| Summary: | The walking beam furnace (WBF) is one of the most prominent process plants
often met in an alloy steel production factory and characterized by high
non-linearity, strong coupling, time delay, large time-constant and time
variation in its parameter set and structure. From another viewpoint, the
WBF is a distributed-parameter process in which the distribution of
temperature is not uniform. Hence, this process plant has complicated
non-linear dynamic equations that have not worked out yet. In this paper, we
propose one-step non-linear predictive model for a real WBF using non-linear
black-box sub-system identification based on locally linear neuro-fuzzy
(LLNF) model. Furthermore, a multi-step predictive model with a precise long
prediction horizon (i.e., ninety seconds ahead), developed with application
of the sequential one-step predictive models, is also presented for the
first time. The locally linear model tree (LOLIMOT) which is a progressive
tree-based algorithm trains these models. Comparing the performance of the
one-step LLNF predictive models with their associated models obtained
through least squares error (LSE) solution proves that all operating zones
of the WBF are of non-linear sub-systems. The recorded data from Iran Alloy
Steel factory is utilized for identification and evaluation of the proposed
neuro-fuzzy predictive models of the WBF process.
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