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By C M. W Boyle, E K. C Phuak, A Lewan, E Ryan, M D. Paine, P H. Mackenzie

"Rio Tinto Iron Ore (RTIO) has an extensive history in the application of geometallurgy to the iron ore value chain. Fundamental to this has been the geological logging of material types which are assigned processing attributes such as lump per cent and density. Initially these were used for product (lump and fines) grade predictions in relation to grade blocks to be used within short-term planning horizons.More recently the predictive reach of the material types and their associated processing attributes has been extended due to their inclusion into geological block models. These predictions can now be used to influence mine plans across multiple planning horizons in such a way that the extremes of geometallurgical properties can be pre-empted and managed thus improving efficiency. Spatial material type domains are also used in the planning of metallurgical drill holes to ensure greater representivity of material than was typically obtained by chemical and/or stratigraphic criteria alone.Material type logging has also been enhanced by the introduction of modern, rapid mineralogical techniques that have complemented visual logging. This has enabled the identification and quantification of contaminant minerals and their relationships with host ore types to be established. This data is used to better manage product grade predictions and identify risks to the short-term plan.Work is ongoing in RTIO to further refine logging based predictions and in addition apply them to new areas of the business. This work requires collaborative and multidisciplinary teams working on an integrated approach combining geology, metallurgy, technology and innovation, mine planning and technical marketing.CITATION:Paine, M D, Boyle, C M W, Lewan, A, Phuak, E K C, Mackenzie, P H and Ryan, E, 2016. Geometallurgy at Rio Tinto Iron Ore – a New Angle on an Old Concept, in Proceedings The Third AusIMM International Geometallurgy Conference (GeoMet) 2016, pp 55–62 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Jun 15, 2016

By V Liebezeit

Data management is a significant, but perhaps unrecognised, consideration for any organisation embarking on a geometallurgy program. While resource geologists are accustomed to dealing with thousands or millions of samples and their associated assays, metallurgists and mineralogists have traditionally carried out only a few expensive and complicated tests and consequently tend to manage the resultant data in spreadsheets. The emerging discipline of geometallurgy arrives with significant challenges as a result of the sheer volume of data generated. As an example, the Olympic Dam geometallurgy program has generated more than:60 000 physical subsamples analysed for up to 30 analytes; 25 000 spreadsheet-based laboratory test results; 30 000 sized fractions analysed via QXRD, MLA and/or QEMSCAN;multiple external providers of data, and more than seven terabytes of data. Considerations for any geometallurgy data management system include storage space, data integrity, accessibility, traceability (sample provenance), maintenance and ease of use, integration with data providers and the ability to modify the system to adapt to changing requirements. Further, aspects of a geometallurgy program, such as conducting a range of tests on one sample, producing subsamples such as concentrates and tailings, compositing and routine QA/QC may not be well handled by existing software and providers geared to standard geochemistry, mineralogy or metallurgical test programs. The development of a geometallurgy database using existing software (AcQuire), and the associated file management system will be described for a large geometallurgy program at Olympic Dam. As with many aspects of geometallurgy there is no approach that meets the needs of every site or program. Recognition of data management as an issue, a structured approach in the program planning phase and commitment to executing the strategy will increase the chances of successfully mining the geometallurgical data.

Sep 5, 2011

Cripple Creek and Victor Gold Mine is an epithermal gold deposit located in the Rocky Mountains of Colorado. Surface operations are continuing along with an expansion for an onsite mill to take place in the next couple of years. The surface pit is currently progressing downward from an oxidized zone into a transitional, more sulfide/ telluride-rich zone. Gold mineralization includes native gold, electrum, and tellurides. Cyanide heap leaching is the recovery process for the oxidized ore with the sulfide/telluride-rich ore to be processed at the new onsite mill. Ore characterization prior to mining is vital when there are two possible recovery processes. This characterization is dependent on geometallurgy. To identify the causes of the variability, multiple parameters are analyzed and assayed. These parameters are measured by a variety of analytical testwork. The analytical testwork provides measurements for the desired parameters. Geometallurgical models are developed for the parameters per recovery process, so estimations can be inserted into the resource model to optimize operations and recoveries.

