Control Systems 2016 1
Gamifcation in web based dynamical
simulations - Trial in an
undergraduate course in Paper
Peter Lingman1*, Tomas Eriksson1,
Joacim Marklund2, Curt Lindström3
1) Optimation AB, Luleå, SWEDEN
2) Softronic AB, Sundsvall SWEDEN
3) Luleå University of Technology, SWEDEN
*) Corresponding: email@example.com
At Swedish universities future process engineers and
process developers are educated in master programs
targeting specific branches of the industry, e.g. pulp and
paper. Learning the fundamental dynamics of both unit
processes and larger process sections encounter in the
industry are of high importance for the students. In an
effort to further improve this aspect of the education and
also highlight the important role of industrial process
automation and control, simulations are a suitable tool.
In this paper cloud based simulations inspired by the
consumer game industry have been tested and evaluated
in a master-level course in pulp and paper technology at
Luleå University of Technology (LTU). The approach
was well received by students and teachers, clearly
encouraging students interest and understanding of
process dynamics and automatic control.
The application of process and control system
simulations in educations are by no means a new
approach. In some programs students build their own
simulators from scratch and in others ready-to-use
simulators are made available for the students. In this
paper the target is set to improve user friendliness and
user experience of ready-to-use simulators, i.e. the focus
is on using simulations as a tool and complement in
education rather than simulator designing. Traditionally
these kind of simulators are installed on a set of
dedicated student computers to which students are given
access at specific time slots. Students are guided
through the exercise (simulation) by instructions and
questions normally documented on paper, very much
like traditional laboratory exercises that many of us are
The motivation of our work is to improve the
pedagogical aspects of using simulators to gain
knowledge of process and control system dynamics and
how process design is affected by controller
design/tuning and vice-versa. Simulations has the
potential to further amplify curiosity and learning rate
and we believe that gamification is one key enabler for
Our solution is based on Modelica® models and a cloud
based simulation platform called Everysim®.
In this section fundamental platform requirements are
described and our view of gamification is explained.
A number of top-level user requirements where
established at the beginning of the project:
·Clear definition and presentation of
task/exercise and mission. The student should
be able to start and run the game without much
·Possibility to start, pause and stop the game
and vary simulation speed in order to manage
real processes with very long time constants
·Possibility to run on many different devices,
e.g. smart phones, PADs and PCs.
·Operating system independent.
·24-7 access to simulations
In addition to these user requirements another important
requirement in education is that the platform must
facilitate interaction between the student and teacher
where the minimum requirement is to provide means for
the teacher to check student progress and understanding
in order to give feed-back.
From a teacher perspective, administration must be
minimized. Software updates like modifications of the
simulation model and management of users may not
take much time. For example, adding new students and
checking on student progress must be very simple.
In  gamification is defined as the application of game
design elements in non-game contexts and the authors
conclude that gamification is related to adding elements
from game industry rather than developing full-fledged
games. In the context of our work we have strived to
enhance the normal user experience when working with
simulators in education by adding gamification, key
ingredients that where prioritized in this project are:
·Achieve short and long term user feedback in
order to encourage strategical thinking (“think
around the corner”) and keeping up the interest
for the game.
·Define a story for the game. The story includes
“what to learn” and “stages of learning”.
·Possibility to advance to new level when
current task is completed - to evoke curiosity.
·Simple graphics to support understanding, e.g.
levels and alarms.
·Simple questioner that is integrated in the
game and further increases student-teacher
Control Systems 2016 2
In the paper technology course at LTU studies of unit
operations in paper production are in focus. Teachers
have recognised that the students are struggling in
understanding process dynamics that involve several
unit operations. Especially how unit operations interact
is difficult to explain. For example, how does an
upstream change in beating energy affect the paper
quality and properties of the long and short circulation.
The process section chosen here range from a pulp
storage to press section, i.e. a relatively complete stock
preparation section as shown in Figure 1.
Figure 1, Stock preparation process to press section.
