Review Article Biochemical Engineering Parameters for ...

Review ArticleBiochemical Engineering Parameters for HydrometallurgicalProcesses Steps towards a Deeper Understanding

K Kundu12 and A Kumar13

1 Department of Biochemical Engineering Harcourt Butler Technological Institute Kanpur Uttar Pradesh 208002 India2Department of Biochemical Engineering and Biotechnology Indian Institute of Technology Delhi Hauz Khas NewDelhi 110016 India3 BIOMATH Department of Mathematical Modelling Statistics and Bioinformatics Faculty of Bioscience EngineeringGhent University Coupure Links 653 9000 Gent Belgium

Correspondence should be addressed to K Kundu kankanakundugmailcom

Received 10 November 2013 Revised 10 March 2014 Accepted 26 March 2014 Published 27 April 2014

Academic Editor Luigi Toro

Copyright copy 2014 K Kundu and A Kumar This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Increasing interest in biomining process and the demand for better performance of the process has led to a new insight toward themining technologies From an engineering point of view the complex network of biochemical reactions encompassed in biominingwould best be performed in reactors which allow a good control of the significant variables resulting in a better performance Thesubprocesses are in equilibrium when the rate of particular metal ion for example iron turnover between the mineral and thebacteria is balanced The primary focus is directed towards improved bioprocess kinetics of the first two subprocesses of chemicalreaction of the metal ion with the mineral and later bacterial oxidation These subprocesses are linked by the redox potential andcontrolled by maintenance of an adequate solids suspension dilution rate and uniformmixing which are optimised in bioreactorsduring mining operations Rate equations based on redox potential such as ferricferrous-iron ratio have been used to describe thekinetics of these subprocesses This paper reviews the basis of process design for biomining process with emphasis on engineeringparameters It is concluded that the better understanding of these engineering parameters will make biomining processes morerobust and further help in establishing it as a promising and economically feasible option over other hydrometallurgical processesworldwide

1 Introduction

Biomining is gaining importance as a unit process whichinvolves biological organisms in mineral extraction indus-tries worldwide With the decreasing high grade ore reservesand increased concern regarding the effect of mining on theenvironment biomining technology which was neverthelessage old deserted technique is now being developed as amain process in the mining industry to meet the demand[1] Another important aspect is the feasibility of biominingtechnologies to treat ores deposits with complex mineralogywhich could be difficult to treat by conventional methods [2]Besides the most attractive feature of biomining is economicfeasibility compared to other competitive techniques [2]Gahan et al [2] comparatively analysed how gold and copperbiomining operations played a role with the increase or

decrease in metal pricing over time Their analysis indicatedthat most biohydrometallurgical innovations have been com-mercially implemented during leaner times [3] Economicfactors such as eliminations of net Smelter Royalties asso-ciated with smelting and refining and possibility of the usebioleaching for on-site acid production to eliminate or reduceacid purchases are the reasons behind this observation Thishas made us look toward the biomining with a broaderinsight into performance and profit oriented research tomeetthe commercial requirements For this purpose the processstoichiometry kinetics and the key parameters involvedin the process have been studied [4ndash7] and the promisingconcepts like design and control of reactors for miningoperations are yet to be practised [8]

Biomining in general is used to describe the exploitationof chemolithotrophicmicroorganisms to assist the extraction

Hindawi Publishing CorporationJournal of MiningVolume 2014 Article ID 290275 10 pageshttpdxdoiorg1011552014290275

2 Journal of Mining

Engineered environment

Solid metal slurry

Biomining microbes

for production of biominig conditions

such as ferric iron and acidity

Adaptation abilitymicrobial diversity

strain activityspatial distribution etc

Microbial factorsPhysiochemical factors

Feed in

Feed out

Fe3+ Fe2+ redox potentialtemperature CO2 pH

inhibitors mass transferlimitation etc

Figure 1 Factors involved in application of bioreactors in biomining

of metals from sulphide or iron-containing ores or concen-trates [9 10] It is a combination of two major operationsbioleaching and biooxidation The metal-solubilisation pro-cess is a blend of microbiology and chemistry Knowledgein microbiology is required as specific microorganisms areresponsible for producing the ferric iron and acid leadingbiooxidation while strong base on chemistry is equallynecessary since the solubilisation of themetal is a result of theaction of ferric iron andor acid on themineral further knownas bioleaching [6 11 12] Conventional biomining is usuallyperformed in heaps of ground ore or in the dumps of waste orspent material Though this process offers several advantagessuch as simple equipment and operation low investmentoperational costs and acceptable yields it is beset with severeoperational limitations such as high heterogeneity of piledmaterial and practically no close process control Moreoverthe low oxygen and carbon dioxide transfer rate and extendedperiods of operation to achieve sufficient conversions arevery challenging [8] From the process engineering pointof view bioreactors are the best choice for regulating thecomplex network of biochemical reactions comprehended inbiomining as they allow for a close control of the variablesinvolved rendering significantly better performances Thereactors are usually arranged in series with a continuousflow of material into the first which overflows to the nextand so on for reduction of reaction volume required [13]Therefore process designing approach along with the definedapplication and monitoring of the abundance and activityof the metal sulfide oxidizing microorganisms will makethe biomining process more industrially popular and as aportfolio of flexible techniques to provide a way of recoveringmetal (Figure 1)

In this paper we discuss many of the theoretical consider-ations regarding the development of process for applicationof continuous-flow stirred-tank reactors in biomining with

focus on different engineering parameters which should betaken into consideration for better control and design ofbiomining processes

2 Mining Mechanisms and Stoichiometry

The most important mineral degrading microorganisms inbiomining applications are the iron and sulphide oxidizingchemolithotrophs which grow autotrophically by fixation ofatmospheric CO

2 However the mineral dissolution mech-

anism is not the same for all metal sulphides Schippersand Sand [14] reported that the oxidation of different metalsulphides proceeds via different intermediates (1) thiosul-phate mechanism for the oxidation of acid-insoluble metalsulphides such as pyrite (FeS

2) and molybdenite (MoS

2) and

(2) polysulphide mechanism for acid-soluble metal sulphidessuch as sphalerite (ZnS) chalcopyrite (CuFeS

2) or galena

(PbS) (Figure 2) [1]In the thiosulphate mechanism solubilisation is through

ferric iron attack on the acid-insoluble metal sulphides withthiosulphate being the main intermediate and sulphates themain end-product Schippers and Sand [14] proposed thereactions using pyrite as an example

FeS2+ 6Fe3+ + 3H

2O 997888rarr S

2O3

2minus+ 7Fe2+ + 6H+ (1)

S2O3

2minus+ 8Fe3+ + 5H

2O 997888rarr 2SO

4

2minus+ 8Fe2+ + 10H+

(2)

In the polysulphide mechanism solubilisation of the acid-soluble metal sulphide is through a combined attack byferric iron and protons with elemental sulphur as the mainintermediate This elemental sulphur is relatively stable but

Journal of Mining 3

Fe3+

Fe2+

Fe3+

Fe2+

MS

MS

M2++ S2O3

2minus

SO42minus

H+

M2++ Sn

2minus S8

Thiosulfate mechanism

Polysulfide mechanism

Tf Lf

Tf Lf (Tf Tt)

(Tf Tt)Tf Tt

SO42+ H+

Figure 2 Scheme of the two metal sulphide oxidation pathways(mechanisms) via thiosulphate or via polysulphides and sulphurbased on the properties of metal sulphides (MS)

can be oxidized to sulphate by sulphur-oxidizing microbes[14] (3)ndash(5)

MS + Fe3+ +H+ 997888rarr M2+ + 05H2S119899+ Fe2+ (119899 ge 2)

(3)

05H2S119899+ Fe3+ 997888rarr 0125S

8+ Fe2+ +H+ (4)

0125S8+ 15O

2+H2O 997888rarr SO

4

2minus+ 2H+ (5)

This explains why strictly sulphur-oxidizing bacteria suchas A thiooxidans or A caldus are able to leach some metalsulphides but not others The ferrous iron produced duringmetal dissolution and biomining might also be reoxidised byiron-oxidizing organisms to ferric iron

2Fe2+ + 05O2+ 2H+ 997888rarr 2Fe3+ +H

2O (6)

The role of the microorganisms in the solubilisation of metalsulphides is therefore to provide sulphuric acid (5) for aproton attack and to keep the iron in the oxidized ferric state(6) for an oxidative attack on the mineral

