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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2282/1241
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| Title: | Empirical modeling, state estimation, and process control with real-life applications to the Czochralski crystallization process |
| Authors: | Komperød, Magnus |
| Issue Date: | 24-May-2012 |
| Abstract: | This PhD thesis presents research work within the field of systems and control
engineering, with emphasis on applications to real-life processes, the Czochralski
(CZ) crystallization process in particular. During the PhD study, two journal
articles and five conference papers have been published. All seven publications are
based on logged data from real-life processes or include examples based on such
data. For four of the publications, logged process data are essential. The seven
publications are referred to as Paper A through Paper G. The publications focus
on data preprocessing, empirical modeling, process control, and state estimation
for the purpose of noise filtering.
The Czochralski (CZ) crystallization process is a batch process that converts
multicrystalline materials into monocrystalline materials, i.e. materials that have
homogeneous crystal structures. Among the most important applications of the
CZ process is production of monocrystalline silicon. This is the only application
of the CZ process that has been considered during this PhD study. Monocrystalline
silicon is used in solar cell wafers and in computers and electronics. Solar
cells based on monocrystalline silicon have higher efficiency than those based on
multicrystalline silicon.
During the CZ batch process, multicrystalline silicon is melted in a crucible.
The silicon is then solidified on a monocrystalline seed crystal, thereby growing a
crystal. The grown crystal is monocrystalline and is referred to as an ingot. There
are several challenges associated with modeling and control of the CZ process: (i)
The process dynamics is challenging to model using mechanistic (first principle)
modeling. (ii) The process has multivariable character. (iii) The process is timevariant
due to its batch nature. (iv) There are several difficulties regarding sensor
technologies. In particular the ingot diameter is difficult to measure online.
The candidate’s literature search indicates that most published research works
considering modeling and control of the CZ process are simulation studies, which
are not validated against real-life processes. Only one publication was found that
documents that a suggested control strategy works on real-life CZ processes. During
the PhD study, the candidate had access to a real-life CZ process at SINTEF
Materials and Chemistry in Trondheim, Norway. As published research results that are validated on real-life CZ processes seem to be rather sparse, the candidate
focused his research on experiments at this plant.
Unfortunately, issues regarding sensor technologies forced the candidate to focus
on other parts of the CZ process than initially planned. However, these issues
have also given useful experiences and provided ideas for further research. The
work of this PhD study has focused on the heating element power and the temperature
of the molten silicon. The ingot diameter has not been considered, partly
because of unreliable diameter sensor, partly because the diameter depends on the
silicon temperature. Hence, it is reasonable not to consider the ingot diameter until
the heating element power and the silicon temperature are properly measured,
modeled, and controlled.
Logged process data from the SINTEF CZ plant are used extensively during
this PhD study. Paper D and Paper E consider empirical modeling of the heating
element power, Paper F suggests a cascade control strategy for improving temperature
control of the molten silicon, and Paper G presents state estimation for
the purpose of measurement noise filtering. Also, logged process data from the
SINTEF CZ plant are used as an example in Paper C.
Paper A and Paper B include work on logged process data from the copper
refining process at Xstrata Nikkelverk in Kristiansand, Norway. These data were
made available from the process by Dr. Tor Anders Hauge. Paper A presents
work on data preprocessing, using the Xstrata data as real-life examples. Paper B
considers system identification and compares two system identification algorithms
using process data from Xstrata. System identification is the science of developing
dynamic, empirical models based on process inputs and the corresponding process
outputs. |
| Keywords: | Czochralski crystallization process
empirical modeling |
| Publisher: | Høgskolen i Telemark |
| Document type: | Doctoral thesis |
| Peer reviewed: | Peer reviewed |
| URI: | http://hdl.handle.net/2282/1241 |
| Rights: | © Copyright The Author. All rights reserved |
| ISBN: | 978-82-7206-337-4 |
| Bibliographic citation: | Komperød, M. Empirical modeling, state estimation, and process control with real-life applications to the Czochralski crystallization process. Doctoral dissertation, Telemark University College, 2012 |
| Appears in Collections: | Doktorgradsavhandlinger i prosess- energi og automatiseringsteknikk
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