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By A Paz, H Lehto, M +stholm, I Roitto

Nowadays, the demand for a finer grind in minerals processing has set new challenges within the industry, particularly for grinding technology. Apart from a finer grind, energy efficiency is also a key driver in optimising comminution technology.Stirred milling technology, already proven in many other industries, delivers a modern, sophisticated, flexible and energy efficient grinding solution for fine and ultra fine grinding in minerals processing. Fine grinding technology is utilised particularly in concentrate regrinding to complete the liberation of valuable minerals after the first stage of concentration. Maintaining the correct particle and particle size distribution of the regrind product is crucial for downstream process performance. Stirred milling technology offers the unique possibility for online control and optimisation of the regrind product particle size. This paper describes the basis of this online control possibility and discusses the implementation of stirred milling technology in minerals processing, particularly in regrinding applications. The paper will also outline management and control of fluctuations in feed rate tonnages as well as particle size fluctuations in concentrate regrinding.CITATION:Roitto, I, Lehto, H, Paz, A and +stholm, M, 2013. Stirred milling technology - A new concept in fine grinding, in Proceedings MetPlant 2013 , pp 190-201 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 15, 2013

By C. M. Rule

Stirred milling using either horizontal or vertical mills has made a rapid entrance into PGM ore and tailings concentrator flow sheets. Currently there are in excess of 40 units installed in either mainstream, ?MIG? or intermediate concentrate regrind, ?UFG?, applications in PGM, primary ore treatment plants or in tailings scavenging and reclamation treatment plants. This represents a total of approximately 70 MW of installed fine grinding equipment. This paper outlines the reasons why the technology take-up has been rapid and illustrates the advantages of this technology over conventional milling. Keywords PGM, platinum group metals, UG2, Merensky, platreef, stirred milling, MIG?mainstream inert grinding, UFG?ultra fine grindingof intermediate flotation concentrates, IsaMill?, Metso SMD?, Deswik, ceremic grinding media, QEMSEM, MLA.

Jan 1, 2011

By Y. Thibault, L. Morin, R. Cameron, D. Gould

"There are an estimated 50 to 100 million dry metric tonnes of low-grade nickeliferous pyrrhotite tailings in the Sudbury area alone, with an average nickel grade ~0.8% (plus cobalt). Pyrrhotite is a highly reactive sulphide mineral that has the potential to generate acidity; consequently, it is stored in large tailings ponds that require perpetual treatment to avoid the production of acidic drainage. Re-processing this material using bioleaching technologies has the potential to recover valuable metals, while reducing the toxicity, reactivity, and acid generating potential of the remaining residues. CanmetMINING is currently developing a stirred-tank bioleaching process to recovery nickel and cobalt from pyrrhotite-rich tailings. Stirred-tank experiments have been conducted with one tailings sample over a range of pH and temperature conditions, resulting in 80 to 90% and 70 to 80% leaching of nickel and cobalt respectively in 10 days.INTRODUCTION Sulphide tailings may represent a significant financial liability for mining companies as these wastes may produce acidic drainage under some disposal conditions. As a result, the companies must post financial bonds large enough to ensure there are sufficient funds for perpetual management of these wastes. At the same time, there are billions of dollars in metals incorporated into these mining wastes waiting to become valorized. Many sulphide tailings are amendable to bacterial oxidation for metal extraction. Re-processing of tailings using bacterial leaching has the potential to recover valuable metals from waste; and reduce the toxicity, the reactivity, and the acid generating potential of the remaining residue. By reprocessing these wastes there is an opportunity to significantly reduce liabilities and financial assurance bonds, while recovering metal values. There are an estimated 50 to 100 million dry metric tonnes of low-grade nickeliferous pyrrhotite-rich tailings in the Sudbury area alone with an average nickel grade ~0.8% (Peek et al., 2011) plus cobalt and copper. Pyrrhotite is separated from pentlandite concentrates by magnetic separation and flotation. It is already finely ground and has often been segregated from other tailings material, making „mining? relatively inexpensive. This represents roughly (> $100/ton) in contained value, with negligible mining costs. This paper reports on the development of a stirred-tank bioleaching process for recovering nickel and cobalt from pyrrhoitite-rich tailings, which is currently underway at Canmet MINING."

