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By P. Schumacher, M. Fein

"INTRODUCTION Newmont’s Twin Creeks operation, part of its Nevada operations, produces gold from two large open pit mines. Mined ore is processed at one of three on-site facilities: a refractory mill with two autoclaves, an oxide mill and heap leaching facilities. Twin Creeks also processes various off-site sourced concentrates and ore including high grade underground ore from the Turquoise Ridge joint venture with Barrick Gold Corporation. This paper discusses Twin Creeks’ strategic mine planning process which is part of an in-depth continuous improvement program called “Full Potential.” The process involves cost analysis, phase design validations, life-of-mine production scheduling optimization, mill blending strategy and stockpile management. Through this Full Potential strategic planning, Twin Creeks is creating significant value by increasing near term free cash flow and life of mine net present value (“NPV”) and achieving cost reductions. TWIN CREEKS OPERATION Twin Creeks is one of the major gold producers in Newmont’s Nevada operations (see Figure 1, 2 and 3). Located approximately 15 miles north of Golconda, Twin Creeks produced approximately 28 % of the Nevada operation’s 2013 gold production of 1.8 million ounces (Newmont 2014). The production included Turquoise Ridge sourced gold ore from the joint venture (75% Barrick Gold Corporation, 25% Newmont).At Twin Creeks, there are two large open pits, named Mega Pit and Vista Pit (see Figure 4, 5 and 6). There are 3 to 5 phases in Mega Pit and 2 to 5 phases in Vista Pit depending on the gold price assumption. There is also an underground exploration project at the bottom of the Vista Pit. Twin Creeks produces gold from a variety of ore types requiring different processing techniques depending on economic and metallurgical characteristics. To ensure the best use of processing assets, engineering and processing groups at Twin Creeks utilize a Mixed Integer Linear Programming (“MILP”) model to guide the flow of mine sequences and material routing to various process facilities (Kawahata, Schumacher and Hufford 2013)."

Jan 1, 2015

By J. L. Ellenberger, G. S. Esterhuizen, T. Miller, M. M. Murphy

A limestone mine in Ohio has had roof instability problems that have led to massive roof falls extending to the surface. This paper describes a collaborative study of these events undertaken by the National Institute for Occupational Safety and Health and the mine owners. The study focuses on the roles that weak, moisture-sensitive floor and weak bands in pillars play in a general instability that led to roof falls at this mine. The NIOSH Stone Mine Pillar Design software (S-Pillar) does not include analysis for weak floor or weak bands and recommends that when such issues arise they should be investigated further using a more advanced analysis. It is important to recognize that without the impact of the floor or weak bands, the calculated inherent stability factor for these pillars is within the recommended design range. Therefore, to further investigate the observed instability occurring on a large scale at the Ohio mine, FLAC3D numerical models were employed to help determine the failure mechanism. Roof-to-floor convergence monitors were also installed in the areas predicted to have further instability. This case study will provide important information to limestone mine operators regarding the impact of weak floor causing the potential for roof collapse, pillar failure, and subsequent subsidence of the ground surface. This recent case demonstrates that weak floor can be a significant factor in pillar stability and should be assessed during the design stage of the mine.

Jan 1, 2015

By Chee S. Theng

As the 2015 president of the Society for Mining, Metallurgy & Exploration Inc., I am pleased to present the 2015 issue of the Transactions of the Society for Mining, Metallurgy & Exploration Inc., Vol. 338. A significant portion of the contribution that we make as a professional society is the publication of professional and technical papers that accelerate the advancement of scientific knowledge and enable the transfer of technology. Communication within the mining community is the key to a successful society. This technology exchange continues a tradition that dates back to the 1871 meeting of 22 mining engineers in Wilkes-Barre, PA, that resulted in the founding of AIME. Those individuals envisioned that these transaction volumes “would form a most valuable and greatly needed addition to our professional literature.” This volume fulfills that pledge. The 2015 Transactions contains some of the most interesting research and insights from the past year and features an international slate of authors spanning industry, academia and government. Papers include the latest findings from the U.S. National Institute for Occupational Safety and Health (NIOSH) and special-focus articles on gold processing, iron ore processing and coal processing. The 2015 Transactions consists of a total of 67 technical papers, encompassing all 22 papers published in 2015 in the Technical Papers Section of Mining Engineering (Vol. 67) and all 29 papers published in 2015 in Minerals & Metallurgical Processing (Vol. 32). Additionally, this volume contains 15 papers and one discussion that are being published for the first time in this issue. The papers presented in this volume were written by some of the most distinguished professionals in the industry worldwide. All of the technical papers were peer-reviewed. The process of review and publication requires much hard work by authors, reviewers and our publications staff. As the 2015 president of SME and on behalf of all our members, I would like to extend our gratitude to all whose volunteer efforts contributed to the publication of this volume.

