Geometallurgical Model Research Articles

Development of a geometallurgical model for a copper concentrator

Development of a geometallurgical model for a copper concentrator

Testing of Ore Comminution Behavior in the Geometallurgical Context—A Review

Comminution tests are an important element in the proper design of ore beneficiation plants. In the past, test work has been conducted for particular representative reference samples. Within geometallurgy the entire ore body is explored in order to further identify the variation within the resource and to establish spatial geometallurgical domains that show the differential response to mineral processing. Setting up a geometallurgical program for an ore deposit requires extensive test work. Methods for testing the comminution behavior must therefore be more efficient in terms of time and cost but also with respect to sample requirements. The integration of the test method into the geometallurgical modeling framework is also important. This paper provides an overview of standard comminution test methods used for the investigation of ore comminution behavior and evaluates their applicability and potential in the geometallurgical context.

Distribution of Sb minerals in the Cu and Zn flotation of Rockliden massive sulphide ore in north-central Sweden

The Rockliden massive sulphide Zn–Cu deposit contains minor amounts of Sb minerals. The Sb mineralogy is complex in terms of composition, micro textures and mineral associations. The main Sb minerals comprise tetrahedrite, bournonite, gudmundite and Sb–Pb sulphides such as meneghinite. The presence of these minerals is especially critical to the quality of the Cu–Pb concentrate. To study how they are distributed in a simplified flotation circuit and what controls their process behaviour Sb-rich drill core samples were selected from the Rockliden deposit and a standard laboratory flotation test was run on the composite samples. Scanning electron microscope-based automated mineralogy was used to measure the Sb mineralogy of the test products, and the particle tracking technique was applied to mass balance the different liberation classes to finally trace the distribution of liberated and locked Sb minerals. The mineralogical factors controlling the distribution of Sb minerals are mineral grain size, the degree of liberation, and associated minerals. Similarities in the distribution of specific particle types from the tested composites point towards systematics in the behaviour of particles and predictability of their distribution which is suggested to be used in a geometallurgical model of the deposit.

Comminution test method using small drill core samples

Comminution tests aim to measure the comminution properties of ore samples to be used in designing and sizing the grinding circuit and to study the variation within an ore body. In the geometallurgy context this information is essential for creating a proper resource model for production planning and management and process control of the resource’s exploitation before and during production.Standard grindability tests require at least 10kg of ore sample, which is quite a lot at early project stages. This paper deals with the development of a method for mapping the variability of comminution properties with very small sample amounts. The method uses a lab-scale jaw crusher, standard laboratory sieves and a small laboratory tumbling mill equipped with a gross energy measurement device. The method was evaluated against rock mechanics tests and standard Bond grindability test. Within this approach textural information from drill cores is used as a sample classification criterion.Experimental results show that a sample of approximate 220g already provides relevant information about the grindability behavior of iron ores at 19% mill fillings and 91% fraction of the critical mill speed. The gross energy measured is then used to calculate an equivalent grinding energy. This equivalent energy is further used for predicting the variations in throughput for a given deposit and process.Liberation properties of the ore connected to grindability elaborates energy required for grinding and significances of it when deciding to move to higher grinding energy considering the improvement of liberation of the desired mineral. However, high energy significantly enhanced the degree of liberation of magnetite and is expected to improve the concentrate grade after downstream treatment. The higher the magnetite content the better is the liberability of magnetite and the lower the energy required to liberate the desired mineral. Liberability of magnetite is also affected by texture classes containing low magnetite content. A methodology that combines this information has been developed as a practical framework of geometallurgical modeling and simulation in order to manage technical and economic exploitation of resource at early, project stages and during mining operations.

Simulation of geometallurgical variables through stepwise conditional transformation in Sungun copper deposit, Iran

Nowadays, geometallurgical modeling, as a rapidly growing discipline in mining engineering, has significant role in mine designing/planning especially for porphyry copper projects. In porphyry copper deposits, the copper ore often is composed of two main parts including oxide and sulfide ores. The oxide rock type consists a fraction of total copper which would be recovered by heap leaching. Flotation plant is used when the fraction of sulfide ore is high enough. The mineralogical inequality constraint linking both grade variables (total copper and oxide copper) is a difficulty which often complicates the joint modeling and simulation of variables. To simplify such complicated joint simulation, the stepwise conditional transformation technique is presented which transforms multiple variables to the univariate and multivariate Gaussian with no cross correlation. This makes it easy to simulate multiple variables with arbitrarily complex relationships. This study addresses the application of stepwise conditional transformation and sequential Gaussian simulation for reproduction of the total and oxide copper in different rock types of the Sungun porphyry copper deposit honoring the relationship between Cu-oxide and Cu-total of the initial data. Validation results show that the simulation model has a good agreement with the sample data and geological facts of deposit.

