A model for the bacterial leaching of copper sulfide ores in pilot-scale columns

International Journal of Mineral Processing, 31 ( 1991 ) 247-264 247 Elsevier Science Publishers B.V., Amsterdam

A model for the bacterial leaching of copper sulfide ores in pilot-scale columns

Heinz J. Neuburg, Jorge A. Castillo, Miguel N. Herrera, Jacques V. Wiertz, Tom~s Vargas and Ricardo Badilla-Ohlbaum t

Department of Chemical Engineering, Faculty of Physical and Mathematical Sciences, University of Chile, Casilla 2777, Santiago, Chile

(Received February 5, 1990; accepted after revision October 19, 1990)

ABSTRACT

Neuburg, H.J., Castillo, J.A., Herrera, M.N., Wiertz, J.V., Vargas, T. and Badilla-Ohlbaum, R., 1991. A model for the bacterial leaching of copper sulfide ores in pilot-scale columns. Int. J. Miner. Pro- cess., 31:247-264.

The kinetics of the bioleaching of a low-grade copper ore was studied in a pilot-scale leaching column over a period of 360 days using an open-circuit leaching regime with a fresh sulfuric acid solution at pH = 2 at the inlet of the column. The effluent solutions were periodically analyzed for Cu, total Fe and Fe(II) concentrations as well as Eh and pH values. Bacterial population was measured both in the solution (free cells) and at the surface of the ore particles. Bacterial growth parameters and the true kinetic constant for the dissolution of the sulfide minerals present in the ore were determined in independent experiments. A generalized phenomenological model for the bioleaching of sulfide ores is presented, for which practically all the required parameters can be independently measured. The model is capable of reproducing well measured values of the process variables, and the results are extended to the simulation of the process under different operating conditions.

I N T R O D U C T I O N

Hydrometallurgical processes based on bacterial leaching/solvent extrac- tion/electrowinning are increasingly applied worldwide, particularly for the recovery of copper from low-grade sulfide ores which are otherwise wasted. Depending upon the characteristics of the deposit and the ore, the technology is applied in different ways: heap leaching, in situ leaching and in place mineral processing operations (e.g., Murr, 1980 ). This technology is also applied to the recovery of other metals such as gold (by bacterial oxidation of refrac- tory ores) and uranium. Process design and operation of these complex metallurgical operations can be better approached with the aid of mathematical

For all correspondence.

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248 H.J. NEUBURG ET AL.

models which provide a useful tool to understand the interaction of the most important operating variables in the process behaviour.

Modelling of the bacterial leaching of copper sulfide ores has been developed mainly with reference to the metallurgical aspects of the process, being the bacterial role included in an implicit form. Madsen et al. (1975) mo- delled the leaching of sulfide ores by ferric iron in a pilot column, assuming a constant ferric ion concentration profile along the column, a condition ful- filled in practice by the action of bacteria on the ore, which catalyzes the reox- idation of ferrous iron. These models are based on the shrinking core concept of the mineral particles and present equations with parameters to be optimized from experimental measurements. In general they consider two reaction stages, the first taking place at the beginning of the process where the conversion rate is controlled by surface reactions and the second occurring at later stages, where the process rate is controlled by diffusion through the reacted product zone. Among these models are the ones developed by Bartlett (1973 ), Braun et al. (1974), Madsen et al. (1975 ) and Madsen and Wads- worth ( 1981 ). Energy transport equations have also been included by Cathles and Apps ( 1975 ) and Gao et al. ( 1983 ). Cathles et al. ( 1975 ) introduced the concept of the bacterial controlling effect in the modelling of the bacterial leaching in heaps. A temperature range was defined (between 10 ° and 50 °C) in which bacteria are active and, therefore, an instant conversion of ferrous iron into ferric iron can be assumed. Outside this temperature range, bacterial activity was considered negligible, thus limiting the supply of ferric iron for the leaching process.

