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Temperature control in copper heap bioleaching
Hydrometallurgy 176 (2018) 26–32
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Temperature control in copper heap bioleaching a,⁎
Wenying Liu , Giuseppe Granata a b
T
b
Department of Materials Engineering, University of British Columbia, 309-6350 Stores Road, Vancouver, BC V6T 1Z4, Canada Faculty of Science and Engineering, Department of Resources and Environmental Engineering, Waseda University, Tokyo, Japan
A R T I C L E I N F O
A B S T R A C T
Keywords: Heap temperature Raffinate flow rate Thermal cover Extent of pyrite oxidation Raffinate temperature
Heap leaching is being increasingly explored as a lower cost metallurgical technology to extract copper from sulfide ores of low grade and quality. Heap temperature is a critical factor in achieving economic copper extraction. The determination and control of heap temperature is challenging due to the intrinsic complexity of the intertwined fundamental processes occurring simultaneously inside a heap. In this study, we applied the HeapSim-2D model, calibrated using data provided by the Quebrada Blanca Mine, to study the response of heap temperature to variations of four key design parameters: raffinate flow rate, raffinate temperature, the extent of pyrite oxidation, and the application of a thermal cover. Note that the reported results have not been validated against experimental data and other possible rate-limiting factors are presently ignored. The modelling results showed that at a fixed raffinate temperature, the average heap temperature approached the raffinate temperature faster at a high flow rate than at a low flow rate. The heat generated by the oxidation of pyrite led to an increase in the heap temperature, but the magnitude of the increase was negligible at high raffinate flow rates, which would remove the generated heat via convection by the bulk movement of the leaching solution. The model predicted that the application of a thermal cover had a positive effect on maintaining the heap temperature, but the effect also depended on the raffinate flow rate. Understanding the effects of these design parameters on heap temperature is critical for achieving optimum copper extraction in heap leaching.
1. Introduction Temperature is among the key factors that control the kinetics of copper leaching from different copper-bearing sulfide minerals, such as chalcocite and chalcopyrite. Extraction of copper from chalcocite using bacterially-assisted heap leaching has been successfully practiced on a commercial scale (Petersen, 2016). In heap leaching, crushed copper sulfide ore is irrigated with leaching solution high in acid and low in dissolved copper (termed raffinate), generating an effluent solution low in acid and high in dissolved copper (termed pregnant leach solution or PLS). Chalcocite leaching is reported to occur in two stages, with the second stage that converts secondary covellite to copper sulfate and elemental sulfur being the rate-limiting step (Dutrizac and MacDonald, 1974; Niu et al., 2015). The kinetics of the second stage is sensitive to temperature as supported by a high activation energy on the order of 98 kJ/mol (Bolorunduro, 1999). A typical chalcocite heap bioleach pad, with temperature rarely exceeding 25 °C, operates between 8 months and 2 years to achieve approximately 80% of copper extraction (Petersen and Dixon, 2007). Dixon (2000) estimated that every 10 °C increase in temperature would theoretically lead to a threefold
⁎
increase in the intrinsic rate of the second-stage leaching. The application of heap leaching to treat chalcopyrite ore can potentially result in reduced cost and attractive environmental benefits in copper production (Pradhan et al., 2008; Robertson et al., 2012). However, heap leaching of chalcopyrite ore has not been successfully practiced on a commercial scale, mainly due to its slow rate of dissolution caused by passivation at low temperatures (Chen and Wen, 2013). It is known that the kinetics of copper leaching from chalcopyrite concentrate is greatly accelerated at elevated temperatures (Hackl et al., 1995; Olvera et al., 2016; Yue and Asselin, 2014). Agitation-based leaching of copper from chalcopyrite concentrate with the assistance of moderately (50 °C or below) and extremely (70–80 °C) thermophilic bacteria has been tested in laboratory, from pilot-scale to full-scale commercial demonstration, such as the BioCOP™ Process (Ahmadi et al., 2011; Batty and Rorke, 2006; Khoshkhoo, 2016). Even though in most cases the total sulfides in the ore possess enough energy to allow for high heap temperatures, poor heat management and lack of temperature control techniques are among the reasons for heap temperature failing to rise to the desired level (Chen and Wen, 2013). Therefore, it is essential to understand the key factors that control the heat balance of a heap if one is to create the conditions for a
Corresponding author. E-mail address: [email protected] (W. Liu).
https://doi.org/10.1016/j.hydromet.2018.01.001 Received 14 September 2017; Received in revised form 26 December 2017; Accepted 8 January 2018 Available online 10 January 2018 0304-386X/ © 2018 Elsevier B.V. All rights reserved.
