Miguel Castaño Arranz CV Structural Process Analysis

Miguel Castaño Arranz
1 Personal Information
Name: Miguel Castaño Arranz
Date of Birth: December 31, 1981.
Gender: Male
Address: Tulegatan 2 Floor 3, Left
Sundbyberg 172 78
Stockholm
Cell: +46 (0) 72 940 5744
Secondary phone: +46 (0) 76 278 4624
Email: miguel.castano@ltu.se
Citizenship: Spanish.
Homepage: http://se.linkedin.com/pub/miguel-castaño-arranz/1a/144/ba9
Current Position
Researcher at Luleå University of Technology (January 2014 - March 2014).
I am participating in a project for detecting damage in track switches with ultrasound measurements. My
responsibilities are:
- Development of ultrasonic imaging algorithms for defect detection.
- Implementation of a prototype software tool.
Previous Position
Researcher at Luleå University of Technology (LTU) (November 2007 - February 2013).
This period includes the commitment of doctoral studies (February 2008 - November 2012).
During this period, I participated in several projects under the SCOPE programme, which groups several
major companies in the pulp & paper industry in Sweden. In these projects, I acquired large practical
experience in designing and realizing plant experiments, process modeling, analysis, and control design.
Products
ProMoVis (Process Modeling and Visualization).
I am a main designer and developer of ProMoVis, which is a software tool for the design of control
structures for complex industry processes, focusing in the pulp & paper industry. ProMoVis can import
models described using Modelica. ProMoVis is owned by OProVAT EF.
2 Educational Degrees
Degrees
- Ph.D. in Engineering in the subject of Automatic Control, Luleå University of Technology, November
2012. The Ph.D. diploma including the list of courses is enclosed at the end of the application.
- Licentiate in Engineering in the subject of Automatic Control, Luleå University of Technology (Swe-
den), 2010. Robust methods for control structure selection in paper making processes.
- M.Sc. in Industrial Engineering: Electronic Systems and Automatic Control, University of Oviedo
(Spain), 2008.

Miguel Castaño Arranz 2
Other courses and programs
- IntelliCIS. Training school in "Intelligent Monitoring, Control and Security of Critical Infrastructure
Systems", Varna, October 2011.
- Vrije Universiteit Brussel (Belgium). Summer school in "Measuring, Modelling and Simulation of
(Non)linear Dynamic Systems", 2009.
- Luleå University of Technology. Student under the Erasmus programme, 2006-2007.
Academical activities
- Luleå University of Technology. Member of the organization committee of Reglermöte 2008. Main
task: organization of activities for Ph.D. students.
- Luleå University of Technology. Orientation guide for new students (Phösare), 2007.
- University of Oviedo. Member of the organization committee of the course study trip, 2004-2005.
3 Scientific Merits
Planned research activities
In this statement, I declare my research interests for my future activities in the structural analysis of
complex industry processes for control structure selection. This field differs from the subject description
for the open position. By including this document, I intend to demonstrate my ability and initiative for
formulating research questions.
North of Sweden is a large industrialized area with industry processes as large as pulp and paper mills,
plants for mineral processing and plants for steel production. These processes present a large topological
complexity, since they are composed by hundreds or even thousands of sensors and actuators as well as
control loops which connect them. This controllers have to be placed within this complex structure and
tuned appropriately for the running the process in optimal conditions.
Optimality is usually defined in terms of production targets, safety requirements and energy consump-
tion. The large scale aspect of these processes and the existence of recirculations and control loops derive
in unpredictable dynamics which have to be understood to avoid loop interaction and performance degra-
dation. Therefore, processes have to be analyzed from a holistic perspective, giving raise to plant-wide
optimization.
My main research interests within the analysis of complex industrial processes are now stated.
Visualization of industrial processes
Visualization and communication tools and techniques are needed in any modern industrial plant. Dia-
grams and flow sheets are used by control and process engineers for communicating process knowledge
to other workers such as operators or to decision boards. There is a need of developing visualization tech-
niques and combine them with mathematical tools in order to create diagrams which allow to understand
and communicate the process behavior (see Fig. 1).
Structural analysis of complex processes
My previous research derived in methods for the analysis of complex processes by using weighted graphs,
resulting in diagrams like the depicted in Fig. 1. The representation of processes as graphs has several
applications in the optimization of complex processes: process decomposition, input-output selection,
controllability and observability analysis, control structure selection, only to mention some.

Figure 1: Functional analysis of the input-output interconnections in a subprocess belonging to the paper
industry.
It is of my interest to find applications for my previous research in the field of plant-wide optimization.
Structural analysis of complex processes from logged process data
The existing methods for control structure selection require that a process model is available. However,
modeling is a time consuming task, and its difficulty increases with the number of process variables.
It is therefore of interest to create methods to estimate structural properties of processes and therefore
removing the need of modeling prior to process analysis.
My previous research generated satisfactory results in this new field, opening new opportunities and
research perspectives.
Software platforms for the integration of methods for process optimization
Nowadays there is a large time gap between research, education and finally industry application. Being
able to integrate the latest research on process optimization on software tools allows the direct application
of research by industry engineers.
My current research includes the development of the software tool ProMovis, which is a platform for
integrating methods for process optimization.
Publication list
Peer-reviewed publications in international journals.
The following publications form part of my Ph.D. thesit:
W. Birk, M. Castaño, A. Johansson, An Application Software for Visualization and Control Structure Se-
lection of Interconnected Processes, Control Engineering Practice, Volume 26, May 2014, Pages 188200.
I worked together with W. Birk in the development and design of ProMoVis and I am the sole pro-
grammer of its computational core. The underlying mathematical framework of ProMoVis for the
representation of dynamic systems was created by A. Johansson.

This publication has been attached to the application for its relevance. It describes the phase of technol-
ogy transfer in which a software tool was created to make research results directly available to industry
application. This has been done in tight collaboration with industry partners which participated in the
design of the tool with requirements, testing and feedback.
M. Castaño, W. Birk, On the Selection of Control Configurations for Uncertain Systems Using Gramian-
Based Interaction Measures, submitted to Automatica.
I have a major contribution in this paper. The contribution of the co-author (W. Birk) was limited to the
generation of the process models on which the case-study (bark oiler) was conducted and the revision
of the paper.
M. Castaño, W. Birk, New methods for interaction analysis of complex processes using weighted
graphs, Journal of Process Control, Volume 22, Issue 1, January 2012, Pages 280-295, ISSN 0959-1524.
I have a major contribution in this paper, including conducting the research and creating the illustrative
examples.
Proceeding in international conferences (Full papers).
M. Castaño, W. Birk, Bounds on a gramian-based interaction measure for robust control structure
selection, IEEE ICCA 2011, December 2011, Santiago de Chile.
M. Castaño, W. Birk, B. Halvarsson, Empirical approach to robust gramian-based analysis of process
interactions in control structure selection, 50th IEEE Conference on Decision and Control and European
Control Conference, December 2011, Orlando.
W. Birk, A. Johansson, M. Castaño, S. Rönnbäck, T. Nordin, N.-O. Ekholm, Interactive modeling and
visualization of complex processes in pulp and paper making, Control Systems 2010, Stockholm.
B. Halvarsson, M. Castaño, W. Birk, Uncertainty Bounds for Gramian Based Interaction Measures ,
WSEAS International Conference on Systems 2010, Corfu.
M. Castaño, W. Birk, New methods for structural and functional analysis of complex processes, IEEE
Multi-conference on Systems and Control 2009, St Petersburg.
M. Castaño, W. Birk, A new approach to the dynamic RGA analysis of uncertain systems, IEEE Multi-
conference on Systems and Control 2008, San Antonio.
Theses
Practical Tools for the Configuration of Control Structures. Ph.D. Thesis, Luleå University of Technol-
ogy, 2012.
Robust methods for control structure selection in paper making processes, Licentiate Thesis, Luleå
University of Technology, 2010.
Sensitivity of Variable Pairing in Multivariable Process Control to Model Uncertainties, Master’s Thesis,
Luleå University of Technology, 2007.
Approved Research Grants
PrOSPr (2012). PrOSPr is a continuation of the project MeSTA (2007-2011). The objective of MeSTA
was to develop robust and reliable methods for structural analysis and optimization of complex industry
processes so that these methods become reliable and sufficiently robust to become packaged in tools. The
software application ProMoVis was a product which resulted form MeSTA, and the goal of PrOSPr is the

open source distribution of ProMoVis. These projects group several major companies in the pulp & paper
industry in Sweden, as well as consultancies. I was a co-author for the funding application for PrOSPr
with Wolfgang Birk.
Network/Research Collaboration
I worked for 5 years in different projects under the SCOPE programme, which is administrated by Process
IT. In these projects, I also participated leading several work packages. The projects are:
- MeSTA (2007-2011). The objective is to develop robust and reliable methods for structural analysis
and optimization of complex industry processes so that these methods become reliable and sufficiently
robust to be automated and packaged into tools. The project groups several major companies in the
pulp & paper industry in Sweden, as well as consultancies. I worked in this project as researcher,
developer, and programmer.
- PrOSPr (2012). The objective is to release the software tool ProMoVis under an open source project. I
worked writing the project application, and is currently working as developer, programmer and tester.
- EQoRef (2012-2013). Energy and quality oriented modeling and control of refiners.
My network of contacts is strongly influenced by his work under the SCOPE programme, with addition
of other industry and academic contacts derived from personal and professional relationships.
Contacts in consultancy for process industry
Optimation AB This consultancy participated l in the previously mentioned projects, and I am currently
collaborating with them in different funding raising activities. Optimation AB is also a a co-owner of
OProVAT together with me and other personal entities.
Eurocon AB This consultancy participated in the research project MeSTA, in the analysis of different
industrial processes and the development of the software tool ProMoVis.
Process industry contacts
SCA Obbola AB I had a strong collaboration with SCA Obbola AB during his Ph.D. studies and worked
at their plant with two of their processes: the bark boiler and the stock preparation plant.
BillerudKorsnäs Karlsborg AB I had a strong collaboration with SCA Obbola AB during his Ph.D.
studies and worked at their plant with two of their processes: the bark boiler and the secondary heating
system.
ArcelorMittal ArcelorMittal is a leading integrated steel and mining company. I keep a strong contact
with many employees, including process engineers, technical staff, operators, administrators or project
leaders. I am currently working with project leaders on the seeking of projects under the FP programme.
Academic contacts
Federico Santa María Technical University, Chile. The department of electronics is a world leading
department in the design of control structures for multivariable processes. The head of the department,
Mario Salgado was the faculty opponent of my Ph.D. thesis.

