  Semi-autogenous grinding (SAG) mills play a pivotal role in the grinding
circuit of mineral processing plants. Accurate prediction of SAG mill
throughput as a crucial performance metric is of utmost importance. The
potential of applying genetic programming (GP) for this purpose has yet to be
thoroughly investigated. This study introduces an enhanced GP approach entitled
multi-equation GP (MEGP) for more accurate prediction of SAG mill throughput.
In the new proposed method multiple equations, each accurately predicting mill
throughput for specific clusters of training data are extracted. These
equations are then employed to predict mill throughput for test data using
various approaches. To assess the effect of distance measures, four different
distance measures are employed in MEGP method. Comparative analysis reveals
that the best MEGP approach achieves an average improvement of 10.74% in
prediction accuracy compared with standard GP. In this approach, all extracted
equations are utilized and both the number of data points in each data cluster
and the distance to clusters are incorporated for calculating the final
prediction. Further investigation of distance measures indicates that among
four different metrics employed including Euclidean, Manhattan, Chebyshev, and
Cosine distance, the Euclidean distance measure yields the most accurate
results for the majority of data splits.