Feb 27, 2013

By Stacey Leichliter

The Cripple Creek and Victor (CC&V) Gold Mine (Cresson Project) (Fig. 1) is undergoing an expansion to extend production to the year 2025. This expansion involves building a high-grade ore mill, another valley leach facility (VLF) for heap leach operations and expanding the overburden storage areas. The current operating pits are progressing downward from the oxide ore into a more sulfide and telluride-rich orebody. There is still oxide ore present in the current pits and planned pits not yet mined. There is an increased need for two recovery processes for these two different types of ore. The key component to this expansion is the high-grade ore mill, which has gravity, flotation and carbon-in-pulp (CIP) circuits. The mill will be the main recovery process for the higher grade, more sulfide/telluride-rich ore. This ore type does not recover well on the heap leach pad due to its refractory nature. Geometallurgy is vital for ore characterization for the two recovery methods. Geometallurgy helps develop indices that are used to predict parameters that influence overall recovery and production, such as hardness, crushability, grindability, pH fluctuations, gravity (coarse) gold, flotability and cyanidability. Using geology and mineralogy along with other analytical data, geometallurgical models are constructed and applied to the resource block model for proper planning and extraction of the gold. Geometallurgy aims to analyze the variability in the different responses to the parameters to help reduce risk (costs) and optimize recovery (profit).

Jan 1, 2013

By G Williams

Somewhat to the surprise of many heavy mineral sands (TiO2 and zircon) producers, there are important geological structures within their typical placers and aeolian sands deposits that affect plant metallurgical performance. After many years of operations, even experienced producers have encountered unexpected changes in production, product quality and recovery. After looking in all the obvious (but wrong) places, it is recognised that the mine has advanced into ore with different separation characteristics. Metallurgical process response can vary significantly between ore sourced from different geological domains. The mineral characteristics that are exploited for effective separation may be associated with differences in the depositional environment, or be post-depositional in origin. Surface coatings that interfere with electrostatic separation might be associated with paleo-water table movements. Major storm events in the deposition history can also leave distorted mineral assemblage through stranded formations or incisions in older deposits.Apart from the influence on capital and operating costs, geometallurgical variances add another dimension to orebody modelling. Each domain could have specific recovery, mineral assemblage and product quality values to be considered in the value model. Project success is often directly dependent on understanding the geometallurgy of the resource. Tailoring the exploration and early process flow sheet development programs to account for such variances should be standard practice. Such practices have had a chequered history in the mineral sands industry, with sophisticated processes having been developed, only to be shelved for ‘economic’ reasons, and then once again ‘discovered’ when problems re-emerge.It is clear that knowledge gained through a higher level understanding of the changing mineral separation characteristics and advance warning of mineral behavioural differences can mean the difference between lost recoveries and disappointed customers or well timed capital plans and maximised revenues.

Oct 5, 2011

By D Gerrard, J Begelhole, J Gerber

The Iron Magnet deposit has been mined since 2007 when Arrium (then OneSteel) commissioned the magnetite concentrator to supply feed to the pelletising plant in Whyalla. The concentrate quality requirement for the customer required consistent product with minimal variability. In the early stages of operation, it was identified that the feed variability introduced significant changes in the concentrate quality, which was outside the acceptable customer limits.In 2011, a geometallurgical regression model was developed that allowed for the estimation of concentrate quality produced from the feed material. This was based on a laboratory-scale Davis Tube Recovery (DTR) test performed on the feed material. The objective of the project was to characterise the raw ore in the pit to allow for blending prior to crushing. By blending the feed to the concentrator, minimal concentrate quality variation within the plant was achieved. The orebody was divided into geometallurgical classes, called ore types, based on the mineral texture and magnetic mass recovery. Each geometallurgical class consisted of material with similar metallurgical performances. For each individual blast pattern, the ore was grouped into the geometallurgical classes and a composite sample was created. The composite sample was put through the laboratory-scale DTR test and the results were used to estimate the metallurgical performance of the material.The characterisation process and feed pile build methodology was applied in early 2013 and went through an optimisation and streamlining phase. Since the implementation of the geometallurgical characterisation process, the plant has seen a significant reduction in concentrate variability and measurable throughput gains. The characterisation process has also increased the amount of material that is now classified as ore.CITATION:Gerber, J, Gerrard, D and Begelhole, J, 2015. Geometallurgy of the South Middleback Ranges magnetite deposit – predicting plant performance from the pit, in Proceedings Iron Ore 2015, pp 555–560 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By J Jackson

