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By Tianzu Yang, Weifeng Liu, Lin Chen, BOYI XIE, DUCHAO ZHANG, HUI XIAO

In this study, the oxygen-rich bath smelting process of copper dross was simulated using Factsage software. It was found that the two-step simulation results agreed better with the industrial data, especially in the speiss. Bismuth (Bi), lead (Pb), antimony (Sb), arsenic (As) and copper (Cu) contents in the speiss by two-step simulation differed from the industrial data by 0.6, 0.8, 2.6, 7.2 and 8.6 weight percent, respectively. The two-step simulation results indicate that the speiss partitioning of Sb, As and Cu increased from 60.1, 32.3 and 27.5 percent to 81.6, 67.8 and 47.2 percent, respectively, with the smelting temperature increasing from 1,050 to 1,250 °C, while the metal partitioning of Sb and Cu increased from 0 and 2.7 percent to 15.2 and 13.3 percent, respectively, with the separation temperature increasing from 400 to 700 °C.

The grinding of ore to a size that is conducive to the extraction of minerals by leaching or flotation is an essential and expensive component of most mineral-processing operations. Overgrinding results in reduced flotability of the minerals, as well as problems in the filtration and thickening processes, while undergrinding leads to a product in which the minerals have not been exposed, and cannot be leached or floated effectively. Furthermore, coarse material causes settling in thickeners and agitator tanks. It is therefore essential, from the perspective of both operational and extraction efficiency, to grind the ore to an optimum size fraction. A well-run milling circuit will yield a product within a narrow size band and at a high throughput. Although the effective operation and control of milling circuits with their many operating parameters is widely acknowledged as a complex as well as a critically important operation, the benefits of such control are difficult to quantify. This is due to the difficulties in relating more stable operation to increased recovery of the valuable components in downstream processes.

Jan 1, 1993

By Newell RB

Grinding circuits are used in most mineral processing plants. Their widespread application makes the efficient running of the circuits essential to almost all resource projects. This paper will show that, with the application of modern multivariable control techniques, improved plant performance can be obtained when compared with conventional single loop control strategies. Results are presented for a simulated closed-circuit ball mill.The grinding circuit was controlled using the rod-mill water and primary and secondary sump dilution flowrates as the manipulated variables. Variables controlled were the primary cyclone feed flowrate, the primary cyclone feed density and the particle-size measurement (percentage smaller than 75 um).

Jan 1, 1985

By D. G. Hulbert, D. Tudors, M. L. Coetzee, I. K. Craig

The paper describes how a multivariable control scheme was developed and implemented for the run-of-mine milling circuit at a gold mine belonging to Vaal Reefs Exploration & Mining Company Limited. The key variables that are controlled are slurry level in the sump, mass of material in the mil, and product size. The control scheme has resulted in reliable control of the dynamics of the plant, and has provided a good basis for the optimization of plant operation by the selection of suitable set Points.

Jan 1, 1990

By F. Hassani, J. Peck, D. J. Lucifora

The performance of a rotary blasthole drill is heavily dependent on the material being drilled. This material is a microcosm of the in-situ geology that makes up the surface mine in which the rotary blasthole drill is deployed. The performance metrics that are influenced by the in-situ geology are penetration rate (distance drilled per unit time), rotary torque and to a lesser extent rotary speed and vibration. For simulation and testing purposes it is useful to have a validated plant model based upon the application of study. Once validated, a plant model can be used to perform simulation based testing as a supplement and possibly in place of more expensive and logistically complex field testing. Previous studies in the field of rock mechanics have supplied proven models that relate the performance of a rotary blasthole drill to the material being drilled for a given rock type with a given drill bit. In this study, field based experiments were conducted in an active surface mine on a working rotary blasthole drill and the corresponding monitoring while drilling (MWD) data was recorded. Data analysis was then performed on the collected field data. The techniques used successfully fit the collected MWD data to the previously proven rock mechanic models. This paper explores the theory relating rotary blasthole drill performance to the material being drilled, illustrates the required data collection and data analysis techniques, and demonstrates the ability of these techniques and the known rock mechanic models to provide a simulated plant model for the drilling process that can then be validated. This validation is also discussed.