In anothermethodology proposed by Byrne and Luo [15]it was experimentally shown that the ferric iron precipitationprocess in the basal medium could be described by thefollowing stoichiometry

X+ + 3Fe3+ + 2SO4

2minus+ 6H2O

997888rarr XFe3(SO4)2(OH)6+ 6H+

(7)

where X+ corresponds to a monovalent cation This stoi-chiometry provides the numeric relation between moles ofprecipitated iron and moles of released protons to be used inthe calculations

For the formulation of microbial metabolic reactionoccurring during this course the three half reactions for cellsynthesis (119877

119888) electron donor (119877

119889) and electron acceptor

(119877119886) should be combined So the overall metabolic reaction

(119877) is the sum of the half reactions [15] given as

119877 = 1198910

119890(119877119886minus 119877119889) + 1198910

119904(119877119888minus 119877119889)

= 1198910

119890119877119886minus 1198910

119904119877119888minus 119877119889

(8)

The negative sign of an electron donor reaction means thatthis reaction should be reversed before adding it to the others

3 Process Kinetics

Kinetics of biomining process depends mainly on the overallsum of the kinetics of the two subprocesses of bioleachingand biooxidation in the mineral matrix These subprocessesare balanced when the rate of ferrousferric iron turnoverbetween them is balanced (119903chemFe2+ = minus119903

bacFe2+) [16] The ferric

leach kinetics can be described by

120585Fe2+ =minus119903Fe2+

120572 [FeS2]

=

120585maxFe2+

1 + 119860 ([Fe2+] [Fe3+])(9)

and the kinetics of the bacterial oxidation of ferrous iron isgiven by

119902Fe2+ =minus119903Fe2+

119883

=

119902maxFe2+

1 + 119870 ([Fe3+] [Fe2+]) (10)

According to the shrinking core model by Lizama et al[6] leaching kinetics involves a surface reaction and porediffusion transport which can be described by the followingequation

1 minus

2

3

120572M minus (1 minus 120572M)23

= 119896 sdot 119905 (11)

The shrinking core rate constant ldquo119896rdquo is related to the surfacearea of leaching samples and should be constant over timeIf the particle shrinks at a uniform rate the leaching rate isproportional to the surface area of the mineral particle

Assuming that the ferrous oxidation kinetics follows aMonod dependence on ferrous iron and includes a termrelated to ferric inhibition the rate of ferrous iron oxidationcan be given [17] as

119881oxi =119881max [Fe

2+]

[Fe2+] + 119870119904(1 + 119870

119868[Fe3+])

(12)

where 119881max = (120583max119884) and 119884 is being the yield for ferrousiron oxidation Using this expression value of 119881max can beobtained from the values of 119881 at particular pH in a systemoperated under broad pH range

The kinetics of the chemical reaction in the biominingcan also be related to the chemiosmotic mechanism [17 18]which can be given as 119881chem where the diffusional processcan be related to diffusion of protons across the ferric ironprecipitates layer formed on the bacteria in the leachingsample Thus 119881max can be expressed as

119881max =119881chem1 + 119896Δ119909

(13)

where Δ119909 represents the thickness of the precipitates layerand 119896 is a constant directly proportional to the ratio betweenthe diffusional and the kinetics constant

4 Journal of Mining

In broad terms the overall microbial growth during bio-mining is modelled via Michaelis-Menten kinetics describedas

119889119873

119889119905

= 119862119896119892[Π119891M (119888Fe2+ 119888O

2

119888H+ sdot sdot sdot ) minus 119896119889119891119889] (14)

where 119862 is the microbial concentration 119896119892and 119896

119889are the

specific growth and death rate the function 119891M represents aseries of Monod terms to account for the effects of ferrousion and dissolved oxygen and acid concentrations and 119891

119889

represent death rateThe variableΠ represents the product ofgrowthsubstrate limiting and inhibitory terms [19] The twokinetics namely microbial oxidation (12) and the microbialgrowth (14) are linked via the Pirt equation given as

119881oxi = 119862(119896119892119891119892Π

119910119892

+ 119896119898) (15)

where 119910119892is the yield constant and 119896

119898is the maintenance

(nongrowth) coefficient These formulations can be made forgrowth and functional kinetics of both sulphur and ferrousoxidizing microbes in the reactor

4 Gas-Liquid Mass Transfer

Though some anaerobic processes for biomining have beenproposed recently [1 20 21] the aerobic processes are morein practice which should be designed to optimise the masstransfer of the gases from the sulphide gas phase to liquidphase via gas-liquid interface and allow the gas to diffusethrough the culture medium to the cell surface and theninto the cell Additionally the biooxidation processes rely onCO2as the key carbon source for microbial growth Many

industrial bioprocesses in fact use air enriched with CO2to

ensure rapid biomass growth However this can also be therate limiting due to high availability of more soluble carbondioxide and hence causing inhibitory effects due to reductionin the solubility of oxygen The principal resistance to gasmass transfer can be assumed to be in the liquid film at thegas-liquid interface for sparingly soluble gas-like oxygen andcan be given by the following relation

119873transported =119870119871119886

119867

(119875119892minus 119875119897) (16)

From a process engineering standpoint different mass trans-fer strategies have been modelled [8 19] Supply of oxygenwhich takes place through diffusion can be estimated as therate of change of oxygen concentration in staticmedium layergiven as

120597119862119877

120597119905

= 1198630(

1205972119862119877

1205971199092) (17)

with the assumption thatmineralmatrix is in contact with thegas phase via gas-liquid interface and oxygen concentrationon the surface is in equilibrium with the gas phase Theoxygen concentration in the mineral matrix can be given as

119889119888119875

119889119905

= 119863eff1198892119888119875

1198891199092minus (

119883 sdot 119902max119881

)(

119888119875

119896119904+ 119888119875

) (18)

00E+00

10Eminus04

20Eminus04

30Eminus04

40Eminus04

50Eminus04

60Eminus04

70Eminus04

Oxygen consumption rate (molmin)

00E + 00

40E minus 06

80E minus 06

12E minus 05

16E minus 05

20E minus 05

CO2

cons

umpt

ion

rate

(mol

min

)

40ndash100ppm

100ndash150ppm

150ndash200ppm

200ndash400ppm

600ndash800ppm

lt40ppm40ndash100ppm100ndash150ppm150ndash200ppm

200ndash400ppm600ndash800ppm800ndash1000 ppm1000ndash1200 ppm

gt800ppm

Figure 3 Plot of O2and CO

2consumption rates in the various

column segments over the length of the entire column experimentgrouped by residual CO

2concentration [19]

Petersen et al [22] demonstrated that the rates of O2and

CO2consumption in the biomining are closely correlated

(Figure 3)When grouping the data in ranges of CO2concen-

trations (average concentration in a segment between thosemeasured at the bottom and top of the segment) it wasshown that beyond a certain threshold value of oxygen con-sumption linear trends between O

2and CO

2consumption

rates (119903O2

119903CO2

) exist This shows that the rate of substrate(O2) consumption 119903O

2

can be linked to biomass growth(proportional to CO

2consumption) using a conventional

substrate consumption model as follows

119903O2

=

119903CO2

119884O2

+ 119898O2

(19)

where 119884O2

is the carbon yield on O2(mol Csdotmol O

2

minus1)and 119898O

2

is the maintenance rate of oxygen consumption(molsdotminminus1)

The effects of dissolved oxygen and carbon dioxideconcentration on microbial activity have been investigatedby de Kock et al [23] considering the microbial activity tobe directly proportional to the Fe3+ ion concentration in themedium

The relative activity data points were fitted using a four-parameter log normal fit function

119910 = 01072 + 09029119897 [minus05(

ln (11990925077)09908

)

2

] (20)

where 119910 and 119909 are the relative activity and dissolved oxygen(mgsdotlminus1) respectively The relative activities of the Fe3+production curves at different dissolved oxygen and gaseousCO2concentration concentrations are given in Figure 4 The

effects of increased CO2concentrations on microbial activity

Journal of Mining 5

0 1 2 3 4 5 6 7 8 9 10

00

02

04

06

08

10

12

14

Rela

tive a

ctiv

ity

02

04

06

08

10

12

14

Rela

tive a

ctiv

ity

0 20 40 60 80 100

Inlet oxygen concentration ( vv)

R2= 092

0 5 10 15 20 25 30

Inlet CO2 concentration ( vv)

R2= 096

Dissolved oxygen (mgL)

