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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.

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