Abstract
Grinding circuits can exhibit strong nonlinear behaviour, which may make automatic supervisory control difficult and, as a result, operators still play an important role in the control of many of these circuits. Since the experience among operators may be highly variable, control of grinding circuits may not be optimal and could benefit from automated decision support. This could be based on heuristics from process experts, but increasingly could also be derived from plant data. In this paper, the latter approach, based on the use of decision trees to develop rule-based decision support systems, is considered. The focus is on compact, easy to understand rules that are well supported by the data. The approach is demonstrated by means of an industrial case study. In the case study, the decision trees were not only able to capture operational heuristics in a compact intelligible format, but were also able to identify the most influential variables as reliably as more sophisticated models, such as random forests.
Highlights
Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Department of Process Engineering, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
Decision tree models constructed with constructed manipulatedwith variables as the target leads to rules with a direct control actionModelling as its prediction
It can be seen that periods of bypass lie outside the edges of the central cluster of normal operation. This suggests that reducing the frequency of these events would decrease the overall variability in the supervisory on production tarautogenous (SAG) circuit
Summary
Control of grinding requires of the fundamental gov- that is that could be usedcircuits for higher level knowledge interpretation of the data, and itprinciples is this aspect erningconsidered circuit operation. This knowledge allows the transformation of observed data and in this paper.
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