Hydrometallurgy 176 (2018) 26–32
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“hot heap” leaching process. Mathematical modelling provides the possibility of assessing the effectiveness of key techniques in controlling heap temperature distribution. Leahy et al. (2005, 2007) used a onedimensional model to investigate heat balance in chalcocite bioleaching and found that leaching occurred in both top-down and bottom-up manners depending on the bacteria types being mesophiles or thermophiles. Dixon (2000) developed a one-dimensional HeapSim model to study how the heap temperature was affected by raffinate flow rate, solution and air heating, and the placing of an evaporation shield on top of the heap. The author concluded that heat loss occurred mainly by evaporation on the heap surface and by effluent solution flow. In this study, a two-dimensional model, referred to as the “HeapSim2D” to differentiate the two versions of the HeapSim model, was applied to simulate the heap temperature distribution in both the vertical and the radial directions under various operating conditions. Prior to its application, the model was calibrated using the historical heap leach monitoring data from the Quebrada Blanca Mine. An overview of the HeapSim-2D model was given elsewhere (Dixon and Afewu, 2011; Liu and Dixon, 2015; Liu and Hashemzadeh, 2017).
Table 1 Input and output parameters for the heat transfer component of the HeapSim-2D model. Description
Category
Unit
θs
Saturated moisture content (porosity) Irrigation rate Bulk density Density of water (solution) Latitude Number of the day Average annual minimum temperature Amplitude of annual minimum Diurnal temperature variation Phase lag Thermal conductivity of solids Specific heat capacity of solids Temperature of raffinate solution Heat transfer coefficient
Design
m3/m3
Design Constant Constant Empirical Empirical Empirical
m3/m2/s kg/m3 kg/m3 Degrees days K
Empirical Empirical Empirical Empirical Empirical Design Empirical, adjusted
°C °C days W/m/K J/kg/K K W/m2/K
Uw ρb ρw l N min T amb
ΔTambmin ΔTambday φambmin ks Cp, s Tw hconv
2. Methods 2.1. General heat balance A brief overview of the HeapSim-2D model is given here. The HeapSim-2D model comprises five components that simulate the five types of fundamental processes occurring in heap leaching: fluid flow, solute and gas species transport, chemical reaction kinetics, microbial activities, and heat transfer. The model divides a leach pad into multiple 2D-axisymmetric cylindrical leach units, the radius of which is equivalent to half drip emitter spacing and the height equivalent to the heap height. As with the heat transfer component, the general assumption is that the solid phase (ore particles), liquid phase (leaching solution), and gas phase (air) involved are in thermal equilibrium. The governing equation that describes the two-dimensional heat balance of a heap under leach is developed based on the conservation of energy (enthalpy) (Eq. (1)). Heat is transferred through the heap by conduction and convection via the flow of air and leaching solution. The heat flux term (q) associated with these two modes of heat transfer is expressed using the constitutive equation as Eq. (2). The heat source term (Q) inside the heap refers to the heat released from oxidation reactions. Even though ferric is the direct oxidant for sulfide oxidation, oxygen is the eventual electron acceptor, whose consumption is directly proportional to the extents of ferrous and sulfur oxidation. Thus, the heat released is estimated based on the total oxygen consumption in the heap, with 4.8 × 105 kJ of heat released per mole of oxygen consumed. The heat source term (Q) at the top boundary, i.e., heap surface, is the sum of three heat sources: (1) oxidation reactions; (2) heat carried by the incoming leaching solution or raffinate (qraff), described by Eq. (3); (3) heat transfer across the heap top via convection driven by the difference between the ambient temperature and the temperature at the top of the heap, described by (Eq. (4)). Even though the radiative heat transfer is combined into the general heat transfer coefficient (hconv) without being explicitly considered, the HeapSim-2D is capable of separately simulating the radiation heat transfer. The ambient temperature (Tamb) in Eq. (4) is simulated by Eq. (5), which is an extended version of the original equation developed by Dixon (2000). The inputs of Eq. (5) can be estimated from the local climatic data. Table 1 summarizes all model parameters.