The dynamical process model is developed in Modelica
 using Dymola  as simulator. Modelling
components from the commercially available VISA 
model library are used to build the model of the process
in Figure 1. The model is composed by the following
·A pulp storage tank modelled as an infinite
reservoir of pulp
·Headbox and wire
·Pumps and valves
·Controllers (LC, FC, QC)
·A pulp medium consisting of water, unbeaten
fibre, beaten fibre and fines.
Pulp refiner model
The pulp refiner model refines the unbeaten fibre from
the pulp storage tank into different components
(unbeaten, beaten and fines). Students can change the
refining by adjusting specific energy [kWh/tonnes] and
Specific edge load (SEL) [J/m], the later corresponding
to, for example, a change of refiner discs.
Generation of beaten fibre from unbeaten fibre is based
on a beating factor and the beating factor is calculated
as a function of refiner power and SEL. In a similar way
the generation of fines from beaten fibre is calculated as
a function of refiner power and SEL. No generation of
fines occur directly from unbeaten fibre.
Calibration of the refiner model is done against
measurement data using trends for specific refining
energy vs tensile index at different specific edge load
(SEL) , . Furthermore, general trend of tensile
index vs fines content  and specific energy vs.
Schopper-Riegler  are used in the calibration phase.
The wire model consists of a series of foil boxes that
extracts the water from the pulp. In the current model
the wire is represented with five foil boxes in series.
Pulp drainability is quantified using the Schopper-
Riegler number and the actual water leaving the wire
foilbox to the short circulation is calculated and
calibrated against measurement data.
In the visualization of the process shown in Figure 1 the
dry line position is calculated and shown. The dry line
(often also called the wet line) is the boundary between
the reflecting and non-reflecting regions of the upper
surface of the fibre mat on a wire.
CLOUD BASED SIMULATIONS - EVERYSIM
Everysim is an application that provides a framework
for commissioning simulation based education to the
web. Everysim supports six main functions that to a
large extent fulfils the requirements outlined in chapter
1, Using dynamical models
2, Graphical user interfaces (GUI)
3, Real time acceleration of simulations
4, Scenario based education setup and execution
5, Student follow-up and teacher student interaction
6, 24-7 access using a web browser on device of choice
Dynamical models are built in the Modelica language
and compiled in Dymola. The graphical user interface
(or operator interface) is built up in Inkscape™ mainly
using a predefined graphical .svg object library that is
supplied with everysim. This enables easy design of
realistic user interfaces. A distributed shared memory,
DSHM,  is used for data exchange between user-
graphical interface-simulation model. DSHM also
synchronises the simulation time with real time.
All files required to generate an everysim simulation are
easily moved to the cloud using drag and drop from a
local repository directly into the web browser. The
built-in possibilities to design student training scenarios
directly in the web browser in everysim enabled direct
and easy transfer of requirements and ideas from the
“story" into pedagogically designed tasks and missions.
When working with education the ability to check on
student progress is an important feature. In everysim it
is possible for the teacher, as administrator, to view the
progress of each student. For example, the teacher can
view time trends of process states (pressure, temperature
Control Systems 2016 3
etc) during a task, how long time the student needed to
complete the task, and how well the student completed
the task (e.g. threshold value compared with achieved
value). In addition, it is possible to design a questioner
to further test knowledge.
Figure 2, Overview of everysim architecture.
Everysim is a fully cloud-based application where the
website, storage and cloud services are all placed in
Microsoft Azure. Each user work through everysim.se
and will get a dedicated cloud service (or instance)
when an exercise is allocated. When an exercise starts
the graphical objects (svg-files) will be uploaded on the
client and the simulation model will be loaded into the
dedicated cloud service.
The communication between the client and the cloud
service uses Websocket in order to manage connection
and enable interaction of up to several thousands of
triggers in the simulation model. Communication speed
is a crucial aspect of Everysim where real-time (or near
real-time) simulations are required in order to achieve a
realistic operator feedback.
Since Everysim is a web application scalability is
inherent. Adding new users is very easy and fast and do
not require hardware upgrades or software installations.
Furthermore, all changes in the simulation solution are
managed in one place and updates are pushed through
the environment to all users at once.