Uppsala University I participated in several publications with Björn Halvarsson, who currently holds a
doctoral degree from Uppsala University. In addition, Prof. Bengt Karlsson formed part of the evaluation
tribunal for my Ph.D. thesis.
Research Awards
- Norrbottens forskningsrÃˇeds award on the honor of Curt BostrÃ˝um (2013). This award is yearly
granted to two thesis in Norrbotten in the field of technology.
Peer-review/charing assignments
- Arabian Journal for Science and Engineering (AJSE). One review in 2013.
- ACC 2012 Reviewer.
- ICCA 2011 Co-chair of session Robust Control and Systems I.
- ICECS 2011 Reviewer
- CDC’2009. Reviewer.
- MSC’2009. Chair of session Complex and Chaotic Systems.
- MSC’2008. Reviewer. Co-chair of session Modeling and Identification.
4 Educational Qualifications
Teacher Portfolio
Teaching philosophy
Teaching is a success when the student acquires the targeted knowledge, but also when the teacher changes
the vision of teaching. This can only be achieved through a deep reflection on teaching experiences.
The complexity of the teaching task forces a good teacher to gain experience and reflect upon it being able
to derive new teaching strategies as well as discard or modify unfruitful ones.
Even the most experienced teacher has to avoid the risk of routines. Routines in teaching lead to a focus
on the subject instead of on the link between the student and the subject. Society evolves rapidly and this
derives in an evolution in the academical environment as well as in the taught disciplines. The teacher has
to keep up to date with progress and adapt his teaching with adequate and motivating strategies, since
motivation is a key for the students to address learning in a deep approach.
How can I develop as a teacher?
The key for developing is to systematically reflect on teaching experiences and actuate in concordance
with the obtained conclusions. In the case of courses, a good tool for reflection is the feedback received in
the course evaluation, which collects the opinion of the students. These course evaluations are usually the
most effective way of identifying the weaknesses and strengths of your teaching or your course material.
Reflecting on teaching experiences is a need but it is not sufficient for the development of the modern
teacher. The current evolution of communication techniques provides excellent channels for the distri-
bution of teaching material as well as quick and efficient interfaces to provide support to the students
and enhance their mutual collaboration. The modern teacher has to master this new technologies and be
able to provide with i.e. online lectures for distance teaching, multimedia tools for creating tutorials and
student feedback or virtual rooms for the interaction with the students.

Reflections on surface and deep approaches to learning.
"Student learning research originated in Sweden, with Marton and Säljö’s ([]) studies of surface and deep approaches
to learning. They gave students a text to read and told them they will be asked questions afterwards. Students
responded in two different ways. The first group learned in anticipation of the questions, concentrating anxiously
on the facts and details that might be asked. They skated along the surface of the text, ... using a surface approach
to learning. What these students remembered was a list of disjointed facts; they did not comprehend the point that
the author was making. The second group on the other hand set to understand the meaning of what the author was
trying to say. They went below the surface of the text to interpret that meaning, using a deep approach.
Taking a surface or deep approach to learning is mainly a preference from the student. However, the
teacher and the course material can influence the students towards one of the approaches or the other.
One of the objectives as a teacher should be to try that most of the students use the deep approach to
learn.
Own experiences with surface learning. After knowing about he research from Marton and Säljö’s I
was able to analyze cases in which I was not maintain any of the contents of the course after taking it, and
identify some of them as cases in which I took a surface learning approach. I consider that my natural
approach to learning is a deep approach, however after a personal reflection I concluded that in some
cases the teacher or the course structure influenced me in such a way that I selected a surface approach.
Surface learning during my university studies.
An interesting case was a course in which out talkative and amusing teacher succesfuly attracted the
attention of the students to his lectures. Most of us got good grades in the course. An evaluation of the
course performance would probably have brought up a very high ranking, since the students were very
happy with the course and the grades were good. However, I find myself as unable to recall any details
of the course. For years I wondered why this happened, until I read the work from Marton and Säljö’s.
The main problem of the course was the exams. A large part of the course evaluation was formed by
multiple choice tests, with the peculiarity that most of the questions were repeated, or were very similar
to the ones in exams of previous years. The way I studied for the exams, was to start by taking all the
questions of previous exams and face them one by one. If I didn’t know the answer, I would look in the
book and only read the paragraph in which the answer is found. In this way, I built my knowledge in a
fast way by learning only the parts which had a large probability of appearing in the exam, being able
to discard a larger part of the book. The good grade in the exams was guaranteed with minimal effort.
However the knowledge was stored in my mind as a set of unconnected facts, without having a clear idea
of the full picture. Those facts faded away from my memory and nothing remains.
Surface learning during my school studies.
I often wondered why after my school studies, my knowledge in history, geography and other human
sciences was so bad. A reflection based on the theories from Marton and Säljö’s brought up an explanation.
I have to admit that the contents of the programmes were quite complete. However, the teachers encour-
aged us to memorize texts. Some of them facilitated it even by forcing us to reedit the textbooks adding
pen annotations with their suggestions for a simplified text. This was done by crossing words or parts of
sentences and adding some other words to the text. All the exams also encouraged the surface approach
since the student was just asked to repeat full sections of the text book. Even if the teachers were saying
that it is better to use our own words in the exam instead of directly repeating the text book, this usually
lead to a degradation of the grades, since the teacher would later argue that some important details have
been distorted. It is obvious that the approach taken by the students in this case would be to just memorize
all the texts word by word during the previous days to the exam without the need of even understanding
the text. It didn’t matter if you don’t understand words such as abdication or democracy, as long as you
have placed them properly in the text.
This means that all the knowledge is these topics is lost as soon as you make the exam and stop doing the
effort of memorizing those texts. Years and years of education in social sciences have been mostly wasted
for me. The only contents I keep from those programmes are those in which I found special interest and

for which I also consulted additional sources myself. Some time after my school studies I started to feel a
significant ignorance in historical and geographical knowledge which I’m still trying to compensate.
Finally I have to say that I never had this problem with any technical disciplines. The need of understand-
ing and applying theoretical concepts made these subjects interesting but also easy to remember. A deep
approach for learning is a natural approach to this sciences almost regardless from the influence of the
teachers.
Teaching contributions. Taught courses at Luleå University of Technology
I participated in the following courses at Luleå University of Technology:
- Modeling and control. 2011-2012. Problem solving lectures. This is the course in which I had a more
relevant participation. The student response related to the problem solving sessions (section 8 in the
evaluation) is included below.
- Multivariable and robust control systems. 2010-2011. Labs and project assistant and examiner.
- Modeling and control. 2009-2010. Labs and project assistant and examiner.
- Nonlinear and optimal control systems. 2009-2010. Labs and project assistant and examiner.
- Automatic control. 2009-2010. Labs and project assistant and examiner.
The credentials for this courses are enclosed at the end of this section. From these course experiences,
the the one which produced a larger impact in me is the lab assistance in the course R0002E during the
academic year 2009-2010. My reflections on this experience are included below.
Reflections on R0002E
This was a very basic course, however it requires preliminary knowledge, mainly in mathematics (i.e.
calculus and differential equations) and physics (i.e. cinematics and electricity). Students from several
different programmes and with different backgrounds participate in the course (mechanical engineering,
chemical engineering, electrical engineering, electronics . . . ). Even if most of the students are or were en-
roled in courses including the preliminary contents it is likely that their knowledge in these preliminaries
is not very solid yet, since they are in the first years of their education.
This meant that students often came with loads of questions on very basic principles of physics and math-
ematics, involving for myself a large time consumption in the demotivating task of reviewing with many
students such basic topics as Newton’s or Hooke’s laws or Taylor expansions. The worst consequence
was the slow and hard progress of the students, and their loss of time waiting long queues in front of my
office.
The success rate of the students at the final lab Project was almost total. However the path to the goal was
tedious for me, the students and the lecturer.
One can think of blaming the educational system or the programmes structure for the poor background
of the students, or blame the students for their laziness in not reviewing the preliminaries by themselves.
Nevertheless, there is little which could be done by me if keeping only these postures.
A reflection brought up that leaving freedom to the students for forming the groups means that they will
tend to team up with their friends and colleges from their programs, and it is then likely that all the people
in the same group will have similar background and knowledge. By forcing/encouraging the students to
team up in groups with people from different programmes, the students would have been more likely to
learn from each other since their backgrounds would be more likely to be complementary, and therefore
the overwhelming amount of questions might have been avoided.
[1] F. Marton and R. Säljö, On Qualitative Differences in Learning: 1–Outcome and Process, British Journal
of Educational Psychology, 1976, volume 46, pages 4-11.

Experience of supervision
Co-supervision of Master’s thesis: Pablo Fernandez de Dios. Implementation of a Visualization Tool
in MatLab for Structural Analysis of Complex Processes. 2011. Principal supervisor: Wolfgang Birk.
For the credential, see the attached Ph.D. diploma under the title Supervision of Master’s Thesis students.
Teacher training
Course: Pedagogy in higher education. 7.5 ECTS. for credentials, see the Pd.D. diploma.
Development work in education
I developed the course General topics in applied control. The development included the study guide and the
lab material. The study guide is after the credentials of participation in courses and the study guide.
Other educational activities
I wrote the user documentation for the software tool ProMoVis, and participating in creating and instruct-
ing several workshops for industry members about ProMoVis.

