Table Of ContentIndustrial Applications of Soft Computing
Studies in Fuzziness and Soft Computing
Editor-in-chier
Prof. Janusz Kacprzyk
Systems Research Institute
Polish Academy of Sciences
ul. Newelska 6
01-447 Warsaw, Poland
E-mail: [email protected]
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http://www.springer.de/cgi-bin/search_book.pl?series 2941
Further volumes of this series can Vol. 60. K.J. Cios (Ed.)
be found at our homepage. Medical Data Mining and Knowledge Discovery.
200}
ISBN 3-7908-1340-0
Vol. 49. L.I. Kuncheva
F/l'l.ZY Classifier Design, 2000 Vol. 61. D. Driankov and A. Saffiotti (Eds.)
ISBN 3-7908-1298-6 Fuzzy Logic Techniques for Autonomous Vehicle
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Vol. 50. F. Crestani and G. Pasi (Eds.)
ISBN 3-7908-1341-9
Soft Computing in Information Retrieval. 2000
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ISBN 3-7908-1303-6
Soft Computing for Control of Non-Linear
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Quo Vadis Computational Intelligence? 2000 Relational Methods for Computer Science
ISBN 3-7908-1324-9 Applications. 200}
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F/l'l.ZY Mathematics in Medicine. 2000 Radial Basis Function Networks 1. 200]
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Rough Set Methods and Applications. 2000
Radial Basis Function Networks 2. 200}
ISBN 3-7908-1328-1
ISBN 3-7908-1368-0
Vol. 57. V. Novak and I. Pertilieva (Eds.)
Discovering the World with Fuzzy Logic. 200} Vol. 68. A. Kandel. M. Last and H. Bunke (Eds.)
ISBN 3-7908-1330-3 Data Minining and Computational Intelligence.
200]
Vol. 58. D.S. Malik and J.N. Mordeson ISBN 3-7908-1371-0
Fuzzy Discrete Structures. 2000
ISBN 3-7908-1335-4 Vol. 69. A. Piegat
Fuzzy Modeling and Control. 200]
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ISBN 3-7908-1339-7 ISBN 3-7908-1387-7
Kauko Leiviska
Editor
Industrial Applications
of Soft Computing
Paper, Mineral and
Metal Processing Industries
With 118 Figures
and 27 Tables
Springer-Verlag Berlin Heidelberg GmbH
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ISBN 978-3-7908-2488-9 ISBN 978-3-7908-1822-2 (eBook)
DOI 10.1007/978-3-7908-1822-2
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Preface
My first contact with Soft Computing dates back to the end of 1970's. I got a letter
from a foreign colleague who asked, if I were interested to study a new control
methodology, fuzzy logic control. I had just finished with my Licentiate of Tech
nology thesis that had led to the first commercial control system for the lime kiln.
I had also started my Doctor of Technology studies and selected production
scheduling algorithms for the pulp mill as my topic. So, I had to turn his proposal
down.
Next contact was about seven years later, 1985. I was working with the lime
kiln again. This time, one my students was testing the expert system technology
for the consulting application on the lime kiln. The program system we were using
had a fuzzy system option, and we made a consulting fuzzy expert system for the
lime kiln operation. The system was never installed on the actual kiln.
I got my own laboratory to manage in 1990 and we started working with the its
strategy. My interest to knowledge-based system and to fuzzy logic and neural
networks had grown during years. We had also discussions with companies and
financing sources and, together with the interest, we chose Intelligent Methods,
how we called them at this time, our strategic growth area.
After couple of years, we were working with the lime kiln control again. This
time we used fuzzy logic control and the studies led to our first successful indus
trial application on this new area. So, it is no wonder that also one of the chapters
in this book deals with the use of Soft Computing methods in lime kiln.
This book concentrates on the application of Soft Computing methods in
Finland. Finland experienced a boom of neural network and fuzzy technologies
during 1990's. There were several reasons for this. Finnish companies have been
pioneering in many areas of industrial control applications for decades. Especially
this agrees with mining and metals and paper industries. Companies have seen the
benefits of better control and improved information, and also in this case it was
easy to find industrial partners ready for testing the new technology. Also the
research policy that the State of Finland was carrying out was positive for new
technologies. As a concrete example, the National Technology Agency, Tekes,
launched an extensive research and technology development project concerning
intelligent and adaptive systems. Its effect for the growth of fuzzy logic and neural
network applications in the Finnish industry was a tremendous one.
