Minerals Engineering xxx (2013) xxx–xxx

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Minerals Engineering

journal homepage: www.elsevier .com/locate /mineng

Ore characterization, process mineralogy and lab automation a roadmapfor future mining q

0892-6875/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.mineng.2013.11.008

q Keynote lecture at Process Mineralogy ’12, Cape Town.⇑ Tel./fax: +1 801 871 7126.

E-mail address: wolfgang.baum@flsmidth.com

Please cite this article in press as: Baum, W. Ore characterization, process mineralogy and lab automation a roadmap for future mining. Miner. Eng.http://dx.doi.org/10.1016/j.mineng.2013.11.008

W. Baum ⇑FLSmidth Salt Lake City, Inc., 7158 S. FLSmidth Drive, Midvale, UT 84047-5559, USA

a r t i c l e i n f o a b s t r a c t

Article history:Available online xxxx

Keywords:Process mineralogyLab automationOre characterizationAutomated mineralogy

Mineralogical laboratory technology has undergone seismic shifts since the introduction of automatedmineral analyzers and other quantitative tools such as XRD Rietveld analysis. During the last 25 years,these changes have positioned mineralogical data into the front line of ore characterization, process con-trol and plant optimization. The continuous deterioration of ore quality in regard to grade, hardness, finerparticle sizes and the increase of metallurgical complexities have made modern process mineralogy anintegral part of new project development. In addition, it has supported improvement of existing plantsand the better utilization of tailings or other residues. Automation in mineralogical (and chemical) lab-oratories from sample preparation to analysis has been the baseline for these improvements. This paperwill highlight key benchmarks of mineralogical work from ore characterization to advanced process min-eralogy including the increasing importance of mineralogical mine site laboratories. A roadmap for thefuture of operations-oriented process mineralogy will be provided.

� 2013 Elsevier Ltd. All rights reserved.

1. Changes in mining and processing

The dramatically increasing cost in mining owing to risingwater, power, reagent and steel prices, less favorable ore character-istics, lesser oxide ores and dominance of primary sulfide feedscoupled with economic aspects of sustainability require a largere-focus of operations on process mineralogy.

� With high(er) throughput operations, lack of adequate mineral-ogical data may inevitably lead to not using the full potential ofthe ore body.� The merging and blending of standard and new process technol-

ogies will carry more risks without continuous information onore variability.� The focus on ‘‘minimum liberation’’ with a coarser grind will

mandate a detailed liberation-locking profile of the ore types.� The focus on minerals with fast float kinetics could conceivably

down-size a float circuit.� Mineralogy-related intelligent circuit design with tailored

equipment (not cookie-cutter, mass-produced machinery) willbe instrumental for future concentrators.

� Mineralogical input for better regrind circuit design and adapt-ability of processing flow sheets (Chadwick, 2012) will emergeas a fundamental parameter avoiding the lingering poor com-minution designs.� Minimizing downtime due to mineralogical ore variability.� Based on recent data (Chadwick, 2011), the major copper pro-

ducers need to replace an average of almost 480,000 t of Cu pro-duction in reserves each year. The new reserves requiresubstantially more mineralogical characterization in order toavoid poor processing performance. Improving plant through-put and process efficiency will not only depend on large equip-ment. It is significantly governed by the ore feed mineralogy,texture and better control of the variance thereof.

Further, the long(er) lead times for mega projects need to beshortened. Changes in process equipment are significant (i.e. big-ger and fewer in the future). This includes the use of ever increas-ing float super cells (>600 m3 and beyond), hybrid energy flotation,large SAGs, >3 m HPGRs, the better correlation of mineralogy withfroth vision control, or better design of regrind mills. These equip-ment changes will rely on more mineralogy to minimize the riskfor mill and float circuit design and throughput guarantees.

Bigger may be better, but only if ‘‘bigger’’ is engineered, flow-sheeted and operated properly. The processing with ‘‘bigger’’ willalso imply ‘‘fewer’’ which makes it imperative that the ore charac-terization has to be ‘‘better’’, ‘‘bigger’’ and ‘‘more frequent’’. Miner-alogy’s impact of detrimental gangue on flotation will not be

(2013),

2 W. Baum / Minerals Engineering xxx (2013) xxx–xxx

smaller with bigger and less equipment. The critical importance ofroutine gangue analysis is highlighted in the following:

Ph

Hardness &abrasion

lease cite this articlettp://dx.doi.org/10.1

Quartz–Feldspar–Garnet–Tourmaline–Magnetite

Sliming

Clays–Micas–Talc–Sulfates–Fe-oxides/Hydrxides–Carbonates

Self floaters

Talc–Pyrophyllite–Serpentine–Chlorite–Sericite

Water solublephases

Cu–Sulfates–Na/K-Salts–Mo-Oxides–Ca–Al–Mg Sulfates

ReagentCONSUMERS

Clays–Micas–Carbonates–Zeolites–FeOxides–Jarosite

pH changers

Alunite–Jarosite–Cu–Al–Mg-Sulfates-Chlorides

Froth de-stabilizers

Clays–Sulfates–Sericite–Talc–Hornblende

High throughput plants will promote metallurgical challengesrelated to the following:

Leach

in press as: Baum, W016/j.mineng.2013.11

Float

Coarser crush

Less residence time Less surface area Less liberation/access Less wetting More middlings Poor agglomeration/permeability More nugget effects Lower 2nd pass recovery Sluggish flotation Poor quality cathodes Coarse/fine sulfide losses

Many of the cost overruns and/or outright flow sheet failures ob-served in recent years are related to poor ore characterization,fast-tracking of metallurgical testing without robust endmemberore type mineralogy and too much use of master composites whichdilute and blur detrimental process mineralogy features. In addi-tion, modeling requires substantially more mineralogy data inputfrom representative samples. The representivity (trueness) of sam-pling (or lack thereof) has been a key factor in biased metallurgicaltesting, poor flowsheeting and misleading models (Lotter et al.,2003, 2010; Baum et al., 2004; Lotter and Laplante, 2007).

Unless mining adopts substantially more use of process miner-alogy, current and future operations will continue to be handi-capped by ‘‘Achilles’ Heels’’ in startup. They may include:

Equipment & Circuit Issues� Start-Up & Ramp-Up Delay.� Throughput Less Than Design.� Equipment/Circuits Undersized.� Higher Wear.� Poor Design of Re-Grind.� Failure of Equipment.Process Issues� Increased Downtime – Lower Availability.� Thickener Overflow Problems.� Flotation Problems.� Inadequate Reagent Use.� Lower Metal Recovery.Poor Plant Economics� Production Loss & Replacement Cost.� Higher Maintenance & Operating Cost.

Although in the past, the purchase of a ‘‘tonnage-enhancingtool’’ (e.g. a new truck) was on the priority list, mining companiesare realizing that top-of-the-line laboratories are equally

. Ore characterization, process miner.008

important when high-throughput is required. With the increasinguse of engineered stockpiles for bio leaching of low-grade ore orwaste rock in copper (and/or lower-grade heap leaching), bio-met-allurgy with high(er) extractions will outperform the expectationsonly if a thorough feed mineralogy is performed in concert with‘‘down-hole’’ chemical–analytical work. Important recommenda-tions include:

� Optimizing the high-cost areas of crushing and grinding withbetter blast indexing and blast fragmentation based on dailymineralogy control (Allen et al., 2007; Brandt et al., 2011).Energy efficiency starts with better blast indices based on actualrock mineralogy, i.e. daily blast hole mineralogy via XRD, NIR orFT-NIR.� Subsequent tracking of ore characteristics in blasted muck via

radio-frequency transponders and optimization of ore routing,reduction of dilution and better stockpile forecasting.� Addition of bulk gangue mineralogy data to the (too frequent)

exclusive use of comminution test data. Gangue mineralogy(more than just hardness, abrasion, and breakage) governsand/or limits flotation performance.

Deeper mining and challenges of more underground operationswill require high-quality and extensive mineralogical informationto permit robust modeling. The skill crisis in the mining industry willmandate more lab automation for operations in remote locations.The path to future automation efforts is described by CSIRO (2009).

2. Process mineralogy and the re-tooling of ore characterizationand plant optimization

Ore characterization-related losses continue to represent a ma-jor cost factor for mining, routing, processing and tailings handling.De-bottlenecking of existing or recent design and plant restarts canbe executed faster and more cost-efficiently with process mineral-ogical support. Although the trend to big plants will alleviate someissues, mineralogy’s troublesome process features (both for leach-ing and flotation) will not disappear with bigger equipment. New(faster and better) mineralogical tools will have to focus on provid-ing 12–24 h data feedback for operations in order to counteractharmful variances in gangue, alteration or sulfide characteristics.

Due to the capability of producing mineralogical data in everincreasing quantities and faster turnaround times, the mineralo-gists of the future also have an obligation to maintain high qualityand thorough interpretation of the data. With the availability ofpowerful lab technology, we need to remember that not every sam-ple or metallurgical problem requires a CT Scan or open heart surgery.

