The ladle metallurgical furnace (LMF) is a very flexible and common unit
operation found in most steelmaking melt shops, and enables the adjustment and
fine-tuning of molten steel's composition and temperature prior to casting.
Despite the importance of ladle metallurgy to the overall steel making process
very little has been achieved in the way of advanced ladle control. Limited
sensors are available to monitor heat progress during refining and current control
methods involve manual procedures. This thesis represents part of an ongoing
study on the modelling of a full-scale LMF in real-time with the forward goal of
improved control and optimization.
The first part of this thesis details a vision-based sensor for analyzing ladle
eye dynamics online using a multivariate image analysis (MIA) technique based
on principal component analysis (PCA). Predictive capabilities of the developed
model are demonstrated using previously published cold model data over a wide
range of operating variables. Further, preliminary work has confirmed the ability
of the sensor for potential use in an industrial setting. The second part of this
study concerns the development of metallurgical models for assessing the state of
a ladle metallurgical furnace. Specifically, a multi-component kinetic model in
combination with developed slag and steel thermodynamic solution models were
used to quantitatively describe the kinetics of slag-metal reactions within 41
industrially sampled heats at ArcelorMittal Dofasco's LMF#2. Metal phase mass transfer coefficients for all elements in steel were assumed to follow the empirical
relation derived from measured sulphur contents, while slag phase mass transfer
coefficients were calculated by fitting the ratio of k^Mm/ k^MxOysl to the experimental
results. On the basis of the fitted results, slag phase mass transfer coefficient
correlations were evaluated using linear regression. Computed results from the
model using these slag phase mass transfer coefficient correlations were found to
be consistent with the experimental data. In regard to the developed
thermodynamic solution models, original contributions to the modified interaction
parameter formalism and cell model are presented. As process model predictions
are invariably uncertain, the final part of this work involves the use of a stochastic
model (extended Kalman filter) to account for process disturbances, model-mismatch
and other sources of uncertainty that may result in significant error
propagation causing poor process control and plant economics. Several case
studies were performed to illustrate the effectiveness of the extended Kalman
filter and its application to optimal sensor selection was introduced. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/16650 |
| Date | 11 1900 |
| Creators | Graham, Kevin James |
| Contributors | Irons, Gordan A., Materials Science and Engineering |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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