Intermediate
Description
Formula
Unit
a d z Q kw
Local hour angle Solar declination Zenith angle of the sun Volumetric heat generation rate Thermal conductivity of water (solution) Thermal conductivity of air Specific heat capacity of water (solution) Specific heat capacity of air Heat carried by raffinate solution Heat loss via convection Ambient temperature
Eq. (5) Eq. (5) Eq. (5) Function of T
– – – W/m3 W/m/K
Function of T Function of T
W/m/K J/kg/K
Function of T Eq. (3) Eq. (4) Eq. (5)
J/kg/K W/m2 W/m2 K
kg Cp,
w
Cp, g qraff qconv Tamb
Output
Description
Formula
Unit
T θ vw ρg vg q qz qr
Heap temperature Heap moisture content Solution velocity Density of air Air velocity Heat flux Heat flux in the vertical direction Heat flux in the radial direction
From solution transport From solution transport From gas transport From gas transport Calculated from T Calculated from T
K m3/m3 m/s kg/m3 m/s W/m2 W/m2 W/m2
q2 = q z2 + qr 2
q = ρw vw Cp,w T + ρg vg Cp,g T − [θkw + (θs − θ)k g + (1 − θs )k s] ∇T (2)
qraff = ρw Uw Cp,w Tw
(3)
q conv = h conv (Tamb − Ttop)
(4)
⎛ N + 284.5 − min min + ∆Tamb Tamb = T amb sin ⎜2π 365.25 ⎝ day ⎧ + ∆T amb ⎨0 ⎩
min φamb ⎞
⎟ ⎠
(cos(z) > 0) (cos(z) ≤ 0)
cos(z)
(5)
(
where cos(z) = sin (l) sin (d) + cos (l) cos (d) cos (a); a = 2π N +
d = 0.40928 sin
(
N + 284.5 2π 365.25
1 2
);
).
2.2. Model calibration The HeapSim-2D was calibrated using the historical monitoring data at the Quebrada Blanca (QB) Mine. QB is a copper mine located in the Chilean Andes, about 240 km southeast of Iquique, at an elevation of approximately 4400 m. At this elevation, the atmospheric pressure was calculated to be 0.58 atm using the barometric formula, with the
∂q 1 ∂rqr ∂ − ∇∙q = − z − = [ρ Cp, s T + θρw C p,w T + (θs − θ)ρg C p,g T] ∂z r ∂r ∂t b −Q
Input
(1)
27
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Table 2 Particle size distribution of the ore. Material size, mm Cumulative pass, %
25.4 100
19 99.6
12.7 79.7
9.51 64.1
4.76 44.0
Daily ambient temperature, ˚C
mole fractions of oxygen and carbon dioxide set at 0.21 and 0.004, respectively. The simulations were done assuming that all air applied flowed through the entire heap and that there were no preferential flow paths causing air loss through sideways. The operation uses heap and dump leaching, solvent extraction (SX), and electrowinning (EW), to produce copper cathodes. The heap studied had a total copper grade of 1.48%, of which 8% was soluble copper (oxides), 74% was cyanide soluble copper (chalcocite), and 18% was insoluble copper (chalcopyrite). The crushed ore, whose particle size distribution is shown in Table 2, was transported by conveyor belt 1.4 km to agglomeration. The heap, as a dynamic pad, had a height of 8 m and a surface area of 66,018 m2. The whole heap was divided into 19 leach modules that began irrigation sequentially, with full heap irrigation being achieved by day 37. Irrigation, provided by drip emitters arranged in a 55 by 55 cm grid, was applied at an average rate of 12 L/ m2/h for the first 100 days, 9 L/m2/h from day 101 to day 245, and 1.8 L/m2/h from day 246 to day 444, the end of the leach cycle. Aeration was provided at an average aeration rate of 0.23 m3/m2/h, but only for the first 250 days of irrigation.
Unit
θs
Saturated moisture content Irrigation rate Bulk density Density of water (solution) Latitude Number of the day Average annual minimum Amplitude of annual minimum Diurnal temperature variation Phase lag Thermal conductivity of solids Specific heat capacity of solids Temperature of raffinate solution Heat transfer coefficient
Design
0.37
m3/m3
Design Constant Constant
12 1600 1000
L/m2/h kg/m3 kg/m3
Empirical Empirical Empirical
− 21 1 January = 1 273.15
°S days K
Empirical
2
°C
Empirical
6
°C
Empirical Empirical
220 1
days W/m/K
Empirical
1000
J/kg/K
Design
299
K
Empirical, adjusted
0.5
W/m2/K
Uw ρb ρw l N min T amb
ΔTambmin ΔTambday φambmin ks Cp,
s
Tw hconv
0.074 9.1
0.037 7.5
14 A
2005 - 2010 10 6 2 -2 -6
Average daily ambient temperature, ˚C
simulated
B
10 6 2 -2 -6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Fig. 1. (A) monitored daily ambient temperature between 2005 and 2010 at the Quebrada Blanca Mine; (B) simulated average daily ambient temperature compared with the monitored average ambient temperature.