The term geometallurgy (or geometallurgical) first appeared in the literature some 40 years ago and has a wide range of definitions and perspectives which increases the barriers of communication between practitioners and across functional groups and management levels within organisations. However the consistent theme running through the diversity of definitions and perspectives is the requirement for close collaboration between the relevant disciplines. This concept of geometallurgy arguably goes back to the earliest days of the exploitation of mineral deposits, through the mid-1500s to the beginning of the 20th century.This holistic and integrated view was diminished during the 20th century due to a number of factors which included the commencement and continuing upward trend of large-scale mining and the expansion in production, the rise of scientific and classical management theories and methodologies, increase in specialisation and the development of traditional project planning. The re-emergence of geometallurgy in the late 20th century required the relevant functional units and disciplines to collaborate as a team. This necessitates a common understanding that the geometallurgical program’s goals must be obtained, which requires clear and consistent communication, particularly with the modern trend of virtual teams.A multi-dimensional construct is proposed to assist in providing a consistent approach in the communication of the context and purpose of both general and specific geometallurgical programs, thus simplifying communication. This geometallurgical construct consists of the dimensions of process span, response knowledge, variability knowledge, spatial knowledge, application and project phase in relation to extraction process(es) applied to geological materials from a mineral deposit. These dimensions are then linked to the phase of mineral project development in which the geometallurgical program is being applied, which ranges from advanced exploration through to operational execution. This paper outlines the multi-dimensional construct with examples of how the context, purpose and scope of geometallurgical programs can be clearly delineated and communicated.

Sep 5, 2011

By L O’Connor, S C. Dominy

"Geometallurgy is an important addition to any evaluation project or mining operation. As a discipline, it seeks to maximise the net present value (NPV) of an orebody while minimising technical and operational risk. It also aims to promote sustainable development and initiatives by ensuring that all stages of extraction are performed in an optimal manner from a technical, environmental and social perspective. To achieve these goals, development of innovative technologies and approaches along the entire commodity value chain are being established (Glass, 2016). Geometallurgy has been shown to increase operational stakeholder collaboration, creating an environment for knowledge sharing and improved data acquisition and interrogation, with the end result being the integration of such data into mine planning and scheduling. All of these aspects create better business optimisation, utilisation of staff and targeted and realistic key performance indicators.The mining industry faces numerous challenges including, but not limited to:declining ore gradesgeometrically and/or internally more complex depositsdeep-seated deposits, with potentially high in situ stress regimesincreasing quantities of mine waste that needs to be managed appropriatelyhigher energy, water and chemical costsprocessing of more challenging and refractory ores – textural complexitiesstricter environmental/permitting conditions – so-called ‘licence to mine’increasing demand for specialist metals (eg rare earth elements, In, Ge, Te, Rh, Se, Ga and Li)commodity market volatilitya difficult funding environment.Given these challenges, geometallurgy provides the opportunity to control or at least manage some of them.CITATION:Dominy, S C and O’Connor, L, 2016. Geometallurgy – Beyond Conception, in Proceedings The Third AusIMM International Geometallurgy Conference (GeoMet) 2016, pp 3–10 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Jun 15, 2016