Jan 1, 2015

By Natalie Caciagli

Catchment basin sampling allows for rapid assessment of large areas. Stream sediment surveys are a common tool used in regional exploration. The difficulty in detecting geochemical anomalies in stream sediment data from large regional datasets lies in finding the background values of pathfinder elements due to variable lithologies that could be sampled within a single basin. An alternative data driven method to discover anomalies in complex environments, without a priori knowledge of background concentrations, is presented here

May 10, 2023

The Rio Tinto Iron Ore group currently estimates grade attributes for mineral resources using ordinary kriging (OK). OK produces a smoothed globally unbiased estimate, but does not capture local variability, which can have significant impact on mine planning and scheduling. The dimensions and the multivariate nature of iron ore deposits make point simulations of whole deposits impractical. With a direct block simulation method the size and speed limitations of point simulations can potentially be overcome, resulting in a viable method for capturing local variability and for providing risk measures into the mine plan and schedules. Some iron ore deposits are highly folded (post mineralisation) and require preprocessing using an unfolding algorithm to properly define the continuity parameters and directions of anisotropy before simulation can proceed. Block co-simulation proceeds in unfolded space and since mine planning require a block-model with regular block-sizes; a regular block-model is unfolded first. This paper discusses a block co-simulation approach that can be used to simulate the irregular blocks resulting from the unfolding process. A section of an iron ore deposit is used for testing the block co-simulation algorithm and the simulation results are compared against the Isatis direct block simulation method. Available blasthole data is also used to validate the behaviour of the block co-simulation algorithm.

Sep 26, 2011

By J. Daraisme

Société le Nickel (SLN) exploits the Dôme lateritic nickel orebody at its Tiébaghi operations in New Caledonia. The site geology has an obvious bearing on recovery and metallurgical performance, and the controls are extremely complex at all scales: lithology, oxidation-reduction conditions, mineralogy, and multivariate geochemistry. In that context, establishing an adapted recoverable resource estimation method that is efficient and transparent proves an interesting challenge. The method must address the key notions of: ? Support effect: the exploration data-set can only warrant the estimation of large panels 20×20×3 m3, which are much larger than the selective mining units (SMUs) of 5×5×3 m3 ? Information effect: the selection at production stage uses estimates based on information much denser than that available for mine planning. The problem is rendered more complex in this type of deposit by the fact that the additive variables used for any estimation process (metal accumulation and ore tonnage) are not the ones used to establish the selection at mining stage (i.e. the nickel grade of SMUs, which is the ratio of accumulation on tonnage). Eventually, the selection criteria involve not only Ni grades but also other constituents such as Al2O3, Fe2O3, MgO, and SiO2. The solution presented in this paper is the construction of a platform of SMU multivariate simulations in the saprolitic horizon, together with efficient post-processing aimed at producing the multivariate recoverable resource estimates. The efficiency constraints imposed by the number of blocks to be simulated (1.5 million) lead to resorting to direct block simulations. The underlying multivariate discrete Gaussian model (DGM), the validity of which is tested beforehand, is put to good use to mimic the selection process at the mining stage, by offering the ability to simulate in each block a composite value located at random within the block. Finally, the paper presents the application of a scenario reduction algorithm to pick a representative subset of a few simulations to help appraise the risk attached to the downstream phases (reserve optimization, mine sequencing) of the project. The implementation presented here is for the first generation of the Scenario Reduction plugin built in Isatis software, where measuring the distance between the initial set of scenarios and the reduced set is based on Ni quantities only. Research is under way to adapt the measure to the true multivariate nature of the problem.

