the conventional supervisionsystem. The operators considered the system’s user interface as very userfriendly. The system gave them easy access to information they else have to
Visualizing Process Information
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gather from different sources in conventional control systems. However,
since the controllers of the plant were quite conservative, they first had to get
used to the decision-support system. Then, after a short time, they esteemed
especially the way how a summary of the plant’s operational state and
suggestions for control actions to keep it in an optimal state were presented
to them. In addition, they found it particularly valuable how the system
presented to them in a graphical environment the locations of faulty
components together with proposed corrective actions.
However, since on-line simulation of biological processes is still a topic
directly at the frontier of research in wastewater treatment, the simulation
model of the cleaning-process could not be kept as close as possible to the
biological processes as intended over a longer period of time without
recalibration. This was a drawback to the application and caused us to shift
our focus for further applications on the coupling of the visualization-system
to the diagnosis-component alone. Here the integrated diagnosis- and
visualization-system proved its usefulness in providing the user detailed
visual information about the location of faulty components in a complex
technical system and thus supporting a quick repair and reducing downtimes of the affected system.
5.
Conclusions
In this paper we presented an approach to support the supervision of
wastewater treatment plants by integrating simulation-, diagnosis and
visualization-components into a plant’s supervision-software. A prototype of
the presented system has been implemented in a sewage plant, where it was
tested with real process-data. On one side the tests revealed weak points in
the available process-models describing the biological processes within
wastewater treatment. On the other hand, the tests showed the usefulness of
enhancing modern diagnosis systems with advanced visualization techniques
to display the location of faulty components to the users, thus providing
valuable information for a quick repair of the affected system.
Our further work will now concentrate on a generalization of the
techniques for coupling simulation, diagnosis and visualization in the
product development process for technical systems. We will transfer the
techniques developed within this project into an integrated product development environment, where we use simulation- and visualization-techniques
for an early prototyping of technical systems under development. For the
later phases it is our objective to use simulation techniques to retrieve
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diagnosis-information for that technical system and use that information in
an integrated operations-support environment. Here we will provide
advanced diagnosis- and visualization-techniques to support the user in
operating the newly developed technical system.
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