Wolfgang Birk, Modellbygge och reglering -
20.12.2011 EvaSys evaluation Page 1
Wolfgang Birk
Modellbygge och reglering (R0002E)
Response rate = 48.1 %
Survey Results
Legend
Question text Right poleLeft pole n=No. of responses
av.=Mean
dev.=Std. Dev.
ab.=Abstention
25%
1
0%
2
50%
3
0%
4
25%
5
Relative Frequencies of answers Std. Dev. Mean
Scale Histogram
1. SjälvbedömningSjälvbedömningSjälvbedömningSjälvbedömning / Self-assessment
Hur stor del av beräknad studietid (helfartskurs 40 h/vecka, halvfartskurs 20 h/vecka, kvartsfartskurs 10 h/vecka) har lagts påHur stor del av beräknad studietid (helfartskurs 40 h/vecka, halvfartskurs 20 h/vecka, kvartsfartskurs 10 h/vecka) har lagts påHur stor del av beräknad studietid (helfartskurs 40 h/vecka, halvfartskurs 20 h/vecka, kvartsfartskurs 10 h/vecka) har lagts påHur stor del av beräknad studietid (helfartskurs 40 h/vecka, halvfartskurs 20 h/vecka, kvartsfartskurs 10 h/vecka) har lagts på
denna kurs, schemalagd tid plus hemarbetstid? /denna kurs, schemalagd tid plus hemarbetstid? /denna kurs, schemalagd tid plus hemarbetstid? /denna kurs, schemalagd tid plus hemarbetstid? / A full-time course is estimated as 40 hours of study; part-time courses are either
20 or 10 hours per week. What percentage of this time did you spend on this course, count the time spent both in class and on
self-study?
1.1)
n=22< 25% 4.5%
26-50% 22.7%
51-75% 27.3%
76 -100% 13.6%
> 100% 31.8%
Jag är nöjd med mina insatser under kursen. /Jag är nöjd med mina insatser under kursen. /Jag är nöjd med mina insatser under kursen. /Jag är nöjd med mina insatser under kursen. / I
am satisfied with my efforts during the course.
1.2)
Instämmer helt/Instämmer helt/Instämmer helt/Instämmer helt/
Strongly agree
Instämmer ej/Instämmer ej/Instämmer ej/Instämmer ej/
Strongly disagree
n=25
av.=3.2
dev.=1.3
8%
1
24%
2
28%
3
24%
4
12%
5
4%
6
Jag har deltagit i kursens allaJag har deltagit i kursens allaJag har deltagit i kursens allaJag har deltagit i kursens alla
undervisningsmoment.undervisningsmoment.undervisningsmoment.undervisningsmoment. //// I have participated in all
teaching instances in the course.
1.3)
Strongly agree
Strongly disagree
n=24
av.=5
dev.=1.3
0%
1
12.5%
2
0%
3
4.2%
4
41.7%
5
41.7%
6
Jag har förberett mig inför allaJag har förberett mig inför allaJag har förberett mig inför allaJag har förberett mig inför alla
undervisningsmoment.undervisningsmoment.undervisningsmoment.undervisningsmoment. //// I have been well
prepared for all teaching instances.
1.4)
Strongly agree
Strongly disagree
n=25
av.=2.9
dev.=1.4
24%
1
8%
2
40%
3
12%
4
16%
5
0%
6
2. Kursens mål & innehållKursens mål & innehållKursens mål & innehållKursens mål & innehåll / Course aims and content
Kursens mål har varit tydliga.Kursens mål har varit tydliga.Kursens mål har varit tydliga.Kursens mål har varit tydliga. / The aims of the
course are clear.
2.1)
Strongly agree
Strongly disagree
n=25
av.=3.6
dev.=1.3
4%
1
20%
2
20%
3
24%
4
28%
5
4%
6
Kursens innehåll har bidragit till att uppnåKursens innehåll har bidragit till att uppnåKursens innehåll har bidragit till att uppnåKursens innehåll har bidragit till att uppnå
kursplanens mål.kursplanens mål.kursplanens mål.kursplanens mål. / The contents of the course
help to achieve/meet the course’s aims.
2.2)
Strongly agree
Strongly disagree
n=24
av.=3.7
dev.=1.1
ab.=1
0%
1
20.8%
2
12.5%
3
45.8%
4
16.7%
5
4.2%
6
Kursplaneringen/studiehandledningen har gettKursplaneringen/studiehandledningen har gettKursplaneringen/studiehandledningen har gettKursplaneringen/studiehandledningen har gett
god vägledning.god vägledning.god vägledning.god vägledning. / The course planning and
supervision are structured and easy to follow.
2.3)
Strongly agree
Strongly disagree
n=25
av.=3.8
dev.=1.3
0%
1
20%
2
28%
3
16%
4
28%
5
8%
6

Handledningen vid laborationerna var till hjälp att
lösa uppgifterna.
7.2)
Instämmer helt/
Strongly agree
Instämmer ej/
Strongly disagree
n=20
av.=1.9
dev.=1
40%
1
40%
2
10%
3
10%
4
0%
5
8. Övningarna
Övningstillfällen har underlättat inlärningen av
kursens teoretiska innehåll.
8.1)
Instämmer helt/
Strongly agree
Instämmer ej/
Strongly disagree
n=18
av.=3.2
dev.=1.1
16.7%
1
0%
2
33.3%
3
50%
4
0%
5
Det fanns tillräcklig med tid under övningarna för
att ställa frågor och diskutera uppgifter
8.2)
Instämmer helt/
Strongly agree
Instämmer ej/
Strongly disagree
n=18
av.=2.9
dev.=1.1
11.1%
1
27.8%
2
27.8%
3
27.8%
4
5.6%
5
Lärarnas insats var ett bra stöd för att lära sig
tillämpa det teoretiska innehåll i kursen.
8.3)
Instämmer helt/
Strongly agree
Instämmer ej/
Strongly disagree
n=17
av.=3.4
dev.=0.9
5.9%
1
5.9%
2
35.3%
3
47.1%
4
5.9%
5

8. Övningarna
På vilket sätt kan övningstillfällen förbättras?8.4)

General topics in applied control
STUDY GUIDE
Author: Miguel Castaño Arranz

1.- Introduction
Control engineering is a rapidly evolving discipline. There is a large number of traditional control
strategies which are still being improved as well as a number of emerging ones. It is hard for the control
engineer to choose which control strategy is suitable for a specific application, as well as to choose a
topic in control engineering for developing his/her skills.
This course is aimed for Ph.D. students in control who want to explore a new control strategy and have
an understanding of which other control strategies exist and how can they contribute to their
professional development.
2.- Intended Learned Outcomes
After taking this course you should be able to:
 Apply acquired theoretical knowledge on a control topic of your choice.
 Communicate control theory concepts and applications of the selected control topic.
 Criticize and compare different control strategies using the work of other students as source.
 Reflect on which control topics you can learn in the future for your professional development.
 Present your work formally correct in both written (technical report) and oral form
(presentation).
3.- Preliminary knowledge.
 Those Ph.D. students who have taken the course Automatic Control R0002E or similar
are considered to have enough knowledge to face any of the advanced topics.
 Ph.D. students without this background which want to learn basic topics in automatic
control should have the following skills:
- Basic knowledge of Matlab. Knowledge of Simulink is desired but not compulsory.
- Knowledge of differential equations and the Laplace transform.
- Basic algebra notions.
- It is desired to have basic knowledge of physical laws, including balances of
masses/energy.
4.- Course activities
The goals of the course activities are:
o Acquire knowledge in a control topic on your choice.
o Design and implement a controller on a benchmark process using the acquired
knowledge.
o Evaluate the implemented controller and the selected strategy.

o Disseminate your results in a presentation form, a formal report, and tutorial sessions
with other students if needed.
o Compare the control strategy that you selected with other strategies.
These activities are arranged as follows:
1.- Choose a control topic from a proposed list or propose a topic of your interest. A proposed list is:
Advanced topics:
Robust Control
Fuzzy Control
Model Predictive Control (MPC)
Adaptive Control
Control with Neural Networks
Decentralized control and interaction measures
Internal model control (IMC) and Smith predictor
Achievable performance of multivariable control structures
Modeling and control of time-varying systems
Sliding mode control
Optimal Control
Flatness Based Control
LMI control
Basic topics:
PID Control (Basic)
Lead/Lag Compensation
2.- Create a plan. After a short period of reading information on that topic, the Ph.D. student will make a
plan for implementing a control strategy on a benchmark project. The plan will be review to see that the
difficulty of the work is in accordance with the scope of the course. A short report of not more than one
page has to be delivered.
3.- Design and implement a control solution on the benchmark process. The selected benchmark
process will be a quadruple-tank system. A full detailed model in MATLAB/SIMULINK will be given to the
students to implement their work. Depending on the availability of a real quadruple-tank process, the
students might be requested to make a demonstration in real life.
4.- Present your results. A poster presentation will be made at the end of the course in which the
student will make an introduction to the used control theory and will show the results when applied to
the benchmark process. The presentation has to be structured as described in section 5.
5.- Compare different control strategies. This is divided in the following two sub-activities:
- Fill a survey on the material presented by other students during the poster presentation.
- Choose the work of other student and compare it with your work, focusing in a qualitative (and
quantitative is possible) comparison of your selected strategy with the strategy of the other student.