Figure 4The effect of dissolved oxygen and gaseous CO2concentration concentrations on ferrous iron oxidation activity during autotrophic

growth of Sulfolobus sp U40813 [22]

were also investigated through a series of batch cultivationswith increasing CO

2concentration [23] The relative activity

data points were fitted using a four-parameter log normal fitfunction where 119910 and 119909 are the relative activity and inlet CO

2

concentration respectively

119910 = 03450 + 08298119897 [minus05(

ln (1199091102)1147

)

2

] (21)

Such empirical models can be used for balanced gasificationand agitation in the stirred tank reactors to achievemaximumcell growth and subsequent improved microbial activitynecessary for controlled bioleaching and biooxidation in areactor The existing stirred-tank reactor design is relativelyinefficient when it comes to aeration Improved aerationdesigns will vastly improve the economics of use of stirred-tank reactors in biomining

5 Temperature pH and Redox Potential

Physical conditions like temperature and pH are the inherentproperties of microorganism and so they vary with microor-ganisms and mainly get decided by the dominant speciesin the consortium like T ferrooxidans and Leptospirillumferrooxidans [24] The temperature has been shown to beclearly impacting the biomining rate on iron oxidation bymesophilicmixed iron oxidizingmicroflora [25] By elevatingfrom 20 to 30∘C the rate was reported to increase andbeyond this temperature the rate decreased due to denat-uration of enzymes and proteins as well as the behaviourof ferric ion Operating the reactor at higher temperature isadvantageous in some cases such as during biooxidation ofsulfidic-refractory gold concentrates it allows growth of ther-mophilic microorganisms rendering oxidisation of partiallyoxidised sulfur compounds that are responsible for increasingcyanide consumption [26] Also bioleaching of chalcopyriteconcentrates has been demonstrated using moderately ther-mophilic bacteria [27] and extremely thermophilic Archaea

[28] However operating the reactor in thermophilic rangeincreases both capital and operating costs of the processHence more promising approach is to use a combination ofmesophilicthermophilic process but such approach requiresmore developments before practice [3] Besides temperaturesince the bacteria involved in biomining are chemolithotropicand acidophilic in character the optimisation of pH is neededfor its growthThemajor reason for the dominance of speciessuch as ldquoLeptospirillumrdquo and T ferrooxidans in industrialprocesses is almost certainly the Fe3+Fe2+ ratio that isredox potential (Eh) [14] To estimate the rate of ferrousiron oxidation throughout the bioleaching process to obtainexplicit data of [H+] and [Fe3+] from the values of Eh andpH a preliminary calibration procedure should be developedFor this purposeNernst expressionswhich relate the solutionredox potential with the ferricferrous ratio in solution wereobtained [17] given as follows

In basal medium

Eh = 0471 + 00579 log([Fe3+][Fe2+]

) (22)

In nutrient-free solution

Eh = 0492 + 00644 log([Fe3+][Fe2+]

) (23)

In continuously operating stirred tank reactors a steadystate is reached once the redox potential inside tank remainsapproximately constant and high Under such conditions theability of T ferrooxidans to oxidize ferrous iron is severelyinhibited by the presence of ferric iron [18] whereas theiron-oxidizing ability of Leptospirillum is much less affectedbecause of its ability to oxidize pyritic ores better at highredox potentials Sundkvist et al [29] also developed amodel on Leptospirillum ferrooxidans-dominated culture inchemostat and suggested that the apparent Fe3+ inhibition

6 Journal of Mining

0

5

10

15

20

Max

Zn

diss

olut

ion

rate

(mg

Lh)

10 12 14 16 18 20 20

pH

A brierleyi (70∘C)A ferrooxidans (30∘C)

S yellowstonensis (50∘C)S thermosulfidooxidans (50∘C)

Figure 5 Effect of pH on the initial (maximum) dissolution rateof zinc from the ore by mesophilic moderately thermophilic andextremely thermophilic bacteria [31]

on specific Fe2+ utilization rate was a direct consequence ofthe declining biomass yield on Fe2+ due to growth uncoupledFe2+ oxidation when the dilution rate was decreased

However there may be many other factors which maycontribute to the dominance of particular species in consor-tia The optimum pH for the growth of T ferrooxidans iswithin the range pH 18ndash25 whereas L ferrooxidans is moreacid-resistant than T ferrooxidans and can grow at a pH of 1-2[16] With regard to temperature T ferrooxidans is more tol-erant of low temperature and less tolerant of high temperaturethan is L ferrooxidans Some strains ofT ferrooxidans are ableto oxidize pyrite at temperatures as low as 10∘C [30] but 30ndash35∘C is considered to be optimal Leptospirillum-like bacteriahave been reported to have an upper limit of temperaturefor growth around 45∘C with a lower limit of about 20∘C[31] Deveci et al [32] showed that the bioleaching activity ofmesophileA ferrooxidans as indicated by the dissolution rateof the zinc Figure 5 was adversely affected with decreasingpH to le14 But above this pH there seemed no significantdifference in the dissolution rate and extent (91ndash93) ofzinc In contrast to A ferrooxidans the extraction rate ofzinc by the extremely thermophilic A brierleyi (at 70∘C)was observed to increase with increasing acidity In case ofthermophilic S yellowstonensis and S thermosulfidooxidansthe increase in the acidity to pH 12ndash14 led to a decrease inthe oxidising activity of both strains indicating the inhibitoryeffect of increased acidity on the strains [32]

In a bioleaching process neutralisation is required atdifferent stages As the microorganisms used in bioleachingprocesses are chemolithotrophic and acidophilic having opti-mum activity at low pH therefore the addition of neutralis-ing agents is required to maintain the desired pH dependingon the reactor configuration Neutralisation of the acid pro-duced during bioleaching of sulphide minerals is generallypractised using limestoneGahan et al [33] concluded in their

study that considerable savings in operational costs could beobtained by the replacement of lime or limestone with steelslag without negative impact on bioleaching efficiency

6 Solid Suspension and Mixing

The reactors which are mostly used in commercial miningplants operating continuously and in biohydrometallurgicallaboratories are the stirred tank reactor (STR) and the airliftreactor (ALR) or pneumatic reactor (PR) of the Pachucatype Aeration in these reactors depends on suspensionagitation and ldquofloodingrdquo may cause some limitations whilevery strong agitation may cause cell damage to the extreme[8] Normally the stirred suspension flows through a seriesof pH and temperature-controlled aeration tanks in whichthe mineral decomposition takes place Thus maintainingthe uniformity of solid-liquid ratio in reactor system solidsuspension is important for efficient biomining The solid-liquid ratio becomes a very critical parameter in the biore-actor applications as the solids packed density that can bemaintained in suspension is limited to 20 in the operationof stirred tank reactors beyond which the physical andmicrobial problems occur The shear force induced by theimpellers causes physical damage to the microbial cells andthe liquid as well becomes too thick for efficient gas transfer[34] In biomining higher mixing characteristics and loweroxygen transfer rates are needed compared to commonindustrial fermentations Hence impellers that generate axialflow patterns are preferable [13] Among the axial flow typeimpellers the hydrofoil design offering several advantagessuch as good mixing characteristics low shear and gastransfer capabilities are most compatible with the biominingprocesses demands [8 35] A disk turbine at the bottom anda propeller at the upper part of the shaft have been proposedto meet higher oxygen demand situations [36]

Most of the existing mixing models concern singleimpeller nonaerated systems in turbulent flow conditionsand consider generally one geometrical factor which is theimpeller to reactor diameter (119889119863 ratio) Only the influ-ence of the off-bottom clearance and the spacing betweenimpellers on the power consumption was studied In thepresent instance it can be assumed that the mixing timein an aerated and agitated vessel is governed by a mul-titude of geometrical (ℎ 1198631015840 119889 1198891015840 11988910158401015840 119911 119861 119897 119908 119899

119887 119899imp 119899baf)

and technological (119873 119866 120588 120578) factors The analysis leads tothe following dimensionless grouping and variables [37]

NT119898= 119870(

ℎ

1198631015840)

119886

(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

(

119889

1198631015840)

119891

times 119899ℎ

119887119883119899119894

im119899119895

baf(119861

119889

)

119896

(

119897

119889

)

119898

(

119908

119889

)

