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By José De La Cruz

Para la construcción del dique de arranque de una planta concentradora es importante considerar la ingeniería y diseño del mismo -en tanto debe realizarse un diseño adecuado y así evitar problemas a largo plazo en la estabilidad de la presa de relaves. En ese sentido, el presente texto señala los costos, calidad y estabilidad del diseño de presas de relaves a través del proyecto de Toromocho S.A, perteneciente a la minera Chinalco, cuyo dique de arranque es CFRD (Concrete Face Rock File Design), método aguas abajo. La modalidad de este método permite diseños con un menor empleo de volúmenes de material de cantera que es aplicable en aquellas zonas con baja producción de material constructivo.

Dec 1, 2014

By Daniel I. Naupari Escobar

El presente texto señala las técnicas de campo empleadas en la mina Cobriza para el estudio base del sistema de ventilación desde el punto de vista psicrométrico, así como la evaluación del comportamiento termodinámico del sistema y las acciones realizadas para la mejora del mismo. Así también se realiza una introducción del análisis para la aplicación de sistemas de acondicionamiento de aire masivos.

Dec 1, 2014

By Iván Arana Sevallos

Dado que la mina Catalina Huancca posee rocas malas dentro de sus labores mineras, surge la inestabilidad de las mismas. En ese sentido, el presente texto señala la forma de aplicar la voladura controlada a través del uso de espaciadores inertes para espaciar los cartuchos de explosivo y reducir la presión que ejerce el explosivo en el taladro al momento de la explosión.

Dec 1, 2014

By Gregg Davidson, Lonnie Jacobs, Brett Zernich, Alan Howard, Robert Pintabona

All Rights Reserved. Printed in the United States of America. Information contained in this work has been obtained by SME, Inc. from sources believed to be reliable. However, neither SME nor its authors guarantee the accuracy or completeness of any information published herein, and neither SME nor its authors shall be responsible for any errors, omissions, or damages arising out of use of this information. This work is published with the understanding that SME and its authors are supplying information but are not attempting to render engineering or other professional services. If such services are required, the assistance of an appropriate professional should be sought. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Any statement or views presented here are those of the authors and are not necessarily those of SME. The mention of trade names for commercial products does not imply the approval or endorsement of SME.

Dec 1, 2014

By T Elkington, A Finch

The theory of constraints (TOC) is a business improvement method that focuses on exploiting a business’s bottleneck to deliver the highest possible profit. Early applications were mostly in the manufacturing industry; however, the theory in itself is completely generic and has now been applied widely in other industries such as services, retail, software and distribution, amongst others. The intent of the approach is attractive – it focuses on the ‘silver bullet’ rather than ‘death by a hundred projects’, and it focuses on growth rather than cost cutting. Application within the mining industry has been limited to date, although there are a few applications emerging. This paper discusses how TOC can be applied to maximise value during the project planning phase and how to maximise profits when the mine is operational. The paper uses two case studies to illustrate the approach. Case study 1 uses the approach to improve tactical mine planning, which delivers an increase in revenue of 58 per cent. The second case study is a strategic mine planning example that delivers an increase in value of 77 per cent.CITATION:Finch, A and Elkington, T, 2014. Application of the theory of constraints to strategic and tactical mine planning, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 129–136 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By P A. Grossman, J H. Rubinstein, M Brazil, K G. Sirinanda

To date, the scheduling and access design of an underground mine have only been considered as two separate optimisation problems. First, access to the mine is designed and then the scheduling is completed. One drawback of this approach is that the costs of access construction fail to be reflected in the net present value (NPV) calculation. In this paper, designing the access and scheduling its construction are formulated as a single optimisation problem.The underground mine access construction process can be classified according to the number of faces being developed concurrently. An underground mine with a single decline branching at a junction point into two declines is considered. After construction reaches the junction, the two faces of the decline can be constructed sequentially or concurrently.This paper proposes an efficient algorithm for optimally locating a junction point to maximise the NPV where two faces are being developed concurrently. The NPV is defined by taking the locations of orebodies and their values, decline construction costs, decline development rate and discount rate into account. The variation of the NPV and the optimal locations of the junction point for one and two concurrent development faces for a range of discount rates are discussed and compared. The proposed algorithm is applied in a simulated case study based on hypothetical values for an underground mine.CITATION:Sirinanda, K G, Brazil, M, Grossman, P A, Rubinstein, J H and Thomas, D A, 2014. Strategic underground mine access design to maximise the net present value, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 235–242 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By P Cesare, S Munro, D Wandel, R A. Bearman