STUDENT TRIAL AND RESULTS
Students were introduced to the subject in several
theoretical lectures before entering the simulation. The
simulation was performed in two double lectures with a
15 min break. These lectures were initiated by
explaining implemented algorithms (models), regulatory
options, visualisation of responses and assumptions
made in the model as well login requirements.
The exercise was divided in different sections starting
with a task to control the first refiner (task I). The
students were asked to deliver two charts; paper strength
(tensile index) and dewatering capacity vs specific
refining energy at different SEL. Regulation was limited
to specific refining energy (specifik energi, Figure 3)
and different SEL (kantbelastning, Figure 3). The charts
were made in excel and would then be used to manage
later tasks in the simulation. This part of the simulation
was ended by questions regarding refining and issues
not implemented in the model.
Figure 3 Refiner interface (kvarn in Swedish) with
input dialog box open.
The following parts of the simulation utilized the full
model and the question to be answered was; what
production level can be reached with maximum paper
strength at headbox concentration 0.3% (task IIa) and
0.2% (task IIb)? The regulatory option was to adjust the
refiner, and the flow (FC004, Figure 4) to the headbox.
After completing this task, an option to regulate an
upstream pulp concentration (QC004, Figure 4) was
introduced using a fixed headbox concentration of 0.3%
Figure 4 Graphical interface of stock preparation
process to press section.
The intended learning outcome was; lowered
dewatering requirement at higher concentrations give
Control Systems 2016 4
higher production. Maximum production was defined
by the limitation in the dewatering capacity on the paper
machine, illustrated by the “dry line” and when moved
to close to the press section, terminating production.
The termination was mimicking a break in paper
production which can be restored with proper settings.
This section was also followed by questions related to
drawbacks using high concentrations in headbox and
during formation. In the last exercise, students were
asked to optimize the production at a fixed refining
energy (250 kWh/t) and a minimum required strength
(70 Nm/g). Optimization was performed at two levels of
retention 90% (taskIIIa) and 98% (taskIIIb). All
regulatory options were available in this exercise.
It was challenging to moderate the simulation at the
accelerated speed of 50 times real-time that most
students selected to use. In the end all students
submitted a report with results and reflections on the
The entire exercise excluding the “follow up” answers
can be summarized in one plot, Figure 5. The plot
shows the maximum production level achieved by
individual students in the different tasks.
Figure 5,Distribution of results from the production
simulations (task II and III).
Despite limited time to complete all tasks, students
reached steady state with their selected settings using
accelerated speed. The wide distribution of results is
expected as the dynamic simulation will give different
optima depending on how the optimization was made.
Going from high to low setting on one parameter and
the order and time of adjustments will give different
The realistic simulation requires a systematic approach
using design of experiment to achieve less variation and
higher production level. Further developments of the
simulator would implement possibility to control and
adjust retention-formation relationships and introduce
graphical representation of pulp/stock and paper
formation on the wire. These graphical representations
would be simplified cross sections describing fibre
distributions, fillers etc. as well as selected paper
Finally, as teachers it was appreciated that no
installations were required. A procedure normally
requiring ordering of software installation to the IT-
service, and thereafter, testing of the installed software
in student computer labs.
In this study we can conclude that elements of
gamification adds value to simulation based education.
The response from students was encouraging requesting
simulations as complement to more traditional teaching.
The students participating in this study had good
insights in a variety of unit operations in the pulp and
paper manufacturing but little or no experience of
automatic control. Due to the design of tasks and
exercises in everysim students were forced to adjust and
run PID controllers in a factory-like interface (control
faceplate). We believe this to be a valuable first
introduction to automatic control on a very practical
level and hope that it enlightens the importance of
automatic control of dynamical processes.
We can also conclude that the adopted cloud based
approach was essential in order to minimize
administration of simulations and achieve high
accessibility. Even though the implemented high level
of accessibility was not fully explored from a user
(student) perspective it was very much utilized in the
implementation phase of the model and story.
This work was partly financed by Vinnova,
Energimyndigheten and Formas through the strategic
innovation program Process Industrial It and
Automation, PiiA. We are very grateful for this support.
We would also like to express our gratitude to the
students of the pulp and paper technology course of
2016 for participation and valuable feedback.
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Control Systems 2016 5
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