A process mineralogy approach to geometallurgical model refinement for the Namakwa Sands heavy minerals operations, west coast of South Africa

The original geometallurgical model for the Namakwa Sands deposit was modified to accommodate ore blends in addition to the various single ore types. A process mineralogy approach was followed in a structured and systematic manner to evaluate the integrity of the adjusted model, particularly for ilmenite and zircon, the minerals of highest intrinsic value. This study reproduced recovery relationships predicted by the geometallurgical model for each of the key process functions, and as a result the integrity of the geometallurgical model is validated. Overall, the recovery potential determined for ilmenite and zircon are well adjusted to model estimates. Poor mineral liberation, an anomalously high abundance of garnet and pyroxene and variation in particle chemistry are recognized as the key recovery penalties. The gangue content is the most significant constraint to ilmenite recovery, whereas zircon chemistry is the most important negative factor in the production of a premium quality zircon product. Results of this study contributed to the refinement of the current geometallurgical model and also identified opportunities to optimise mineral resource utilisation in the future.

Development of recovery domains: Examples from the Prominent Hill IOCG deposit, Australia

New methods and concepts for recovery domaining with outputs that are suitable for geometallurgical modelling are being investigated. In the work reported here, mineralogical and textural information obtained for drill core samples by a combination of techniques has been investigated to develop models for predicting recovery. The simplest and cheapest method involves assay data only and gives predicted results with ∼±4% RMS error. Since this model involves only assay data it could be used to predict Cu recovery values for the remainder of the deposit. The predicted Cu recovery values can be used to rank samples and divide the deposit into recovery domains that are suitable for integration into the planning process for mining, mineral processing and scheduling.

Geometallurgical Modeling at Olympic Dam Mine, South Australia

Modeling of geometallurgical variables is becoming increasingly important for improved management of mineral resources. Mineral processing circuits are complex and depend on the interaction of a large number of properties of the ore feed. At the Olympic Dam mine in South Australia, plant performance variables of interest include the recovery of Cu and U3O8, acid consumption, net recovery, drop weight index, and bond mill work index. There are an insufficient number of pilot plant trials (841) to consider direct three-dimensional spatial modeling for the entire deposit. The more extensively sampled head grades, mineral associations, grain sizes, and mineralogy variables are modeled and used to predict plant performance. A two-stage linear regression model of the available data is developed and provides a predictive model with correlations to the plant performance variables ranging from 0.65–0.90. There are a total of 204 variables that have sufficient sampling to be considered in this regression model. After developing the relationships between the 204 input variables and the six performance variables, the input variables are simulated with sequential Gaussian simulation and used to generate models of recovery of Cu and U3O8, acid consumption, net recovery, drop weight index, and bond mill work index. These final models are suitable for mine and plant optimization.

Detailed characterisation of antimony mineralogy in a geometallurgical context at the Rockliden ore deposit, North-Central Sweden

The antimony (Sb) content of the Rockliden complex Zn–Cu massive sulphide ore lowers the quality of the Cu–Pb concentrate. The purpose of this study is to characterise the Sb mineralogy of the deposit. The Sb-bearing minerals include tetrahedrite (Cu,Fe,Ag,Zn)12Sb4S13, bournonite PbCuSbS3, gudmundite FeSbS and other sulphosalts. On a microscopic scale these minerals are complexly intergrown with base-metal sulphides in the ore. Based on these observations mineralogical controls on the distribution of Sb-bearing minerals in a standard flotation test are illustrated. Deposit-scale and rock-related variation in the Sb-content and distribution of Sb-bearing minerals were found. This underlines the importance in understanding the geological background as a basis of a 3D geometallurgical model for Rockliden. Such a model is expected to predict the Sb content of the Cu–Pb concentrate, among other process-relevant factors, and helps to forecast when the Cu–Pb concentrate has to be treated by alternative processes, such as alkaline sulphide leaching, before it is sold to the smelter.

A novel approach to the geometallurgical modelling of the Collahuasi grinding circuit

Compañía Minera Doña Inés de Collahuasi SCM initiated the development of a new geometallurgical model to characterize its Rosario deposit in terms of its comminution circuit capacity and flotation performance. The comminution component of the model is now complete and is described in detail in this article. The model uses a combination of simulation and power-based approaches to relate ore hardness and flotation feed size to grinding circuit throughput. Ore hardness values are provided by Collahuasi’s block model which has been populated with ore characterisation data derived from Bond ball work index tests, JK drop-weight tests and SMC Tests ® on diamond drill cores. The influences of planned and unplanned maintenance downtime are taken into account both in terms of frequency and duration. The accuracy of the model is demonstrated using weekly production data taken from the period January–December 2008, showing an average relative error of 5.2% and an R 2 value of 0.95. The model is now in successful routine use at the mine for planning purposes and is currently in the process of being further developed to incorporate the response of the flotation circuit.