Consideration of the processing plant operational envelope is a key factor in production scheduling,and therefore mine planning, for any mining operation. In many cases hard-wired or regressionbased tonnage-grade-recovery relationships are used as mine planning constraints and as a means of valuing the system output. In extensions of this type of approach, various regressions can be used to describe throughput limits, based on a number of global metallurgical parameters.The ultimate position for the optimisation of a mine plan and production schedule is to have a full process plant model running as an integrated part of the mine planning exercise. A plant model suitable for this needs to have all the required sensitivities to input parameters such asfragmentation, grade, mineralogy, strength and hardness. The model then needs to convert these to performance measures for the plant, which include recovery, throughput, product size distributions, energy consumption, water use and operating cost.In this paper an approach is outlined that takes this view and then goes beyond it to provide a full, time-based bottleneck identifi cation and valuation tool.Using such an approach it becomes possible to run mine plans and production schedules in windows of days, weeks, months and over the life-of-mine with a time-enabled process model acting as the active constraint. In this way virtual bottlenecks can be identifi ed for various mine plans and schedules well ahead of time. The value of de-bottlenecking can be defi ned, not just at a plant scale, but in terms of the overall mining operation, and the data can be used as the basis for capital investment decisions.CITATION:Bearman, R A, Cesare, P, Munro, S and Wandel, D, 2014. Incorporation of a full process plant model as an active constraint for mine planning and production scheduling, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 121–128 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M de Freitas Silva

One of the main steps during the decision-making process of long-term mine planning is the definition of the optimal sequence of extraction, which usually is synonymous with maximising the discounted cash flow of the project subjected to several constraints arising from aspects of technical, physical, and economic limits.Open pit mine production scheduling (OPMPS) comprises several intricacies related to its size and uncertainty of input parameters. Due to its complexity and prohibitive size, traditional mine planning usually relies on heuristic or metaheuristic methodologies which are able to provide good solutions in a reasonable amount of time. However, most of the uncertainty that surrounds the mining complex is ignored leading to non-realistic results.In this paper, a new heuristic approach is explored in order to solve a stochastic version of the OPMPS problem accounting for geological uncertainty in terms of metal content, multiple processing streams, and stockpiling option. The methodology involves generating an initial solution by solving a series of subproblems and this initial solution is improved using a network flow based algorithm.The algorithm was applied to a relatively large gold deposit with more than 119 thousands blocks. Results have shown that the methodology is promising to deal with large-size mine instances in reasonable time.CITATION:de Freitas Silva, M, 2014. Solving a large stochastic integer programming model for production scheduling at a gold mine with multiple processing streams and uncertain geology, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 389–396 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By T Wambeke, J Benndorf

A risk-robust development of a heavy mineral resource requires an assessment of the geological uncertainty and spatial variability of the key factors impacting the mining and processing operation. Attributes of interest are the total heavy mineral grade, the slime content and the amount of oversized particles. The modelling of a mineral sand deposit presents two main challenges. Firstly, the exact location of the geological boundaries are obscure due to the complex interaction of the deposit formation processes. Secondly, the properties of interest exhibit a rather complex transitional behaviour across the so-called soft boundaries. Traditionally, the modelling exercise is restricted to a consecutive stochastic simulation of distinct geological facies and orebody properties. This results in different zones separated by hard boundaries, ignoring the typical transitional behaviour.This contribution presents an alternative approach which models the entire sand body as a single domain while accounting for the complex trends present. Error curves are used to infer covariance model parameters without the need of a prior removal of the trend. The principle is based on minimising the differences between empirical and theoretical errors resulting from two methods of prediction calculated for different rings of influence. The added value results from the unambiguous decomposition of the spatial variation into a deterministic trend and a stochastic residual. The trend model neither overfits nor underfits the data. The calculated residuals are thus unbiased and the uncertainty can be correctly assessed.The effort results in a characterisation of the resource uncertainty which provides a more realistic image of the spatial variability of the key properties in and across the geological domains of an existing heavy mineral sand deposit.CITATION:Wambeke, T and Benndorf, J, 2014. Optimal inference of modelling parameters to simulate complex trends across soft boundaries – a case study in heavy mineral sands, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 73–84 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By S Coward, C Xu, P A. Dowd

"This paper provides an assessment of the current challenges in strategic mine planning and design and suggested approaches for addressing them. The specific challenges covered are: realistic quantification of downstream processes applied to orebody models to provide an integrated approach to mine design and optimisation modelling, estimation and simulation of geometallurgical variables and their integration into resource and reserve estimation and mine planning modelling, estimation and simulation of new variables for new forms of mining – deep mining, particularly block caving, and solution mining flexibility in planning and design to manage risk and minimise its impact information technology (IT) infrastructure and platforms for rapid online data collection, storage, access and processing.Most of these challenges require new types of data, variables, modelling and estimation methods. Foremost among the new types of variables and data are geometallurgical and dynamic rock mass characterisation variables. New types of data and data collection include rapid generation of very large amounts of online sensor data and the consequent need for rapid processing and modelling of these data.This paper outlines the challenges and strategies in each of these areas and uses examples of models and outputs to illustrate approaches and potential solutions.CITATION:Dowd, P A, Xu, C and Coward, S, 2014. Strategies for mine planning and design, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 41–50 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014