A mathematical model that would jointly consider biological and metallurgical aspects of bacterial leaching operations of copper ores is still lacking. The present work describes the modelling work for the bacterial leaching of a copper sulfide ore in a pilot column. Bacterial activity is related with the oxidation of ferrous to ferric iron and described by means of a modified Mi- chaelis-Menten mechanism, distinguishing between the catalytic action of the populations attached to the ore and free in solution. The model, using parameters obtained from the literature, describes well the behaviour of the metallurgical and biological phenomena in a column experiment. The results are used to analyze the interaction between metallurgical and biological factors in the leaching process.

MATERIALS AND METHODS

Sulfide ore The column was fed with a -4 .699 + 0.833 mm particle size fraction pre-

pared from an ore sample from the Mine of El Teniente. This narrow particle size range was selected in order to minimize solution chanelling and air supply limitations throughout the reaction zone, usually found in materials con-

BACTERIAL LEACHING OF COPPER SULFIDE ORES 249

taining high proportions of fines (Madsen and Groves, 1976). Copper and iron contents in the initial ore, determined by chemical analysis, were 1.0 wt% Cu and 5.8 wt% Fe; soluble copper and iron were 0.085 wt% and 0.44 wt%, respectively, determined from a 24-h leaching test of a - 0 . 1 4 7 m m particle size sample in a 5 wt% sulfuric acid solution. Specific acid consumption was determined to be around 4.2 kg H2SO 4 per kg of Cu. Mineralogical reflec- tion microscope analysis indicated that copper was mainly present as chalcopyrite, with much smaller amounts of chalcocite and covellite. Pyrite was the main iron-containing species, with some amounts of magnetite and hematite also present. The gangue was in nature mainly siliceous with the presence of small amounts of gypsum.

Leaching column and procedures A 180-cm high PVC column with a 28-cm diameter was used and loaded

with around 185 kg of the ore. Temperature measurements and gaseous and solid samples were taken at 36-cm intervals starting from the top of the column. The leaching solution was circulated by means of a Cole Palmer Model 7554-20 peristaltic pump and sprinkled onto the ore through a perforated spiral shaped 1/4" tygon pipe attached to the lead of the column. The column was sealed at the top with a gas trap connected to the head and a water seal at the bottom. A flow of 24 I h-~ air (saturated in water at room temperature) was fed countercurrently to the solution throughout the experimental run. The ore bed was leached with a fresh sulfuric acid solution at pH = 2 in an open circuit (no recirculation) with a flow of 1.91 I h -~. The temperature of the column varied in the range of 14 ° to 28°C, following room conditions and no significant variations along the column were observed at any time during the 360 days run.

Analysis of the leaching solutions The leaching process was monitored by means of daily analysis of free-bac-

teria counts, Cu 2÷, Fe 2+ and total Fe concentrations as well as Eh and Ph determinations in the effluent leaching solutions. Cu concentration was determined by atomic absorption spectrophotometry whereas ferrous and total iron concentrations were determined by the o-phenanthrolin spectrophoto- metric method (Muir and Anderson, 1977). Eh and pH values were measured in a Cole Palmer Model 5982-10 pH-Eh meter.

Bacterial activity The iron oxidation activity arises from free bacteria in the solution and

from cells attached to the surface of the ore particles. Free-bacteria numbers were evaluated by determining viable bacteria concentrations in the solution by microscopic counts of colonies formed in solid plate cultures (Harrison, 1984; Espejo and Ruiz, 1987). The population of iron-oxidizing microorga-


nisms attached to the ore along the column was estimated through the measurement of the initial ferrous iron oxidation rate of a sample of ore. Two grams of ore from each sampling point were contacted with 100 ml of MS-9B incubation medium, using HCI instead of H2SO 4 (Espejo and Romero, 1987 ) and the Fe(II) oxidation rate during the first 2-3 h was measured. The attached bacteria population was then estimated by comparing the observed Fe (II) oxidation rate with the specific ferrous iron oxidation rate of a strain of T. ferrooxidans (ATCC 19859) iron oxidizing bacteria (Espejo et al., 1989).