A poor mineralogical analysis from fast-track automated min-eral analysis can be more detrimental than no analysis. Experi-enced process mineralogists will play a different role in thefuture. They will become the ‘‘flight deck managers’’ of highly auto-mated laboratory cockpits. Filtering out the process implicationsfrom the plethora of automated mineral data will be at the cross-roads for top process mineralogy vs. mere high volume dataspreadsheets based on short-cut analytical approaches.

Emerging metallurgical sectors (such as bio leaching of low-grade chalcopyrite ores) with innovative leach concepts (e.g. hy-brid stockpile and heap leaching involving large in-pit HPGRs) willrequire strong mineralogical guidance. As recent efforts in engi-neered stockpile bio leaching (Ekenes and Caro, 2012) have dem-onstrated, large-scale sampling (>5000 samples) andcharacterization programs are essential for successful metallurgy.

Clearly, sufficient ore characterization and, especially, metallur-gical testing have suffered in recent years as it is assumed that aclearly defined mineralogical analysis and metallurgical testingplan can be substituted with modeling and simulation:

alogy and lab automation a roadmap for future mining. Miner. Eng. (2013),

W. Baum / Minerals Engineering xxx (2013) xxx–xxx 3

� The impact of clay minerals on reduced grinding efficiency,packing of clays in lifters, and on mill loading is significant.Yet, many projects are developed without a quantitative claymodel.� The interference of alteration minerals with pulp viscosity, air

dispersion and froth characteristics.� The impact of middlings on the recycle streams.� Sulfide/gangue mineralogy on collector–frother optimization.� Minor changes of alteration and/or aged ore on collector dosage.� The formation of Run-Away-Float Conditions caused by acidic

minerals, clay and other gangue slimes.� Hydrophobic gangue’s impact on froth structure, froth drainage

issues and dry froths.

The more aggressive application of benchmark concentrator andheap leach surveys (‘‘audits’’) constitutes one of the most important(continued) process optimization tools. Many circuits are over-reagentized and/or do not even have a baseline mineralogical mate-rial balance for the grinding, rougher and cleaner circuits.

In the past, these surveys have generated tremendous values(Baum et al., 1989,1996; Kendrick et al., 2003; Baum et al., 2004;Rule and Schouwstra, 2011; Bristette and Roman, 2012). Followingare some examples from copper-moly concentrators:

1. Gold recovery increases of 5–6%.2. Reduction of lime consumption by 1–3%.3. Quantifying the molybdenite losses in the rougher and cleaner

tails.4. Introduction of high intensity conditioning.5. Identify areas (pH and particle size) for good, mediocre and

poor Cu recovery.6. Identification of ‘‘Re-Grind Sweet Spot’’ for pyrite rejection.7. The use of ‘‘Kicker–Collectors’’ for recovering previously lost

copper.

The preventive/prophylactic value of quantitative, baseline,process mineralogy in combination with rougher kinetics flotationtesting has been outlined by Zahn et al. (2007).

The introduction of seawater for processing will mandate evenmore mineralogical scrutiny as we are just on the threshold ofunderstanding the impact of this water type. The use of de-sali-nated water will increase the operating cost and reducing mineral-ogical variance will also be more critical. Capital discipline equalsmore discipline in ore characterization.

Re-tooling for process mineralogy also encompasses the imper-ative effort to include downhole logging tools in heap or stockpileleach applications. Such as:

Ph

Parameter

lease cite this article inttp://dx.doi.org/10.101

Conventional

press as: Baum, W. Ore char6/j.mineng.2013.11.008

Potentiadown-hole tool

Chemistry

ICP/Leco ECS Acid

consumption

Auto TiT, EM, DCR ECS, PGNAA

WireLog

Moisture Sensors, ER APT pH/Eh pH/Eh meters ? Oxygen O2 sensors ECS? Imaging Optical Various tools Hydraulic

conductivity

Piezometer CMR + NMR

Temperature

IR + TC Fiber optics Porosity/pore

size

– CMR, NMR,

resistivity

Permeability NIR Array induction/

laterolog

acterization, process mineralo

q (continued)