Jan 1, 2014

By J Deraisme, P Epinoux, O Bertoli

"Société le Nickel exploits the Dôme lateritic nickel orebody at its Tiébaghi operations in New Caledonia. The site geology has an obvious bearing on recovery and metallurgical performance, with controls that are extremely complex at all scales, including lithology, oxydo-reduction conditions, mineralogy and multivariate geochemistry. In this context, establishing an adapted recoverable resource estimation method that is efficient and transparent proves an interesting challenge. The method must address the key notions of: support effect – the exploration data set can only warrant the estimation of large panels of 20 × 20 × 3 m3 that are much larger than the selective mining units (SMUs) of 5 × 5 × 3 m3 information effect – the selection at the production stage uses estimates based on information that is much denser than that available for mine planning.The problem is rendered more complex in this type of deposit by the fact that the additive variables used for the estimation (metal accumulation and ore tonnage) are not the ones used to establish the selection at the mining stage (ie the nickel grade of SMUs, which is the ratio of accumulation on tonnage). Eventually, the selection criteria not only involve Ni grades, but also other elements such as Al2O3, Fe2O3, MgO and SiO2.The solution presented in this paper is to construct an SMU multivariate simulation in the saprolitic horizon and construct an efficient post-processing aimed at producing multivariate recoverable resource estimates. The efficiency constraints imposed by the number of blocks to be simulated (1.5 million) lead to resorting to direct block simulations. The underlying multivariate discrete Gaussian model, which had its validity tested beforehand, is put to good use to mimic the selection process that happens at the mining stage by offering the ability to simulate a composite value located at random within each block.The paper also presents the application of a scenario reduction algorithm to pick a representative subset of a few simulations to help appraise the risk attached to the downstream (reserve optimisation and mine sequencing) phases of the project. The original version of the paper (see Deraisme, Bertoli and Epinoux, 2014) used an early version of the scenario reduction plug-in (named S2RM) built in Isatis® software, where measuring the distance between the initial set of scenarios and the reduced set was based on Ni only. The implementation presented here is for the second generation of S2RM, where measuring the distance between the initial and the reduced set of realisations is based on all study variables. The benefits of the measure adapted to the true multivariate nature of the problem are highlighted in this updated version of the paper.CITATION:Bertoli, O, Deraisme, J and Epinoux, P, 2014. Multivariate block simulations of a lateritic-type nickel deposit and post-processing of a representative subset, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 99–108 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014

Mine planning studies of iron ore deposits are often based on resource models generated by ordinary kriging. Ordinary kriging aims for local accuracy to minimise the variance of expected estimation error but this is at the expense of reproducing variability of the original sampling data. Hence mine planning studies using kriged models are likely to underestimate product variability, and the required blending and stockpiling capacity to reduce variability to an acceptable level. Instead, conditional simulation models of the main chemical variables in iron ore are able to reproduce likely variability to be encountered during mining and processing. This approach is demonstrated in a case study of the Weld Range bedded iron ore deposit in Western Australia, with a comparison of ordinary kriging and conditional simulation models. Conditional simulation models suggest that, compared with the single ordinary kriging estimation, there is likely to be far greater block variability for iron and impurities, a greater requirement for stockpiling and blending capacity, highest variability in initial years of mining and less variability in later years of mining for deeper ore. The conditional simulations provide a more realistic range of mining outcomes for production of high-grade ore, which may be seven per cent less than in the kriged model, as well as for lower expected project net present value (NPV). In year one, the kriged model is conservative for NPV; however, in later years two to five the kriged model has higher NPV than most conditional simulations, suggesting expected NPV based on the kriged model may not be achieved. For improved decision-making in mine planning, a complete range of possible outcomes from conditional simulations of the deposit should be considered rather than relying on a single kriging estimation that may be an end member case in the range of possible mining outcomes.