temperature (Tamb) was used for the subsequent calculation of the convective heat transfer flux over the heap surface by Eq. (4). The HeapSim-2D model was then calibrated by fitting multiple model outputs with the corresponding monitoring data. The model outputs include heap temperature, PLS copper concentration, PLS acid concentration, and PLS redox potential (Fig. 2). The equations and procedures for simulating the solute transport and reaction kinetics are not given here and can be found in Liu and Hashemzadeh (2017) and Dixon and Afewu (2011). The model could be successfully calibrated to simulate the PLS copper concentration (Fig. 2B), PLS acid concentration (Fig. 2C), and PLS redox potential (Fig. 2D). The simulation results have been reported in Liu and Hashemzadeh (2016). As with the heat transfer component (Fig. 2A), the model calibration was done by adjusting the heat transfer coefficient (hconv) to simulate the average heap temperature. The rest of the parameters, as shown in Table 3, were fixed. Since there were no direct monitoring data on the heap temperature profile, the monitoring data on the temperatures of the PLS and the raffinate solution were used as the benchmarks in the calibration process. Another benchmark was that the temperatures at the top and the bottom of the heap, measured by Lizama (2001), were 16 °C and 27 °C, respectively. Fig. 2A shows that once irrigation was initiated, the heap average temperature increased from the initial temperature of 6 °C to approximately the same as the raffinate temperature over a period of 50 days. Between 50 and 250 days, the average heap temperature closely followed the raffinate temperature at around 27 °C, during which period
Table 3 Values of model input parameters. Value
0.149 11.7
monitored
Prior to the model calibration, the ambient temperature (Tamb) at which the heap leach pad was operated was simulated by Eq. (5). The estimated input parameters associated with this equation are given in Table 3. The estimation was done using the monitoring data on the daily ambient temperatures over a six-year period from 2005 to 2010 (Fig. 1A). The simulated daily ambient temperature was then compared with the monitored average ambient temperature over the period (Fig. 1B). Fig. 1B shows that Eq. (5) could satisfactorily simulate the seasonal trend of the daily ambient temperature, with the summer maxima from December to February, and the winter minima from June to August in the southern hemisphere. The simulated ambient
Category
0.42 16.0
14
3.1. Model calibration
Description
0.841 22.4
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3. Results and discussion
Input
2 30.7
28
Hydrometallurgy 176 (2018) 26–32
W. Liu, G. Granata
30
Temperature, ºC
raffinate
A
simulated Cu concentration, g/L
40
30
20 heap average
PLS
10
B
monitored
20 15 10 5 0
0 0
100
200 300 Leaching time, days
0
400
100
200 300 Leaching time, days
850
8 simulated
C
simulated 800 Eh (vs SHE), mV
monitored
6 Acid, g/L
25
4
2
400
D
monitored
750 700 650 600
0 0
100
200 300 Leaching time, days
0
400
100
200 300 Leaching time, days
400
Fig. 2. Model calibration using the historical monitoring data at the QB Mine: (A) monitored raffinate and PLS (pregnant leach solution) temperature compared with the simulated heap average temperature; (B) monitored and simulated copper concentration in PLS; (C) monitored and simulated acid concentration in PLS; (D) monitored and simulated redox potential of PLS.
the raffinate flow rates were maintained at high levels (9 L/m2/h). The heap average temperature started to drop on day 245 when the raffinate flow rate was reduced to 1.8 L/m2/h. The simulation of the heap temperature was considered valid for two reasons: the model output fell within the measured 17 °C and 27 °C; the model output fell within the measured PLS temperature and the raffinate temperature. The latter was predicted because the other major heat contributor, the oxidation of pyrite originally present in the ore, was expected to occur to a minimal extent. The model output corresponded to a heat transfer coefficient (hconv) of 0.5 W/m2/K. This seemingly small value could be explained by the application of a thermal cover over the leach pad at the QB mine.
