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.

翻译：半自磨机在选矿厂磨矿回路中起着关键作用。准确预测作为关键性能指标的半自磨机处理量至关重要。遗传编程在该领域的应用潜力尚未得到充分研究。本研究提出一种增强型遗传编程方法——多方程遗传编程，用于更精确地预测半自磨机处理量。在所提出的新方法中，提取多个方程，每个方程针对特定训练数据簇准确预测磨机处理量。随后采用多种方法利用这些方程预测测试数据的处理量。为评估距离度量的影响，在MEGP方法中使用了四种不同的距离度量。对比分析表明，最佳MEGP方法相比标准遗传编程，预测精度平均提升10.74%。在该方法中，利用所有提取的方程，并综合每个数据簇的数据点数量及与簇的距离来计算最终预测值。对距离度量的进一步研究表明，在欧氏距离、曼哈顿距离、切比雪夫距离和余弦距离这四种度量中，欧氏距离在多数数据划分中获得了最准确的结果。