Applied Machine Learning for Geometallurgical Throughput Prediction—A Case Study Using Production Data at the Tropicana Gold Mining Complex

With the increased use of digital technologies in the mining industry, the amount of centrally stored production data is continuously growing. However, datasets in mines and processing plants are not fully utilized to build links between extracted materials and metallurgical plant performances. This article shows a case study at the Tropicana Gold mining complex that utilizes penetration rates from blasthole drilling and measurements of the comminution circuit to construct a data-driven, geometallurgical throughput prediction model of the ball mill. Several improvements over a previous publication are shown. First, the recorded power draw, feed particle and product particle size are newly considered. Second, a machine learning model in the form of a neural network is used and compared to a linear model. The article also shows that hardness proportions perform 6.3% better than averages of penetration rates for throughput prediction, underlining the importance of compositional approaches for non-additive geometallurgical variables. When adding ball mill power and product particle size, the prediction error (RMSE) decreases by another 10.6%. This result can only be achieved with the neural network, whereas the linear regression shows improvements of 4.2%. Finally, it is discussed how the throughput prediction model can be integrated into production scheduling.

An Improved High-Pressure Roll Crusher Model for Tungsten and Tantalum Ores

An improved approach is presented to model the product particle size distribution resulting from grinding in high-pressure roll crusher with the aim to be used in standard high-pressure grinding rolls (HPGR). This approach uses different breakage distribution function parameter values for a single particle compression condition and a bed compression condition. Two materials were used for the experiments; altered Ta-bearing granite and a calc-silicate tungsten ore. A set of experiments was performed with constant operative conditions, while varying a selected condition to study the influence of the equipment set-up on the model. The material was comminuted using a previously determined specific pressing force, varying the feed particle size, roll speed and the static gap. A fourth group of experiments were performed varying the specific pressing force. Experimental results show the high performance of the comminution in a high-pressure environment. The static gap was the key in order to control the product particle size. A mathematical approach to predict the product particle size distribution is presented and it showed a good fit when compared to experimental data. This is the case when a narrow particle size fraction feed is used, but the fit became remarkably good with a multi-size feed distribution. However, when varying the specific pressing force in the case of the calc-silicate material, the results were not completely accurate. The hypothesis of simultaneous single particle compression and bed compression for different size ranges and with different parameters of the distribution function was probed and reinforced by various simulations that exchanged bed compression parameters over the single particle compression distribution function, and vice versa.

Prediction model to estimate the specific energy and product particle size distribution of semi-autogenous mill

The optimal use of energy for grinding of ores in mills is one of the key objectives of this study. An attempt has been made to present an experimental model for the specific energy and product particle size distribution of semi-autogenous mills for the simulating of real conditions of the particles collision. A novel semi-autogenous mill test has been suggested in order to measure the required energy for grinding. The effect of the mill’s operation time and various energy levels on the particle size distribution and feed ore hardness has been investigated with samples collected from the Sarcheshmeh Copper Complex, Kerman, Iran. The obtained results demonstrated that there is a high congruence between the experimental product particle size distribution and energy, which are predicted by the proposed novel method and ball-drop test. Using the proposed analytic relationships, the required comminution energy and semi-autogenous mill operating time to achieve the specific product particle size distribution can be estimated with high accuracy ( R2 = 0.96). Finally, the accuracy of the results was evaluated by a ball drop-weight test.

Influence of the Feed Moisture, Rotor Speed, and Blades Gap on the Performances of a Biomass Pulverization Technology

Recently, a novel biomass pulverization technology was proposed by our group. In this paper, further detailed studies of this technology were carried out. The effects of feed moisture and crusher operational parameters (rotor speed and blades gap) on product particle size distribution and energy consumption were investigated. The results showed that higher rotor speed and smaller blades gap could improve the hit probability between blades and materials and enhance the impacting and grinding effects to generate finer products, however, resulting in the increase of energy consumption. Under dry conditions finer particles were much more easily achieved, and there was a tendency for the specific energy to increase with increasing feed moisture. Therefore, it is necessary for the raw biomass material to be dried before pulverization.