Jan 1, 2017

By M. S. Shishvan, J. R. van Duijvenbode

The results of dig limit delineation in open pit mining are never truly optimized due to gaps in the underlying data, such as insufficient sampling. Aside from the data uncertainty, there is also an influence on the final dig limit by either humans or by the heuristic character of an optimization method like simulated annealing. Several dig limit optimizers have been published, which can replace the manual dig-limits designing process. However, these dig limit designs are generally not adapted to account for this heuristic character. In this paper we present a stochastic analysis tool that can be used with the results of heuristic dig-limit optimization to increase confidence in the obtained results. First, an enhanced simulated annealing algorithm for dig limit optimization is presented. Then, this algorithm is tested on ten different blasts at the Marigold mine, Nevada, USA, as a case study. Finally, the results are analysed with a destination-based ensemble probability map and an analysis conducted of the final solution data distribution. The generated dig-limit designs of the algorithm include high revenue areas that are excluded in comparable manual designs and show improved objective and revenue values. The analysis tool provides block destination probabilities and box plots with the distribution of opportunity value for the dig limit. Furthermore, with the analysis tool, it is possible to make well-informed design decisions in areas of uncertainty.

Dec 13, 2022

By Francois Charette

Corrosion is a significant factor affecting the capacity and working life of ground support. The paper explores a relatively simple method to predict the load capacity of a support system using a Monte Carlo stochastic approach. The analysis uses published field data on existing ground support to evaluate the applicability of the method and its predictive performance. Discussion on corrosion rates found in the field and predicted from laboratory measurement is provided.

Jun 1, 2012

By K. Kim

A site characterization strategy formulated for the probabilistic design of underground structures is presented, which takes into account the variability of rock properties and the stochastic nature of the rock failure process. It entails determining probability distributions of rock properties by utilizing the theory of extremes and Monte Carlo simulation. Laboratory and field test data are analyzed and presented to illustrate the methodology. The results indicate that the distributive characteristics of rock mass properties can be estimated using empirical relationships currently available. This results of analyses can be incorporated into the design of underground structures, and then, the relative uncertainties of the failure probability of the structures can be assessed.

Jan 1, 1995

By K. Kim, H. Gao

A site-characterization strategy formulated for the probabilistic assessment of rock mass properties is presented. The strategy, which takes into account the variability of rock properties and the stochastic nature of the rock-failure process, entails determining probability distributions of rock properties by utilizing extreme value statistics and Monte Carlo simulation. Laboratory and, field test data are analyzed and presented to illustrate the methodology. The results indicate that the distributive characteristics of rock mass can be estimated by using this proposed method. The results of the analysis can be incorporated into the design of underground structures. Then, the relative uncertainties of the failure probability of the structures can be assessed.

Jan 1, 1997

By T. M. Yegulalp

This article summarizes some of the results of the continuing research activity at the Henry Krumb Schools of Mines, Columbia University, directed towards a better understanding of the stochastic behavior of slurry flows in underground coal-slurry pipeline systems. Fundamental ideas supported by field observations are presented and a Semi-Markov model of the slurry flows is developed. As a result of this formulation, behavior of combined slurry flows from N independent coal faces can be expressed in analytical form. The contents of a slurry surge tank receiving slurry flows with certain stochastic characteristics is investigated in a function of discharge pump characteristics and time.

Jan 1, 1983

By Tuncel M. Yegulalp

This article summarizes some of the results of the continuing research activity at the Henry Krumb School of Mines, Columbia University, directed towards a better understanding of the stochastic behavior of slurry flows in underground coal-slurry pipeline systems. The slurry system under consideration here replaces all conventional haulage systems between the coal face and the coal preparation plant on the surface. At the beginning of the pipeline system, flow rate and frequency of coal slurry is very closely related to the activities of production equipment at the coal face. Due to several scheduled and unscheduled interruptions in continuous miner's coal mining activity, resulting slurry flows possess stochastic characteristics. Proper understanding of these characteristics is believed to lead to a more realissic and economic design of slurry handling equipment. Fundamental ideas supported by field observations are presented and a Semi-Markov model of the slurry flows is developed. As a result of this formulation, behavior of combined slurry flows from N independent coal faces can be expressed in analytical form. The contents of a slurry surge tank receiving slurry flows with certain stochastic characteristics is investigated as a function of discharge pump characteristics and time.

Jan 1, 1981

By Juan J. Monsalve, Mario Karfakis, AMAN SONI, Nino Ripepi, Jim Hazzard

Pillar collapses are events that due to their severe consequences can be classified as high risk. The design of pillars in underground room-and-pillar operations should migrate to risk-based design approaches. The authors of this work proposed a risk-based pillar design methodology that integrates stochastic discrete element modeling for pillar strength estimation, and stochastic finite volume modeling (FVM) for stress estimation. This paper focuses on the stochastic FVM component for stress estimation. The mining and geomechanical aspects of a case study mine (CSM) are described and pillar stresses are estimated by using three approaches: (1) analytical solutions, (2) 2D finite element modeling, and (3) 3D finite volume modeling. This operation extracts a 30° dipping deposit, which makes current underground stone mine design guidelines inapplicable for this CSM. This work compares results from each stress estimation approach and discusses uses the point estimate method as a simplified stochastic approach to evaluate the effect of rock mass elastic properties variability on pillar stress distribution. Results from this work show that the three estimation approaches lead to different estimations, possibly, due to the wide range of assumptions each estimation approach considers. It was also determined that the horizontal to vertical stress ratio has a significant impact on pillar stress magnitude. Therefore, it is recommended to perform in situ stress measurements, or assume worst-case-scenario values to account and reduce uncertainty due to this parameter. The stochastic stress estimation approach used in this paper provides results that can integrate a risk-based pillar design framework.