Parameter

gy and lab automation

Conventional

a roadmap for future minin

Potentiadown-hole tool

Mineralogy

XRD/NIR/FTIR/AMA/SEM/NAA

ECS, NCS

ICP: Inductively Coupled Plasma Spectrometer.Leco: Leco Carbon & Sulfur Analyzer.Auto TiT: Auto-Titrator.EM: Electromagnetic Measurements.ER: Electric Resistivity.Optical: Optical Camera Imaging.IR: Infrared Probes.TC: Thermocouples.NIR: Near Infrared Analysis.XRD: X-ray Diffraction.FTIR: Fourier Transform Infrared.AMA: Automated Mineral Analyzer.SEM: Scanning Electron Microscope.NAA: Neutron Activation Analyzer.ECS: Elemental Capture Spectroscopy.PGNAA: Prompt Gamma Neutron Activation.APT: Acceleration Porosity Tool.CMR: Combinable Magnetic Resonance.NMR: Nuclear Magnetic Resonance.NCS: Nuclear Capture Spectroscopy.

3. Modeling and forecasting

Modeling, simulation and geo-metallurgy are contingent on:

� High quality quantitative mineralogy.

And� An interpretation of the mineralogy data – ‘‘What do the data

mean for metallurgy?’’ (Schouwstra and Smit, 2011).With the persistant lack of skilled technical staff and dismal

improvement prospects for the near future, the mining industryhas been using modeling and simulation extensively. Althoughthese models contain some very good data, many of them still havedeficits in mineralogical information. Models for copper or goldheap leaching, copper or moly flotation without good quantitativegangue mineralogy, are prescriptions for ‘‘casino metallurgy’’.

Bond Work index (BWi) tests are costly, time consuming andprone to operator/SOP errors. Rapid, quantitative XRD analysiscould provide large data bases which may actually be more reliablethan a small amount of BWi data (Carrasco, 2007). Ausburn andBaum (2013) have shown that various minerals can be used to per-form considerably more robust grinding and mill throughput mod-els/forecasts than the exclusive use of conventional BWi tests withlow sample populations. The grinding efficiency (and relatedthroughput) of porphyry copper ores in conventional mills is moreimpacted by certain alteration minerals than by the traditional‘‘hardness mineral suite’’. Consistent and daily mine/plant supportmineralogy can provide major technical and economic improve-ments on vital KPIs such as:

1. Blast Indexing.2. Agglomeration.3. Acid curing.4. Permeability.5. Slope Stability.6. Acid Consumption.7. Mill Throughput.8. Froth Stability.9. Pulp Density.

g. Miner. Eng. (2013),

4 W. Baum / Minerals Engineering xxx (2013) xxx–xxx

Thompson (2012) has shown that test errors made in designand metallurgical work can result in a very large gross revenue im-pact. For a hypothetical 200,000 t/d copper concentrator with a0.5% head grade at a Cu price of $3.5/lb, the impact of a 5% errorwould be as follows:

Ph

SAG design

lease cite this article in press as: Baum, W. Orttp://dx.doi.org/10.1016/j.mineng.2013.11.00

±$ 120 million/year

Bond work index ±$ 10–15 million/year Flotation ±$ 22 million/year

Design and metallurgical testing can be more reliable and pre-cise with the use and assistance or process mineralogical data.Likewise, modeling of flotation circuits without the proven Miner-alogical (baseline) Surveys, could result in inefficient or potentiallymisleading decisions on geo-metallurgy, grinding, and flotation.Jeltema et al. (2013) have shown that efficient process design,accurate equipment sizing and timely startup require a reliablemineralogical ore characterization and thorough metallurgical testprogram.

4. Lab automation in mining

The increasing shift to underground mining mandates more andfaster mineralogical and chemical lab support and/or cross beltanalysis. With the growing size of new and expanded concentra-tors (150,000–250,000 t), the process risks can be substantially re-duced through automated mineralogical lab technology. Forexample routine daily XRD/NIR analysis can optimize blast index-ing, crusher operations, reduce mill performance and/or avoid ma-jor flotation problems. In leach operations, it can also remediatepoor agglomeration, large swings in acid consumption along withpermeability problems on the heap, slope failures in lifts, low PLSgrades and, finally, lower-than-expected metals extraction. If theinvestment in and the change to cutting edge lab technologymay not be a priority for management or considered expensive,the alternatives of increasing cost, poor recovery, loss of competi-tiveness have considerably more down-sides.

Automated Central Labs or Smaller Automated Mine Site Labscan provide high efficiency, better quality and fast lab data forexploration/mine geology samples, geo-metallurgy programs, dailyblast holes, and all other production samples (Best et al., 2007;Baum, 2009; Baum, 2013) . Automation opportunities exist in thefollowing areas:

� Sample preparation (for Mineralogy & Chemistry).� Optical microscopy.� XRD and NIR.� Automated Mineral Analysis (QEMSCAN, MLA, TIMA or others).� Digestion/XRF/Leco/Titrators/Density/Others.