By S Robinson, A Turner, R Dale, C Butler

"Sandfire Resources NL- (‘Sandfire’) DeGrussa definitive feasibility study (DFS) identified geometallurgy as a major factor in achieving optimum plant performance at the DeGrussa mine. A geometallurgical initiative was created to proactively investigate this issue, and a key initial achievement was the 2013 paper by Condon et al (2013) on developing a working geometallurgical model from first principles. Significant advancements in Sandfire’s geometallurgical understanding since that publication now shape how information is presented and communicated.The main geometallurgical parameters so far identified as critical factors affecting plant performance are Cu:S ratio, iron sulfide mineralogy, talc carbonate content and sphalerite content. Apart from specific mill strategies, geometallurgical issues have been managed through communication between departments and strategic run-of-mine (ROM) pad management.This paper uses the current theories of geological evolution of the volcanogenic massive sulfide (VMS) deposit at DeGrussa, to discuss the variability between the orebodies C1, DeGrussa (sea floor mounds) and C4, C5 (sub-sea floor replacement) to highlight the different issues each orebody is expected to encounter when processing.A zone of altered low-grade mineralisation surrounds the main orebodies at DeGrussa, referred to as a halo. Talc minerals are often patchy within this halo and often cause significant recovery issues during processing. This halo has been successfully modelled using MgO as a proxy. The ability to model the talc material assists with both identifying potentially problematic zones for processing and geological interpretation of the ore deposits evolution.Early in geometallurgy studies at DeGrussa it became apparent that it would be useful to identify processing performance prior to mining. This necessitated the incorporation of specific sulfide species into the mining block model. These attributes are interpolated from the logged sulfide estimates allowing a clear picture of the abundance of each mineral type within a defined area.In conjunction with the block model a geometallurgical factor (GMF) graph was developed, identifying sulfide mineralogy based upon mineral assays. These two recent additions to the model provide a clear picture of the nature of the ore sources that will be presented to the processing plant.The future of geometallurgy at DeGrussa will see these tools used to aid mine scheduling with a view to further improve processing recoveries.CITATION:Butler, C, Dale, R, Robinson, S and Turner, A, 2016. Geometallurgy – Bridging the Gap between Mine and Mill: a Case Study of the DeGrussa Geometallurgy Program, in Proceedings The Third AusIMM International Geometallurgy Conference (GeoMet) 2016, pp 77–88 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Jun 15, 2016

By J Beniscelli

Some words to honour Pedro:Pedro was among the few persons with a very high level in geology modelling, resource and reserve estimation, geometallurgy development, sampling theory applications and a deep understanding of the mining industry needs. He had brilliant ideas about new developments on economic geology. He was always looking at the long-term and proposed challenging questions a decade in advance on their solution.The fundamental aim of geometallurgy is to add economic value to the mining of mine resources by timely providing information and knowledge about the relevant metallurgical variables represented by their space models. In this manner, geometallurgy allows obtaining the optimal design of a project, mitigating processing risks, optimising and supporting production plans and identifying and assessing mining business opportunities.This paper describes the conceptual framework and geometallurgical definitions and some of Codelco’s experience during the last 15 years.

Sep 5, 2011

By B. D. Frater

Geometallurgy or process mineralogy is an integrated part of the scoping, pre-feasibility and feasibility phases of a project as well as an existing operation. A well planned and executed geometallurgical testwork programme during the early phases, will result in a more accurate decision making process during the selection phase. The better this integration between geological, mining and metallurgical data gathering during the early phases, the more accurate the predictions will be with regards to throughput and recovery during modelling and process and equipment selection in later phases. This will enhance and improve the mine and process scheduling and subsequent NPV. Geometallurgical information gathered during plant operation and testwork can for example be used to statistically model the pit optimisation, plant throughput and optimise the plant for certain feed material (characterised by statistical models).