Jan 1, 2015

By R. Halatchev

This paper treats the spatial aspect of mine sequence optimization, revealing it in the context of determining the optimum number of cutbacks of an openpit mine. Cutback mining is currently a dominant method in the mining of base and precious metals. It is based on a pseudo-economic discretization of the pit space using advanced pit design algorithms. The result of implementing such an approach of pit design is the generation of pit shells, which need to be grouped into an optimum number of cutbacks to maximize project value. In this paper, the development of a mathematical solution of this problem using combinatorial analysis is presented. The solution provides a multivariate investigation of the optimum mine sequence with the implementation of a method for production scheduling optimization that uses Net Present Value (NPV) as a criterion for assessing the economic efficiency of mine sequencing. A case study using the conditions of a hypothetical openpit gold mine is provided to illustrate the applicability of the approach. The results obtained reveal the complex nature of NPV variation as a function of the cutback geometry.

Jan 1, 2015

By C. S. Todd, R. L. McNearny, S. Dun, P. Clark, P. W. Conrad, X. Liu, J. Q. Wu, H. Rhee, W. J. Elliot

Soil erosion is strongly related to soil hydraulic properties. Understanding how surface coal mining affects these properties is therefore important in developing effective management practices to control erosion during reclamation. To determine the impact of mining activities on soil hydraulic properties, soils from undisturbed areas, areas of roughly graded mine spoil, areas with replaced topsoil before seeding, and revegetated areas at the Rosebud Mine in Eastern Montana were evaluated. Field and laboratory studies were conducted to determine the soil hydraulic properties of the four representative areas, including particle-size distribution, organic matter content and saturated hydraulic conductivity. Using this data, Water Erosion Prediction Project (WEPP) simulations and risk analyses were performed to investigate mining’s effects on changes in soil erosion potential. Significant differences were seen in the hydraulic properties of the soils in the representative areas due to the mining and reclamation practices at the mine. The WEPP simulations show that the potential for soil erosion increases as a result of mining activities disturbing the soil. The simulations also indicate that the erosion potential may return to pre-mining levels over time with effective revegetation practices.

Jan 1, 2015

By G. Bylapudi, A. Bhagwat, K. Mondal, A. J. S. (Sam) Spearing

Minimizing the effects of corrosion improves the long-term performance of the rock anchors and ground control systems in underground mines, which is fundamental for safe operation over the design period. The primary objective of this study is to explain the problems due to stress corrosion cracking (SCC), strain-induced corrosion cracking (SICC) and other forms of corrosion, and why the mining industry’s ground control professionals should take this into serious consideration. It is important to clearly determine the kind of SCC mechanisms that causes roof bolt failures in order to suggest the necessary action plan to mitigate or eliminate the possibility of the cause. Pertinent literature and studies around the globe on this complicated subject are reviewed. The secondary objective is to encourage the use of corrosion-resistant ground control products in the underground mining industry in areas of high corrosion potential. Characteristics of SCC on high strength steel are also reviewed. The literature and preliminary studies to date indicate that research work is necessary in United States.