6.- Report your conclusions. This is done by delivering your implementation with a report which must
include the contents specified in section 5.
5.- Course evaluation and reporting.
To pass the course, you have to:
 Successfully implement the selected advanced control technique on the quadruple tank
system.
It is not sufficient to design a stable controller. The controller has to achieve a satisfactory
performance (track steps on the reference, reject process disturbances, …).
It is possible to pass the course with a poor performance controller, but only if this is a
consequence of the chosen control strategy and not of a poor design. In this case, the
limitations on the achievable performance which are imposed by the selected control strategy
have to understood and described by the student both in the oral presentation and the report.
 Disseminate your results.
- Give a poster presentation with the structure described in the table below.
- Deliver your poster and implementation after the presentation.
- Give the needed support to the student who will be comparing his control strategy
with yours.
The dissemination of results is considered as passed when the previous activities have been
done and the supported student shows that he has understood your work by reporting a
comparison with his own work. If the supported student fails to successfully report this
comparison, the examiner will evaluate if the dissemination tasks area passed. In this case, the
surveys filled by the other students will be considered as the main tool to judge the
dissemination of results.
 Fill a survey on the different control strategies. The survey will be handed to the students at the
poster presentation and has to be filled with the results reported by the other students.
 Report your conclusions. Deliver a report with the structure described in Activity 5. The report
as to be graded as passed by the course supervisor.
To show success the student has to deliver a portfolio with the following files:
 Report of activities (Excel file).
 Work plan.
 Implemented control strategy (Matlab file).
 Poster.
 Survey.
The structure of these documents and the delivering deadline are summarized in the following table:

DOCUMENT DEADLINE STRUCTURE OF THE DOCUMENT
Draft for the work plan At Meeting 2 1 page describing the control strategy to be
designed and implemented on the quadruple
tank.
Work plan 1 day after meeting 2 Same as above.
First Simulation Meeting 3 Deliver the files needed to run your simulation on
the quadruple tank.
Poster Meeting 4 The poster has to follow a given structure:
1.- Description of the used theory.
2.- Details on the control design and
implementation.
3.- Simulation results.
4.- Critical evaluation of the implemented control
strategy.
Survey Meeting 4 The survey will be distributed and filled at the
poster presentation.
Final Simulation 1 week after meeting 4 Provide in a rar the needed files to run the
simulation. Include also a README.txt file
describing technical details in how to run/tune
the simulation.
Report 1 week after meeting 4 The report given structure:
1.- Description of the used theory supported by
references.
2.- Details on the control design and
implementation.
3.- Simulation results.
4.- Critical evaluation of the implemented control
strategy.
5.- Qualitative(and quantitative if possible)
comparison of your method with the method
chosen by other student.
6.- Timeline
This section describes the meetings which will take place during the course. It is important to
check which assignments you have to have prepared for the meeting.

Tasks which have to be ready
before the meeting
Activities at the meeting Outcome
Meeting 1.
Introduction.
Day 1.
None * The supervisor presents brief
information on several control
topics.
* Students choose control topic
trying to avoid repeated topics if
possible
* Worksheet
summarizing the
individual
choices.
Meeting 2.
Review of
work plan.
Day 15
* Work plan in written form.
(Maximum 1 page)
* Students present their plan.
* The plans receive feedback
from other students as well as
from the course supervisor.
* The course supervisor accepts
the plans after possible
modifications.
* Reviewed work plan.
Meeting 3.
Follow up
meeting.
Day 60
* It is desired to have a
simulation of the
implemented controller at this
stage.
* Students present the current
status of their work.
* The plans receive feedback
from other students as well as
from the course supervisor.
* The produced results in
the projects are reviewed
and the projects are
steered if needed.
Meeting 4.
Dissemination
of results
(poster
session).
Day 70
* Poster with the structure
described in Section IV.
* Final implemented
controller.
* Present your results in a
poster session.
* Fill a survey which helps you
to criticize and compare the
control strategies chosen by the
other students.
* Receive feedback from
industry personal who will
participate in the meeting.
* Survey.
* Selection of the work of
other student to compare
with your own.
7.- Plagiarism
Detected plagiarism will be reported and will involve the failing of the course. The following will be
considered plagiarism:
1. The reuse of unreferenced material.
2. The unreferenced reuse of control designs/algorithms. The implementation and design of the
control strategy has to be original.
Allowed material.
Only the following toolboxes can be used Matlab toolboxes can be used:
- Control Systems Toolbox
- Symbolic Math Toolbox

Using other toolboxes or functions in your final work is only allowed under the explicit permission of the
teacher. The delivering of work with forbidden functions/toolboxes might derive in a rejection and a
resubmission but will not be considered as plagiarism.
8.- Missing a deadline.
Failing to present the required results at the poster session will involve the failing of the course.
9.- Course contact & support.
Course supervisor:
Name: Miguel Castaño
Mail: miguel.castano@ltu.se
Office hours: Monday 10:00 – 12:00. Thursday 15:00-17:00.
10.- Course literature.
There is no course book. Each student has to select the literature which is relevant to the
selected topic with the support of the teacher. The literature might include but not be restricted
to: academic books, journal publications, conference papers, ...
11.- Course credits.
Passing the course awards 7.5 ECTS. The expected amount of time in the course activities is
summarized in the worksheet distributed in Appendix I. You are encouraged to fill in the Excel
worksheet with the time you spent in the activities. The number of awarded credits will be
reviewed in case of large deviations from the total expected spent working hours.
APPENDIX I. WORKSHEET FOR THE ACTIVITY REPORT.
The following worksheet will be used by the students to report the spent time in course activities:

APPENDIX II. REFLECTIONS ON THE COURSE STRUCTURE.
This appendix is not part of the study guide. It collects a set of reflections which motivate the contents of
this study guide.
 Most of the Ph.D. students in control engineering have to read a new topic in control. If the
student reads the topic with no supervision, there is a large probability that the student will just
read a book and take a surface approach to leaning (see Pages 22-24 in [1] ). One of the main
goals of this course is ensuring a deep learning approach by planning appropriate activities (see
Pages 24-25 in [1]). These activities are the design and implementation of a control solution, and
the dissemination of results.
 The amount of credits to be awarded by this course is a bit uncertain. The original plan is to give
7.5 ECTS. Nevertheless, the students are asked to report the spent time by filling the Excel
worksheet included in Appendix I. This will help to adjust the course credits in case of large
deviations.
 An important objective of the course is the dissemination of results. An appropriate
dissemination will ensure that the students receive a broad picture of which other control
techniques exists and how can they contribute to their professional development.
 It is of importance that the students are able to compare the different selected control topics.
For this purpose, the design and implementation work will be done on the same benchmark
process. The selected benchmark process is the quadruple-tank system due to its well
demonstrated pedagogic advantages (see [2]).

 It is of importance that the documents and simulations generated by the students are collected
and kept as source of information for future students. Therefore, all the deliverables have to
follow a predefined structure in order to improve their readability.
 The fact that the dissemination of results is an important task imposes hard deadlines, since the
results have to be ready at the date of the presentation. Section 8 mentions explicitly the
consequences of missing a deadline.
 The ILOs have been aligned with the course activities and the evaluation as described in Chapter
4 in [1].
 As teacher, I want to to be able to ask students for resubmission of their work if they fail to
report properly. My interest is educational, but also maintaining good documentation as an
outcome from the course. To be able to do this, the following ILO was specified:
”Present your work formally correct in both written (technical report) and oral form
(presentation).”
 The ILOS have been designed using the procedure described in [1] (Pages 83-85). A review of the
ILOs as indicated in Page 85 was performed in a final review of the study guide. This review led
to the inclusion of the ILO: “Reflect on which control topics you can learn in the future for your
professional development.”
 From my previous experiences as teacher I concluded that it is good both for the students and
the teacher to have a clear and explicit definition of what is considered plagiarism in the study
guide. For this purpose section 7 was added.
[1] John Biggs and Catherine Tang, Teaching for quality learning at University. Third Edition. Mc Graw
Hill.
[2] Karl Henrik Johansson, The quadruple-tank process. A multivariable laboratory process with an
adjustable zero. IEEE transactions on control systems technology. Vol 8, No 3, May 200.

5 Additional Assignments
Board member at OProVAT EF (since June 2012).
OProVAT has as goal the Open source distribution of Process Visualization and Analysis Tools. OProVAT
resulted from a research project in which the software tool ProMoVis was created. OProVAT and ProMoVis
are maintained by funded projects with strong industry collaboration.
My tasks in OProVAT include:
- Write funding applications which comprise partners in Swedish industry and academia.
- Develop the software tool ProMoVis, being up to the date the sole programmer of its mathematical
part, which is programmed using MATLAB.
- Actively research in the ﬁeld of control structure selection.
- Organize seminars, workshops and tutorials regarding control structure selection and ProMoVis.
- Identify industry needs and synthesize new development tracks.
6 References
Reference #1
Name: Wolfgang Birk
Title: Associate Professor
Company: Luleå University of Technology
Primary e-mail: wolfgang.birk@ltu.se
Primary phone number: +46 725 39 09 09
Secondary phone number: +46 920 49 19 65
Wolfgang was my supervisor during my Ph.D. studies. He was the main manager in the projects in which
I participated. We collaborated in the conceptual design of the software tool ProMoVis. Together with
other partners, we founded the company OProVAT EF, which is the copyright owner of ProMoVis.
Reference #2
Name: Björn Halvarsson
Title: Ph.D., Research Engineer
Company: Ericsson AB
Primary e-mail: bjorn.halvarsson@ericsson.com
Primary phone number: +46 107 17 45 05
Secondary phone number: +46 722 44 15 05
Björn Halvarsson graduated as Ph.D. from Uppsala University in 2010. We ﬁrst met in a conference in
2008 and a series of technical discussions brought important research results for my doctoral thesis. We
have 2 conference publications together.