than the heap bottom did, which was more pronounced in the case of a lower flow rate. Another key factor simulated is the extent of pyrite oxidation. Note that the pyrite here refers to the hypothetical blending of finely ground pyrite concentrate from flotation with the agglomerated ore. The percentage of pyrite blended in the agglomerated ore was set at 2% by weight. Due to its fine particle size and large surface area, the blended pyrite concentrate was expected to react to a much greater extent than the pyrite associated with the copper ore resulting from its coarse grain size and low degree of liberation (Bouffard and Dixon, 2002). However, the oxidation of pyrite associated with the copper ore can still play an important role if it can be easily accessed by the leaching solution. Fig. 4A shows the effect of blended pyrite oxidation on the heap average temperature under three raffinate flow rates and at a fixed raffinate temperature of 20 °C. The contribution of the pyrite oxidation to the heap temperature was much more significant at lower raffinate flow rates than at higher ones. Even though the lowest raffinate flow rate of 1 L/m2/h corresponded to the lowest extent of pyrite oxidation of 51%, the heap temperature rose to the highest among the three raffinate flow rates tested. In contrast, there was almost negligible effect of pyrite oxidation on the heap temperature at the two higher flow rates, even though the extents of pyrite oxidation were higher in both cases. This negligible effect was attributed to the heat generated by the pyrite oxidation at higher flow rates being transferred away by the bulk movement of the raffinate solution. The significant contribution of pyrite oxidation at a low raffinate flow rate was also seen at the raffinate temperatures of 30 °C (Fig. 4B). Not only the heap average temperature but also the heap temperature distribution was vastly different at different flow rates. In the case of the high flow rate (Fig. 4C), the temperature at different depths quickly approached the raffinate temperature within 50 days and remained constant for the rest of the leaching cycle. Conversely, in the case of the low flow rate (Fig. 4D), the heap temperature at the bottom (8 m) increased more slowly than at the top; the contribution of the pyrite oxidation was more pronounced at increasing depth and only the temperature on the very top of the heap (2 m) was controlled by the
3.2. Heap temperature distribution in response to variations of key design factors The calibrated model was used for the sensitivity study that assessed the response of the heap temperature distribution to the variations of the critical design factors. In the sensitivity studies, all empirical parameters were fixed and only controllable design parameters were varied. The design parameters under investigation are raffinate temperature, raffinate flow rate (irrigation rate), the extent of pyrite oxidation, and the application of a thermal cover. The heap initial temperature was set at 6 °C, lower than the raffinate temperature. Fig. 3A and B show the simulation results of the effect of raffinate temperature and flow rate on the heap temperature. The simulation results were obtained under the assumption that no chemical reactions occurred and that no heat loss via convection over the heap top (hconv = 0). At a fixed raffinate temperature, the average heap temperature gradually approached the raffinate temperature, but the time required for the heap to reach this temperature was controlled by the raffinate flow rate. As the raffinate flow rate was decreased from 12 to 1 L/m2/h (Fig. 3A), the time required increased from 22 days to 300 days. As shown by Fig. 3C and D, the time required to reach the raffinate temperature increased with increasing depth. In other words, the temperature at the heap top approached that of the raffinate faster 29
Hydrometallurgy 176 (2018) 26–32
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A
Heap average temperature, ºC
Heap average temperature, ºC
30
20
10 12 L/m²/h 6 L/m²/h 1 L/m²/h
raffinate: 20 ˚C 0 0
50
100 150 200 Leaching time, days
250
B
20
10 raffinate: 20 °C irrigation: 12 L/m2/h
raffinate: 30 °C
0
300
0
20
40 60 Leaching time, days
80
30
30
C
raffinate: 20 ˚C irrigation: 6 L/m2/h
Heap temperature, ºC
Heap temperature, ºC
30
20 increasing depth 10 0.5 m: heap top
100
D
raffinate: 20 ˚C irrigation: 1 L/m2/h 20 increasing depth 10 0.5 m: heap top
8 m: heap bottom
8 m: heap bottom 0
0 0
20
40 60 Leaching time, days
80
0
100
50
100
150
200
250
300
Time, days
Fig. 3. (A) Simulated heap average temperature at different raffinate flow rates and a fixed raffinate temperature; (B) simulated heap average temperature at different raffinate temperatures and a fixed raffinate flow rate; (C) simulated heap temperature profile at a fixed raffinate temperature and a higher raffinate flow rate; (D) simulated heap temperature profile at a fixed raffinate temperature and a lower raffinate flow rate. All simulations were done under the assumption that no chemical reactions occurred, and no heat loss via convection over the heap top (hconv = 0).
