1. Modelica Modeling Language as a Tool on Control
Engineering Education: Simulation of a Two-Tank
System
João Figueiredoa
, José Machadob
,Vitor Carvalhoa,c
and Filomena Soaresc
a
IPCA-EST, Campus do IPCA, 4750-810 Barcelos, Portugal
b
CT2M Research Centre, School of Engineering of University of Minho, 4800-058 Guimarães, Portugal
c
ALGORITMI Research Centre, School of Engineering of University of Minho, 4800-058 Guimarães, Portugal
(joaogmfigueiredo@gmail.com;jmachado@dem.uminho.pt;vcarvalho@ipca.pt;fsoares@dei.uminho.pt)
Abstract—Engineering education requires considerable
innovation in the used methodologies. In this context, attention
should be given to the use of simulation programs as they
provide students with the knowledge of complex physical
systems before their physical implementation. Moreover, the
fact that sometimes there is a reduced time to combine practical
components and tutorial lectures, enables the use of virtual and
remote laboratories essential tools to engineering education.
Control systems are of utmost importance in industrial
applications. The simulation of control systems assumes an
essential role as it allows the complete previewing of the
behavior of the systems in physical environment, including
possible damages during operation. Thus, the costs of physical
implementations can be reduced and optimized. Besides that
there is a high complexity and demands in what refers to
dependability of automation systems controllers because their
design process is a hard and complex task. However, there are
several analysis techniques that provide robust and dependable
controllers. This paper presents the simulation of a two-tank
system operated in closed-loop with discrete controllers.
Moreover the importance of Modelica modeling language and
Dymola simulation environment are highlighted to model
complex closed-loop systems.
Keywords—Modelica; Dymola; Control; Education;
Simulation.
I. INTRODUCTION
The process of simulating physical systems is present across
several areas of engineering [1]. It allows the prediction of
desired or undesired behaviors that can be guaranteed or
avoided before the practical implementation of the system in
analysis. On the other hand, throughout the study of the
system’s operation it is possible to evaluate and implement
possible optimizations. The simulation allows students to
better conciliate the concepts taught through theoretical and
practical lessons. Moreover, it is possible to perform a more
precise analysis of the system operation and to infer
improvements and new possible approaches.
Currently it is denoted a strong increase in the development
and delivery of solutions in order to simulate complex
systems including the emergence of new programming
languages and new software with several features. Regarding
the teaching/learning process in Control Systems curricular
unit we may have tutorial lessons, practical classroom work,
virtual labs, and remote labs. In tutorial lessons the teacher
exposes the subject, he/she is still the main actor. The
practical classroom work is conducted during lessons where
students perform experiments, usually defined by a script in
for applying the learned theoretical concepts in practical
terms. Virtual laboratories [1] are meant to simulate real
systems, allowing the animation of the experiments on an
interactive environment. The connection to the virtual server
lab is performed through a remote client. The process
consists of selecting the system to simulate, change the setup
and the parameters and run the simulation. Typically the
results are presented to the user through a graphical interface
or by storing them in any other format.
In what concerns remote laboratories [1], a substantial
increase in the use and development of these simulation tools
is denoted as the growth of available resources on the
Internet and the number of user increases. In these labs,
students can interact with physical systems via the Internet
where intuitive interfaces are provided to the students
allowing the changing of setup parameters in order to verify
the behavior of the system. However, the practical teaching
component should not be excluded but optimized. Thus the
approach to more complex systems is promoted by using
remote labs so that students feel totally familiarized with
such systems. Moreover, there are important advantages in
the preparation of practical works in order to predict possible
failures and inherent optimizations.
This project aims, through a case study, to explore the
features of simulation systems for educational purposes.
Through modeling and simulation, the user will be able to
test several operating modes according to the change of
parameters in order to test the system’s behavior. This

2. project includes the development and analysis techniques on
industrial controllers, which allow the design of more
reliable controllers as well as the optimization of all
parameters.