119899

Re119875Fr119902119865119903119892

(24)

with NT119898being the dimensionless mixing number Re =

1198731198892120578 the impeller Reynolds number Fr = 119873

2119889119892 the

impeller Froude number Fg = 1198661198731198893119899imp the modified

aeration number related to a single-impeller system and(ℎ1198631015840) (1198891015840119889) (11988910158401015840119889) (119911119889) (1198891198631015840) (119861119889) (119897119889) (119908119889)

dimensionless geometric groupsThe use of a specific reactor

Journal of Mining 7

requires maintaining some of the dimensionless geometricgroups constant Therefore the general expression becomes

NT119898= 1198701(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

Re119875Fr119902119865119903119892 (25)

After evaluating the coefficients 119887 119888 119890 119901 119902 and 119903 by trial anderror the final form of the equation can be determined for thespecific system

A more complete model was given by Norwood andMetzner [38] which takes account of the influence of theRe and Fr numbers and shows clearly the existence of threezones corresponding to the laminar transitory and turbulentflow regime In this model the mixing time number isindependent of the Re number Scale up of mixing uses acorrelation of Reynolds number with Power number 119873

119901

given as

119873119901=

119875

12058811987331198895 (26)

Effective mixing is mandatory for all material transportprocesses involved in microbial and biooxidation kinetics ofbiomining and it attains its maximum effectiveness when allthese processes are optimised in the bioreactor Howevermechanically agitated reactors have limitations with respectto their sizes due to increase in cost and mechanical com-plexity related to agitation above the volume of 500m3 [13]Pneumatically agitated reactors could be a better optionwhenhigh reaction volumes are needed as their construction andoperation costs are lower due to the absence of an agitator

7 Continuous Process Dilution Rate

In order to maintain solids packed density below 20 duringthe operation of stirred tank reactors which deviates due tothe feeding of mineral slurry it is necessary to account theeffect of dilution rate Carrying the mass balance across thereactor the final biomass accumulation rate can be describedas

119889119862

119889119905

= 120583119862 minus (

119865

119881

)119862 (27)

Dilution rate in agreement with the equation of product (119875)balance in a CSTR vessel in steady state is given by Aiba et al[39]

119889119875119899

119889119905

= 119863 (119875119899minus1minus 119875119899) + (

119889119875119899

119889119905

)

production (28)

where the desired dilution rate 119863 = 119865119881 (timeminus1) is arate at which the mineral slurry was fed continuously tothe bioreactor and the effluent is removed by means ofvariable-speed pumps At steady state 119889119875

119899119889119905 = 0 and

assuming that 119875119899minus1= 0 in a single reactor vessel thus

(

119889119875

119889119905

)

total= 0 = (

119889119875119899

119889119905

)

productionminus 119863119875

that is (119889119875119899

119889119905

)

production= 119863119875

(29)

Steady state can also be expressed in terms of growth rateof microorganisms which equals dilution rate 119863 or thereciprocal of the residence time 1120591 Using this method thecalculation of dilution rate of the general model of industrialscale biomining processes can be done (generally between005 and 06 hminus1) A continuous bioreactor for biominingoperating at steady state ideally follows the dilution rate equalto the growth rate of the microorganismsThe rates of micro-bial oxidation obtained from (15) remain coupled for dilutionrates yielding moderate concentrations of the individualcations However with increasing cation concentrations thereaction rate reduces This was shown by Ojumu et al [40]where microbial oxidation occurred better at lower ferric-to-ferrous ratios These rates levelled off or began to decreasewith increasing dilution rates in those runs with high cationconcentrations As120583max was lower at higher concentrations ofdissolved ions washout would be expected to occur alreadyat lower dilution rates

8 Microbial Community Structure

Although microbial community structure is not an engineer-ing parameter it is an integrated part of biomining processeswhich catalyses the biochemical reaction involved and shouldbe considered properly in process designing approach Forthe robust modelling of the system knowledge regardingmicroorganisms present should be acquired as each species iskinetically different from the other Therefore the simplisticassumption of lack of diversity in the characteristics ofthe species within the microbial groups may lead to over-or underestimation of the model output when comparedto observed data There have been considerable studies inthe last years to understand the biochemistry of iron andsulphur compounds oxidation bacteria-mineral interactionsand several adaptive responses allowing the microorganismsto survive in a bioleaching environment which has beenreviewed by Valenzuela et al [41] There are different groupsof microorganisms capable of growth in situations whichsimulate biomining commercial operations [31] The biologyof leaching bacteria becomes more and more complex due toincreasing data on 16S rRNA gene sequences as many newspecies of leaching bacteria have been described and knownspecies have been reclassified [42] Moreover it was believedthat only mesophilic bacteria play a role in biomining butthe discovery of new genera of moderate and extremelythermophilic bacteria belonging to Sulfolobus AcidianusMetallosphaera and Sulfurisphaera has made the bioleach-ing process more economically attractive to the industrieswhere coiling used to be a necessary step to operate thereactors atmesophilic temperature [41 43] Change inmicro-bial community dynamics during moderately thermophilicbioleaching of chalcopyrite concentrate has been recentlystudied by Zeng et al [44] and it was observed that Acaldus was dominant at the early stage while L ferriphilumpredominated at the later stage in a stirred tank reactor

8 Journal of Mining

The bioleaching performance of chalcopyrite for varioushydraulic residence times (HRTs) was estimated by Xia et al[45] and they observed that Leptospirillum ferriphilum andAcidithiobacillus ferrooxidans compete for oxidising ferrousiron leading to large compositional differences in the micro-bial community structure under different HRTs It wasobserved thatLeptospirillum ferriphilum andAcidithiobacillusthiooxidans dominated under the longer HRT (120 h) whileAcidithiobacillus ferrooxidans was dominant when the HRTwas decreased It has been suggested that for the properestablishment of active biomining consortia a substantialperiod of adaptation is necessary as consortia (mesophiles ormoderate thermophiles) cannot be assembled ldquooff the shelf rdquo[46]

9 Conclusions and Perspectives

The future of biomining is much demanding and the incor-poration of engineering principles in the field will make theprocess industriallymore acceptable Stirred-tank bioreactorshave clearly opened new opportunities for processing inmining industries as it allows better control than conven-tional technologies leading to superior leaching efficiencyand metal recovery Aeration temperature pH and so forthcan be continually adjusted to achieve maximum microbialactivity in stirred-tank reactors Present studies demonstratethat in designing a biomining process the bioleaching andbiooxidation performance microbial growth kinetics andother characteristic properties (gas-liquidmass transfer solidsuspension andmixing and the characteristic dilution rate ofa continuous process) should be reasonably well understoodTo evaluate the feasibility and effectiveness of the controlledprocess in wide-scale metallurgy it is essential to makepredictions of the biomining performance The performanceassessment and prediction based on both equilibrium anddynamic studies will help in the extended process controlincreasing the commercial viability

There are several specific needs for more advancementin the commercialisation of biohydrometallurgy A prolificarea for research is a comprehensive study of microbial com-munity structure with rapid accurate and simple techniquesfor monitoring the microbial activity in bioleachmineral-biooxidation systems which will enable the operators to gainmore control on these processes More advanced researchin applied microbiology will throw light in this regard Inmost instances these advancements will help the metallur-gical engineers understand the role and specific operationalneeds for microorganisms and to improve the designingapproaches maximizing the systemrsquos efficiency Another areafor development is in reactor engineering such as materialsof construction New materials for high temperature highlycorrosive conditions that are of relatively low cost wouldalso advance the technology Such reactors would amplifyopportunities to bioleachwhole ores Robustmodelling of therelevant parameters and processes including the microbialcommunity dynamics and structure would make biominingmore established and economically feasible option over otherhydrometallurgical processes worldwide

Symbols

120572M Metal recovery120572 Specific surface area of pyrite (m2molminus1)119903chemFe2+ Chemical ferrous iron production rate

(mol Fe2+ lminus1 hminus1)119903bacFe2+ Bacterial ferrous iron production rate

(mol Fe2+ lminus1 hminus1)119903Fe2+ Ferrous iron production rate

(mol Fe2+ lminus1 hminus1)119860 Kinetic constant in pyrite oxidation

(dimensionless)119888119877 Oxygen concentration in static medium

layer (mol lminus1)119888119875 Oxygen concentration in mineral matrix

(mol lminus1)1198630 Diffusion coefficient (m2 sminus1)

119863eff Diffusion coefficient in surface matrix ofmembrane (m2 sminus1)

120577Fe2+ Area specific ferrous iron production rate(mol Fe2+mminus2 hminus1)