Intrinsic oxidation kinetics of the ore The true mineral oxidation rate coefficient of the sulfide minerals present

in the ore was determined through the measurement of the copper dissolution kinetics of a flotation concentrate, obtained from a - 0.417 + 0.104 mm particle size fraction of the ore. A 2-g sample was leached in an acidic ferric solution in a shake flask at 30°C, from which the value of 4.63× 10 -7 cm s -~ was calculated for the kinetic coefficient (referred as fl in eq. 11 ) for the chalcopyrite oxidation.

M O D E L F O R M U L A T I O N

Stoichiometry of the leaching process

Copper ores nearly always contain a mixture of oxide and sulfide minerals (Dana and Ford, 1971). Oxide minerals are easily solubilized by just acid solutions, whereas sulfide minerals are solubilized only under strong oxidizing conditions and, therefore, the ore mineral composition varies continu- ously during leaching. Oxide species are completely dissolved during the early stages of the process, whereas sulfide species are gradually leached, with the secondary sulfide minerals being depleted first. In this study the main sulfide species present in the ore were chalcopyrite and pyrite, and therefore, from a practical point of view, only these two species are considered for modelling purposes. Although many nonstoichiometric compounds are formed on the surface of these minerals during leaching, the overall stoichiometry for the reactions that take place under the pH and Eh conditions in the column can be represented as:

chalcopyrite (Dutrizac et al., 1969 )

CuFeS2 +4Fe 3+ ,5Fe 2+ +Cu 2+ +2S ( 1 )

pyrite (Garrels and Thompson, 1960)

FeS2 + 8 H 2 0 + 14Fe 3+ ,15Fe2+ +2SO42- + 16H + (2)

It has been demonstrated elsewhere (Herrera et al., 1989 ) for the bioleaching

BACTERIAL LEACHING OF COPPER SULFIDE ORES 2 51

of low-grade chalcopyritic ores, that the microbial effect on the overall leaching rate is mainly related to the bacterial oxidation of ferrous ions, by both bacteria free in solution and attached onto the surface of the ore particles (Almendras et al., 1987; Espejo et al., 1989 ). Thus, the resulting ferrous ions are metabolically oxidized by bacteria according to:

bacteria 2Fe2+ +O2 + 2 H + ,2Fe3++H20 (3)

Mass balances in the flowing phases

The mass balance for the chemical species i present in the system is represented by the continuity-equation in the axial direction of the column:

O( ¢~C~) ~_vz00~ 0 (Da OC~ Ot Oz\ --~-z ] = Y.R, (4)

where es is the liquid fraction (volume of solution per unit volume of ore bed), C~ concentration of species i in solution, vz superficial liquid velocity and Oai the axial dispersion coefficient of species i. ~R~ is the net rate of mass change of the species i by production or consumption through chemical and biochemical reactions and/or transport from or into another phase per unit volume of packing, Since the process is slow and the ore particle size distri- bution used in the present work is very narrow, a small value for the Da~ dispersion coefficient will prevail (Satterfield, 1975 ), and therefore, a plug-flow behaviour for both liquid and gaseous phases and pseudo-steady-state conditions can be assumed throughout the column. Thus the mass balance eq. 4 can be reduced for each species i to:

v~ d C

Eq. 5 is applied to the oxygen balance in the gas phase and to oxygen, ferrous ion, ferric on, cupric ion and hydrogen ion mass balances in the liquid phase. Changes in bed porosity with liquid-flow rate were estimated through stan- dard correlations (Coulson and Richardson, 1979 ), whereas unit mass transfer surface area per unit bed volume was estimated as suggested by Onda et al. (1968 ). Overall gas-liquid mass transfer coefficients were estimated by the correlations of Bolles and Fair ( 1979 ). Liquid holdup was estimated using the correlations presented by Kafarov et al. (1969).

In the present study, mainly isothermal conditions throughout the column were observed, so that the heat balances were not considered in the model.

252

Bacterial mass balances

H.J. NEUBURG ET AL.