By J Whittle

This paper describes what is involved in carrying out a comprehensive optimisation of a long-term operating schedule for a mining complex. It details the data that must be assembled and discusses the different approaches to optimisation and what limitations they put on the variables and constraints that can be handled.CITATION:Whittle, J, 2014. Not for the faint-hearted, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 3–6 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M Bloss, B Roberts

BHP Billiton Olympic Dam required a well-tested software product suitable for strategic optimisation and scheduling of options for a potential underground mining project. The company has a standard software package called Blasor® that is used for open pit optimisation of complex deposits such as iron ore, copper-gold-uranium and silver-lead-zinc. It applies a mixed integer programming formulation (MIP) to produce conceptual pushback designs and an optimised pushback sequence. Schedules can then be run on the resulting mine designs. Existing pushback designs can also be imported into the model and an optimum schedule generated. One of the key advantages of Blasor is that multiple strategic mine schedules can be quickly generated according to the various scenarios, options and constraints that are being investigated.It was realised that Blasor, if carefully configured, could be adapted for use in underground planning. The concept of using Blasor as an optimiser for underground strategic scenarios is to represent the underground stope and development data in the form and structure of an open pit mine. In simple terms, underground stoping areas are modelled in the form of pushbacks (phases) with vertical walls. The phase dependency tools in Blasor are used to link mine blocks and their access development drives. These underground phases are imported into the optimiser. Artificial block models are constructed using stope design tonnage and grade data at various fixed cut-offs. A variable cut-off grade model has also been developed using a method of parcels to represent each cut-off grade increment.So far, this approach has been successfully used to investigate the optimum stope design cut-off for various strategic options, the economic potential of a series of mining and processing configurations and to provide guidance on the optimum sequence for developing and mining the various stoping blocks in the resource. Examination of the results has highlighted the most suitable development paths for more detailed study.CITATION:Roberts, B and Bloss, M, 2014. Adaptation of an open pit optimiser for underground strategic planning, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 249–256 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By J Zhang, R Dimitrakopoulos

A two-stage stochastic mixed integer non-linear program is formulated for a mining complex to optimise strategic and tactical plans. The objective is to find the near optimal decisions for a mineral supply chain in the context with uncertainties in both ore supply and the commodity market (price and demand). The endogenous spot price in the commodity market and long-term sales contracts are considered in the formulation of the mining complex’s optimisation model and an ad hoc heuristic is developed to deal with the throughput – and head-grade-dependent recovery rate in processing plants. Numerical results indicate that the proposed heuristic is effective and efficient in numerical tests. Based on the proposed model and heuristic, a long-term contract design strategy is proposed for making decisions on the contract price and strategic investments. A shadow price based method is also proposed to evaluate the existing mining schedule.CITATION:Zhang, J and Dimitrakopoulos, R, 2014. Optimising a mineral supply chain under uncertainty with long-term sales contracts, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 25–32 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M W. A Asad, E Topal, D S. S Sandanayake

Underground mine planning requires an optimal stope layout as a prerequisite for the definition of an optimal production schedule. The procedure to generate an optimal stope layout considers an orebody model and economic parameters as inputs. It evaluates all possible combinations of mining blocks and creates a set of stopes by satisfying the physical mining and geotechnical constraints. Thus, the procedure is computationally complex, and generating a solution to the problem is a challenge. This paper presents an implementation of a new heuristic algorithm that solves the stope layout problem. In two case studies, the algorithm achieves 11.2 per cent and 22.5 per cent superior solutions to the industrially available maximum value neighbourhood approach.CITATION:Sandanayake, D S S, Topal, E and Asad, M W A, 2014. Implementing a new heuristic approach to the underground mine stope layout problem, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 243–248 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

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 H M. Parker, G Verly, T Postolski

Assessing the uncertainty and risk in mineral resource estimation is required for best practice and is implied by the public reporting codes in the categorisation of mineral resources and reserves. One industry best practice is quantifying the risk on future production for a given period, which can be done using two geostatistical approaches: conditional simulation and drill hole spacing studies based on estimation variances. Conditional simulation can provide a more complete and flexible answer to the problem, but it takes a fair amount of time to complete. Drill hole spacing studies are simpler, faster and can provide reasonable assessments of mineral resource uncertainty.This paper is a presentation of the principles, theory and practice of drill hole spacing studies. Various types of risk assessment are considered. Difficulties encountered in practice are discussed, and solutions are proposed. The important parameters impacting the results are reviewed. Examples of results and sensitivities are shown.CITATION:Verly, G, Postolski, T and Parker, H M, 2014. Assessing uncertainty with drill hole spacing studies – applications to mineral resources, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 109–118 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By J Balzary, A Mohais