120577maxFe2+ Maximum area specific ferrous iron

utilization rate (mol Fe2+mminus2 hminus1)1198910

119890 Electrons from the donor transferred to

the terminal electron acceptor1198910

119904 Electrons from the donor that is used for

net cell synthesis119870 Kinetic constant (dimensionless)119896 Rate constant119905 Time of bioleaching (day)120583max Specific growth rate of the bacterial cell

(hminus1)119865 Total feed rate (kg hminus1)[Fe2+] Ferrous iron concentration (mg lminus1)[Fe3+] Ferric iron concentration (mg lminus1)Fr Mixing time number[H+] Proton concentration (mg lminus1)119867 Henryrsquos law constant (mol dmminus3 atmminus1)119870119904 Michaelis-Menten constant (mg lminus1)

119870119868 Ferric iron inhibition constant (no units)

119896119871119886 Overall gas mass transfer coefficient

119875119892 Substrate partial pressure in gas phase

(atm)119875119897 Substrate partial pressure at equilibrium

with the substrate concentration in thebulk liquid phase (atm)

119873transported Gas mass transfer rate per unit volume ofreactor (mol sdot sminus1 sdot lminus1)

119902Fe2+ Bacterial specific ferrous iron oxidationrate (mol Fe2+(molC)minus1 hminus1)

119902maxFe2+ Maximum bacterial specific ferrous iron

oxidation rate (mol Fe2+(molC)minus1 hminus1)119902 Specific uptake rate (mol cellminus1 hminus1)119902max Maximal cell-specific oxygen uptake rate

(mol cellminus1 hminus1)

Journal of Mining 9

119881 Volume of processing system (l)119881119871 Liquid volume (l)

119881oxiSpecific ferrous iron oxidation rate(mg (hr cell)minus1)

119881chemMaximum specific ferrous ironoxidation rate non affected by ferric(mg (hr cell)minus1)

119881maxMaximum specific ferrous ironoxidation rate (mg (hr cell)minus1)

119883 Cell density (cells lminus1)

119884 Bacterial growth yield on ferrous ironoxidation (cellmgminus1 Fe)

119911 Bottom clearance (m)1198631015840 Tank diameter (m)

119889 Impeller diameter (m)1198891015840 Impeller spacing (m)11988910158401015840 Top clearance (m)119861 Baffle width (m)119897 Impeller blade length (m)119908 Impeller blade width (m)119899119887 Number of blades

119899imp Number of impellers119899baf Number of baffles119873 Impeller rotation speed (rpm)119866 Volumetric gas flow rate (m3 hminus1)120588 Liquid density (kgmminus3)120578 Liquid kinematic viscosity (Pasdots)119886 119887 119888 119890 119891 ℎ119894 119895 119896119898 119899 119901119902 119903

Numerical coefficients in (24) and (25)

119875 Power required to drive the agitator(W)

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors wish to acknowledge the Ministry of HumanResource and Development (MHRD) Delhi India for pro-viding financial assistantship during this project

References

[1] A Schippers S Hedrich J Vasters M Drobe W Sandand S Willscher ldquoBiomining metal recovery from oreswith microorganismsrdquo in Advances in Biochemical Engineer-ingBiotechnology pp 1ndash47 Springer Berlin Germany 2013

[2] C S Gahan H Srichandan D J Kim and A Akcil ldquoBiohy-drometallurgy and biomineral processing technology a reviewon its past present and futurerdquo Research Journal of RecentSciences vol 1 pp 85ndash99 2012

[3] C L Brierley ldquoBiohydrometallurgical prospectsrdquo Hydrometal-lurgy vol 104 no 3-4 pp 324ndash328 2010

[4] M BoonTheoretical and experimental methods in the modellingof bio-oxidation kinetics of sulphide minerals [PhD thesis] DelftUniversity of Technology 1996

[5] M J Nicol and I Lazaro ldquoThe role of EH measurements in theinterpretation of the kinetics and mechanisms of the oxidationand leaching of sulphidemineralsrdquoHydrometallurgy vol 63 no1 pp 15ndash22 2002

[6] H M Lizama M J Fairweather Z Dai and T D AllegrettoldquoHow does bioleaching startrdquo Hydrometallurgy vol 69 no 1ndash3 pp 109ndash116 2003

[7] P Gonzalez-Contreras J Weijma and C J N BuismanldquoKinetics of ferrous iron oxidation by batch and continuouscultures of thermoacidophilic Archaea at extremely low pH of11ndash13rdquo Applied Microbiology and Biotechnology vol 93 no 3pp 1295ndash1303 2012

[8] F Acevedo ldquoThe use of reactors in biomining processesrdquoElectronic Journal of Biotechnology vol 3 no 3 pp 184ndash1942000

[9] D E Rawlings Biomining Theory Microbes and IndustrialProcesses Springer Berlin Germany 1997

[10] D E Rawlings ldquoHeavy metal mining using microbesrdquo AnnualReview of Microbiology vol 56 pp 65ndash91 2002

[11] S R Hutchins M S Davidson J A Brierley and C L BrierleyldquoMicroorganisms in reclamation of metalsrdquo Annual Review ofMicrobiology vol 40 pp 311ndash336 1986

[12] W Sand T Gerke R Hallmann and A Schippers ldquoSul-fur chemistry biofilm and the (in)direct attack a criticalevaluation of bacterial leachingrdquo Applied Microbiology andBiotechnology vol 43 no 6 pp 961ndash966 1995

[13] F Acevedo and J C Gentina ldquoBioreactor design fundamentalsand their application to gold miningrdquo inMicrobial Processing ofMetal Sulfides pp 151ndash168 Springer 2007

[14] A Schippers and W Sand ldquoBacterial leaching of metal sulfidesproceeds by two indirect mechanisms via thiosulfate or viapolysulfides and sulfurrdquo Applied and Environmental Microbiol-ogy vol 65 no 1 pp 319ndash321 1999

[15] R H Byrne and Y Luo ldquoDirect observations of nonintegralhydrous ferric oxide solubility productsrdquo Geochimica et Cos-mochimica Acta vol 64 no 11 pp 1873ndash1877 2000

[16] D E Rawlings H Tributsch and G S Hansford ldquoReasonswhy ldquoLeptospirillumrdquo-like species rather than Thiobacillus fer-rooxidans are the dominant iron-oxidizing bacteria in manycommercial processes for the biooxidation of pyrite and relatedoresrdquoMicrobiology vol 145 no 1 pp 5ndash13 1999

[17] M S Liu R M R Branion and D W Duncan ldquoThe effects offerrous iron dissolved oxygen and inert solids concentrationson the growth of Thiobacillus ferrooxidansrdquo The CanadianJournal of Chemical Engineering vol 66 no 3 pp 445ndash4511988

[18] O LevenspielChemical Reaction EngineeringWiley NewYorkNY USA 1972

[19] J Petersen and D Dixon ldquoModeling and optimization ofheap bioleach processesrdquo in Biomining D Rawlings and D BJohnson Eds pp 153ndash176 Springer Berlin Germany 2007

[20] K A Third R Cord-Ruwisch and H R Watling ldquoControlof the redox potential by oxygen limitation improves bacterialleaching of chalcopyriterdquo Biotechnology and Bioengineering vol78 no 4 pp 433ndash441 2002

[21] K B Hallberg E Gonzalez-Toril and D B JohnsonldquoAcidithiobacillus ferrivorans sp nov facultatively anaerobicpsychrotolerant iron- and sulfur-oxidizing acidophiles isolated

10 Journal of Mining

from metal mine-impacted environmentsrdquo Extremophiles vol14 no 1 pp 9ndash19 2010

[22] J Petersen S HMinnaar and C A du Plessis ldquoCarbon dioxideand oxygen consumption during the bioleaching of a copper orein a large isothermal columnrdquo Hydrometallurgy vol 104 no 3-4 pp 356ndash362 2010

[23] S H de Kock P Barnard and C A du Plessis ldquoOxygenand carbon dioxide kinetic challenges for thermophilic mineralbioleaching processesrdquo Biochemical Society Transactions vol32 no 2 pp 273ndash275 2004

[24] K Bosecker ldquoBioleaching metal solubilization by microorgan-ismsrdquo FEMSMicrobiology Reviews vol 20 no 3-4 pp 591ndash6041997