Heap average temperature, ºC
Heap average temperature, ºC
50
50
A
raffinate: 20 ˚C 40 30 20
12 L/m²/h, 69% reacted 6 L/m²/h, 61% reacted 1 L/m²/h, 51% reacted
10 0 0
100
200 300 400 Leaching time, days
30 20 raffinate: 20 °C, 51% reacted
10
raffinate: 30 °C, 51% reacted
0 0
500
100
200 300 400 Leaching time, days
50
50
C
raffinate: 20 ˚C irrigation: 6 L/m2/h 61% reacted
40
Heap temperature, ºC
Heap temperature, ºC
B
irrigation: 1 L/m2/h 40
30 20 0.5 m: heap top
10
D
raffinate: 20 ˚C irrigation: 1 L/m2/h 51% reacted
40
500
30 20 0.5 m: heap top
10
8 m: heap bottom
8 m: heap bottom 0
0 0
100
200
300
400
0
500
Leaching time, days
100
200
300
400
500
Time, days
Fig. 4. (A) Simulated heap average temperature at different raffinate flow rates and a fixed raffinate temperature; (B) simulated heap average temperature at two raffinate temperatures and a fixed flow rate; (C) simulated heap temperature profile at a high flow rate; (D) simulated heap temperature profile at a low flow rate. The simulations were performed under the assumption that no other sulfide reactions other than the blended pyrite oxidation occurred, and no heat loss via convection over the heap top (hconv = 0). The percentages reacted represent the extents of the oxidation of the added pyrite.
30
Hydrometallurgy 176 (2018) 26–32
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30
40
A
average top bottom
Heap temperature, ºC
Heap temperature, ºC
40
20
10
raffinate: 20 ˚C; hconv = 1 W/m2/K irrigation: 1 L/m2/h 51% reacted
30
B
20 raffinate: 20 ˚C; hconv = 1 W/m2/K irrigation: 6 L/m2/h 62% reacted
10
0
0 100
200 300 400 Leaching time, days
40
20 raffinate: 20 ˚C; hconv = 0.5 W/m2/K irrigation: 1 L/m2/h 51% reacted
10
100
200 300 400 Leaching time, days
40
C
average top bottom
30
0
500
Heap temperature, ºC
0
Heap temperature, ºC
average top bottom
0
average top bottom
30
500
D
20 raffinate: 20 ˚C; hconv = 0.5 W/m2/K irrigation: 6 L/m2/h 61% reacted
10
0 0
100
200 300 400 Leaching time, days
500
0
100
200 300 400 Leaching time, days
500
Fig. 5. Effect of applying a thermal cover over the top of the heap on the heap temperature at a fixed raffinate temperature of 20 °C: (A) a lower raffinate flow rate of 1 L/m2/h and heat transfer coefficient of 1 W/m2/K representing the case without a thermal cover; (B) a higher raffinate flow rate of 6 L/m2/h and heat transfer coefficient of 1 W/m2/K representing the case without a thermal cover; (C) a lower raffinate flow rate of 1 L/m2/h and heat transfer coefficient of 0.5 W/m2/K representing the case with a thermal cover; (D) higher raffinate flow rate of 6 L/m2/h and heat transfer coefficient of 0.5 W/m2/K representing the case with a thermal cover; 51% and 62% reacted referring to the extents of the blended pyrite oxidation.
arbitrary. It is extremely challenging to accurately estimate this value because of its dependence on the local climatic conditions. However, the choice of 1 W/m2/K served the purpose of comparing the effect of thermal cover application. The simulation results show that the decrease in the heat transfer coefficient due to the application of a thermal cover led to an increase in the heap temperature, reflecting the reduced heat loss across the warmer heap top by convection to the cooler ambient. However, the impact of the heat transfer coefficient depended on the raffinate flow rate. In the case of a higher heat transfer coefficient (without thermal cover) and a lower raffinate flow rate (Fig. 5A), the heap top temperature exhibited oscillation, which was in sync with the seasonal change of the ambient temperature. This was attributed to the heap top temperature being more responsive to the ambient temperature at the low flow rate than at the high flow rate.