An Application Software For Visualization and Control Configuration Selection of
Interconnected Processes$
Wolfgang Birka,∗
, Miguel Castañoa
, Andreas Johanssona
aControl Engineering Group, Department of Computer Science, Electrical and Space Engineering, Lule˚a University of Technology,
SE-971 87 Lule˚a, SWEDEN
Abstract
This paper presents a new application software for control configuration selection of interconnected industrial processes,
called ProMoVis. Moreover, ProMoVis is able to visualize process models and process layout at the physical level
together with the control system dynamics. The software consists of a builder part where the visual representation
of the interconnected process is created and an analyzer part where the process is analyzed using different control
configuration selection tools.
The conceptual idea of the software is presented and the subsequent design and implementation of ProMoVis is
discussed. The implemented analysis methods are briefly described including their usage and implementation aspects.
The use of ProMoVis is demonstrated by an application study on the stock preparation process at SCA Obbola AB,
Sweden. The results of this study are compared with the currently used control strategy.
The study indicates that ProMoVis introduces a systematic and comprehensive way to perform control configu-
ration selection. ProMoVis has been released under the Apache Open Source license.
Keywords: Visualization, signal flow graphs, interaction measures, control structure, control configuration,
multivariable control, process control, interconnected systems, pulp and paper industry
1. Introduction
Continuity is an important aspect of industrial process plants. It means that the industrial plant has a certain
level of availability for production and evolves with maintenance and optimization efforts. Nowadays, availability
of production plants need to be very high and the production quality needs to be well aligned with customers’
requirements, (El-Halwagi, 2006). In turn, the requirements on performance of processes, their control and maintenance
are high, and any changes in hardware should lead to adaptations in the control systems more or less right away.
However, these industrial process plants are interconnected systems where hundreds or even thousands of variables
are connected through dynamic systems, resulting in a so-called topological complexity, (Jiang et al., 2007). These
connections can be physical connections between components, plant-wide access of information by the control system,
or control actions by the control system on a plant-wide scale. Examples of physical interconnections are material
flows and reflows, like discarded material which is returned to a previous process step and thus gives rise to large
recycle loops.
A consequence of this topological complexity is that adding control loops to a process in an ad-hoc manner may
result in a system with obscure causality and unforeseen dynamics. Understanding of such systems becomes a challenge
which makes the control configuration task very difficult. Remember, control configuration selection (CCS) addresses
the problem of finding a low complexity structure for a controller for an industrial process that has the potential to
render a control system with desirable performance. It does not involve the parametrization of the controller.
The first methods date back more than four decades, initiated by the work published in (Bristol, 1966) and
(Rijnsdorp, 1965) where small scale multivariable problems were addressed. Since then, the host of methods has
$The work has been conducted within the MeSTA project that is hosted at ProcessIT Innovations at Lule˚a University of Technology
and run within the branch framework SCOPE. Funding provided by VINNOVA, Hjalmar Lundbohm Research Centre and the participants
of the SCOPE consortium, is hereby gratefully acknowledged. The authors also want to thank the reviewers and associate editor for their
constructive comments that helped to improve the article.
∗Corresponding author: wolfgang.birk@ltu.se, +46 725 390909
Preprint submitted to Elsevier January 13, 2014

increased largely and can now be used to determine feasible control configurations for problems of larger scale. This
has also led to the introduction of the control structure selection problem which contains the I/O selection problem
and the control configuration selection problem as sub-problems. A good overview of the topic and available methods
is given in (van de Wal and de Jager, 2001), (Skogestad and Postlethwaite, 2005) and (Khaki-Sedigh and Moaveni,
2009). It is also important to mention that these methods are not viable on a plant-wide scale, where the total number
of inputs and outputs exceeds a few dozen.
Despite the vast host of proposed methods for CCS, there are no up-to-date toolboxes available for industrial use
of the methods. To the knowledge of the authors, the only toolbox reported in the literature is by Nistazakis and
Karcanias (2004), but it does not seem to be widely available.
As indicated in (Rohrer, 2000), visualization is important both from a collaborative perspective as well as to
provide a comprehensive understanding of processes. Within the areas of construction, manufacturing, or production
management, visualization is recognized as an important tool, see (Bouchlaghem et al., 2005; Browning and Ramasesh,
2007), but when it comes to the design and maintenance of control systems in process industries, the use of visual-
ization is still very limited. Available software that can be used for visualization focuses mainly on simulation of the
process dynamics, such as ChemCAD, MATLAB/Simulink, LabView, Extend, or Dymola, the latter based on the
generic modeling language Modelica. However, there is a lack of user-friendly toolboxes or software aiming at control
configuration selection.
The aim of this paper is to propose a new application software, called ProMoVis, that combines a graphical repre-
sentation of a process plant and control system with analysis of the dynamic interconnections for control configuration
selection. The underlying mathematical framework is the directed graph which is a highly abstract way of representing
topological complexity in various applications.
Based on this mathematical framework a set of selected control configuration methods is implemented and can
be used to analyze interconnected processes. Thereby, even mathematically complex methods become available for
industrial use. Obviously, analyses performed by ProMoVis have the same limitations as the implemented control
configuration methods, which means that the user has to select at most a few dozen variables for an individual
analysis. These variables do not need to belong to the same part of the process plant, may be selected on a plant-wide
scale, and may include variables in the control system, like e.g. estimated variables. It should be noted that ProMoVis
is not limited to the selected set of methods, and other analysis methods for interconnected systems can be added.
The software is currently in use at several industry partners of the SCOPE consortium within ProcessIT Innovations,
(ProcessIT Innovations, 2012), and is made available by the open-source project ProMoVis at Sourceforge, (OProVAT
EF, 2012).
The paper is arranged as follows. First, the interface for modeling and visualization is discussed and some
necessary notation is introduced. Thereafter, the implemented CCS methods are shortly summarized including their
usage, properties, and limitations. Then the stock preparation process of SCA Obbola AB is introduced as a case
study. It is shown how the stock preparation process can be represented in ProMoVis and how the CCS task is
performed. Finally, the results from the CCS are compared with the currently implemented control strategy and are
discussed. The paper is concluded with some final remarks.
2. Application software ProMoVis
Selection of a control configuration for processes with many interconnections is facilitated by a systematic ap-
proach, which is based on process knowledge in terms of dynamic models of the interconnected process. To the
knowledge of the authors there is no software available which can visualize process variables including their dynamic
interconnections and control configuration analysis results in a comprehensive way. For this end, we now propose the
software ProMoVis, (Process Modeling and Visualization).
From a practical perspective, selection and assessment of a control strategy would require the following actions
by a practitioner:
1. Derive a dynamic model for the process
2. Select a set of manipulated and controlled variables (I/O selection)
3. Determine a controller configuration
4. Design of the individual controllers according to the configuration
5. Implementation of the controllers
6. Assessment of the control performance
2

(a) (b)
Figure 1: (a) Sketch of the quadruple tank process. (b) SFG for the quadruple tank process which contains informative labeling of the
signals. Red edges indicate the model interconnections, red nodes indicate the actuators (pumps), yellow nodes represent the disturbances
(leakage flows), white nodes represent the internal states (level in the upper tanks) and green nodes represent the measurement signals
(levels in the lower tanks).
For all actions, besides action three, there exist software tools that support the control engineer. For modeling
of processes and control design, toolboxes in MATLAB, (The Mathworks, Inc., 2012), or multivariate analysis and
modeling tools from MKS Umetrics AB, Sweden (2012), are available. For the selection of I/O sets with manipulated
and controlled variables the tools from MKS Umetrics AB, Sweden (2012) can be used from a multivariate perspective,
whereas the methods proposed in (Skogestad, 2000), address the problem from a feedback control perspective. For the
implementation of controllers in the control system there are tools proposed that support the automatic generation of
control system code (Estévez et al., 2007) and (Vyatkin, 2012). Additionally, control systems provide standard blocks
for certain types of controllers, like for example the PID. Further, many industrial control systems possess online tools
to monitor the performance of control loops as part of the control system. The remaining gap is action three, where
ProMoVis aims at providing support for CCS.
2.1. Software concept
In this section the required mathematical framework and notation is introduced and based on that the software
concept is explained.
The signal flow graph (SFG) was proposed by Mason (1953) to represent interconnected dynamic linear systems,
where the nodes represent the signals and the edges elementary linear dynamic systems, and will be used as the
mathematical framework for the application software. Thus, the modeling task in ProMoVis reduces to the effort of
collecting and combining information on the process plant and its control system. We will now state the algebraic
form of the signal flow graph as given in (Johansson, 2010).
Let xi, i = 1, ..., p represent all exogenous signals, i.e. those variables that are not affected by any other variables
in the interconnected system and let zi, i = 1, ..., n be all other variables of interest. The models are assumed to be
formulated as
zi = Φi1z1 + ... + Φinzn + Γi1x1 + ... + Γipxp (1)
for i = 1, ..., n where Φij and Γij are linear dynamic systems that may represent process model interconnections
as well as controllers. The set of exogenous signals may include e.g. external disturbances and manipulated variables
but also set points. When a control loop is closed using a manipulated variable xi and a variable zj, then xi will
become an element in z and the associated set point variable will be introduced in x. Now, let us associate each signal
xi and zi with a node, each Φij = 0 with an edge from zj to zi, and each Γij = 0 with and edge from xj to zi. Then
the SFG is obtained as a graphical representation of the model interconnections. Moreover, by collecting the signals
xi and zi into vectors x and z and defining the multivariable, dynamic systems Φ and Γ whose i, jth element are Φij
and Γij respectively, the signal flow graph representation may now be formulated as (Johansson, 2010)
z = Φz + Γx (2)
In the example in Fig. 1 a process sketch (a) and a signal flow graph (b) of a quadruple tank (Johansson, 2000b)
are depicted. While the process sketch provides information on the construction and the variables in the process, the
3