[25] D Kim D Pradhan K Park J Ahn and S Lee ldquoEffect of pHand temperature on iron oxidation by mesophilic mixed ironoxidizingmicroflorardquoMaterials Transactions vol 49 no 10 pp2389ndash2393 2008

[26] P C van Aswegen J van Niekerk and W Olivier ldquoThe BIOXprocess for the treatment of refractory gold concentratesrdquo inBiomining pp 1ndash33 Springer 2007

[27] C Brierley and A Briggs ldquoSelection and sizing of biooxidationequipment and circuitsrdquo in Mineral Processing Plant DesignPractice and Control pp 1540ndash1568 Society of Mining Engi-neers Littleton Colo USA 2002

[28] C A du Plessis J D Batty and D W Dew ldquoCommercialapplications of thermophile bioleachingrdquo in Biomining pp 57ndash80 Springer 2007

[29] J Sundkvist C S Gahan and A Sandstrom ldquoModelingof ferrous iron oxidation by a Leptospirillum ferrooxidans-dominated chemostat culturerdquo Biotechnology and Bioengineer-ing vol 99 no 2 pp 378ndash389 2008

[30] P R Norris J C Murrell and D Hinson ldquoThe potential fordiazotrophy in iron- and sulfur-oxidizing acidophilic bacteriardquoArchives of Microbiology vol 164 no 4 pp 294ndash300 1995

[31] D E Rawlings and D B Johnson ldquoThe microbiology ofbiomining development and optimization ofmineral-oxidizingmicrobial consortiardquo Microbiology vol 153 no 2 pp 315ndash3242007

[32] H Deveci A Akcil and I Alp ldquoBioleaching of complexzinc sulphides using mesophilic and thermophilic bacteriacomparative importance of pH and ironrdquoHydrometallurgy vol73 no 3-4 pp 293ndash303 2004

[33] C S Gahan M L Cunha and A Sandstrom ldquoComparativestudy on different steel slags as neutralising agent in bioleach-ingrdquo Hydrometallurgy vol 95 no 3-4 pp 190ndash197 2009

[34] D E Rawlings ldquoMicrobially assisted dissolution of mineralsand its use in themining industryrdquo Pure and Applied Chemistryvol 76 no 4 pp 847ndash859 2004

[35] D W Dew E N Lawson and J L Broadhurst ldquoThe BIOXprocess for biooxidation of gold-bearing ores or concentratesrdquoin Biomining pp 45ndash80 Springer 1997

[36] F Bouquet andDMorin ldquoBROGIM a new three-phasemixingsystem testwork and scale-uprdquoHydrometallurgy vol 83 no 1ndash4pp 97ndash105 2006

[37] D Hadjiev N E Sabiri and A Zanati ldquoMixing time inbioreactors under aerated conditionsrdquo Biochemical EngineeringJournal vol 27 no 3 pp 323ndash330 2006

[38] K W Norwood and A Metzner ldquoFlow patterns and mixingrates in agitated vesselsrdquo AIChE Journal vol 6 pp 432ndash4371960

[39] S Aiba A E Humphrey andN FMillis Biochemical Engineer-ing University of Tokyo Press Tokyo Japan 1965

[40] T V Ojumu J Petersen and G S Hansford ldquoThe effectof dissolved cations on microbial ferrous-iron oxidation byLeptospirillum ferriphilum in continuous culturerdquo Hydromet-allurgy vol 94 no 1ndash4 pp 69ndash76 2008

[41] L Valenzuela A Chi S Beard et al ldquoGenomics metagenomicsand proteomics in biomining microorganismsrdquo BiotechnologyAdvances vol 24 no 2 pp 197ndash211 2006

[42] T Rohwerder T Gehrke K Kinzler and W Sand ldquoBioleach-ing review part A progress in bioleaching fundamentalsand mechanisms of bacterial metal sulfide oxidationrdquo AppliedMicrobiology and Biotechnology vol 63 no 3 pp 239ndash2482003

[43] P R Norris N P Burton and N A M Foulis ldquoAcidophiles inbioreactor mineral processingrdquo Extremophiles vol 4 no 2 pp71ndash76 2000

[44] W Zeng G Qiu H Zhou et al ldquoCommunity structure anddynamics of the free and attachedmicroorganisms duringmod-erately thermophilic bioleaching of chalcopyrite concentraterdquoBioresource Technology vol 101 no 18 pp 7068ndash7075 2010

[45] L Xia C Yin S Dai G Qiu X Chen and J Liu ldquoBioleachingof chalcopyrite concentrate using Leptospirillum ferriphilumAcidithiobacillus ferrooxidans and Acidithiobacillus thioox-idans in a continuous bubble column reactorrdquo Journal ofIndustrial Microbiology and Biotechnology vol 37 no 3 pp289ndash295 2010

[46] C G Bryan C Joulian P Spolaore et al ldquoThe efficiency ofindigenous and designed consortia in bioleaching stirred tankreactorsrdquo Minerals Engineering vol 24 no 11 pp 1149ndash11562011

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of


EarthquakesJournal of


Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining


Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of


Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering


GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of


OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in


MineralogyInternational Journal of


MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Geological ResearchJournal of


Geology Advances in

2 Journal of Mining

Engineered environment

Solid metal slurry

Biomining microbes

for production of biominig conditions

such as ferric iron and acidity

Adaptation abilitymicrobial diversity

strain activityspatial distribution etc

Microbial factorsPhysiochemical factors

Feed in

Feed out

Fe3+ Fe2+ redox potentialtemperature CO2 pH

inhibitors mass transferlimitation etc

Figure 1 Factors involved in application of bioreactors in biomining

of metals from sulphide or iron-containing ores or concen-trates [9 10] It is a combination of two major operationsbioleaching and biooxidation The metal-solubilisation pro-cess is a blend of microbiology and chemistry Knowledgein microbiology is required as specific microorganisms areresponsible for producing the ferric iron and acid leadingbiooxidation while strong base on chemistry is equallynecessary since the solubilisation of themetal is a result of theaction of ferric iron andor acid on themineral further knownas bioleaching [6 11 12] Conventional biomining is usuallyperformed in heaps of ground ore or in the dumps of waste orspent material Though this process offers several advantagessuch as simple equipment and operation low investmentoperational costs and acceptable yields it is beset with severeoperational limitations such as high heterogeneity of piledmaterial and practically no close process control Moreoverthe low oxygen and carbon dioxide transfer rate and extendedperiods of operation to achieve sufficient conversions arevery challenging [8] From the process engineering pointof view bioreactors are the best choice for regulating thecomplex network of biochemical reactions comprehended inbiomining as they allow for a close control of the variablesinvolved rendering significantly better performances Thereactors are usually arranged in series with a continuousflow of material into the first which overflows to the nextand so on for reduction of reaction volume required [13]Therefore process designing approach along with the definedapplication and monitoring of the abundance and activityof the metal sulfide oxidizing microorganisms will makethe biomining process more industrially popular and as aportfolio of flexible techniques to provide a way of recoveringmetal (Figure 1)

In this paper we discuss many of the theoretical consider-ations regarding the development of process for applicationof continuous-flow stirred-tank reactors in biomining with

focus on different engineering parameters which should betaken into consideration for better control and design ofbiomining processes

2 Mining Mechanisms and Stoichiometry

The most important mineral degrading microorganisms inbiomining applications are the iron and sulphide oxidizingchemolithotrophs which grow autotrophically by fixation ofatmospheric CO

2 However the mineral dissolution mech-

anism is not the same for all metal sulphides Schippersand Sand [14] reported that the oxidation of different metalsulphides proceeds via different intermediates (1) thiosul-phate mechanism for the oxidation of acid-insoluble metalsulphides such as pyrite (FeS

2) and molybdenite (MoS

2) and

(2) polysulphide mechanism for acid-soluble metal sulphidessuch as sphalerite (ZnS) chalcopyrite (CuFeS

2) or galena

(PbS) (Figure 2) [1]In the thiosulphate mechanism solubilisation is through

ferric iron attack on the acid-insoluble metal sulphides withthiosulphate being the main intermediate and sulphates themain end-product Schippers and Sand [14] proposed thereactions using pyrite as an example

FeS2+ 6Fe3+ + 3H

2O 997888rarr S

2O3

2minus+ 7Fe2+ + 6H+ (1)

S2O3

2minus+ 8Fe3+ + 5H

2O 997888rarr 2SO

4

2minus+ 8Fe2+ + 10H+

(2)