Sep 24, 2022

By C. Ketata, M. C. Rockwell

During the operation of a mineral processing plant, the unit processes are investigated by analyzing the data extracted by means of stream sampling. The data structure is identical to the time series form. Data analysis is a crucial step in understanding the plant performance. To control the plant performance efficiently, it is very important to avoid sampling errors, which is impossible. However, it is possible to minimize these errors. The factors influencing these errors are divided into two categories: material properties illustrating its heterogeneity, and sampler features. The purpose of this paper is to study the influence of the material composition variation on sampling errors, and to compare our evaluation method with that of Gy. Note that the stream mass flowrate is assumed to be constant during the sampling process.

Jan 1, 2001

By Roussos Dimitrakopoulos

Conventional approaches to estimating reserves and optimizing mine planning and production forecasting result in single, often biased forecasts. This is largely due to the non-linear propagation of errors in understanding orebody attributes from a limited finite number of drilling data, throughout the chain of mine planning and mining. A new mine planning paradign is considered herein to include two interacting and potentially fusing elements: stochastic simulation and stochastic optimization. These two elements provide an expanded mathematical framework that allows the modelling of orebody uncertainty and its direct integration to mine design, production planning and valuation of mining projects and operations. This stochastic framework has shown to increase the value of production schedules in the order of 25%. In addition, case studies show that stochastic optimal pit limits are about 15% larger in terms of total tonnage, compared to the conventional (deterministic) optimal pit limits. This difference extends life of mine and adds approximately 10% of net present value (NPV) to the NPV reported above from stochastic production scheduling within the conventionally optimal pit limits. Furthermore, results suggest a potential new contribution to the sustainable utilization of natural resources.

Oct 1, 2009

By Nasser Madani, SULTAN ABULKHAIR

The geological and geostatistical modeling of mineral deposits requires densely sampled drillholes that provide accurate and reliable hard data. Furthermore, quantifying orebody uncertainty through geostatistical simulation can allow mining engineers to assess long-term risk in mine planning. Nevertheless, ore deposits can sometimes lack dense drillhole information, reliable hard data with proper quality assurance/quality control (QA/QC), or sometimes both. This study is based on a coal mine located in the Republic of Kazakhstan, where an iron dataset is based on data from three newly drilled drillholes, and geological information comes from a massive collection of legacy drillhole data with no evidence of proper QA/QC. For this reason, a workflow was introduced to construct a representative training image from legacy data and stochastically model geological domains within these three drillholes using a multiple-point geostatistics technique. Once the geological model was obtained, a two-point geostatistics algorithm was applied to model the iron inside each geological domain. The direct sampling algorithm was chosen for modeling geological domains, and sequential Gaussian simulation for iron grade calculation. Both methods were extensively evaluated using different statistical tools and analyses.

Aug 1, 2022

By Nasser Madani, SULTAN ABULKHAIR

The abstract of the paper discusses the challenge of quantifying iron content in a coal deposit in Kazakhstan. The iron dataset is limited, coming from only three drill holes and some stope samples, while a large amount of geological information is available from legacy drill hole data. The paper proposes a workflow using multiple-point geostatistics to model geological domains and two-point geostatistics to model iron content within these domains. The results show that the direct sampling (DeeSse) algorithm effectively reproduces the complex seam layers, and sequential Gaussian simulation models the iron within each domain.

Mar 30, 2022

By R. W. Chappell, M. W. Hills, R. L. Olsen

A stochastic approach was developed and applied to the Butte, Montana Hillside abandoned mining site for modeling stormwater runoff and subsequent receiving stream loadings. This approach enabled capture and quantification of the uncertainty associated with stormwater quality data and allowed the prediction of copper and zinc concentrations in the receiving stream caused by runoff from the Butte Hillside during storm events. Runoff flows and stormwater concentrations were input to the model as probability distribution functions. Monte Carlo simulation allowed prediction of expected instream concentrations in the form of cumulative distributions functions. The modeling results allowed evaluation of current best management practices (BMPs), targeting of future BMPs, and identification of key data gaps to fill during future sampling activities. This modeling approach is considered extendable to other active and inactive mining sites. Output from a model of this type is easily understood and can be used to communicate effective stormwater management to site regulators. Furthermore, application of this modeling approach will facilitate the development and implementation of a site-specific stormwater management plan and will optimize efforts to achieve standards for compliance with stormwater discharge permits.