signal flow graph provides information on the dynamic interconnections. There, the exogenous inputs are the nodes
d1, d2, u1, and u2 and constitute x = [d1, d2, u1, u2]T
, while the nodes h1 to h4 are the measurement signals and the
internal states, which make up the vector z = [h1, h2, h3, h4]T
. Therefore, Φ13, corresponding to the arrow from node
h3 to h1, is a linear system modeling how the level in Tank 3 affects the level in Tank 1. Similarly, Γ43 is a model for
how Pump 1 affects the level in Tank 4, and so on.
In the SFG framework variables are the interface between dynamic models, and some of them constitute the
interface between process and control system. In ProMoVis, the process layout at the physical level is represented by
interconnected entities referred to as components. These components do not contribute to the dynamics of the plant,
but provide important information on the geographical location of the process variables and how they relate to the
process physics.
In Fig. 2, this concept is captured and depicted for the quadruple tank example. Naturally, one could think of
three layers: components, process models, and controllers. In each of these layers, the process variables are visible
and represent the interface between the layers. This concept is very much in line with the industrial understanding of
a plant where process variables and their properties are the central element. Performance requirements for processes
and product qualities are always related to variables that are measured online, estimated, or derived from laboratory
assessments. Therefore, components and process variables are the natural point to start modeling and visualizing
a process, which is the component layer, similar to Fig. 1a. The process model layer then represents the dynamic
interconnections in the process, which is the same SFG as already shown in Fig. 1b. The controller layer represents
the dynamic interconnections in the control system, in this case an SFG of two SISO controllers for the quadruple
tank with their associated set point variables r h1 and r h2.
A visualization can become very complex when all elements are visible at the same time, which might be of
interest during composition or building, but unadvisable during analysis and decision making. In the latter case it is
of interest to select certain information that should be visible, which can be achieved by the use of layers and their
visibility. Such a complete representation of a plant in ProMoVis will be denoted a scenario.
2.2. Objects in ProMoVis
In ProMoVis a process plant including its control system is modeled using generic objects that are connected and
arranged in different layers. There are four classes of objects: Variables, process models, controllers, and components.
Process models, components, and controllers are collected in separated layers, which enable a differentiation of the
view based on the class of the objects.
2.2.1. Variables
The variables represent the signals (nodes) in the SFG and can be divided into categories based on their character.
For each category a color code is used in the interface in order to increase clarity for the user. Here, the default color
setting is used but the user can reconfigure it.
Measured variables (green) represent the sensor input from the process into the control system.
User reference variables (blue) represent set points for controllers and can be interpreted as a manual setting by an
operator. As such, they are the interface between the operator and the control system.
Manipulated variables (red) represent the interface from control system to process. Usually, actuator signals are
manipulated variables.
Disturbance variables (yellow) represent exogenous disturbance signals, which may be induced by another process
of the plant.
Estimated variables (orange) represent the result of a computation based on manipulated, controlled, or reference
variables.
Internal or state variables (white) represent all variables which do not belong to any of the previous categories.
These represent internal variables of the process or the control system, which are of importance for the control
engineer.
Intermediate variables (white) are added automatically when two objects of the control system are connected with
no interface variable. They are needed for the implementation of the SFG framework. They are considered as
internal variables but have no user defined properties.
4

Figure 2: Diﬀerent layers in the modeling and visualization concept. Components (top), Process models (middle), Controllers (bottom).
Manipulated variables (red), Measured or controlled variables (green), Reference variables (blue).
5

Table 1: Applicability of variable properties depending on the variable category
Property
Variable type
Manipulated
Measured
Reference
Disturbance
Estimated
Internal
Intermediate
Range X X X X
Limit X X X X X
Variance X X X X
Sensor noise X
Operating point X X X X X
User set value X
Delay X X X X X
These categories are of importance as they determine how variables can be interconnected and how they interact
with the information in the layers. It is important to note that controlled variables are either measured or estimated
variables. In the sequel, the term controlled variables is used when the variables can be either estimated or measured.
Variables have different process related properties that can be set by the user, see Table 1. Some of these properties
form part of all the dynamic models which connect a specific variable. These properties are:
• Limit (Saturation), which determines the allowed operating range of a variable.
• Delay, which allows the user to define input or output delays.
The value of the delay is integrated into the process models during the analysis. The remaining properties allow
the user to specify process operating conditions which can be used for the scaling of the process variables during the
analysis.
2.2.2. Process models
The process models correspond to the edges of the SFG and are the interconnections between variables representing
the dynamic behavior of the plant. Generally, process models can be defined on a single-input-single-output basis, but
multi-input-multi-output models are supported as well. In both cases, a process model can be defined as a transfer
function or state space system in continuous or discrete time. When a process model is defined it is represented by a
red edge, as shown in middle layer of Fig. 2.
In order to simplify adding process models, some model structures which are used within system identification of
process models are pre-defined, like for example
Γij(s) =
K
Ts + 1
e−Ls
or Φij(s) =
K
Ts + 1
e−Ls
where the user only has to provide the parameters K, T and L in order to define the dynamics.
Currently, only linear time invariant models are supported. Clearly, a dependency on the operating points of the
different variables arises, but most available CCS methods are only applicable on linear models.
It has to be noted that ProMoVis is an offline tool and does not derive the process models and their parameters.
This has to be done in a previous step by the user.
2.2.3. Controllers
In most cases, controllers do not differ from process models in their implementation. Single-input single-output
controllers can be represented by two edges, from reference and controlled variable to manipulated variable, see
bottom layer in Fig. 2. Alternatively, Single-input single-output controllers can also be defined as blocks with two
inputs (reference variable, controlled variable) and one output (manipulated variable). Either way, the resulting edges
or blocks are then automatically generated. The reason is to simplify for users to create and connect controllers
properly and thereby to avoid incorrect connections. Similar to process models, some controller types are pre-defined,
such as PID controllers and filters. The user can choose between the block or edge representation. Multivariable
controllers can be defined with multiple input and output ports.
6

Figure 3: Software architecture of ProMoVis.
2.2.4. Components
The process layout of a plant at the physical level can usually be decomposed into smaller building blocks which
are components. These components can have a graphical representation which can be used to create a visualization
of the plant.
In ProMoVis, components have no functionality other than providing an understanding of the layout and con-
struction of the plant with a rather coarse level of detail and realism. An effective representation of components can
be created by using symbols according to industry standards (see for example SSG Standard Solutions Group AB,
2007a,b), or bitmap images of drawings or sketches.
For the design of symbols a simplistic script language is implemented that enables the user to create new sets of
symbols and libraries. At the moment, there are sets of symbols available for the pulp and paper industry and mining
industry. The script language is mainly composed of drawing commands for lines, polygons, ovals, coloring, and text.
ProMoVis will interpret the commands and then draw the component symbols accordingly.
2.3. Software implementation
Building a representation of an interconnected process does not require any intense computations. Additionally,
the focus is on interactivity and a graphical user interface which is versatile and easy to use on any computer platform.
CCS methods depend on many mathematical operations that have to be performed on the SFG.
Therefore it was decided to implement the modeling and visualization in Java and the computational engine in
MATLAB. A schematic of the software architecture is shown in Fig. 3. There, it can be seen that the Java GUI is
configured using configuration files. The information flow between the Java GUI and the computational engine is
limited to the transfer of the model data, the analysis commands and the reporting of the result data back to the Java
GUI.
After startup, ProMoVis enters the building mode, where the user can create new scenarios or load existing
scenarios from stored files. The user can then switch between building mode and analysis mode using menu commands.
In the analysis mode, the user makes a selection of the analysis that should be performed and selects the parts of
the scenario which should be considered. As soon as the analysis is called, the current model data is transferred
to the computational engine where it is buffered until the user leaves the analysis mode. Additionally, the analysis
coordination is executing the necessary analysis functions. Thereafter the result arbitration will combine the results
from the analysis functions and report them back to the result display in the Java GUI.
The interface between the Java GUI and the computational engine is well defined and enables the porting of the
MATLAB code onto other platforms without significant changes to the Java GUI. For industrial use, it is possible to
combine the computational engine with the Java GUI into a stand-alone software.
7

Table 2: Available options for each method implemented in ProMoVis.
RGA DRGA NI PM HIIA Σ2 SET FETr FETc FDPTr FDPTc
Consideration of time delays X X X
Frequency options X X X
Scaling options
saturation, range X X X X X X X X
input scaling X X X X X
output scaling X X X X X
Filter options X X X
Plot type X
3. Analysis methods for the selection of control configurations
The goal is to select a set of Interaction Measures (IMs) which is sufficient to solve the CCS problem for most
of the cases. It is the belief of the authors that this includes traditional IMs like relative gains for the selection of
input-output pairings, Niederlinski Index for testing the stabilizability of the resulting decentralized configurations, as
well as more modern gramian-based IMs which are used for the design of sparse control configurations.
We define now the selected CCS methods and discuss their implementation. A typical procedure for CCS using
IMs is described for the user of ProMoVis.
3.1. Implementation of the analysis tools
The implemented tools depend on the availability of accurate process models, which have to be derived prior to
the analysis.
When the method to be used is selected, the user is required to choose an input/output set for which the analysis
is performed. In general, the inputs are restricted to be manipulated variables, however future consideration of
hierarchies will require including controller references in order to select higher level structures like the outer loops of
cascades. Depending on the selected method, a different set of options is available, with predefined default values.
These options are grouped in the following subsets:
• Consideration of time delays. For those methods which are sensitive to time delays, the user can decide if
these are considered in the computation. If so, the order of the Padé approximation has to be given for the case
of continuous-time systems.
• Frequency options. For those methods which result in an array of diagrams in the frequency domain, it is
allowed to select the frequency unit, as well as the set of frequencies considered for analysis.
• Scaling options. Usual methods for scaling signals involve dividing each variable by its maximum expected or
allowed change (Skogestad and Postlethwaite, 2005). For those methods which are sensitive to the scaling of the
process variables, it is allowed to choose to scale the process variables by using the values entered in either the
Saturation or the Range fields of the process variables. As an alternative, it is allowed to manually introduce
input and/or output scaling matrices depending on the method.
• Filter selection. For the gramian-based IMs, it is possible to restrict the analysis to a range of frequencies of
interest, e.g. around the crossover frequency, which is where most of the control action is usually present. This
is done by filtering the input-output channels such that frequencies outside the selected range are attenuated
(Birk and Medvedev, 2003). In ProMoVis, such filters can be declared in the calculation options.
• Plot type. This option is exclusive of the Dynamic Relative Gain Array (DRGA), which results in a complex
array represented in the frequency domain. The user can choose to represent its magnitude, phase, real part or
imaginary part.
The options which are available for each of the subsequently defined methods are summarized in Table 2.
For the analysis methods described here, the transfer function matrix G(s) from the selected subset u of the
exogenous inputs into the selected subset y of the process outputs is required and will be derived now from (2).
Provided that (2) is well-posed (see (Johansson, 2010) for details) we may infer that the variables z are related to the
exogenous inputs x as
z = (I − Φ)−1
Γx (3)
8