The Modelica [2] programming language has achieved a high
success rate as it covers different technologies through
specific libraries that include several systems such as
hydraulic, electrical, pneumatic, mechanical and has also
available a StateGraph library [3] concerning the modeling of
discrete controllers behavior. This modeling language
comprises a high level modeling through the composition of
pre-defined mathematical equations.
Dymola [4] has several examples of systems, modeled with
Modelica modeling language, making it more accessible to
the user. The modeling is performed via functional blocks,
which correspond to a given component where there are
various levels of abstraction, according to the system.
In this paper it is shown how to model and simulate a closed-
loop system using Modelica modeling language and Dymola
simulation environment, improving industrial controllers’
dependability and robustness in operation. Furthermore, it is
shown how to promote the optimization of operating
conditions such as functional aspects of the system, reducing
and predicting errors and solving emergency situations. In
the near future this system is aimed to be used as a learning
tool in Automation and Control courses.
The paper is organized in six sections, as follows: Section 2
presents the case study that involves a two-tank system;
Section 3 is dedicated to the controller specifications; Section
4 details the implementation in Dymola software; Section 5
presents the simulation results and the associated discussion
thereof in view of a desired behavior of the system and
finally, Section 6 presents the conclusions and the future
perspectives of work.
II. CASE STUDY DESCRIPTION
This case study is a benchmark system, proposed by
Kowalewski [5], that consists in a physical plant composed
by two tanks and respective control valves level sensors as
well as, components related with possible dangerous
behavior of the system that could lead to emergency
situations. However, in the studied approach there are some
adaptations when comparing this system with the original
one presented at [5]. As it can be seen from fig. 1 there is a
cooling system consisting of two tanks in which a solution is
processed.
For the solution, Tank1 must have a given concentration
obtained by heating the solution via a resistance (H1), which
causes the formation of steam from the heating of the liquid.
Furthermore, there is a condenser (C) where cooling is
triggered by circulating a cooling fluid if the valve V13 is
open. There were considered two specific operation modes
for this system: normal behavior and malfunction behavior.
Regarding the normal operation mode, the system operates as
follows: the Tank1 must be filled to its top level with a fluid
by opening the valve V12. When Tank1 is full, the heating
and condensing systems are simultaneously turned on (by
opening the valve V13 regarding the condenser). When the
steam is produced, condensation occurs through the
condenser C. When the concentration of Tank1 is desired, the
heating and cooling systems of the condenser are turned off.
The solution flows from Tank1 for Tank2 and should be
ensured that Tank2 is always empty in this process to avoid
catastrophic problems in the system.
Fig.1 – Complete scheme of the global system
As the crystallization of the fluid should be avoided, it is
necessary to heat the fluid. It can be heated in a certain time
interval or until a pre-defined temperature is registered by the
temperature sensor in Tank2.
The system malfunction occurs when the coolant fluid is in a
low level which is detected by the FIS sensor. When this
problem occurs, an increase in pressure and temperature
values is verified in the condenser and in Tank1 if the heating
system remains powered on. It is therefore necessary to
ensure that the pressure in the condenser C does not exceed
the maximum allowed value, in order to avoid its explosion.
For this purpose, it must be ensured that Tank1 heating is
switched off before opening the emergency valve V16. The
H1 heating resistance must be switched off to prevent that
crystallization never occurs in the fluid. There are two
different ways to switch off H1: during a certain time after
the sensor FIS has detected fluid reduction or via the
temperature sensor TIS1. As a first approach, it is possible to
infer an initial set of inputs/outputs as Table 1 indicates.
III. CONTROLLER SPECIFICATIONS
The Sequential Function Charts (SFC) [6] specifications,
regarding to the controller behavior in normal and
malfunction modes are presented in fig. 2 and fig. 3,
respectively. The behavior was translated to the library
StateGraph. This SFC (fig.4) was created to comprehend the

3. normal and malfunction operations. As it might be observed,
there is a Malf variable, which relates to the system
malfunctioning. It is a Boolean variable and it turns to the
logical «1» value, in a random way, because the conditions
for malfunction cannot be predicted in a precise moment.