Jan 1, 2002

By R Dimitrakopoulos, R Goodfellow

One of the primary objectives when optimising a mining complex is to maximise its value for the primary stakeholders. In order to achieve this objective, it is necessary to holistically optimise all aspects of the mining complex, including decisions of when to extract materials from the available sources, how to blend or stockpile these materials and how to best use the available processing streams to satisfy customer demand. Existing methods for global, or holistic, optimisation ignore the compounded effects that risk has on the performance of a mining complex. Over the past decade, several stochastic optimisation approaches have been proposed to integrate various forms of uncertainty into the open pit mine design and production scheduling. These methods, however, are limited in their ability to simultaneously optimise the production schedules for the portfolio of mines, material destination policies, the use of the available processing streams and the various products that are produced at each location of the mining complex.This paper aims to discuss a new method for the global optimisation of open pit mining complexes with geological uncertainty. The proposed generalised methodology is capable of modelling and holistically optimising mining complexes, including aspects related to production scheduling, blending, stockpiling and non-linear interactions that often occur in practice, but are over-simplified in existing models. Two case studies are discussed to highlight the need for these complex, stochastic optimisers. First, a case study for a nickel laterite blending operation highlights the need to integrate geological uncertainty into the optimisation in order to ensure product quality constraints are respected. Second, a case study for a copper-gold mining complex highlights the added value when simultaneously optimising the production schedule and the stockpiling and treatment of extracted materials.CITATION:Goodfellow, R and Dimitrakopoulos, R, 2014. Stochastic optimisation of mineral value chains – developments and applications for the global optimisation of mining complexes with uncertainty, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 13–22 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By Liam Findlay

Optimizing a production schedule with semi-mobile in-pit crushing & conveyance (IPCC) requires integrating extraction sequence, destination policy, crusher relocation, conveyor layout, and truck fleet investment decisions. An integrated stochastic optimization framework is proposed to produce long-term schedules for mining complexes using multiple semi-mobile IPCC systems. The optimizer selects the crusher locations and designs the conveyor routes from anywhere in the mines. The stochastic framework considers simulated multi-element orebody realizations and manages associated risk. A hybrid metaheuristic based on simulated annealing and evolutionary algorithm is implemented for the optimization. The method is demonstrated using an iron ore mining complex.

Jun 25, 2023

By Roussos Dimitrakopoulos, Liam Findlay

Semimobile in-pit crushing and conveying (IPCC) systems can help reduce truck haulage in openpit mines by bringing the crusher closer to the excavation areas. Optimizing a production schedule with semimobile IPCC requires integrating extraction sequence, destination policy, crusher relocation, conveyor layout and truck fleet investment decisions. A mining complex with multiple mines and IPCC systems should be optimized simultaneously to find an optimal schedule for the entire value chain. An integrated stochastic optimization framework is proposed to produce long-term production schedules for mining complexes using multiple semimobile IPCC systems. The optimization model has flexibility to select the crusher locations and conveyor routes from anywhere inside the pits. The framework uses simulated orebody realizations to consider multielement grade uncertainty and manage associated risk. A hybrid metaheuristic solution approach based on simulated annealing and evolutionary algorithms is implemented. The method is demonstrated using an iron ore mining complex

Nov 1, 2024

By Roussos Dimitrakopoulos

Overview ?Uncertainty matters: The economic side ?Models of geological uncertainly ?Shifting the paradigm: Stochastic mine planning ?Using uncertainty to improve project performance ?Conclusions: Quantified uncertainty based models are great!

Oct 1, 2010

By S Chatterjee

The production scheduling of an open pit mine determines the optimal extraction sequencing that significantly impacts a mine’s life as well as net present value. The optimisation of production scheduling of large open pit mines with geological uncertainty is a computationally very intensive process. In this paper, an approximation algorithm is proposed to schedule an open pit mine, where instead of solving the whole problem at once, the production schedule is generated by sequentially solving subproblems. The subgradient method is used to generate the upper bound solution of the Lagrangian relaxed subproblem. If the upper bound relaxed algorithm is not a feasible solution, a mixed integer programming is solved on the generated upper-bound solution. An application at a copper mine with an orebody represented by 16 532 mining blocks is presented using the proposed algorithm. Results show that all constraints are satisfied. The net present values are also calculated and results reveal an 11 per cent higher net present value (NPV) is generated when compared to the NPV generated when the same approach is applied to the deterministic model of the deposit where uncertainty is not accounted for.CITATION:Chatterjee, S, 2014. Stochastic production scheduling – solution through Langrangian relaxation with the branch-and-cut algorithm, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 323–328 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014