Jan 1, 2015

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 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 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 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 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

By C Fricke, R Wood, P Velletri

"In a climate characterised by an ever increasing focus on cost reduction and maximising the utilisation of existing assets and infrastructure, understanding the uncertainty associated with alternative investment and operational choices becomes paramount to minimising the risk of making an ineffective decision. To assist in this process, TSG Consulting has developed an analytic framework to capture the underlying complexity of a mineral supply chain, and then quantify the impact of the uncertainty associated with the key drivers of business productivity. The technical basis of this approach follows three distinct steps: analyse the current system and identify the major factors that drive value build detailed models that can measure the response of the system to potential changes in the factors identified quantify the sensitivity of financial and capacity forecasts to these factors.Each of the above steps draws on a different area from the field of applied analytics, namely: data analytics dynamic modelling and designed experimentation meta-modelling and optimisation.Following the three-step approach described above ensures a rigorous modelling and analysis process is executed to support strategic mine planning decisions. The approach is equally applicable to the analysis of fully integrated system supply chains (pit-to-port), the evaluation of incremental capital investments to targeted areas of a supply chain, or when considering revisions to the operating philosophy of a system in the short term. The basis of each of the three analytic steps is explored in detail in this paper. The result is the definition of a structured mechanism for a business to actively manage risk and drive productivity as a component of the strategic mine planning process.CITATION:Fricke, C, Velletri, P and Wood, R, 2014. Enhancing risk management in strategic mine planning through uncertainty analysis, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 275–280 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014

By J MacNeil, R Dimitrakopoulos

As open pit mining of a deposit deepens, the cost of extraction may increase up to a threshold where transitioning to mining through underground methods is more profitable. This paper provides an approach to identifying an optimal depth at which a mine should transition from open pit to underground mining. The value of a set of candidate transition depths is investigated by optimising the production schedules for each depth’s unique open pit and underground operations. By considering the sum of the open pit and underground mining portions’ value along with the cost of transitioning corresponding to each candidate transition depth, the optimal transition depth can be identified. The optimisation model presented is based on a stochastic integer program that integrates geological uncertainty. As an input, the stochastic integer program utilises a set of several stochastic simulations that represent equally probable scenarios of the mineral resource. This group of simulations describes the uncertainty in the deposit, while the optimiser aims to maximise value based on discounted profits of both the open pit and underground components of the deposit.CITATION:MacNeil, J and Dimitrakopoulos, R, 2014. A stochastic optimisation formulation for the transition from open pit to underground mining within the context of a mining complex, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 363–368 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By K Dagdelen, I Traore

This paper presents an iterative net present value (NPV) maximisation method to determine the optimum surface to underground transition depth for an orebody to be mined by multiple open pits and an underground mine. The determination of transition depth from open pit to underground mining is based on global production scheduling optimisation of open pit and underground mines using mixed integer linear programming. The method is applied to a case study coming from a gold mining complex with six open pits and a large underground mine using longhole open stoping. The results indicate potential improvements of the NPV of global operations when compared to the traditional techniques based on independently optimised open pit first, followed by underground mining.CITATION:Dagdelen, K and Traore, I, 2014. Open pit transition depth determination through global analysis of open pit and underground mine production scheduling, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 195–200 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M Wortley, J Korteniemi, D La Rosa, L Rajavuori

Geometallurgical modelling of an orebody provides benefits to a mine by gaining a better understanding of the ore characteristics and how they affect the performance of the concentrator. With this knowledge, plant operating conditions can be adjusted to optimise throughput and recovery in advance of the arrival of particular ore types. Therefore, it is extremely important that the origin of the ore being processed is known as accurately as possible. Depending on the homogeneity of the ore characteristics, reliance on assumptions about stockpile residence times, scheduling and material handling can render the best geometallurgical models useless.The solution adopted at Agnico Eagle’s Kittilä mine was to utilise Metso SmartTags™ and in-house expertise to develop a system that continuously and accurately links geotechnical and lab data from the mine to the performance of the plant. This application presented several unique opportunities and challenges. For example, this was the first installation of a SmartTag™ system for geometallurgical modelling in an underground mine. Challenges included the fact that the system installation is routinely subjected to temperatures below -20°C.The system was installed and commissioned in early 2013 and has been operating continuously since. Kittilä has begun to see the benefits of the system, with an increased understanding of how different ores are processed in the concentrator. Other advantages include the ability to alert operators about the arrival of difficult ores and a better understanding of the ore handling systems.This paper describes the installation and use of the system at Kittilä and details some of the geometallurgical relationships that have been developed using the data collected so far.CITATION:La Rosa, D, Rajavuori, L, Korteniemi, J and Wortley, M, 2014. Geometallurgical modelling and ore tracking at Kittilä Mine, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 291–298 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By C A. Myburgh, S Craig, K Deb

