Real time furnace froth state detection using Hidden Markov Models
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Date
2013-07-15
Authors
Harker, William Gordon
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Abstract
In this dissertation the feasibility of developing a soft sensor utilising Hidden Markov
Models (HMM) was evaluated. Specifically, this methodology was tested for use as a
soft sensor to detect furnace froths in a real time environment.
Initially, a review of Hidden Markov Models was undertaken to gain an understanding
of the mathematics and algorithms associated with HMM's. A simple HMM example
was constructed to highlight practical problems associated with HMM's. One such
problem identified was that HMM's are unsuitable for real time use without
modification.
Potential modifications were then researched to improve the real time performance of
the HMM. This research yielded a real time variant of the HMM Viterbi algorithm,
labelled Real Time Viterbi (RTV), as a potential modification. In addition a new hybrid
algorithm, labelled the Hidden Markov Model Fixed State Test (HMM FST), was
developed by the Author.
Comparative studies of the respective real time performances of the RTV and HMM
FST algorithms concluded that the HMM FST algorithm was the most suitable for use
in the real world application.
A final HMM FST real time algorithm was developed which incorporated the use of KMeans
Clustering techniques. Data files, consisting of electrode positions from real
furnace froths, were then replayed into the HMM FST algorithm to evaluate its
performance. Four scenarios, incorporating different HMM FST tuning parameters,
were then executed to determine the impact of the model parameters on its froth
detection ability and false positive response. A final tuning set was recommended for
the HMM FST Furnace Froth Detector.
This research proved that this approach can be used as a practical soft sensor to
detect furnace froths in electric arc furnaces with any structural or electrode
configuration. The HMM FST model could be tuned to various levels of sensitivity
and was found to generate low false positives due to its treatment of plant sensors as
a collective.