Experimental determinations of bacteria numbers throughout the column indicated that the iron-oxidizing activity of both attached and free bacteria is relevant to the leaching process. Therefore, equations for the simultaneous growth of both populations are to be considered in the model. Since both populations are growing using primarily the same substrate (ferrous ion ), the specific bacterial growth rate can be assumed to be essentially the same for free and attached cells (Espejo et al., 1989). Therefore, the variation of the number of attached bacteria along the column can be expressed as (Kargi and Weissman, 1984):

d X ' Vzes( 1 - - eL)--d~--2 = f i x ( 1 - eL) -{ . -gadsX(Xsa t - X ' ) ( 1 - - eL) -- gdesX' ( 1 -- eL)

(6)

and the variation of bacteria in solution along the column as:

dX Uz-~Z=esX]~t--KadsX(Xsa t - X ' ) ( 1 - - e L ) "~ KdesX ' ( 1 --eL) (7)

where e L represents the ore bed void fraction, X' the concentration of attached cells per unit volume of ore,/t the specific bacterial growth rate, X the concentration of bacteria in solution, whereas Kads and Kdes represents the absorption and desorption constants and X~at the saturation concentration of bacteria attached to the surface of ore particles.

The total number of bacteria per unit volume of solution at any point in the column that are involved in the oxidative process can be then written as:

XT =X-+ ) - - e L ~ " X ' ( 1 (8) es

The rate of ferrous iron oxidation is related to the total bacterial growth rate through a Monod equation such as:

dFe 2+ vzdXv --RFe2+ =Vz d - ~ - Y dz (9)

where Y is the bacterial yield and XT is the total bacterial population present in the column. The specific bacterial growth rate/~ can be related to the maximum rate/~max through a Michaelis-Menten type of mechanism and the de- pendance of the bacterial specific growth rate with pH and dissolved oxygen levels. The following expression can be derived for the rate of microbial ferrous iron oxidation:


es _, ( 1 - eL ) (FEZ+) )-F, X F2 (10) -- RFe2 + "~- 'Urnax U "[" "~" es (K+ (Fe~ + )

where the factor F~ accounts for the dependence of Pmax on the pH of the leaching solution (Ingledew, 1982) and F2 takes into account the dependence of ]~max from the dissolved oxygen concentration, being an inhibitory term which becomes less than unity when the dissolved oxygen level in the solution is rate limiting. K represents the bacterial saturation constant.

Ore particle mass balances

Considering the shrinking core model including external and internal mass transfer resistances (Braun et al., 1974; Madsen et al., 1975), the rate of leaching of the sulfide minerals contained in an ore particle at any point in the column can be stated as:

4nr .2, (Fe 3+ ) - 2 ( 1 1 )

- RcuFeS2 0iorlLG_~+D_~e~ r~o ( a r i rio -- ri) + ~-c(r--i-£o) ] ° , , ~ r i

in which rio and ri are the particle radius and reaction front radius respectively, 0io the particle shape factor, G the overall grade of the ore, fl the true mineral oxidation kinetic constant, a a stoichiometric factor, Defrthe effective diffusion coefficient of ferric ions inside the ore particle and kc the mass transfer coefficient of ferric ion in the liquid film surrounding the ore particle. In the present work, since the main copper-bearing mineral present in the ore is chalcopyrite, Rcuves2 represents actually the rate of chalcopyrite leaching where ,8 was determined from an independent experiment (as explained in the experimental section). In the case of a mixed mineral ore, the effect the progressive change of the ore composition in the kinetic factor ,8 must be taken into account through the utilization of an instantaneous grade coefficient in order to consider the mineralogical composition and changes during the leaching process (Herrera et al., 1989).

The reacted fraction of the mineral particles can be defined as:

(ri) a = 1-\r--£o / (12)

The rate equation for chalcopyrite consumption can be transformed into:

G,8(1-0¢) 2/s t-~9-~erfri° ( 1 - or) 1/34- kkc J ~-~e rio do~= 0io Gr~oprFe3+ dt

(13)


where M is the molecular weight of chalcopyrite and Pr the bulk ore density. The particle porosity changes during the process, due to the dissolution of

sulphide minerals and the formation of sulfur and other precipitates inside the ore particle during leaching. Sulfur formed on the pore structure within the ore particle will not be oxidized by bacteria, since cells can not penetrate the pores due to their size. The effect of intraparticle diffusion on the leaching rate is given in eq. 13 by the effective diffusivity coefficient Deff, for the transport of ferric ions through the pores. Deff will be directly influenced by the particle porosity, which in turn can be empirically correlated as a linear function of the reacted mineral conversion, c~, as suggested by Ramachandran and Doraiswamy ( 1982 ):

e = % + z ~ (14)

where Eo is the initial particle porosity and z a numerical constant that de- pends on the characteristics of the ore.