Jan 1, 2017

By J Vann

The key functions of project evaluation are to assess potential profitability and to develop an effective operational design. For mining projects, design aspects include mining methodology, ore treatment methodology and production rates. All of these characteristics impact on project economics and overall value. Resource models, which form the ultimate basis of project evaluation, typically consist of tonnes above cut-off, grade above cut-off and the spatial distribution (connectivity) of tonnes/grade above cut-off, for one or more variables. Dilution, ore loss and metallurgical recovery are all modifying factors applied to the resource model during evaluation and determination of profitability. A key issue is: how is the value determined? In addition to traditional combination of in situ tonnes/grade, the following merit serious consideration: the concentration of deleterious elements, throughput rates, mining/processing costs and û importantly û metallurgical recovery. Geometallurgy is an emerging field targeted at integrating these issues by identifying either direct measures or proxies for throughput (hardness, grindability), recovery (liberation, mineral shape/texture, etc) and concentrate quality from easily collected macro-, meso- and microscopic data. These ægeometallurgical variablesÆ drive project costs and revenues in a fundamental way. For geometallurgical characterisation to have a real impact on business design, it must enable improved mine planning. When creating spatial estimates for such variables, it is preferable to find proxies that can be measured as close to the intact rock mass as possible. However, spatial estimation of proxies for geometallurgical properties and responses is complex and requires special consideration. Unlike grades, both proxies and absolute measures of geometallurgical variables are not necessarily linear or additive and therefore require very careful geostatistical consideration. In some cases, models must predict extreme values of geometallurgical attributes rather than averages. Incorrect characterisation of metallurgical recovery/throughput can (and has) led to misspecification of the scale of projects, and thus can be seriously value destructive. Aspects of geometallurgy have long been important in iron, bauxite, manganese and coal deposits and are increasingly on the radar for base and precious metal miners. However, as more metallurgically complex deposits are developed and mining of large-scale, low-grade deposits becomes more commonplace, the importance of characterising the metallurgical response in order to generate properly optimised projects will rapidly increase. It is possible that in many ætraditionalÆ (ie grades and tonnes) reserve models, the variables that drive the project are missing!

Jan 1, 2007

The key functions of project evaluation are to assess potential profitability and to develop an effective operational design. For mining projects, design aspects include mining methodology, ore treatment methodology and production rates. All of these characteristics impact on project economics and overall value. Resource models, which form the ultimate basis of project evaluation, typically consist of tonnes above cut-off, grade above cut-off and the spatial distribution (connectivity) of tonnes/grade above cut-off, for one or more variables. Dilution, ore loss and metallurgical recovery are all modifying factors applied to the resource model during evaluation and determination of profitability. A key issue is: how is the value determined? In addition to traditional combination of in situ tonnes/grade, the following merit serious consideration: the concentration of deleterious elements, throughput rates, mining/processing costs and - importantly - metallurgical recovery. Geometallurgy is an emerging field targeted at integrating these issues by identifying either direct measures or proxies for throughput (hardness, grindability), recovery (liberation, mineral shape/texture, etc) and concentrate quality from easily collected macro-, meso- and microscopic data. These 'geometallurgical variables' drive project costs and revenues in a fundamental way. For geometallurgical characterisation to have a real impact on business design, it must enable improved mine planning. When creating spatial estimates for such variables, it is preferable to find proxies that can be measured as close to the intact rock mass as possible. However, spatial estimation of proxies for geometallurgical properties and responses is complex and requires special consideration. Unlike grades, both proxies and absolute measures of geometallurgical variables are not necessarily linear or additive and therefore require very careful geostatistical consideration. In some cases, models must predict extreme values of geometallurgical attributes rather than averages. Incorrect characterisation of metallurgical recovery/throughput can (and has) led to misspecification of the scale of projects, and thus can be seriously value destructive. Aspects of geometallurgy have long been important in iron, bauxite, manganese and coal deposits and are increasingly on the radar for base and precious metal miners. However, as more metallurgically complex deposits are developed and mining of large-scale, low-grade deposits becomes more commonplace, the importance of characterising the metallurgical response in order to generate properly optimised projects will rapidly increase. It is possible that in many `traditional' (ie grades and tonnes) reserve models, the variables that drive the project are missing!

