Abstract
A step-wise algebraic routine is used to fit a dynamic non-linear model, specifically developed for process control, to steady-state process data of an industrial single-stage grinding mill circuit. Step-test data from the industrial plant is used to validate the response of the non-linear model. The results indicate that the model provides a qualitatively accurate response of the main process variables. Because the non-linear model parameters can be calculated from steady-state data, it provides an advantage over classical system identification methods as it does not require an expensive and disruptive step-test campaign to develop linear transfer function models. The model is ideal for model-based predictive process control.
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