Development and Testing of a Method to Estimate the Mineral Composition of Ore from Chemical Assays with a View toward Geometallurgy: Application to an Iron Ore Concentrator

For complex orebodies in which the valuable metal is carried by several minerals that respond differently to the concentration process, an ore block model should not be characterized solely with elemental assays, as this information is not sufficient to anticipate the mill performances. Data from an iron ore concentrator is used to demonstrate the idea. A method is then proposed to estimate the mineral contents of ore samples from elemental assays. The method can readily be extended to combine the estimation of the mineral contents in the feed of the mill with an estimation of the recovery of these minerals into the products of the concentrator. These mineral recoveries can subsequently be incorporated into a block model to predict the concentrator response to the processing of an ore block.

Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes

This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes.The datasets for the throughput prediction model include penetration rates from blast hole drilling(measurement while drilling),geological domains,material types,rock density,and throughput rates of the operating mill,offering an accessible and cost-effective method compared to other geometallurgical programs.First,the comminution behavior of the orebody was geostatistically simulated by building additive hardness proportions from penetration rates.A regression model was constructed to predict throughput rates as a function of blended rock properties,which are informed by a material tracking approach in the mining complex.Finally,the throughput prediction model was integrated into a stochastic optimization model for short-term production scheduling.This way,common shortfalls of existing geometallurgical throughput prediction models,that typically ignore the non-additive nature of hardness and are not designed to interact with mine production scheduling,are overcome.A case study at the Tropicana Mining Complex shows that throughput can be predicted with an error less than 30 t/h and a correlation coefficient of up to 0.8.By integrating the prediction model and new stochastic components into optimization,the production schedule achieves weekly planned production reliably because scheduled materials match with the predicted performance of the mill.Comparisons to optimization using conventional mill tonnage constraints reveal that expected production shortfalls of up to 7%per period can be mitigated this way.

On the impact of grinding conditions in the flotation of semi-soluble salt-type mineral-containing ores driven by surface or particle geometry effects?

Grinding and flotation processes are often studied independently, despite the well-established grinding influence on flotation performance, which affects not only particle size and thus liberation but also shape and leads to complex changes in pulp chemistry affecting the particle surface properties relevant for selective bubble attachment. Yet, no study jointly investigated these possible causes and many are limited to single mineral flotation. We relate grinding conditions to changes in pulp chemistry and particle surface properties and assess their impact on upgrading. We studied three non-sulfide ores with different feed grades and valuables: scheelite, apatite, and fluorite. These were dry-, wet-, and wet conditionedground before flotation in a laboratory mechanical cell. Results were evaluated with bulk-and particle-specific methodologies. The selectivity of the process is higher after dry grinding for the fluorite and apatite ores and irrelevant for the scheelite ore. Variations in flotation kinetics of individual particles associated to their size and shape are not sufficient to explain these results. The higher concentration of Ca2+and Mg2+observed in the pulp after wet grinding, altering particle surface properties, better explains the phenomenon. Additionally, we demonstrate how particle shape impacts are system specific and related to both entrainment and true flotation.

Recent Technological Innovation for the New Generation of CRIST Sensors—A Practical Approach in China’s Largest Underground Nonferrous Mine

Located in Shangri-La county, Yunnan Province, China’s biggest underground nonferrous mine Pulang Copper Mine is under construction. To date, the defined copper reserves at the Pulang Copper Mine are 4.8 million tonnes of copper and an average grade of 0.34%. The mineralized zone is 2300 m long, 600 - 800 m wide, and 1000 m high in a dome shape. The first-stage mining and processing capacity is 12.5 million tonnes of ore per year. By geotechnical investigation, ore haulage is adopted via a drift and ore pass development system. From mineralogical analysis, a majority of the Pulang copper ore body is classified as a type III rock, which is generally considered to be suitable for block-caving methods. As an update to the traditional mine-to-mill approach, a cave-to-mill integrated production concept is then introduced. This is essentially the integration of underground mine production scheduling and monitoring with surface mineral processing management based on fragment size and geometallurgical ore characteristics. Several unique challenges experienced during the project design and construction, as well as a number of features aimed at mitigating these problems, are also discussed in this paper.

Geochemical and Behavioral Modeling of Phosphorus and Sulfur as Deleterious Elements of Iron Ore to Be Used in Geometallurgical Studies, Sheytoor Iron Ore, Iran

Sheytoor Iron Ore deposit is located in Yazd province of Iran (Bafq). The most abundant ore is magnetite, which can be seen in the form of mass and granular tissue in various forms of self-shaped, semi-self-shaped and amorphous. The main purpose of this study is to identify the geochemical relationship of phosphorus and sulfur elements and also three-dimensional modeling of mineralization of these elements in iron ore. In order to achieve the research goal, methods such as k-mean clustering technique, concentration-volume fractal as well as block modeling with kriging estimator and Inverse Distance Weighting (IDW) interpolator were used. The model of geochemical behavior of phosphorus and sulfur elements compared to iron is of great importance because these two elements are known as deleterious elements in mineral processing and steelmaking processes, which are the post-mining stages. Existence of geochemical model and identification of elements’ behavior towards each other play a key role in optimizing mining operations in order to achieve geometallurgical goals. The results of this study are the three-dimensional model of mineralization of iron, phosphorus and sulfur elements, separation of phosphorus and sulfur mineralization communities and also presenting the model of enrichment community of these two elements. All the results are in line with geometallurgical studies and can optimize the next steps by optimizing the mining process.