Jan 1, 2009

By P. Babak, E. Insalaco, O. Babak, P. Henriquel

"Detection of outliers is an important primary step in geomodelling. Geological datasets often contain errors due to problems such as measurement device limitations and recording glitches. In this paper, a robust Mahalanobis distance–based approach is successfully applied to particle size distributions of the Joslyn lease, an oil sands development site near Fort McMurray, Alberta, Canada. Automatic outlier detection and geological review were combined. Outliers from the Mahalanobis distance approach were compared with their respective core photographs to determine if laboratory measurements and facies classification were correct. A checking procedure for laboratory technicians is proposed.RÉSUMÉ La détection des valeurs aberrantes constitue une étape primaire importante de la modélisation géologique. Les ensembles de données géologiques contiennent souvent des erreurs en raison de problèmes tels que les limitations des instruments de mesure et des pépins d’enregistrement. Dans le présent article, une méthode robuste basée sur la distance de Mahalanobis a été mise en oeuvre avec succès aux granulométries du bail minier Joslyn, un site de sables bitumineux en développement à proximité de Fort McMurray, Alberta, Canada. La détection automatique des valeurs aberrantes a été combinée à une étude géologique. Les valeurs aberrantes de la méthode de la distance de Manalanobis ont été comparées avec leurs photographies respectives de carottes de forage afin de déterminer si les mesures en laboratoire et la classification des faciès étaient correctes. Une procédure de vérification pour les techniciens de laboratoire est proposée."

Jan 1, 2015

By Oy Leuangthong

Red Dog mine is the world's largest Zn producer. The deposit consists of sulphide ore zones in sedimentary exhalative (sedex) deposits, and is characterized by the presence of multiple metals and multiple ore types. The objective of the case study is to characterize seven different minerals, Zn, Pb, Fe, Ba, sPb, Ag and TOC, within eight different rock types. Geostatistical models were constructed for each variable within the eight rock types, and subsequently assembled to give 40 realizations for six 25ft benches. The stepwise conditional transformation was used to account for the complex multivariate relations. In addition to reproducing the input data, histogram and variogram, the resulting models also respect multivariate relations locally and globally. This paper documents the methodology to construct these models. A thorough validation procedure was implemented. Comparisons with blasthole data show that the conditional simulations and the long-term models agree in expected value. The main advantages of the conditional simulations are (1) the explicit accounting of multivariate relations between the data in model construction, and (2) the ability to quantify variability and uncertainty at any scale. A small exercise comparing the profit from a kriged and a simulated model is also examined. Results show that the simulation approach showed a 3% increase in profit, relative to the conventional estimation approach.

May 1, 2004

By Graeme Lyall, Oy Leuangthong, Clayton V. Deutsch

There are many variables to consider in the evaluation of nickel laterite deposits including the concentrations of nickel, iron, silica, and magnesia. These variables show complex relationships with each other such as mineralogical constraints, non-linear correlation, and heteroscedasticity. The complex multivariate relationship between the variables cannot be reproduced by conventional Gaussian or indicator geostatistical techniques. We introduce the stepwise conditional transformation method as a pre- and post-processing multivariate transform for the stochastic simulation of a nickel laterite deposit. Transformed data are multiGaussian and independent at h = 0; thus, cosimulation can be avoided after verification that variables are independent at all lag distances. Back transformation restores the features of the input multivariate distribution to the simulated realizations. The complex multivariate correlations are reproduced and sensitivity issues are addressed.

