Abstract:
With the increase in available data and the stricter control requirements for mineral
processes, the development of automated methods for data processing and model creation are
becoming increasingly important. In this paper, the application of data quality assessment methods
for the development of semirigorous and empirical models of a primary milling circuit in a platinum
concentrator plant is investigated to determine their validity and how best to handle multivariate
input data. The data set used consists of both routine operating data and planned step tests. Applying
the data quality assessment method to this data set, it was seen that selecting the appropriate subset
of variables for multivariate assessment was difficult. However, it was shown that it was possible
to identify regions of sufficient value for modeling. Using the identified data, it was possible to fit
empirical linear models and a semirigorous nonlinear model. As expected, models obtained from
the routine operating data were, in general, worse than those obtained from the planned step tests.
However, using the models obtained from routine operating data as the initial seed models for the
automated advanced process control methods would be extremely helpful. Therefore, it can be
concluded that the data quality assessment method was able to extract and identify regions sufficient
and acceptable for modeling.