Now, let B be a matrix selecting the variables u from x, i.e. u = Bx. Then ΓBT
will contain those columns from
Γ that correspond to u. Similarly, let C be a matrix selecting the variables y from z, i.e. y = Cz. Then, for the
continuous-time case transfer function matrix from u to y is
G(s) = C(I − Φ(s))−1
Γ(s)BT
(4)
In ProMoVis, the calculation (I −Φ(s))−1
Γ(s) = G0(s) is done only once in order to reduce computational effort.
Selecting different sets of inputs and outputs, i.e. multiplication by different C and BT
is then accomplished by picking
out the appropriate rows and columns from G0(s).
After this computation, the selected method is applied to G(s) and the result is appropriately displayed.
3.2. Analysis tools based on relative gains
The most popular tool based on relative gains is the RGA, introduced by Bristol (1966) to design decentralized
control configurations based on steady-state gain information. Later, several authors addressed some of the limitations
of the RGA, usually by introducing variants of this IM. This includes different extensions of the RGA to consider
process dynamics, like evaluating the RGA at different frequencies by Witcher and McAvoy (1977), which was named
Dynamic RGA (DRGA).
In the default set of CCS methods in ProMoVis, the RGA and DRGA methods have been implemented for the
design of decentralized control configurations as well as the Niederlinski Index for discarding unstable configurations.
Other advanced techniques based on relative gains are candidates for future versions of ProMoVis, like the Block RGA
introduced by Manousiouthakis et al. (1986) for the design of block diagonal control structures and the partial relative
gains introduced by Häggblom (1997) for the selection of sparse control configurations.
3.2.1. Relative Gain Array (RGA)
The RGA of a continuous process described by (2) and with input-output transfer function G as in (4) is:
RGA(G) = G(0) ⊗ G(0)−T
(5)
where ⊗ denotes element by element multiplication, and G(0)−T
is the transpose of the inverse of the steady-state
gain matrix. The normalization used in this calculation implies that the sum of all the elements in the same row or
column of the RGA add up to 1.
Each of the values of the RGA is the steady-state gain of the corresponding input-output channel when all the
other loops are open divided by the steady-state gain when the rest of the process is in closed loop under tight
control. Based on this definition, the following rules have been formulated for the selection of a decentralized control
configuration:
• The preferred pairings are those with RGA values close to 1 (Skogestad and Morari, 1992).
• The selection of positive values for the decentralized pairing is a necessary condition for closed-loop integrity,
provided that all elementary subsystems are linear time invariant, finite dimensional, stable, and strictly proper
(Campo and Morari, 1994). Integrity is a desirable property of the decentralized control system, which means
that the closed-loop system should remain stable as each of the SISO controllers is brought in and out of service
(Bristol, 1966). This is not applicable to time delayed systems due to their infinite dimensional aspect.
• Large values should not be selected since they are related to ill-conditioned behavior of the plant (Chen et al.,
1994). Values exceeding 1 by more than a few tenths are very sensitive to model uncertainty and the nominal
value can be easily perturbed to a large value, as indicated in the studies on 2 × 2 systems by Castaño and Birk
(2008).
Note that these properties imply that the RGA might not indicate any appropriate decentralized control configuration,
requiring other tools to design configurations. Moreover, the RGA is insensitive to input and output scaling and to
time delays.
In addition, the RGA has certain limitations which need to be considered. Several of these limitations have been
resolved by different authors, and some of these solutions have been implemented in ProMoVis, like the application
to non-square plants with the use of the pseudo-inverse (Chang and Yu, 1990), or the computation of the RGA for
systems with pure integrators (Arkun and Downs, 1990; McAvoy, 1998).
An important limitation is that the RGA is originally evaluated only at steady state, and therefore is not reflecting
the dynamic properties of the process.
9

3.2.2. Dynamic RGA (DRGA)
The DRGA of a continuous process described by (2) and with input-output transfer function G as in (4) is:
DRGA(ω) = G(jω) ⊗ G(jω)−T
(6)
The DRGA is an array of complex numbers and has a more obscure interpretation than that of the RGA. Usually,
it is preferred to use its magnitude as indicator due to the gain interpretation, however only the sums of the rows or
columns of the resulting complex array (or its real part) add up to 1. Moreover, by evaluating the magnitude alone,
the sign of the DRGA is lost as an indicator, which is often used to rule out certain input-output pairings.
A shortcoming of the DRGA is that perfect control for all frequencies is assumed in its computation. This
assumption is only valid for a very low frequency range. Other dynamic versions of the RGA have been defined to
overcome this situation, like the Effective RGA (ERGA) introduced by Xiong et al. (2005). Nevertheless, the DRGA
version implemented here has been selected for its simplicity and widespread use.
3.2.3. Niederlinski Index (NI)
For a system under decentralized control, and assuming that the process is described by (2) and with input-
output transfer function G as in (4) which has been reordered so that the controller is a diagonal matrix, the Niederlinski
Index (NI) can be computed as (Niederlinski, 1971):
NI = det(G(0))/
n
i=1
Gii(0) (7)
This indicator is traditionally used to test the stabilizability and/or integrity of a decentralized configuration.
Under the assumptions of stability of all the elementary subsystems represented by rational functions Gij(s), a
value of NI < 0 is a sufficient condition for the instability of the closed loop system when all the SISO loops are
under integral action. This condition is widely used for discarding unstable decentralized control structures prior to
the design of the multi-loop controller.
Integrity can be verified by testing the stabilizability of the systems which result from opening each of the SISO
loops. This is done by computing the value of NI for any of the principal sub-matrices Gii
(0) resulting from removing
the ith
row and column from G(0). The system will not possess integrity if NI < 0 for any of the principal sub-matrices.
More restrictive conditions for stability and/or integrity exist like the tighter conditions derived by Chiu and
Arkun (1991) for 2 × 2 plants. However, these tests are more complicated, and the reader can refer to the surveys in
Chapter 10 by Skogestad and Postlethwaite (2005) or Chapter 2 by Khaki-Sedigh and Moaveni (2009).
3.3. Gramian-based IMs
For the design of control configurations other than decentralized, the modern gramian-based IMs can be used.
The gramian-based IMs are Index Arrays (IAs) in which a gramian-based operator is applied to each of the
single-input single-output subsystems in order to quantify its significance. The use of different operators results in
different IMs. The Hankel Interaction Index Array (HIIA) introduced by Wittenmark and Salgado (2002) uses the
Hankel norm. The Participation Matrix (PM) introduced by Salgado and Conley (2004) uses the trace of the product
of controllability and observability gramians, and the Σ2 introduced by Birk and Medvedev (2003) uses the H2 norm.
For a continuous process described by (2) and with input-output transfer function G as in (4), the IAs are
calculated as:
[IA]ij =
[Gij(s)]p
m,n
i,j=1
[Gij(s)]p
(8)
where [·]p denotes the corresponding operator of the used IA.
As a result of the normalization, all the elements of any of these IAs add up to one. The selection of the control
configuration is made by selecting a subset of the most important input-output subsystems, which will form a reduced
model on which control will be based. Choosing a configuration with a total contribution of the selected input-output
channels larger than 0.7 is likely to result in satisfactory performance (Salgado and Conley, 2004).
An advantage of the gramian-based IMs over the RGA is their ability to be used for designing sparse control
configurations. A disadvantage is that the quantification of the significance of the input-output subsystems depends
on the scales used to represent the inputs and outputs.
10

At the moment there is no clear procedure for interpreting the gramian-based IMs in the presence of time delays.
For the case of the operators used by the HIIA and the PM, the quantified significance of an input-output channel
increases as the channel delay increases (Castaño and Birk, 2012). This might result in inadequate configurations,
since channels exhibiting large time delays but low gain and bandwidth might end up forming part of the reduced
model. This was revealed in Halvarsson (2008), where simulation work indicated that the presence of a time delay
itself is not sufficient for saying that a particular input-output channel should be used in the controller. Due to this
property of the PM and HIIA, it was decided that the user can select if the time delays will be neglected or not in the
computation. No decision needs to be taken in the case of using Σ2 due to its insensitivity to time delays.
3.4. Methods for structural analysis using weighted graphs
ProMoVis is able to visualize analysis results together with the process, e.g. as an overlayed weighted directed
graph that shows the significance of the connections as the thickness of the edges. For this purpose, the analysis
methods described by Castaño and Birk (2012) have been implemented: SET and FET.
These methods use the squared H2 norm as operator for quantifying the significance of the process interconnections
in terms of signal energy transfer.
3.4.1. Structural graphs.
The method Structural Energy Transfer (SET) is applied to obtain a weighted structural graph describing the
importance of the direct process interconnections.
Structural graphs have been extensively used for the design of control structures, and the work in Nistazakis and
Karcanias (2004) describes its importance for deriving properties such as decomposability (Sezer and ˇSiljak, 1986) and
structural controllability and observability (Lin, 1974).
The novelty of the method SET is adding weights to these structural graphs. This gives an enhanced visual
understanding of the process and allows application of advanced methods for CCS which consider weighted graphs
such as described by Johansson (2000a).
3.4.2. Functional graphs.
In Functional Energy Transfer (FET) a normalized weighted directed graph is derived for the input-output chan-
nels, which quantifies their significance. Two different normalizations are used such that either the weights of the
edges entering an output node or the weights of the edges leaving an input node add up to 1. These normalizations
are denoted as FETr and FETc, and for a process described by (2) and with input-output transfer function G as in
(4) they are calculated as:
[FETr]ij =
||Gij||2
2
n
l=1
||Gil||2
2
; [FETc]ij =
||Gij||2
2
m
k=1
||Gkj||2
2
(9)
For each output, the relative effect of the selected process inputs is described by FETr. For each input, the relative
effect on the selected outputs is described by FETc.
It should be noted that FETr is insensitive to output scaling, and FETc is insensitive to input scaling.
Several case studies indicated the usefulness of FETr in CCS. A controlled variable should be associated with the
minimum number of actuators which result in a value of the sum of their contributions (edge widths) larger than a
designed threshold. Previous work indicates that a value larger than 0.7 should be achieved in order to expect a well
behaving closed loop system (Castaño and Birk, 2012).
These measures can also be assessed in the frequency domain resulting in a function of frequency instead of a scalar
number for each edge. This is done by normalizing the squared magnitude of each of the input-output interconnections
so either all the edges entering a node add up to one or all edges leaving a node add up to one. These operations result
in the methods named FDPTr and FDPTc. For a process described by (2) and with input-output transfer function
G as in (4), FDPT is calculated as:
[FDPTr(ω)]ij =
|G(jω)ij|2
n
l=1
|G(jω)il|2
; [FDPTc(ω)]ij =
|G(jω)ij|2
m
k=1
|G(jω)kj|2
(10)
11