Deteriorating economics will be rapidly alerting the operationsthat the ‘‘shake & bake approach of the past’’ will not be enough forcompetitive mining in the 21st century. On the contrary, the pastapproach may lead to more circuit shortfalls than we have seento date.

5. The roadmap for mining, geo-metallurgy and competitiveoperations

Resource control, mine planning, production forecasts and plantsupport via 24/7 mineralogy data will become imperative for com-petitive operation and best-practice metallurgical performance.One can even go as far as concluding that high-quality quantitative

e characterization, process miner8

on-line mineralogy may permit the processing of resources thus farconsidered uneconomic (Baum, 2007).

It is tentatively concluded that the mines of the future will beoutfitted with down-hole probes, cross-belt technology and auto-mated central labs or in-plant robotics mineralogy modules. Anefficient metallurgical operation with excellent ore characteriza-tion and frequent process mineralogy support, superlativelyequipped and run, will overcome the inferiority of an ore deposit.Process mineralogy and lab automation in the future need to con-tinue to make mineralogical data more applicable for daily produc-tion support and establish mineralogy ‘‘lab modules’’ which canprovide 12–24 h turnaround on mine and plant samples.

Specifically, the following areas require continued bold devel-opment and rapid implementation:

� Site labs, central labs or smaller plant lab modules.� Mineralogy needs to be available 24/7.� Daily blast hole XRD & NIR should become a routine (as much as

assays).� Clay content.� Permeability control of heaps.� Daily rougher feed mineralogy to monitor gangue and sulfides.� The use of Down-Hole Analysis for heaps and ROM stockpiles.

6. Conclusions

The mineralogical ore characteristics govern and drive anychoice and design of process technology.

Mining companies, consulting groups, engineering firms andequipment manufacturers need to re-focus their future efforts onmineralogy-related process and equipment selection. Further, con-tinuous, daily production mineralogy (analogous to daily chemicalanalyses) will become a key business and operating parameter foroptimal metallurgical performance and mine economics. It repre-sents the most powerful ‘‘risk reduction factor’’ for process plants.Step-changes for future mineralogy labs may include:

Sample Preparation� Robot Circuits & 24/7 Labs.Characterization� Optical Microscopy/Imaging.� XRD–NIR–FTIR.� LA–ICP–MS & LIBS.� X-ray Micro CT.Automated Mineral Analyzers� Fast-High-Capacity Lab Systems.� Field Systems/Plant Modules.Mine Site Mineralogy Labs.� Cerro Verde, Climax, Anglo, Xstrata, Rio Tinto, Barrick,

Others.On-Line/Cross Belt/Downhole Technology� PFTNA, PGNAA Wirelog, ECS, APT, NMR, et al.� Cross Belt Neutron Analyzers, Blue Cube.� NIR or LIF Systems & Others.

al

LA–ICP–MS

ogy and lab autom

Laser Ablation Inductively Coupled MassSpectrometry

LIBS

Laser Induced Breakdown Spectroscopy X-ray Micron

CT

Computed Tomography

PFTNA

Pulsed Fast Thermal Neutron Analysis PGNAA Prompt Gamma Neutron Activation Blue Cube In Line Diffused Reflective Spectroscopy LIF Laser Induced Fluorescence

ation a roadmap for future mining. Miner. Eng. (2013),

neering xxx (2013) xxx–xxx 5

Process diagnosis, flow-sheet design and optimization are mosteffectively and efficiently achieved through a combination or robust

metallurgical testing (including sufficient piloting) and reliable pro-cess mineralogy (Lotter et al., 2011). Lotter et al. (2011) also indi-cated that this approach has shown an internal rate of return of92% p.a. for the investment in the required laboratory equipment,sampling and cost of plant modifications. This author’s experiencefrom the copper-moly industry indicates that investments in mod-ern process mineralogy generate $3–5 for every $ 1 dollar invested.

With the focus on Autonomous Mining, the future mineralogylabs will play an increasingly critical role if they can adapt to robot-ics modules in order to accommodate 12–24 h turnaround needs.Despite the auto-piloted labs, the mineralogy labs will continueto need highly experienced Process Mineralogists to monitor thelaboratory systems, sort the gigabytes of data for essential pro-cess/metallurgy input.

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alogy and lab automation a roadmap for future mining. Miner. Eng. (2013),