The reaction rates of the remaining reacting species in the system were related to kinetic eqs. 10 and 11 through stoichiometric coefficients as given in reactions ( 1 ) - (3). The ratio of the leaching rates between pyrite and chalcopyrite is calculated through the parameter FPY (Cathles and Apps, 1975 ) which can be considered a constant throughout the process:

RFesJ RcuFeS2 = F P Y (15)

Using this expression the rate of pyrite leaching can be related to the rate of chalcopyrite oxidation.

Computational procedures

The resulting system of differential equations of the model described above must be integrated to obtain the concentration profiles along the column. Once the concentration profiles are known along the column at any time interval, it is necessary to establish the evolution in time of the profiles as well as the consumption of the mineral ore, to ultimately calculate copper recovery. Eq. 13 was integrated between two levels of fractional conversion or, at a certain column height, over a period of reaction time. Since the leaching process of the particles is very slow, the time interval over which the integration is performed can be reasonably large.

The time dependence of the local ferric ion concentration must be known in order to be able to integrate the right hand side of eq. 13. However, since the variation of this concentration at a certain column height between two successive time integration intervals is small, it is reasonable to assume a linear dependence between the values at the beginning and at the end of the time interval at a given column height. The integrated function then leaves frac-

B A C T E R I A L L E A C H I N G O F C O P P E R S U L F I D E O R E S 255

tion a depending upon the local Fe 3+ concentration at the new time interval. Both variables were then calculated simultaneously through an iterative procedure, in conjunction with the set of differential equations. Since the em- ployed column was operating countercurrently, only the characteristics of the inlet flows are known. An iterative calculation procedure over the whole column length is therefore needed to determine all variables of the outlet flows.

Finally, the instantaneous overall copper recovery from the ore was calculated at each time interval, by averaging the local fractions of reacted mineral particles over the whole column, according to:

1 fzf CR= J ° o~l,=tfdz (16)

where CR is the copper recovery, Zf the column height, and tf the specific reaction time interval.

RESULTS AND DISCUSSION

Column and modelling results

Experimental results and model predictions for the overall copper recovery, free-bacteria concentrations and oxidation potential in the effluent solutions of the column over the period of 350 days are shown in Figs. 1 and 2. Measurements (not shown) of the attached bacteria population in the ore by means of the initial rate of Fe E + oxidation indicated an average population of 106 bacteria per gram ore along the column. Thus, the attached population is larger than the free cells in solution. The rate of copper recovery shows a con-

40

8

3 5 -

3 0 ~

25 /

15

10

5

0 , 0 1 O0

[] Experimental Model

~0 ~o Time, days

Fig. 1. Experimental and model predicted copper recovery versus leaching time in the pilot scale leaching column.


6 . 0 - ,900

~ 3.0 o,+Experimental

- - Model

2.0 - ~

Time, days

Fig. 2. Experimental and model predicted free bacteria counts and Eh values versus leaching time in the effluent solutions of the leaching column.

tinuous decay with time. The slight increase in the copper recovery rate observed between day 150 and day 240 days of operation was due to the sea- sonal temperature changes in the building where the pilot column was placed. It can be observed from the results in Fig. 2 that after an induction period of about 25 days, the solution oxidation potential (Eh) as well as the number of bacteria in the effluent solutions reached constant values of about 620 mV and 105 cells per ml respectively. Even though in the last experiments, the inlet solutions contained only acidic water with no recirculation of the leaching solutions, these values remained essentially constant throughout the leaching experiment. This indicates the presence of a stationary population of attached bacteria on the ore.