Copper recovery, %
100 80 60 monitored simulated aeration raffinate 37 °C raffinate 37 °C & aeration
40 20 0 0
100
200 300 Time, days
400
500
3.3. Effect of different temperature control techniques on copper extraction at QB
Fig. 6. The impact of three hypothetical scenarios on the current chalcocite leach operation at the QB Mine: (1) maintain aeration; (2) increase raffinate temperature by 10 °C, and (3) apply both scenarios at the same time
The findings of this work have implications on the current chalcocite leaching operation at the Quebrada Blanca Mine. Since blending pyrite with the agglomerated ore was not a feasible option, three whatif scenarios were tested to improve the current operation: the first scenario maintained the aeration for the entire leach cycle as opposed to terminating it on day 250; the second scenario increased the raffinate temperature by 10 °C from the current 27 °C to 37 °C; the third scenario was a combination of the two previous ones. Fig. 6 shows that all three scenarios had positive effects on the leaching recovery to different extents. The recovery slightly increased by approximately 3% by maintaining aeration for the entire leach cycle. Increasing raffinate temperature by 10 °C had a pronounced positive effect on the copper recovery, with an increase of 17% in copper extraction. The combination of both scenarios resulted in a further 3% increase in the extraction rate, because more oxygen would be required by the bacterial activities.
raffinate temperature. It was therefore concluded that with increasing raffinate flow rate, the raffinate temperature played an increasingly dominant role in controlling the heap temperature in a top-down manner until to a certain flow rate at which the temperature of the entire heap was fully controlled by the raffinate temperature. Fig. 5 shows the simulation results of the effect of applying a thermal cover over the top of the heap on the heap temperature at low and high raffinate flow rates. The effect of thermal cover application was reflected in the change in the heat transfer coefficient. The case of applying a thermal cover was represented by a heat transfer coefficient of 0.5 W/m2/K (Fig. 5C and D) as opposed to 1 W/m2/K (Fig. 5A and B) representing the case without a thermal cover. The choice of 1 W/m2/K to represent the case without a thermal cover is, to a certain extent, 31
Hydrometallurgy 176 (2018) 26–32
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4. Conclusions
electrochemical bioleaching of chalcopyrite concentrates by moderately thermophilic bacteria at high pulp density. Hydrometallurgy 106, 84–92. Batty, J.D., Rorke, G.V., 2006. Development and commercial demonstration of the BioCOP™ thermophile process. Hydrometallurgy 83, 83–89. Bolorunduro, S., 1999. Kinetics of Leaching of Chalcocite in Acid Ferric Sulphate Media: Chemical and Bacterial Leaching. M.A.Sc. Thesis. University of British Columbia. Bouffard, S.C., Dixon, D.G., 2002. On the rate-limiting steps of pyritic refractory gold ore heap leaching: results from small and large column tests. Miner. Eng. 15, 859–870. Chen, B.-W., Wen, J.-K., 2013. Feasibility study on heap bioleaching of chalcopyrite. Rare Metals 32, 524–531. Dixon, D.G., 2000. Analysis of heat conservation during copper sulphide heap leaching. Hydrometallurgy 58, 27–41. Dixon, D.G., Afewu, K.I., 2011. Mathematical modelling of heap leaching under drip irrigation. In: Percolation Leaching: The Status Globally and in Southern Africa. The Southern African Institute of Mining and Metallurgy, pp. 1–29. Dixon, D.G., Petersen, J., 2003. Comprehensive modeling study of chalcocite column and heap bioleaching. In: Riveros, P.A., Dixon, D.G., Dreisinger, D.B., Menacho, J. (Eds.), Copper 2003 - Cobre 2003, Santiago, Chile, pp. 493–515. Dutrizac, J.E., MacDonald, R.J.C., 1974. The kinetics of dissolution of covellite in acidified ferric sulphate solutions. Can. Metall. Q. 13, 423–433. Hackl, R.P., Dreisinger, D.B., Peters, E., King, J.A., 1995. Passivation of chalcopyrite during oxidative leaching in sulfate media. Hydrometallurgy 39, 25–48. Khoshkhoo, M., 2016. Chalcopyrite (bio)leaching in Sulphate Solutions: An Investigation Into Hindered Dissolution With a Focus on Solution Redox Potential. PhD Thesis. Luleå University of Technology. Leahy, M.J., Davidson, M.R., Schwarz, M.P., 2005. A model for heap bioleaching of chalcocite with heat balance: bacterial temperature dependence. Miner. Eng. 18, 1239–1252. Leahy, M.J., Davidson, M.R., Schwarz, M.P., 2007. A model for heap bioleaching