Jan 1, 2002

By S. A. Hosseini, O. Asghari

"The quantification of mineral resources and evaluation of process performance in mining operations at Gole Gohar iron ore deposit requires a precise model of the spatial variability of three variables (Fe, P, and S) which must be determined. According to statistical analysis there are complex multivariate relationships between these variables such as stoichiometric constraints, nonlinearity, and heteroskedasticity. Due to the impact of these complexities in decision-making, they should be reproduced in geostatistical models. First of all, in order to maintain the compositional and stoichiometric constraints, additive log-ratio (alr) transformation has been applied. In the next step cosimulation, using stepwise conditional transformation (SCT) and sequential Gaussian simulation (SGS) has been used to simulate multivariate data. Through statistical and geostatistical validations it is shown that the algorithms were able to reproduce complex relationships between variables, both locally and globally. IntroductionThe key steps in mining projects are the quantification of mineral resources, definition of mining reserves, and production scheduling. They rely on the construction of a block model that is used to represent basically the spatial distribution of ore grades (Montoya et al., 2012). The determination of grades and tonnages affects risk assessment and economic evaluation of mining projects. Evaluation of process performance in mining operations requires geostatistical modelling of many related variables (Barnett and Deutsch, 2012). Iron ore quality is characterized by multiple variables: not only the iron grade but also the contaminants that interfere in the subsequent steel manufacturing processes. Consequently, the spatial variability of multiple variables must be determined. Key variables are frequently correlated, and such correlations must be honoured during estimation and simulation. Data from iron ore deposits constitutes compositional data; furthermore, relationships the between assay data are often heteroskedastic."

Jan 1, 2016

By J Ferreira

To speed up multivariate geostatistical simulation it is common to transform the set of attributes into spatially uncorrelated factors that can be simulated independently. One decorrelation method is the method of minimum/maximum autocorrelation factors (MAF), where a two structure linear model of coregionalisation is assumed. The transformation decorrelates the theoretical model exactly, but the actual data are only approximately decorrelated. The MAF transformation can therefore be seen as a special case of a non-orthogonal approximate diagonaliser of a set of symmetric matrices. A more general approach for approximate joint diagonalisation (AJD) has been developed in the context of blind source separation. For these AJD algorithms no assumptions are made beyond symmetry of the individual matrices and so they can be applied to a family of experimental semivariogram matrices. In their application there are no restrictions on the number of structures in the linear model of coregionalisation (LMC) thus removing one of the conditions placed on the subsequent modelling of the spatial structure of the factors. In this paper the general background for the U-WEDGE AJD method is presented. It is shown that the U-WEDGE method achieves better spatial decorrelation than the MAF method. The factors derived from U-WEDGE are simulated using conditional turning bands simulation and then backtransformed to attribute space.

Sep 26, 2011

By David Verrelli, Melissa Kistner, Leanne Smith, Chris Aldrich, Lidia Auret, Warren Bruckard

In this investigation, different mixtures of realgar and orpiment particles were floated in a laboratory batch flotation cell and multivariate image analysis was used to estimate the arsenic content in the froths. The analysis was based on the froth colour, as well as extraction of three groups of textural features, namely those based on grey level co-occurrence matrices (GLCMs), wavelets and local binary patterns (LBPs). Collectively, these features provided better information on the arsenic content of the froths than any one of the individual groups of features. Partial least squares models could explain approximately 78% of the variance in the arsenic by using all the features combined. The colour content, particularly the green component in the red-green-blue (RGB) features, provided almost as much information as each of the three sets of texture-base features individually.

Jan 1, 2014

By H. Karakul

Determination of P-wave velocity (Vp), which is closely related to intact rock, is a nondestructive, easy and less complicated procedure. Due to these advantages, there is an increasing trend to predict the geomechanical properties of rocks from Vp. In addition to mineral content, cementing material and porosity, degree of saturation (Sr) are also other factors influencing geomechanical properties of the intact rock. However, up to the present, in the studies on the prediction of geomechanical properties of rocks from Vp, the effect of Sr has not been investigated. By considering this issue, in this study, it was aimed to correlate strength properties (uniaxial compressive and tensile strengths) and Young’s modulus with Vp of various rock types under different degrees of saturation. In addition, geomechanical properties of rocks were estimated from theoretical P- and S-wave (Vs) velocities, which were calculated based on their mineralogical compositions. For this purpose, fourteen different rock types were collected from several parts of Turkey and a comprehensive laboratory testing program was conducted. In the statistical analyzes, a series of different prediction relationships were developed by using independent variables such as Vp, Sr and effective clay content (ECC). Simple regression analysis results indicated that although prediction of the geomechanical properties of rocks from Vp at different degrees of saturation was possible, the equations developed would yield some under- or over-predictions. On the other hand, the multivariate equations developed have very high prediction performances and are very useful tools to estimate the geomechanical properties from the measured Vp at any Sr value. In addition to mineralogy, the variation in geomechanical properties with increasing Sr is also associated with the combined effect of porosity and clay content, which is first described in this study by the term Effective Clay Content (ECC). It is also noted that strength properties of rocks at any Sr may also be estimated using the empirical relations based on the theoretical Vp and shear-wave velocity (Vs) values, which are calculated from their mineralogical compositions.