Dynamic market and operating conditions, coupled with an environment in which multiple objectives and trade-offs are common, pose major challenges for planners and schedulers working in any mining entity. Many mining companies recognise the need to shift from a siloed mining-focused push model to an integrated value chain, demand-driven approach, but there are still fundamental barriers in business process and the supporting technology preventing a consideration of end-to-end optimality.This paper presents some elements of experiences working with companies to adopt such advanced approaches. In addition to algorithmic elements, an approach to phased and gradual deployment of progressively more sophisticated optimisation models is described. From a practical software adoption perspective, it is believed that this last concern is also of primary importance. Next generation approaches to the optimisation of complex bulk commodity demand chains, namely iron ore and coal are presented, with case studies in the world’s largest integrated operations in Western Australia and Queensland from the raw material mined through to market. Utilising accurate simulation models supported by metaheuristic optimisation techniques, a range of ways to engineer a dynamic decision support framework that can adapt and change with the inevitable changes in commodity markets is explored. Objectives such as total revenue, margin, cost, NPV, throughput, asset utilisation, contractual penalties and bonuses, and energy consumption can be managed simultaneously across the mine, plant, logistics network, port operation, shipping and sales domains.CITATION:Balzary, J and Mohais, A, 2014. Consideration for multiobjective metaheuristic optimisation of large iron ore and coal supply chains, from resource to market, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 213–222 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By S Q. Liu, E Kozan

This paper proposes a new multiresource multistage scheduling problem for optimising the open pit drilling, blasting and excavating operations under equipment capacity constraints. The flow process is analysed based on the real-life data from an Australian iron ore mine site. The objective of the model is to maximise the throughput and minimise the total idle times of equipment at each stage. The following comprehensive mining attributes and constraints have been considered: types of equipment; operating capacities of equipment; ready times of equipment; speeds of equipment; block-sequence-dependent movement times of equipment; equipment-assignment-dependent operation times of blocks; distances between each pair of blocks; due windows of blocks; material properties of blocks; swell factors of blocks; and slope requirements of blocks. It is formulated by mixed integer programming and solved by ILOG-CPLEX optimiser. The proposed model is validated with extensive computational experiments to improve mine production efficiency at the operational level. The model also provides an intelligent decision support tool to account for the availability and usage of equipment units for drilling, blasting and excavating stages.CITATION:Kozan, E and Liu, S Q, 2014. An Open pit multistage mine production scheduling model for drilling, blasting and excavating operations, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 329–334 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By R Dimitrakopoulos, I Minniakhmetov

Geostatistical simulation techniques are used to quantify uncertainty of spatial attributes of interest describing mineral deposits, petroleum reservoirs, hydrogeological horizons, environmental contaminants and so on. The majority of existing methods consider second-order spatial statistics and Gaussian processes, while the more advanced multiple point-based simulation approaches are algorithmic and do not consistently account for the high-order spatial relations in data. Recently, simulation techniques for complex and non-Gaussian spatially distributed variables have been developed, based on high-order spatial cumulants, and make no assumptions on data distribution or require data transformations. In this paper, the previous developments are extended and a new approach for the joint simulation of multiple correlated variables using high-order spatial statistics is proposed. The technique is based on a new algorithm for the decorrelation of correlated variables into factors, using the so-termed diagonal domination condition of high-order cumulants. The decorrelated factors are then simulated using high-order simulation and back-transformed into the initial correlated variables. The decorrelation using diagonal domination of high-order statistics is tested with a data set from a multielement iron ore deposit, and then a fully known two-dimensional data set with two correlated variables is used to demonstrate the practical intricacies of the proposed method.CITATION:Minniakhmetov, I and Dimitrakopoulos, R, 2014. Joint simulation of correlated variables using high-order spatial statistics for orebody modelling, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 53–60 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M Aros, L Reilly

Iron ore is normally sold in bulk at certain specifications. These include iron grade and specific contaminant grades, typically silica, alumina, phosphorous and sulfur. These target grades are normally agreed between the mining company (producer) and the steelmaking company (customer).This study presents an alternative to traditional cut-off grade calculation, using linear programming that maximises the total ore inventories within a predefined mining envelope while meeting the specified targets. Providing a feasible mining sequence that maintains the target grade is not covered in this study.The method was applied to Fortescue’s operations. The complexity arising from differing ore quality within each mining horizon, variable yields and upgrades and the need to control iron and contaminant grades provided a good opportunity to test an alternative method of ore selection. The new method significantly reduced the time spent in cut-off selection and sensitivity analysis, and provided an optimised solution.CITATION:Aros, M and Reilly, L, 2014. Ore selection optimisation for an iron ore deposit using linear programming, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 345–350 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014