While choosing papers for this book I was in front of multitude of possibilities.
I have tried to balance the material at least in two ways. First, I tried to find new
VI
interesting industrial applications that have not been too reported earlier in the
literature. Secondly, I tried to give a chance to as many research groups as possi
ble to tell about their research.
After four short introductory chapters, applications in mineral and metal proc
essing start. The first application tells about fuzzy logic control of the sintering
plant. It is a good example, how a commercial software is used controlling a com
plicated process. The second example tells about an application Kohonen self
organising map to describe the behaviour of wall temperature profiles in the blast
furnace. It shows the strength of SQM in visualising complicated process
phenomena in giving improved information for operators. The third example tells
about the smelt shop control. It is an example, how neural network models are
integrated with existing models to a hybrid system with increased performance. It
shows the ability of neural networks to cope with huge amounts of data.
The fourth example of this topic takes us to mineral processing. It shows the
results of a long research line concerning the modelling and control of a rotary
dryer for minerals drying using conventional and advanced methods. The fifth,
and the final example introduces even more difficult example from minerals
processing. It deals with using image processing and an expert system in flotation
plant control.
Next we turn to the paper industry. These applications start from modelling.
The first paper in this sequence deals with the comparison of different fuzzy mod
elling methods using continuous pulp cooking as an example. The second example
concerns with a conventional fuzzy logic control. It concern with a fuzzy control
system for TMP-pulping installed at the Swedish mill. The third example is the
"obligatory" lime kiln example. The existing control system combines fuzzy logic
control, neural networks and an expert system framework. This system is in
operation and the results have been good. The forth example is once again dealing
with TMP- pUlping. It shows how fuzzy logic can be combined with Statistical
Process Control to develop a flexible system to help operators in process
monitoring. Finally comes an example from the paper machine control. It
concentrates on paper grade changes that are usually too complicated for manual
operation and the risks of production losses are also high in automatic changes.
As the Editor of this book, I have many thanks to give. I thank the Series Editor
for the possibility to write this book and good advises along the way. I thank my
writer colleagues for good co-operation and patience, because the road was long.
I thank my wife, Eija and my daughter, Minja, for just being there. This effort
took a lot of our time together. Especially, I thank my son, Marko, for collecting
the background material for the Chapter "Applications in Paper Industries". It was
a good work.
Qulu, November 20, 2000 Kauko Leiviska
Contents
v
Preface
Part I: Introduction
Basics of Soft Computing Methods 3
K. Leiviskii
Soft Computing Applications in Mineral and Metal Industries 23
K. Leiviskii
Soft Computing Applications in Pulp and Paper Industries 35
K. Leiviskii
Enhancing Maintainability of Knowledge-Based Soft Computing Systems 47
T. Rauma
Part II: Mineral and Metal Industries
Fuzzy Logic Control of a Sintering Plant 65
P. Myllymliki, 1. Poutiainen
Pattern Recognition for Blast Furnace Temperature Classification 79
H. Saxen, L. Lassus
Temperature Control of a Smelting Plant 93
T. Palovaara, M. Sipola
Adaptive Fuzzy Control of a Rotary Dryer 109
L. Yliniemi
Soft Computing at a Flotation Plant 133
H. Hyotyniemi, R. Ylinen, 1. Miettunen
VIII
Part III: Paper Industries
Intelligent Modelling of Continuous Pulp Cooking 147
K. Leiviskii, E. Juuso, A.Isokangas
Fuzzy Quality Control of a TMP Plant 159
J. Myllyneva, L. Karlsson, l. Joensuu
Intelligent Supervisory Control of an Industrial Rotary Kiln 175
E. Juuso, M. Jiirvensivu, O. Ahava
Fuzzy Logic and SPC 197
K. Latva-Kiiyrii
Estimation of Target Values for Grade Changes in Paper Machine 211
P. Viljamaa, H. Peltonen, H. Koivo
Index 223
List of Contributors 225
Part I
Introduction