In focusing on open pit mine scheduling, one of the biggest areas that a mining engineer can add value is in the area of cut-off grade optimisation. It is not atypical to add up to an extra 20 per cent in value by bringing forward higher-grade ore and deferring costs into the future.There are many techniques available to maximise net present value (NPV) when applied to open pit mine scheduling. That in itself raises the question of why so many of these techniques exist. The short answer is that none of these techniques are robust. Every time the problem changes, the algorithms also have to change.Instead of trying to make a problem fit the constraints of an existing technique, there is much to be said for usefully hybridising different approaches. This paper describes an innovative approach where evolutionary algorithms are combined with both local search and linear programming. The goal is maximising NPV through optimisation of cut-off grade policy and extraction sequence for an open pit mine. The cut-off/cut-over grade policy produced caters for multiple processing streams and stockpiles.Finally we demonstrate the quality of the algorithm by comparing some results with a well-known mathematical solution.CITATION:Myburgh, C A, Deb, K and Craig, S, 2014. Applying modern heuristics to maximising net present value through cut-off grade optimisation, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 155–164 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 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

By M Recabarren, A Echeverria, J Ronda, J I. Guzman, R Dodds, G Juarez, E Vila-Echague

A strategic mine plan is a fundamental part of any mining operation. It must provide a realistic and operative extraction and processing schedule, and yield the highest value for the business. For open pit mines, the problem can be modelled as a set of blocks representing the deposit, for each of which we need to decide if it is going to be mined, if so, when and where it will be sent (process, stock or dump).This problem can be proven to be of NP-Hard complexity. The traditional approach used by mining engineers to tackle this complexity is to separate the problem into more tractable components, solving each separately. The first step is to use the Lerchs-Grossman (LG) algorithm to obtain the final pit limit, as well as a set of optimised pit shells (nested pits), using different input prices. Then, using this input, operational phases are designed. Third, the set of phases can be used as an input for a phase-bench style optimisation, usually using a mathematical programming-based algorithm.There are, however, several problems with this approach. First, dividing the problem into simpler parts has the consequence that any compromises made at early stages limit the potential value left for the following steps. Second, LG is a static algorithm, with no concept of the time-value of money, capacity limits, opportunity costs, etc. Phase design is essentially a manual step, for which nested pits provide insufficient information in practice, and therefore the quality of the result in terms of potential net present value (NPV) depends strongly on the experience and expertise of the designer. This means that key input data such as commodity prices, different processes capacities and mine movement limits, are considered only at scheduling, when there is little room for optimisation. This sequential approach limits the potential value in each step, where a simplified problem is addressed, instead of addressing the real, and more complex problem, which is dynamic and should be solved as a whole.We present in this paper an algorithm, and its commercial software implementation, that addresses the dynamic problem as a whole, simultaneously and automatically creating a high-value strategic mine plan that adheres to basic operational constraints, determining dynamic cut-off grades and delivering a set of phases which can serve as a much stronger guide than nested pits for the final design. We discuss some of the key elements of the algorithm and propose a new methodology for strategic mine planning, where phase design dynamically responds to mining strategy definition. Results using different mining and operational parameters on a fictitious copper deposit are used as a demonstrative example.CITATION:Juarez, G, Dodds, R, Echeverria, A, Guzman, J I, Recabarren, M, Ronda, J and Vila-Echague, E, 2014. Open pit strategic mine planning with automatic phase generation, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 147–154 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014