In the polysulphide mechanism solubilisation of the acid-soluble metal sulphide is through a combined attack byferric iron and protons with elemental sulphur as the mainintermediate This elemental sulphur is relatively stable but

Journal of Mining 3

Fe3+

Fe2+

Fe3+

Fe2+

MS

MS

M2++ S2O3

2minus

SO42minus

H+

M2++ Sn

2minus S8



Tf Lf

Tf Lf (Tf Tt)

(Tf Tt)Tf Tt

SO42+ H+




(3)

05H2S119899+ Fe3+ 997888rarr 0125S

8+ Fe2+ +H+ (4)

0125S8+ 15O

2+H2O 997888rarr SO

4

2minus+ 2H+ (5)


2Fe2+ + 05O2+ 2H+ 997888rarr 2Fe3+ +H

2O (6)



X+ + 3Fe3+ + 2SO4

2minus+ 6H2O

997888rarr XFe3(SO4)2(OH)6+ 6H+

(7)







119877 = 1198910

119890(119877119886minus 119877119889) + 1198910

119904(119877119888minus 119877119889)

= 1198910

119890119877119886minus 1198910

119904119877119888minus 119877119889

(8)


3 Process Kinetics




120585Fe2+ =minus119903Fe2+

120572 [FeS2]

=

120585maxFe2+

1 + 119860 ([Fe2+] [Fe3+])(9)


119902Fe2+ =minus119903Fe2+

119883

=

119902maxFe2+

1 + 119870 ([Fe3+] [Fe2+]) (10)


1 minus

2

3

120572M minus (1 minus 120572M)23

= 119896 sdot 119905 (11)



119881oxi =119881max [Fe

2+]

[Fe2+] + 119870119904(1 + 119870

119868[Fe3+])

(12)



119881max =119881chem1 + 119896Δ119909

(13)


4 Journal of Mining


119889119873

119889119905

= 119862119896119892[Π119891M (119888Fe2+ 119888O

2



119889are the


119889


119881oxi = 119862(119896119892119891119892Π

119910119892

+ 119896119898) (15)








119873transported =119870119871119886

119867

(119875119892minus 119875119897) (16)


120597119862119877

120597119905

= 1198630(

1205972119862119877

1205971199092) (17)


119889119888119875

119889119905

= 119863eff1198892119888119875

1198891199092minus (

119883 sdot 119902max119881

)(

119888119875

119896119904+ 119888119875

) (18)

00E+00

10Eminus04

20Eminus04

30Eminus04

40Eminus04

50Eminus04

60Eminus04

70Eminus04


00E + 00

40E minus 06

80E minus 06

12E minus 05

16E minus 05

20E minus 05

CO2

cons

umpt

ion

rate

(mol

min

)

40ndash100ppm

100ndash150ppm

150ndash200ppm

200ndash400ppm

600ndash800ppm



gt800ppm




2concentration [19]





2and CO

2consumption

rates (119903O2

119903CO2


2




119903O2

=

119903CO2

119884O2

+ 119898O2

(19)

where 119884O2


2

minus1)and 119898O

2




119910 = 01072 + 09029119897 [minus05(

ln (11990925077)09908

)

2

] (20)



Journal of Mining 5

0 1 2 3 4 5 6 7 8 9 10

00

02

04

06

08

10

12

14

Rela

tive a

ctiv

ity

02

04

06

08

10

12

14

Rela

tive a

ctiv

ity

0 20 40 60 80 100


R2= 092

0 5 10 15 20 25 30


R2= 096







2


119910 = 03450 + 08298119897 [minus05(

ln (1199091102)1147

)

2

] (21)





In basal medium

Eh = 0471 + 00579 log([Fe3+][Fe2+]

) (22)


Eh = 0492 + 00644 log([Fe3+][Fe2+]

) (23)


6 Journal of Mining

0

5

10

15

20

Max

Zn

diss

olut

ion

rate

(mg

Lh)

10 12 14 16 18 20 20

pH











119887 119899imp 119899baf)


NT119898= 119870(

ℎ

1198631015840)

119886

(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

(

119889

1198631015840)

119891

times 119899ℎ

119887119883119899119894

im119899119895

baf(119861

119889

)

119896

(

119897

119889

)

119898

(

119908

119889

)

119899

Re119875Fr119902119865119903119892

(24)



2119889119892 the




Journal of Mining 7


NT119898= 1198701(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

Re119875Fr119902119865119903119892 (25)



119901

given as

119873119901=

119875

12058811987331198895 (26)




119889119862

119889119905

= 120583119862 minus (

119865

119881

)119862 (27)


119889119875119899

119889119905

= 119863 (119875119899minus1minus 119875119899) + (

119889119875119899

119889119905

)

production (28)


119899119889119905 = 0 and


(

119889119875

119889119905

)

total= 0 = (

119889119875119899

119889119905

)


that is (119889119875119899

119889119905

)


(29)




8 Journal of Mining





Symbols



























Journal of Mining 9














Acknowledgments


References


























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

Journal of Mining 3

Fe3+

Fe2+

Fe3+

Fe2+

MS

MS

M2++ S2O3

2minus

SO42minus

H+

M2++ Sn

2minus S8



Tf Lf

Tf Lf (Tf Tt)

(Tf Tt)Tf Tt

SO42+ H+




(3)

05H2S119899+ Fe3+ 997888rarr 0125S

8+ Fe2+ +H+ (4)

0125S8+ 15O

2+H2O 997888rarr SO

4

2minus+ 2H+ (5)


2Fe2+ + 05O2+ 2H+ 997888rarr 2Fe3+ +H

2O (6)



X+ + 3Fe3+ + 2SO4

2minus+ 6H2O

997888rarr XFe3(SO4)2(OH)6+ 6H+

(7)







119877 = 1198910

119890(119877119886minus 119877119889) + 1198910

119904(119877119888minus 119877119889)

= 1198910

119890119877119886minus 1198910

119904119877119888minus 119877119889

(8)


3 Process Kinetics




120585Fe2+ =minus119903Fe2+

120572 [FeS2]

=

120585maxFe2+

1 + 119860 ([Fe2+] [Fe3+])(9)


119902Fe2+ =minus119903Fe2+

119883

=

119902maxFe2+

1 + 119870 ([Fe3+] [Fe2+]) (10)


1 minus

2

3

120572M minus (1 minus 120572M)23

= 119896 sdot 119905 (11)



119881oxi =119881max [Fe

2+]

[Fe2+] + 119870119904(1 + 119870

119868[Fe3+])

(12)



119881max =119881chem1 + 119896Δ119909

(13)


4 Journal of Mining


119889119873

119889119905

= 119862119896119892[Π119891M (119888Fe2+ 119888O

2



119889are the


119889


119881oxi = 119862(119896119892119891119892Π

119910119892

+ 119896119898) (15)








119873transported =119870119871119886

119867

(119875119892minus 119875119897) (16)


120597119862119877

120597119905

= 1198630(

1205972119862119877

1205971199092) (17)


119889119888119875

119889119905

= 119863eff1198892119888119875

1198891199092minus (

119883 sdot 119902max119881

)(

119888119875

119896119904+ 119888119875

) (18)

00E+00

10Eminus04

20Eminus04

30Eminus04

40Eminus04

50Eminus04

60Eminus04

70Eminus04


00E + 00

40E minus 06

80E minus 06

12E minus 05

16E minus 05

20E minus 05

CO2

cons

umpt

ion

rate

(mol

min

)

40ndash100ppm

100ndash150ppm

150ndash200ppm

200ndash400ppm

600ndash800ppm



gt800ppm




2concentration [19]





2and CO

2consumption

rates (119903O2

119903CO2


2




119903O2

=

119903CO2

119884O2

+ 119898O2

(19)

where 119884O2


2

minus1)and 119898O

2




119910 = 01072 + 09029119897 [minus05(

ln (11990925077)09908

)

2

] (20)



Journal of Mining 5

0 1 2 3 4 5 6 7 8 9 10

00

02

04

06

08

10

12

14

Rela

tive a

ctiv

ity

02

04

06

08

10

12

14

Rela

tive a

ctiv

ity

0 20 40 60 80 100


R2= 092

0 5 10 15 20 25 30


R2= 096







2


119910 = 03450 + 08298119897 [minus05(

ln (1199091102)1147

)

2

] (21)





In basal medium

Eh = 0471 + 00579 log([Fe3+][Fe2+]

) (22)


Eh = 0492 + 00644 log([Fe3+][Fe2+]