All the chemical, biochemical and physical parameters of the model described before were established from independent experiments or from infor- mation available in the literature, as described in Table I. Only the value of the parameter 3, which represents an intrinsic characteristic of the ore that leads to the estimation of the variation of the porosity with the copper recovery, included in eq. 12, was varied until an optimum fit by the model of the data presented in Figs. 1 and 2 was obtained. Model predicted bacterial numbers are in general a bit higher than those measured, as shown in Fig. 2, but it must be pointed out that the bacterial growth kinetics parameters were established in separate experiments under somewhat different conditions (shake flask) than those prevailing in the column for bacterial growth (i.e., growth under no nutrient limitations).

BACTERIAL LEACHING OF COPPER SULFIDE ORES

TABLE 1

Model parameters used for simulation

257

Parameter Symbol Value References

Initial panicle radius Ro 0.12 cm experimental Mineral grade G 1.0 X 10- 2 experimental Maximum specific #max 0.25 h-~ experimental bacterial growth rate Bacterial saturation K~ 0.84 g ml-~ experimental constant Bacterial yield Y 2.08 × 109 bact/g experimental Molecular diffusion Do 8.82 X 10 -6 cm 2 s - ~ Perry and Chilton coefficient of Fe 3+ in ( 1973 ) water Particle shape factor ~io 0.65 Cathles and Apps ( 1975 ) Ore densi ty Pr 2.70 g cm experimental Adsorption constant /(ads 3.36 X 10- L cm 3 g - ~ s - ~ experimental Desorp t ion constant Kdes 0.15 h - l experimental Kinetic factor fl 4.63 X 10- 7 cm s - 1 experimental Initial particle Eo 1.5 × 10 - 2 experimental porosity Pyrite to chalcopyrite F P Y 5.0 X 10 -4 Cathles and Apps ( 1975 ) leaching rate ratio Stoichiometric factor t7 4.0 calculated

Model simulations

In spite of the fact that only one parameter out of the many involved in the model was optimized to fit the copper recovery data, the close correspondence between model predictions and experimental measurements supports the validity of the phenomenological assumptions made in the development of the model. Therefore, it can be used to analyze the behaviour of the leaching process along the column in relation to changes in the process conditions, starting from the basic case presented in Table I. From a practical point of view, the most important parameters of the process are the panicle size dis- tribution of the ore, liquid flow rate and iron concentration in the inlet solutions as well as column height. For the simulation studies, a constant pressure difference of 4.725 X 10 - 6 N m -2 across the column was considered to esti- mate the gaseous flow rates across the column in each case.

Figure 3 shows the predicted influence of the initial particle size of the ore on the overall copper recovery as a function of leaching time. At the early stages of the process, the smaller particles present a significatively higher copper leaching rate (curves a and b) in comparison with the larger particle sizes (curves c and d) due to the relatively higher specific external surface area exposed to the leaching solution at conditions in which the process is essen-


I O0 200 300

Time, days

Fig. 3. Effect of the ore particle size (r) in the predicted copper recovery versus leaching time.

tially kinetically controlled. However, once the reaction front has progressed far enough within the ore particles, the process becomes practically controlled by intraparticle diffusion, and the rate of copper recovery decreases to very low values (depending upon the porosity of the ore) independent to the ore particle size. From a practical pont of view, this illustrates the need of the utilization of a minimal ore particle size if most of the leachable minerals present in the ore are to be recovered efficiently. However, the utilization of small particle sizes normally requires agglomeration (Rauld et al., 1986 ) in order to avoid plugging of the ore bed.

