Search Results (6)

This study was conducted on a copper porphyry deposit located in Espinar, Cusco (Peru), with the objective of developing and comparing predictive models for processing capacity in SAG grinding circuits. A total of 174 samples were used for the JK Drop Weight Test (JKDWT) and 1172 for the Bond Work Index (BWi), along with 36 months of operational plant data. Three modeling methodologies were evaluated: DWi-BWi, SGI-BWi, and SMC-BWi (Mia, Mib), all integrated into a geometallurgical block model. Validation was performed through reconciliation with actual plant data, considering operational constraints such as transfer size (T₈₀) and maximum throughput (TPH). The model based on SMC parameters and BWi showed the best predictive performance, with a root mean square error (RMSE) of 143 t/h and a mean relative deviation of 1.5%. This approach enables more accurate throughput forecasting, improving mine planning and operational efficiency. The results highlight the importance of integrating geometallurgical and operational data to build robust models that are adaptable to ore variability and applicable to both short- and long-term planning scenarios. Full article

Most grinding operations are the process of reducing the particle size of ore materials under the combined action of impact and abrasion. The action mechanism of impact damage and abrasion damage of materials in the grinding process is different, and the ability of each constituent mineral of ore to resist impact damage and abrasion damage is different. In order to study the independent action mechanism and interaction law of impact and abrasion in grinding, mineral ores calcite, chalcopyrite, and sphalerite are studied in this paper. The JK drop weight test method and batch grinding test method are used to study the changes and laws of various indexes of three mineral ores under a single impact condition, a single abrasion condition, and the coexistence of the two effects. The results show that the impact crushing parameters of the three mineral ores and the corresponding hardness grade of the ores are related to the particle size. The smaller the particle size of the material, the smaller the value of the impact crushing capacity parameter A × b. The order of impact crushing resistance of the three mineral ores is consistent with the characterization results of ore Mohs hardness. Under the same particle size condition, the order of impact crushing parameter A × b of the three mineral ores is calcite > sphalerite > chalcopyrite. The first-order linear model can better fit the grinding kinetics in the cascading state, and its kinetic parameters are related to ore hardness and feed particle size. The t₁₀ is more suitable to characterize the grinding effect in the dropping state than in the cascading state. Full article

During the grinding process, the crushing of minerals mainly depends on the impact action of the grinding medium. Based on the JK drop weight test data of quartz and pyrrhotite and the research results of their impact crushing characteristic parameters, this paper calculates the specific crushing energy (E_cs) of mineral samples subjected to impact in a ball mill using the grinding medium motion theory and then calculates the cumulative particle size distribution under screening under any mesh size using the JK drop weight test method. On this basis, the breakage distribution function of mineral samples is calculated, and a selection function is obtained based on grinding experiments. Finally, using Matlab programming and function-fitting mathematical methods, as well as a particle size population balance dynamics simulation of grinding, the particle size distribution characteristics of the grinding products of the two mineral samples in the mill that are only subjected to impact action are calculated. The results show that the selection function of quartz and pyrrhotite decreases overall with the prolongation of the grinding time, and the selection function of the coarse particle size changes more significantly than that of the fine particle size. At the same time, the selection function decreases with the decrease in feed particle size, and the smaller the feed particle size, the lower the probability of impact crushing. The E_cs values of quartz and pyrrhotite at each particle level in the mill are different, and the degree of mineral crushing is closely related to the impact energy, feed particle size, and mineral properties. Full article

Based on the JK Drop Weight test and principle of selective crushing, a multicomponent complex ore with its component minerals, i.e., pyrrhotite, sphalerite, and quartz, was used to explore the impact crushing characteristics and relationship between the complex ore and its component minerals. Results show that the order of impact crushing resistance is quartz > pyrrhotite > ore > sphalerite. The particle-size-distribution characteristic curve of ore crushing products is always “sandwiched” between the curves of pyrrhotite, sphalerite, and quartz within the same feed-size range. When the particle size is −63 + 53, −45 + 37.5, −31.5 + 26.5, and −22.4 + 19 mm, the component mineral pyrrhotite has a negative effect on the impact crushing of ore, while the component mineral sphalerite has a positive effect. When the particle size is −16 + 13.2 mm, the component mineral pyrrhotite has a positive effect on the crushing effect of the ore, while the component mineral sphalerite has a negative effect. The component mineral quartz always has a negative effect on the impact crushing effect of ore in all the studied particle sizes. Full article

The degree of difficulty in crushing an ore depends on the composition of the ore itself. Due to different types and compositions of ores, the crushing mechanism of ores during the crushing process is also different. In order to quantitatively analyze the impact crushing characteristics of mineral components in ores, this paper takes pure mineral quartz, pyrrhotite, and pyrite as the research objects and uses the universal drop weight impact crushing test equipment and standard test methods developed by the JK Mineral Research Center of the University of Queensland, Australia, to conduct JK drop weight tests on these three pure mineral samples. The results show that the particle size distribution of impact crushing products is wide, covering all particle sizes from “0” to close to the feed particle size, and the yield distribution of each product particle size is relatively uniform. There are critical values and “energy barrier” effects for the impact-specific crushing energy. The impact-specific crushing energy has a significant impact on the particle size composition and crushing effect of the crushing product, and there is an interactive effect between the impact-specific crushing energy and the feed particle size and mineral type. The impact crushing resistance of the sample can be characterized by using Mohs hardness, impact crushing characteristic parameters, impact crushing resistance level, and the yield limit value t₁₀ of the characteristic crushing particle size. The overall characterization results have good consistency. Full article

The particle size composition of grinding products will significantly affect the technical and economic indexes of subsequent separation operations. The polymetallic complex ores from Tongkeng and Gaofeng are selected as the research object in this paper. Through the JK drop-weight test, the batch grinding test, and the population-balance kinetic model of grinding with the Simulink platform, the grinding characteristics of the two types of ores and the particle-size-composition prediction methods of grinding products are studied. The results show that the impact-crushing capacity of Tongkeng ore and Gaofeng ore are “medium” grade and “soft” grade, respectively. The crushing resistance of Tongkeng ore increases with the decrease in particle size, and the crushing effect is more easily affected by particle size than that of Gaofeng ore. For the same ore, the accuracy order of the three methods is: PSO–BP method > JK drop-weight method > B_III method. For the same method, only the B_III method has higher accuracy in predicting Gaofeng ore than Tongkeng ore, and other methods have better accuracy in predicting Tongkeng ore than Gaofeng ore. The prediction accuracy of the B_III method is inferior to that of the JK drop-weight method and the PSO–BP method and is easily affected by the difference in mineral properties. The PSO–BP method has a high prediction accuracy and fast model operation speed, but the accuracy and speed of the iterative results are easily affected by parameters such as algorithm program weight and threshold. The parameter-solving process of each prediction method is based on different simplifications and assumptions. Therefore, appropriate hypothetical theoretical models should be selected according to different ore properties for practical application. Full article