) (23)


6 Journal of Mining

0

5

10

15

20

Max

Zn

diss

olut

ion

rate

(mg

Lh)

10 12 14 16 18 20 20

pH











119887 119899imp 119899baf)


NT119898= 119870(

ℎ

1198631015840)

119886

(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

(

119889

1198631015840)

119891

times 119899ℎ

119887119883119899119894

im119899119895

baf(119861

119889

)

119896

(

119897

119889

)

119898

(

119908

119889

)

119899

Re119875Fr119902119865119903119892

(24)



2119889119892 the




Journal of Mining 7


NT119898= 1198701(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

Re119875Fr119902119865119903119892 (25)



119901

given as

119873119901=

119875

12058811987331198895 (26)




119889119862

119889119905

= 120583119862 minus (

119865

119881

)119862 (27)


119889119875119899

119889119905

= 119863 (119875119899minus1minus 119875119899) + (

119889119875119899

119889119905

)

production (28)


119899119889119905 = 0 and


(

119889119875

119889119905

)

total= 0 = (

119889119875119899

119889119905

)


that is (119889119875119899

119889119905

)


(29)




8 Journal of Mining





Symbols



























Journal of Mining 9














Acknowledgments


References


























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

4 Journal of Mining


119889119873

119889119905

= 119862119896119892[Π119891M (119888Fe2+ 119888O

2



119889are the


119889


119881oxi = 119862(119896119892119891119892Π

119910119892

+ 119896119898) (15)








119873transported =119870119871119886

119867

(119875119892minus 119875119897) (16)


120597119862119877

120597119905

= 1198630(

1205972119862119877

1205971199092) (17)


119889119888119875

119889119905

= 119863eff1198892119888119875

1198891199092minus (

119883 sdot 119902max119881

)(

119888119875

119896119904+ 119888119875

) (18)

00E+00

10Eminus04

20Eminus04

30Eminus04

40Eminus04

50Eminus04

60Eminus04

70Eminus04


00E + 00

40E minus 06

80E minus 06

12E minus 05

16E minus 05

20E minus 05

CO2

cons

umpt

ion

rate

(mol

min

)

40ndash100ppm

100ndash150ppm

150ndash200ppm

200ndash400ppm

600ndash800ppm



gt800ppm




2concentration [19]





2and CO

2consumption

rates (119903O2

119903CO2


2




119903O2

=

119903CO2

119884O2

+ 119898O2

(19)

where 119884O2


2

minus1)and 119898O

2




119910 = 01072 + 09029119897 [minus05(

ln (11990925077)09908

)

2

] (20)



Journal of Mining 5

0 1 2 3 4 5 6 7 8 9 10

00

02

04

06

08

10

12

14

Rela

tive a

ctiv

ity

02

04

06

08

10

12

14

Rela

tive a

ctiv

ity

0 20 40 60 80 100


R2= 092

0 5 10 15 20 25 30


R2= 096







2


119910 = 03450 + 08298119897 [minus05(

ln (1199091102)1147

)

2

] (21)





In basal medium

Eh = 0471 + 00579 log([Fe3+][Fe2+]

) (22)


Eh = 0492 + 00644 log([Fe3+][Fe2+]

) (23)


6 Journal of Mining

0

5

10

15

20

Max

Zn

diss

olut

ion

rate

(mg

Lh)

10 12 14 16 18 20 20

pH











119887 119899imp 119899baf)


NT119898= 119870(

ℎ

1198631015840)

119886

(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

(

119889

1198631015840)

119891

times 119899ℎ

119887119883119899119894

im119899119895

baf(119861

119889

)

119896

(

119897

119889

)

119898

(

119908

119889

)

119899

Re119875Fr119902119865119903119892

(24)



2119889119892 the




Journal of Mining 7


NT119898= 1198701(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

Re119875Fr119902119865119903119892 (25)



119901

given as

119873119901=

119875

12058811987331198895 (26)




119889119862

119889119905

= 120583119862 minus (

119865

119881

)119862 (27)


119889119875119899

119889119905

= 119863 (119875119899minus1minus 119875119899) + (

119889119875119899

119889119905

)

production (28)


119899119889119905 = 0 and


(

119889119875

119889119905

)

total= 0 = (

119889119875119899

119889119905

)


that is (119889119875119899

119889119905

)


(29)




8 Journal of Mining





Symbols



























Journal of Mining 9














Acknowledgments


References


























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

Journal of Mining 5

0 1 2 3 4 5 6 7 8 9 10

00

02

04

06

08

10

12

14

Rela

tive a

ctiv

ity

02

04

06

08

10

12

14

Rela

tive a

ctiv

ity

0 20 40 60 80 100


R2= 092

0 5 10 15 20 25 30


R2= 096







2


119910 = 03450 + 08298119897 [minus05(

ln (1199091102)1147

)

2

] (21)





In basal medium

Eh = 0471 + 00579 log([Fe3+][Fe2+]

) (22)


Eh = 0492 + 00644 log([Fe3+][Fe2+]

) (23)


6 Journal of Mining

0

5

10

15

20

Max

Zn

diss

olut

ion

rate

(mg

Lh)

10 12 14 16 18 20 20

pH











119887 119899imp 119899baf)


NT119898= 119870(

ℎ

1198631015840)

119886

(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

(

119889

1198631015840)

119891

times 119899ℎ

119887119883119899119894

im119899119895

baf(119861

119889

)

119896

(

119897

119889

)

119898

(

119908

119889

)

119899

Re119875Fr119902119865119903119892

(24)



2119889119892 the




Journal of Mining 7


NT119898= 1198701(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

Re119875Fr119902119865119903119892 (25)



119901

given as

119873119901=

119875

12058811987331198895 (26)




119889119862

119889119905

= 120583119862 minus (

119865

119881

)119862 (27)


119889119875119899

119889119905

= 119863 (119875119899minus1minus 119875119899) + (

119889119875119899

119889119905

)

production (28)


119899119889119905 = 0 and


(

119889119875

119889119905

)

total= 0 = (

119889119875119899

119889119905

)


that is (119889119875119899

119889119905

)


(29)




8 Journal of Mining





Symbols



























Journal of Mining 9














Acknowledgments


References


























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

6 Journal of Mining

0

5

10

15

20

Max

Zn

diss

olut

ion

rate

(mg

Lh)

10 12 14 16 18 20 20

pH











119887 119899imp 119899baf)


NT119898= 119870(

ℎ

1198631015840)

119886

(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

(

119889

1198631015840)

119891

times 119899ℎ

119887119883119899119894

im119899119895

baf(119861

119889

)

119896

(

119897

119889

)

119898

(

119908

119889

)

119899

Re119875Fr119902119865119903119892

(24)



2119889119892 the




Journal of Mining 7


NT119898= 1198701(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

Re119875Fr119902119865119903119892 (25)



119901

given as

119873119901=

119875

12058811987331198895 (26)




119889119862

119889119905

= 120583119862 minus (

119865

119881

)119862 (27)


119889119875119899

119889119905

= 119863 (119875119899minus1minus 119875119899) + (

119889119875119899

119889119905

)

production (28)


119899119889119905 = 0 and


(

119889119875

119889119905

)

total= 0 = (

119889119875119899

119889119905

)


that is (119889119875119899

119889119905

)


(29)




8 Journal of Mining





Symbols



























Journal of Mining 9














Acknowledgments


References


























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

Journal of Mining 7


NT119898= 1198701(

1198891015840

119889

)

119887

(

11988910158401015840

119889

)

119888

(

119911

119889

)

119890

Re119875Fr119902119865119903119892 (25)



119901

given as

119873119901=

119875

12058811987331198895 (26)




119889119862

119889119905

= 120583119862 minus (

119865

119881

)119862 (27)


119889119875119899

119889119905

= 119863 (119875119899minus1minus 119875119899) + (

119889119875119899

119889119905

)

production (28)


119899119889119905 = 0 and


(

119889119875

119889119905

)

total= 0 = (

119889119875119899

119889119905

)


that is (119889119875119899

119889119905

)


(29)




8 Journal of Mining





Symbols



























Journal of Mining 9














Acknowledgments


References


























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

8 Journal of Mining





Symbols



























Journal of Mining 9














Acknowledgments


References


























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

Journal of Mining 9














Acknowledgments


References


























































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in





































Volume 2014

Mining


Journal of



Geophysics







Journal of




Advances in











Geology Advances in

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