Jan 1, 2007

By R. Bridge

Mining companies are progressively finding themselves having to develop operations for more complex, deeper ore bodies, which require the use of more complicated mining and mineral processing methods. This coupled with the generally increasing trend of higher capital and operational costs have led to a need in the industry for improving ore body knowledge, mining and mineral processing. Geometallurgy is a relatively new concept that can link the three aspects, and when used effectively it is a method that can produce the necessary information for designing and building operations for reliable and optimum performance. Geometallurgy not only involves the integration of geological, mining, and metallurgical fields, but it can also be expanded to include environmental and economic information to produce a block model that can be used to obtain the best outcome in terms of plant response and plant sustainability. The geometallurgical program should not end once the block model has been populated. A detailed geometallurgical approach should be applied to all stages of the operation encompassing sample selection, through to equipment sizing and production forecasting. It can also become a useful optimisation tool thereby allowing the operation to be proactive rather than reactive against changes in the ore characteristics. With the development of metallurgical performance predictors it is possible to quantify how the ore will behave in the process plant in terms of throughput, recoveries, reagent consumption, etc. Therefore, in terms of responsible mining and sustainable resource development, a geometallurgical approach to operations is the logical step for plant development. In this paper we describe the state of the technology with respect to the application of geometallurgical modeling and its utilisation in the fields of design, forecasting and optimisation of mining operations.

Aug 1, 2013

By Michel Brissette, Shaheer Sanuri, Veroljub Mihajlovic

"The main purpose of geometallurgy is to improve the mine planning by mapping hardness and throughput into the mine block model using geostatistics to eliminate the large production fluctuations caused by the ore hardness variability coming from different parts of the pit. However, not all grinding testwork available commercially can be applied to geometallurgy because of large amount of sample required and the associated cost per test. Geometallurgy is now being used in designing grinding mill but as the mining project progress, the accuracy of engineering must improve from 30-35% during the scoping study to 10-15% at the bankable feasibility stage and less than 5% for Engineering Procurement and Construction Management. For SAG milling, the SAG Variability Test (SVT) is an abbreviated version of the standard SAGDesign test developed for this geometallurgical need. The same amount of ore than the original test is ground for three cycles into an open batch SAG test and then the SAG pinion energy is predicted to the standard end point of 80% passing 1.7 mm. For ball milling, a Bond Variability Test (BVT) was also developed using the SAG ground ore from the SVT. The Bond ball mill grindability (Gpb), corresponding to the average of the net grams per revolution from the last three cycles of the Bond test, is also predicted from the third cycle. The relative error for both the predicted SAG ore hardness (WSVT) and Bond Work Index on SAG Variability ground ore (Sv-BVI), in kWh/t, averaged 5.5% and 3.8% respectively compared to the SAGDesign testwork values and are within 7% and 10% respectively from plant benchmark and within 7% from the samples sent to all partner laboratories. Both tests can be continued to measure the true hardness necessary for mill sizing. This article discusses the development of these two new tests, SVT and BVT, their potential use and accuracies in any geometallurgical program."

