Abstract
M.Ing. (Mechanical)
The goal of this study was to determine whether a SLIRN direct reduction process could be
modelled with a neural network. The full name of the SLIRN process is the Stelco, Lurgi, Republic
Steel, and National Leadprocess. A parallel goal was to identify, and test an alternative method to
reduce the dimensionality of a model. A neural network software package named Process Insights
was used to model the process. Two independent data reduction methods were used along with
various Process Insights functions, to build, train, and test models. The best model produced by
each of the two data reduction methods was used to report on.
The results showed that a SLIRN direct reduction process could be modelled successfully with a
neural network. The large number of variables normally identified with such a process can be
reduced without significant loss in model performance, The results also showed that the removal of
the most significant variable does not affect the model accuracy significantly, which bodes well for
the fault tolerance of the model in terms of individual sensor failures. The Process Insights
functions important to the modelling process were highlighted.