Analysis of Using Genetic Programming (GP) for SAG Mill Throughput Prediction

Semi-autogenous grinding (SAG) mills are critical components in mineral processing plants, and accurately predicting their throughput is of utmost importance for optimal operation. While previous studies have developed empirical models for SAG mill throughput prediction, the potential of using machine learning (ML) techniques, specifically genetic programming (GP), for this purpose has been underexplored.

This study aims to explore the application of GP for predicting SAG mill throughput and introduces five new GP variants to enhance prediction performance. One advantage of using GP is that it provides a transparent equation, unlike black-box ML models, which allows for better understanding and interpretation of the predictions.

These five new GP variants are designed to extract multiple equations, each accurately predicting mill throughput for specific clusters of training data. This approach takes into consideration the heterogeneity of the data and allows for more accurate predictions. By employing various approaches to utilize these equations for test data, the GP variants’ performance can be evaluated.

In order to assess the effect of different distance measures on the accuracy of the new GP variants, four different distance measures are employed. The results of the comparative analysis indicate that the new GP variants achieve an average improvement of 12.49% in prediction accuracy compared to the previously developed empirical models.

Furthermore, the investigation of distance measures reveals that the Euclidean distance measure yields the most accurate results for the majority of data splits. This finding suggests that the Euclidean distance is a reliable measure for determining the similarity between data points.

The most precise new GP variant, which considers all equations and incorporates both the number of data points in each data cluster and the distance to clusters when calculating the final prediction, shows promise. This approach takes into account both the local and global characteristics of the data and results in improved prediction accuracy.

In conclusion, the developed GP variants presented in this study offer a precise, transparent, and cost-effective approach for predicting SAG mill throughput in mineral processing plants. By utilizing ML techniques, specifically GP, and considering the heterogeneity of the data, these variants demonstrate improved prediction accuracy compared to empirical models. The findings also highlight the importance of choosing an appropriate distance measure for data similarity when applying GP for throughput prediction.

Read the original article