The calculated effect of the liquid flow rate on the overall copper recovery in time and dissolved oxygen profiles along the column are shown in Figs. 4 and 5, respectively. It can be seen in Fig. 4 (a -d) that as the liquid flow rate increases, the overall copper recovery decreases. As the flow rate increases, the mean residence time of the solution in the column decreases resulting in relatively lower bacteria numbers in solution. Ferric ion concentration is therefore also reduced; the overall effect being a reduction in the leaching rate. Figure 5 shows the expected dissolved oxygen profiles throughout the column at the leaching time of 350 days. It can be readily seen that under these liquid flow rate ranges, there are no oxygen limitations in the leaching kinetics. As shown in Fig. 5, the model predicts that at very low liquid flow rates the process may also be inhibited in the upper section of the column as oxygen concentration reaches very low values. The high rate of bacterial growth in the upper part of the tower demands consumption of dissolved oxygen at a rate which affects the concentration profile in a short section, where the gas-phase oxygen supply rate may become rate limiting. For lower liquid rates, this oxygen deficiency becomes more pronounced over a shorter length


L"

k

8

4O

35

30

25

20

1 5 -

1 0 -

5 -

r r , 0 1 oo 200 300

Time, days

Fig. 4. Effect of the irrigation flowrate (Ls) on the predicted copper recovery versus leaching time. Ls (1 m -2 h - t ); a: 69.00, b: 24.00, c: 13.88, d: 3.47.

' ~ ' ,0 ' 6o ' 8o 1 0 o ' 1~o 1~0 ' 1;o la0 Dlsts .I lCe f r o m c a l l l m n top.em.

Fig. 5. Predicted dissolved oxygen profiles along the leaching column at the 75th day of leaching for different irrigation flow rates (Ls - 1 m -2 h-1; a: 69.00, b: 24.00, c: 13.88, d: 3.47).

of the column. Oxygen concentration in the liquid reaches again equilibrium values as the solution trickles down the column, through contact with the countercurrently fresh air. This effect occurs because the bacterial growth rate is gradually reduced with the simultaneous reduction of oxygen consumption as the solution potential reaches high values. The effect of the lack of oxygen on bacterial growth has been accounted for by introducing an empirical inhib- iting factor in eq. 10 of the model.

Figure 6 shows calculated profiles of bacteria in the solution along the column for different liquid flow rates after 350 days of operation. Results shown

260 H.J. N E U B U R G ET AL.

i

3

d

' ~ ' , 0 ' ~0 ' ~0 ' , 0 0 ' ~3o ' ~ ;0 ' ~ ; 0 ' , 3o

Distance from colllm~ top,cm.

Fig. 6. Predicted free bacteria counts profiles along the leaching column for different irrigation flow rates (L~ - 1 m -2 h - l ; a: 69.00, b: 24.00, c: 13.88, d: 3 .47) .

0

8

*u I

3O

25

2O

15

10

o o ,oo ~o

Time, days

F e o a + ~ l - l l a: 1 0 " - 3 b: 10"~ c: 10 "x

3oo

Fig. 7. Effect of the Fe s ÷ concentration in the inlet solution in the predicted copper recovery versus leaching time.

in Fig. 6 indicate that cell numbers reach a steady population in the first 10 cm of the column. Moreover, the column length required for bacteria to reach steady concentration is shorter for lower liquid flows (higher residence times); the ultimate bacterial concentration also being larger for lower liquid flows. Bacterial growth rates and concentrations have a direct effect on the solution potential, as it is apparent from the stoichiometry and rate of the ferrous iron oxidation reactions ( 3 ) and (10).

The predicted effect of ferric iron concentration in the inlet solution on the overall copper recovery is shown in Fig. 7 in the ranges of 0.001-0.1 g 1- ~. As


the inlet ferric iron concentration is increased, the rate of copper recovery is significantly enhanced (curves a-c); a large fraction of the extractable amount of copper being practically recovered after 150 days of operation. From then on, copper recovery rates are extremely slow due to the intraparticle diffusional control of the rate process. These results illustrate the advantage of recirculating a fraction of the leaching solutions after copper is extracted, to keep a sufficiently high ferric iron concentration, but not too high to allow the precipitation ofjarosites or other iron containing compounds. The advantage ofrecirculating iron is also seen in the results in Fig. 8 which shows calculated copper recovery profiles along the column for different leaching periods, for levels of 0.001 and 0.1 g 1-l of ferric ion concentration in the inlet solutions. It can be seen that, when the iron concentration is increased, copper recovery in the first half of the column is greatly enhanced, reaching a more homoge- neous profile for leaching along the column.