So, the passage to the second stage will only happen when
Tank1 and Tank2 are empty and, furthermore, Malf variable
logic value is «0». In step 2, V1 and V2 are active and the
next transition occurs only when Tank1 is full. In step 3, MR
is actuated which corresponds to the condenser’s operation.
Table 1 – Inputs/Outputs
Inputs Function Outputs Function
LIS1 Superior
level
V12 Fluid
source
Tank 1 LII1 Inferior level V16 Fluid
drain
TIS1 Maximum
fluid
temperature
(alarm)
H1 Heating
resistor
V15 Fluid
transfer
between
tanks
Common FIS Cooling
fluid flow
from the
condenser C
LII2 Superior
level
V17 Heating of
the tank
Tank 2 LII2 Inferior level V18 Emptying
of the tank
TIS2 Fluid
temperature
P1 Emptying
pump
After two time units, it passes to step 4 where both MR and
the H1 heating resistance are active. Here two alternative
processes can occur: if twenty time units have passed from
the last state, then both MR and V3 are actuated in the
following step. The next transition refers to Tank1 emptying
and thirty-two time units later V4 is actuated and then, Tank2
is drained and the process returns to start. However, if Malf
is active and it did not pass twenty time units, V4 and alarm
are active in the next stage; the transition occurs when Tank2
is drained, so the next state activates V3 and alarm. Next
transition occurs when Tank1 is also drained and state V4 and
alarm are activated. However, there is a final condition that
occurs when Malf signal is active and both tanks are full, the
alarm is activated but when Malf value becomes a logical
«0» and both tanks are drained, the system goes back to
where it started. Both SFCs, referring to malfunction and
normal operation were considered to increase the pressure in
the evaporator during the heating process in Tank1. The
concentration of the resulting solution will depend on the
mass of the evaporated water as a consequence of the
temperature increase. Finally, cooling Tank1 occurs when
Tank2 is empty and the heat loss is the only significant term
relating to the heat balance that promotes a decrease in
temperature over time. Due to the discrete change between
the two continuous systems, Tank1 and Tank2, V15 and V18,
but also considering that cooling occurs when Tank2 is
drained at the boiling point of water, a hybrid model was
developed.
Fig. 2 – SFC specification for normal behavior
The configuration of a temperature alarm is due to the fact
that it needs to fulfill two fundamental opposite properties.
On one hand, the temperature must be low enough to avoid
temperature that could damage the system and, on the other
hand, the temperature should be high enough to prevent
crystallization of fluid before Tank1 is empty.
IV. DYMOLA IMPLEMENTATION
As noted above, the programming language Modelica has
reached a high level of demand due to its ability in modeling

4. of several hybrid systems. The software used was Dymola,
which provides the user an intuitive modeling environment,
also allowing the manipulation of complex models. In
mathematical terms, Modelica includes several types of
equations such as differential ordinary equations, Petri nets,
between others.
Fig. 3 – SFC specification for malfunction behavior
Given all this potential shown by Dymola, it was developed a
global model of the system under study. The modeling of the
general plant was carried out using the Modelica
programming language. Although it was developed an
overall plant, dedicated blocks were modeled and analyzed in
a separate mode to avoid errors. Besides that, plant
comprises three separate controllers: main, normal and
failure operations. This problem could only be solved using a
controller, as it will be observed in fig. 5. The main
controller receives real signals and converts them into
Boolean signals so they can be used in normal and
emergency controllers.
Fig. 4 – SFC specification for global solution
Fig. 5 – Main plant
For the modeling of controllers it was used the Modelica
library of StateGraph [7], included in Dymola.
Also, in relation to the plant, the source was modeled to fill
the two tanks, the heating block, the condenser and their
valves. Fig. 5 presents the model corresponding to the
studied system. However, given the complexity of the plant,
it is plausible that it would be better to employ the SFC
diagram corresponding to the integration of the malfunction

5. and normal modes. Fig. 6 shows the controller internal
configuration obtained through the Modelica State Graph
library. By this way it would just be necessary the use of a
controller in what it refers to normal and malfunction
operation modes. So, with this approach the system would
probably become simpler and effective. Using this system,
students should be able to test different controllers with
several different parameterizations and setups to study its
influence over the system simulation results.