3.5. Tools for the reconfiguration of control structures
A control deficiency could be the consequence of a tuning deficiency or of a structural deficiency in the controller.
In the latter case, a redesign of the control structure should be done, preferably by adding or removing a minimal
amount of controller interconnections.
The development of tools which identify if there is a structural deficiency in the controller and suggest a redesigning
of the controller configuration has only recently received attention. A method was proposed by Birk (2007), that makes
use of the factorization of the closed loop sensitivity function matrix and has been implemented in ProMoVis. This
method quantifies the performance loss due to neglected interconnections in the process and considers the currently
used controller. The method was further analyzed and assessed in a comparative study in (Birk and Dudarenko, 2012).
An appropriate output scaling is required for the application of the method, which is limited to control systems
with decentralized or block diagonal control structures.
In ProMoVis, this method can be used if a 1-DOF controller is used and the parameters of the controller are
declared.
3.6. Typical procedure for CCS using IMs
The following procedure can be used for selecting control configurations based on the IMs.
Step 1. Seek a decentralized control structure using methods based on relative gains. If a decentralized structure is
indicated by the use of the RGA as described below, then the DRGA will help to determine if the structure is
still feasible at other frequencies different to steady-state. The value of the DRGA at the crossover frequency is
of special interest, since it is usually the range of frequencies at which control is more active.
Step 2. Check the stabilizability of candidate decentralized configurations. Decentralized structures with negative
values of NI must be discarded for being unstable under integral action in all the SISO loops. Several other
tests for stability and/or integrity of the decentralized control structure using NI and the RGA can be used.
The reader can refer to Chapters 10 and 2 respectively in the books by Skogestad and Postlethwaite (2005) and
Khaki-Sedigh and Moaveni (2009) for surveys on these tests.
Step 3. Design a sparse control configuration if needed. It is recommended to contrast the indications obtained using
relative gains with other CCS methods. One reason is that the RGA might indicate severe loop interaction if
a decentralized structure is to be used. Another reason is that there might be severe loop interaction which
is not captured by the RGA, i.e. in triangular plants. These cases present severe difficulties for decentralized
configurations, and the gramian-based IMs can then be used to design a sparse control configuration. As an
alternative to the gramian-based IMs, the method FETr can be used, which provides a visual and intuitive
analysis as well as being insensitive to the scaling of controlled variables.
4. Case study: A stock preparation process.
The stock preparation process in SCA Obbola AB, Sweden is described below and will be used as illustrative
example for the typical work flow with ProMoVis. At the moment of the described work, the plant was operating with
a decentralized controller under stable conditions but exhibiting significant perturbations in the controlled variables.
The case study will therefore be considered a success if an analysis with ProMoVis indicates the same decentralized
configuration as feasible, and gives insight in potential modifications on the control structure relating to the deficiencies.
Prior to the use of ProMoVis, process information has to be acquired in the form of mathematical models and/or
process flow charts. First the process model is implemented in ProMoVis by creating a visual representation of the
flow charts and declaring the mathematical process models. Then the control structure can be selected using the
implemented methods.
4.1. Description of the stock preparation process.
The stock preparation process is present in many paper plants for the refining of pulp and chemical treatment. In
conventional refining, the pulp is pumped through the gap between two grooved discs. A moving disc can be rotated
and displaced in the axial direction, and the friction of the fibres with the discs and with each other creates the refining
effects. Refining creates major changes in pulp properties as described by Annergren and Hagen (2004). Its goal is to
improve web strength, but also results in decreasing the dewatering capacity of the paper web and thus needs to be
tightly controlled for optimum results.
12

Figure 4: Schematics of the stock preparation process at SCA Obbola. Pipes with indicated flow directions are wide solid lines, descriptions
or component names are in italics and variables are in capital letters. Symbols are in accordance with the SSG standard.
A schematic of the process is depicted in Fig. 4. First the pulp is pumped from a storage tank and the flow
bifurcates towards two parallel refiners. Note that a fraction of the pulp is recirculated again for balancing the
mechanical load in the refiners. The pulp is then diluted to the required concentration for the chemical treatment,
being finally discharged to a storage tank, in which starch is added, and from where the pulp is pumped to subsequent
tanks to continue with the chemical treatment. The structural complexity of the process requires a deep analysis of
the process interconnections in order to design a control configuration. The set of considered sensors and actuators is
summarized in Table 3.
The refiners have internal controllers to track a set point for the specific energy that is used to affect the pulp.
Safety, quality, and production depend on well-maintained set points for the considered flows and the pressure at
the entrance of the refiners. In the current control of the process, four independent single-input single-output PID
controllers are used to maintain the flows at the desired operating points. The centrifugal pump is then used as
actuator in another control loop to keep the pressure before the refiners at the operating point. The dilution water is
delivered to each of the branches with the use of cascade structures, which have as outer loops the desired concentration
for the pulp, and as inner loops the needed flow of pulp to achieve such concentration. In both branches, the pressure
at which the pulp is discharged to the storage tank is controlled by a valve with a PID controller.
4.2. Implementation of the stock preparation process model in ProMoVis
The visual representation resulting from implementing the stock preparation process in ProMoVis is depicted
in Fig. 5. First, a visualization of the process layout at the physical level was created by connecting components
representing elements such as pipes, valves, pumps, and refiners. Secondly, the corresponding process variables were
declared.
In order to collect significant process data for the modeling task, the process was excited during normal operation
by perturbing the actuators with additive white noise. In a first modeling step, a model structure was created by
selecting a subset of controlled variables and actuators to be considered for control, and identifying which actuators
generate an observable impact on certain controlled variables. System identification techniques were used to model
the input-output channels reflected by the identified model structure, and the resulting transfer functions of the
13

Table 3: Considered sensors and actuators in the refining section.
Actuators
Tag Name Description
PA Pump Actuator Pumps the flow through the refiners
VA1 Valve Actuator 1 Valve after refiner 1
VA2 Valve Actuator 2 Valve after refiner 2
VA3 Valve Actuator 3 Valve at the recirculation from refiner 2
VA4 Valve Actuator 4 Valve at the recirculation from refiner 1
VA5 Valve Actuator 5 Valve at the dilution for the pulp form refiner 1
VA6 Valve Actuator 6 Valve at the dilution for the pulp form refiner 2
VA7 Valve Actuator 7 Valve before discharge to the storage tank
(branch from refiner 1)
VA8 Valve Actuator 8 Valve before discharge to the storage tank
Sensors
PI1 Pressure Indicator 1 Pressure before the flow bifurcation
PI2 Pressure Indicator 2 Pressure at the entrance of refiner 1
PI3 Pressure Indicator 3 Pressure at the output of refiner 1
PI4 Pressure Indicator 4 Pressure at the entrance of refiner 2
PI5 Pressure Indicator 5 Pressure at the output of refiner 2
PI6 Pressure Indicator 6 Discharge pressure before the storage tank
PI7 Pressure Indicator 7 Discharge pressure before the storage tank
FI1 Flow Indicator 1 Pulp flow through refiner 1
FI2 Flow Indicator 2 Pulp flow through refiner 2
FI3 Flow Indicator 3 Pulp flow recirculated from refiner 2
FI4 Flow Indicator 4 Pulp flow recirculated from refiner 1
FI5 Flow Indicator 5 Dilution water for pulp from refiner 1
FI6 Flow Indicator 6 Dilution water for pulp from refiner 2
CI1 Concentration Indi-
cator 1
Concentration before the flow bifurcation
CI2 Concentration Indi-
cator 2
Concentration before the flow bifurcation
Estimated Variables
CE1 Concentration Esti-
mation 1
Average of two redundant concentration sensors
before flow bifurcation.
mation 2
Concentration of pulp to be diluted after refiner
1
mation 3
Concentration of pulp to be diluted after refiner
2
FE1 Flow Estimation 1 Flow of pulp from refiner 1 which is not recir-
culated
FE2 Flow Estimation 2 Flow of pulp from refiner 2 which is not recir-
culated
interconnections in the model of the stock preparation prcess are summarized in Table 4. Each of the obtained
transfer functions was declared in ProMoVis, resulting in red interactive edges represented in Fig. 5, which can be
used to access and edit the parameters of the associated process model.
Finally, the controllers representing the current control of the process were defined in order to visualize and
maintain the information on the control system.
Notice that controlled variables can be either measured or estimated. The distinction is used in order to make
the user aware of the fact that estimated variables are the result of a calculation in the control system, represented by
observers. Therefore, measured variables may only be connected to other process variables, while estimated variables
may be connected to variables in the control system as well.
CE1 is the average of two redundant concentration sensors. FE1 and FE2 are the flows of pulp before adding the
dilution water, and they are computed as the difference between the flow through the refiners and the recirculation
flow. CE2 and CE3 are the concentrations of pulp before the dilution; they are the controlled variables of the outer
loops in the cascades to control the addition of dilution water, and they are estimated as being the concentration of
pulp before the refiners with a transport delay which depends on the flow of pulp before adding the dilution water.
Note that reference variables can be part of a control loop referring to an operating point for a controlled variable,
or the manual setting of an actuator. As an example, in the pressure control loops actuating VA7 and VA8 in Fig. 5,
the user can switch from manual to automatic mode. The position of the switches determine different operational
modes for the analysis.
4.3. Analysis of the stock preparation process with ProMoVis
A control configuration for the stock preparation process will now be selected using ProMoVis.
The existing controllers of the process for the pressures PI6 and PI7 were causing large oscillations during the
experiment. For this reason, the valves VA7 and VA8 were manually placed at a certain opening during most of the
experiments, which means that the collected data was not informative enough to create models which include these
variables. Therefore further experiments need to be conducted in order to include those variables in the CCS problem.
14

Figure 5: ProMoVis screenshot. Reﬁning section of the stock preparation process at SCA Obbola.
15

Miguel Castaño Arranz CV Structural Process Analysis

Miguel Castaño Arranz CV Structural Process Analysis

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