In the industrial practice, one crucial design parameter is the heap or dump height. Figures 9 and 10 show the predicted effect of increasing the column height from the base case (175 cm) to 300 or 600 cm (curves a, b and c respectively) on the overall copper recovery versus time and dissolved oxygen profiles along the column at 25, 100 and 175 leaching days. For these results, process conditions were the same as those shown in Table I, except for the column height. For the same liquid flow rate of 24 1 m - 2 h -1 the resulting gaseous flow rates are 0.47, 0.26 and 0.181 m -2 h - i for columns heights of 175,300 and 600 cm, respectively for the same overall pressure drop along the column. At these process conditions, there is a significant effect of the column height on the bioleaching rates, due to the differences in dissolved oxygen concentration profiles along the columns at different heights, shown

4O

3(1

10

5

0 0 20 40 60 80 100 120 140 160 180

Distance from c o l - m ~ top.era.

Fig. 8. Effect of the Fe 3+ concentration in the inlet solution in the predicted copper recovery profile along the column for three different leaching intervals.

262 H.J . N E U B U R G E T AL.

M

4o

:I 25

2 0 -

1 5 -

1 0 - Lo:lcm]

s - a : 1 7 5 b: 3 0 0 c: 6 0 0

0 100 200 300

T i m e , d a y s

Fig. 9. Effect of the leaching column height (Lo) on the predicted copper recovery versus leaching time.

8 -

7 -

4 -

I

I c Length[cm] Time[daysl "7__.k ~ao .a: !7a

D b: I 0 0 _ _ _ 3 0 0 c : 2 5

0',2 ~ ' ' 0,4 0,6 018

N o n n a l l z e d d i s t a n c e f r o m co l~smn t o p

Fig. 10. Predicted dissolved oxygen profiles along the column for different column height and leaching intervals.

in Fig. 10. While dissolved oxygen concentrations in the liquid are reduced in the upper part o f the column, partial pressures o f oxygen in the gas phase are practically unchanged along the column due to the high oxygen concentration in air. From a practical point o f view, the higher the column height, the higher is the copper recovery rate per unit leaching area o f heap or dump, provided that the dissolved oxygen concentration in the solution does not reach values below 10 -6 g ml-1 inside the ore bed, where the bioleaching process is halted due to lack o f oxygen.


CONCLUSIONS

A mathematical model was developed to describe bacterial leaching of a low-grade chalcopyrite mineral in a pilot-scale column. Experimental measurements of copper recovery, bacterial concentration and solution potential at the column exit with time were well reproduced by the model predictions. Although a number of assumptions were made to have the simplest possible representation of the present bacterial leaching process, the model developed closely represents phenomena taking place in the system. All parameters used to contrast the model with the measured data were obtained independently, except for the change ofintraparticle porosity with time, which was correlated as a linear function of the reacted mineral fraction by adjusting the copper recovery data.

From the experimental results and their subsequent interpretation by the model, it is concluded that bacterial leaching of the chalcopyrite-bearing ore is mainly controlled by intraparticle diffusion of ferric ions to the reaction front. Only at the very early stages of the leaching process, the intrinsic kinetics is of the same order of magnitude as the intraparticle diffusional resistance in the overall mass transfer process. This fact greatly limits the possibilities of changes in the process bioleaching conditions in order to either speed-up leaching rates or to increase ultimate copper recoveries. The proposed model provides a good starting point to develop more complex models for heap and dump industrial bioleaching operations. In these cases, multidimensional mass and heat balances must be considered as well as the fluid dynamics of gas and liquid phases and the chemistry of iron precipitation.

ACKNOWLEDGEMENTS

This work was supported by Projects UNDP CHI/88/003 and FONDE- CYT 88-626/90-1276. Model Calculations were performed on an IBM 4361 computer of the Computing Center the Faculty of Physical and Mathematical Sciences, University of Chile.

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A model for the bacterial leaching of copper sulfide ores in pilot-scale columns

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