Jan 1, 2015

By Bergh. L. C.

On line monitoring and diagnosis systems of process operating performance are becoming an important part of industrial programs, leading to improve process operation and therefore product quality over time. In processes, such as the contact section of a sulphuric acid plant, a large number of input variables, highly correlated, are presented. These characteristics usually pose more difficulties in modelling the process for monitoring and diagnosis purposes. Multivariate statistical projection methods, such as Main Component Analysis, have been proposed to effectively deal with these situations. The application of these ideas to a section of a 2350 MTD sulphuric acid plant, located at the Caletones Smelter, including dry tower, gas blowers, catalytic reactor and S03 absorber processes, is discussed here. A PCA model with eight main components is used for monitoring and diagnosis of more than seventy operating variables. Hottelling's T2 and Q residuals test were used for detecting measurement problems in relevant variables, and for identifying which set of variables were responsible for driving the process outside its normal operating region. Furthermore, considerable information for the correction of the tested operating problems was given by the use of these techniques.

Jan 1, 2007

By C. Montoya

This paper shows, through a case study, the impact of multivariate grade modelling upon mine design and mine planning. A deposit explored by drill holes is considered, in which the grades of five elements (copper, silver, molybdenum, arsenic, and antimony) are of interest. Forty alternative models of the deposit are constructed by fitting the joint correlation structure of the grade variables and using conditional cosimulation. In addition, a reference model, obtained by averaging the alternative models, is also considered. The study shows that the resulting mine design (final pit characteristics and production schedules) is sensitive to the grade model under consideration, and that the design based on the reference model may not be optimal when compared to the alternative models based on cosimulation. However, when assuming a given long-term plan and extraction sequence, the grades and net present value (NPV) calculated on the reference model are unbiased with respect to those calculated on the alternative models with the same extraction sequence. The latter allow assessing the possible dispersion of the actual grades and NPV around their expected values, and are useful for the planner in order to determine the probability of meeting given production targets and of exceeding or falling short of given threshold grades. Additionally, unlike cosimulation, the separate simulation of each grade variable leads to unrealistic resource models and to biased results in mine design and mine planning. This approach should therefore be avoided, unless the grade variables are spatially uncorrelated.

Jan 1, 2012

By C. Montoya

This paper shows, through a case study, the impact of multivariate grade modelling upon mine design and mine planning. A deposit explored by drill holes is considered, in which the grades of five elements (copper, silver, molybdenum, arsenic, and antimony) are of interest. Forty alternative models of the deposit are constructed by fitting the joint correlation structure of the grade variables and using conditional cosimulation. In addition, a reference model, obtained by averaging the alternative models, is also considered. The study shows that the resulting mine design (final pit characteristics and production schedules) is sensitive to the grade model under consideration, and that the design based on the reference model may not be optimal when compared to the alternative models based on cosimulation. However, when assuming a given long-term plan and extraction sequence, the grades and net present value (NPV) calculated on the reference model are unbiased with respect to those calculated on the alternative models with the same extraction sequence. The latter allow assessing the possible dispersion of the actual grades and NPV around their expected values, and are useful for the planner in order to determine the probability of meeting given production targets and of exceeding or falling short of given threshold grades. Additionally, unlike cosimulation, the separate simulation of each grade variable leads to unrealistic resource models and to biased results in mine design and mine planning. This approach should therefore be avoided, unless the grade variables are spatially uncorrelated.

Jan 1, 2012