  In mineral processing plants, grinding is a crucial step, accounting for
approximately 50 percent of the total mineral processing costs. Semi-autogenous
grinding mills are extensively employed in the grinding circuit of mineral
processing plants. Maximizing SAG mill throughput is of significant importance
considering its profound financial outcomes. However, the optimum process
parameter setting aimed at achieving maximum mill throughput remains an
uninvestigated domain in prior research. This study introduces a hybrid
intelligent framework leveraging expert knowledge, machine learning techniques,
and evolutionary algorithms to address this research need. In this study, we
utilize an extensive industrial dataset comprising 36743 records and select
relevant features based on the insights of industry experts. Following the
removal of erroneous data, a comprehensive evaluation of 17 diverse machine
learning models is undertaken to identify the most accurate predictive model.
To improve the model performance, feature selection and outlier detection are
executed. The resultant optimal model, trained with refined features, serves as
the objective function within three distinct evolutionary algorithms. These
algorithms are employed to identify parameter configurations that maximize SAG
mill throughput while adhering to the working limits of input parameters as
constraints. Notably, our analysis revealed that CatBoost, as an ensemble
model, stands out as the most accurate predictor. Furthermore, differential
evolution emerges as the preferred optimization algorithm, exhibiting superior
performance in both achieving the highest mill throughput predictions and
ensuring robustness in predictions, surpassing alternative methods.