Fig. 6 – Global solution, StateGraph in Dymola
V. SIMULATION RESULTS
As previously mentioned in the Introduction section, the
results can be presented in several data formats. Dymola
allows the presentation of results through a GUI (Graphical
User Interface), where by selecting the functions to observe,
the system’s response is displayed through several charts and
graphs regarding to each specific component. Furthermore,
the possibility to observe different graphs simultaneously is
provided to the user by opening more than one window of
data. It is also possible to check two different variables at the
same referential in order to make comparisons, as it will be
presented.
This section presents the simulation results in order to study
the behavior of the two-tank system. These results can be
seen as an overall analysis, in the point of view of the
operations of filling and drainage, heating and cooling. For a
correct behavior of the system, it must be ensured that the
process of filling and draining of the tanks is carried out as
expected for their normal operation.
As such, the results concerning the operation of the valves in
the modes of normal operation and malfunction are
displayed. As it can be observed in fig. 7, in normal
operation, the level of the tanks corresponds to the operation
described in the SFC shown in fig. 2. Regarding the heating
of the Tank1, it was effected using a circuit in which it was
employed a heating resistor. As it can be seen from fig. 8,
there is an increase in temperature with a considerable good
speed rate compared to the ambient temperature (283K). To
increase the temperature value it is important because it is
through this heater that it is possible to control the
temperature increase in Tank1. The condenser block has a
key role in this system and requires special care during its
operation because it is susceptible to explosions when its
maximum value of operation is exceeded so it needs to be
guaranteed that the heating of Tank1 is switched off before
the opening of the safety valve. In the condenser block it
occurs the phenomenon of condensation, namely the passage
of the gaseous to the liquid state. As such, a high temperature
decrease before a subsequent heating is required. This block
also takes into account a prior increase of temperature carried
a significant lowering. As it can be observed in fig. 9, the
rate of temperature decrease in speed is quite high so that the
occurrence of this phenomenon is as reliable as possible in
order to avoid the crystallization of the fluid.
Fig. 7 – Tanks filling and draining normal operation
Fig. 8 – Heating resistor temperature increase

6. Fig. 9– Heat capacitor temperature decrease for cooling
purposes
VI. CONCLUSION AND FUTURE WORK
The simulation assumes a key role in the education of
various engineering-related areas such as Electrical,
Mechanical or even Aerospacial. Thus, it is possible to
predict possible problems and study optimizations inherent to
the studied system such as the definition of a failure
operation controller. Moreover, the process of simulation
allows students to gain further experience for complex
physical systems. Thus, the use of virtual laboratories and
remote laboratories becomes quite advantageous as it allows
students to acquire a critical spirit relative to control systems.
In addition, the simulation offers a more complete education,
enabling student to take part of theoretical, practical and
simulated scenarios. This paper presents the simulation of a
benchmark two-tank system considering its operation
requisites using Modelica modeling language and Dymola
simulation environment. An efficient modeling and
simulation has been achieved. It was possible to obtain a
plant model using specific blocks and their controllers in a
closed-loop system behavior. Some parameters have been
optimized in order to prevent possible occurrences of damage
in terms of physical implementation. This simulation tool
seems to have a high potential as a learning tool for students
of control engineering, enabling to test different control
strategies and different setup parameterizations within an
immediate feedback over the simulation results. As future
work, the physical model of the system will be built, in order
to allow a real observation of the simulation
parameterization. Moreover, using this tool the feedback
from the students will be collected in order to allow its
further development and to infer about its learning
effectiveness.
Acknowledgments
The authors of this paper are grateful to national funds by
FCT – Fundação para a Ciência e Tecnologia in the scope of
the project: PEst-OE/EEI/UI0319/2014.
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