Jan 1, 2014

By Creasey J. W, Huntington J. F

Faults cutting the Lithgow and Katoomba seams in some collieries of the Western Coalfield can be classified, on the basis of spatial pattern, into three principal groups: meridional swarms of closely spaced minor faults and associated joints, with throws of less than 0.5 m and with exceptional faults of up to 5 m; isolated interswarm strike-slip faults in the Lithgow seam and widely spaced strike-slip faults in swarms in the Katoomba seam; and low-angle normal faults associated with the Lidsdale-Lithgow seam split.Orientations of faults and coal cleats in the coalfield are presented on stereoplots and histograms, as a basis for an analysis of fault dynamics. The low-angle normal faults were found to have been formed only along the Lidsdale-Lithgow seam split, and before the high-angle faults. The origin of the high-angle normal faults in the meridional swarms is discussed and it is suggested that basementcover interactions must have occurred. This suggestion is supported by geophysical evidence. Low-amplitude monoclines in the coal seam, and the spacing of swarms 0.9 to 4.5 km apart, may be due to the underlying basement structure.It is clear that postextensional compressive deformation of swarms in the coalfield gave rise to shortening by minor thrust development, and probably also caused the synchronous strike-slip movement on pre-existing normal faults. There is evidence to show that isolated interswarm strike-slip faults in the Lithgow seam, and widely spaced strike-slip faults in the Katoomba seam, may have originated synchronously with such thrusts. The axis of maximum principal compression ('71) for the thrust event was inferred to be near horizontal and aligned approximately E-W. Recent rock mechanics measurements of the present day stress field indicate a similar stress configuration.

Jan 1, 1981

By J Cornejo, A Pinochet, C Caviedes

The ore extraction rate and the incorporation of new areas are key operational parameters. In massive deposits mined by caving techniques they both control the cave back geometry, which is mostly responsible for the induced stress state around productive levels. Early detection of deviations from admissible geometries will prevent stability problems related to geomechanical hazards, helping to improve safety and working conditions and reduce the potential for lost reserves.This paper describes a mine planning tool currently in use at the El Teniente mine. This tool involves the control of the cave geometry based on rules that have been defined as a result of different studies and experience collected during the operational history of the mine.This mine planning strategy achieves an extractive mining process with lower risk than conventional planning methods, ensuring ore reserves extraction if followed as planned while reducing unexpected changes in mining sequences.CITATION:Cornejo, J, Pinochet, A and Caviedes, C, 2016. Geometric control for strategic mine planning at El Teniente Mine, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), pp 191–196 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2016

By R. C. Klimpel

Size reduction of material in aqueous pulps by the use of tumbling media overflow discharge mills is an important and common unit operation in industry. There also has been extensive attention given in recent decades on developing a more fundamental understanding of the breakage processes involved, especially in ball mills. These studies have mostly emphasized the use of size-mass balance or population balance approaches for quantifying the analysis and design of size reduction circuits, ego Austin, et al (1984).

Jan 1, 1989

By T. M. Yegulalp

Geometric programming is a mathematical optimization technique, first formulated by Zener [1], for dealing with certain types of functions which typically arise in engineering design problems. The theory provides algorithms for finding the optimal solution, existence theorems by which one can show that a particular design is possible or not; and characterization theorems that especially make possible to obtain the relationships among the optimal design parameters at optimum. It has been demonstrated [2] that this technique can be applied to several design problems in chemical, electrical and mechanical engineering. Design problems in mining, metallurgical and mineral engineering have many aspects similar to that of other engineering problems. Thus it is only natural to consider geometric programming as a potential tool for solving certain optimization problems in mineral industries.

Jan 1, 1974

The need for geological control in orebody estimation and modelling is self- evident to geologists and mining engineers engaged in practical day-to-day estimation. It is only fairly recently, however, that attempts have been made to incorporate explicitly such requirements into published accounts of geostatistical estimation; see, for example, Dowd and Scott (1982, 19841, Rendu and Readdy (1982), Dagbert et al. (1984), Dowd (1984), Benest and Winter (1984). In practice, many geological controls in automatic or semi-automatic estimation and modelling are essentially geometrical controls. The aim of this paper is to present two techniques for geological/geometrical control. Whilst the applications are new, neither technique is entirely original and both have been adapted from other fields and sources. Both techniques are, in some sense, types of estimators and/or interpolators of shapes. The first is a method for fitting spline surfaces to control orebody boundary estimation and to unroll orebodies. The second is an application of Minkowski addition to interpolate shapes such as orebody outlines or stope boundaries and to control estimations.

Jan 1, 1986