of chalcocite with heat balance: Mesophiles and moderate thermophiles. Hydrometallurgy 85, 24–41. Liu, W., Dixon, D.G., 2015. Modelling solution flow in copper heap bioleaching – a case study. In: Proceedings of the 3rd International Heap Leach Solutions Conference, InfoMine, September 12–16, Reno, Nevada, USA, pp. 63–72. Liu, W., Hashemzadeh, M., 2016. Modelling copper recovery, total iron concentration and acid balance in chalcocite heap bioleaching. In: Proceedings of the 4th International Heap Leach Solutions Conference, InfoMine, October 18–20, Lima, Peru. Liu, W., Hashemzadeh, M., 2017. Solution flow behavior in response to key operating parameters in heap leaching. Hydrometallurgy 169, 183–191. Lizama, H.M., 2001. Copper bioleaching behaviour in an aerated heap. Int. J. Miner. Process. 62, 257–269. Niu, X., Ruan, R., Tan, Q., Jia, Y., Sun, H., 2015. Study on the second stage of chalcocite leaching in column with redox potential control and its implications. Hydrometallurgy 155, 141–152. Olvera, O.G., Rebolledo, M., Asselin, E., 2016. Atmospheric ferric sulfate leaching of chalcopyrite: thermodynamics, kinetics and electrochemistry. Hydrometallurgy 165, 148–158. Petersen, J., 2016. Heap leaching as a key technology for recovery of values from lowgrade ores – a brief overview. Hydrometallurgy 165 (Part 1), 206–212. Petersen, J., Dixon, D.G., 2007. Principles, mechanisms and dynamics of chalcocite heap bioleaching. In: Microbial Processing of Metal Sulfides. Springer, pp. 193–218. Pradhan, N., Nathsarma, K.C., Srinivasa Rao, K., Sukla, L.B., Mishra, B.K., 2008. Heap bioleaching of chalcopyrite: a review. Miner. Eng. 21, 355–365. Robertson, S.W., Van-Staden, P.J., Seyedbagheri, A., 2012. Advances in High-temperature Heap Leaching of Refractory Copper Sulphide Ores. Vol. 112. The Southern African Institute of Mining and Metallurgy, pp. 1045–1050. Yue, G., Asselin, E., 2014. Kinetics of ferric ion reduction on chalcopyrite and its influence on leaching up to 150°C. Electrochim. Acta 146, 307–321.
The HeapSim-2D model, calibrated using the historical data of the Quebrada Blanca Mine, was applied to assess the effect of four temperature control techniques on the heap temperature distribution: raffinate temperature, raffinate flow rate, the extent of pyrite oxidation, and the application of a thermal cover. The simulations were done under the assumption that other factors, such as acid supply, air permeability and aeration were not rate-limiting. It is understood that these factor could become rate-limiting at elevated temperatures (Bouffard and Dixon, 2002; Dixon and Petersen, 2003). Note that the results reported here were only model predictions and not validated against experimental data. The heap temperature was found to be controlled by raffinate temperature at high raffinate flow rates. The contribution of pyrite oxidation to raising heap temperature was significant only at low raffinate flow rates. The application of a thermal cover led to an increase in the heap temperature, but the magnitude of the increase was also controlled by raffinate flow rate. It was therefore concluded that with increasing raffinate flow rate, the raffinate temperature played an increasingly dominant role in controlling heap temperature in a top-down manner until to a certain extent at which the temperature of the entire heap was fully controlled by the raffinate temperature. Using the knowledge gained from the modelling study, three hypothetical scenarios were tested to assess the response of the QB operation to three temperature control techniques. Maintaining aeration for the entire leach cycle in combination with increasing raffinate temperature by 10 °C could potentially lead to a 20% increase in the copper extraction from the current 66%. This conclusion is of particular importance because both techniques are very simple to implement from an operational standpoint. Portable low-pressure air blowers are low cost and simple to operate. Likewise, diesel heat exchangers for solution heating are easy to install. Understanding the effects of these temperature control techniques on heap temperature distribution is critical for achieving optimum copper extraction from low-grade sulfide ores using heap leaching. Given that the reported results are only model predictions, further work is required to validate the modelling results against experimental data. Acknowledgement The authors would like to thank Teck Metals Applied Research and Technology for providing the heap leach data at the Quebrada Blanca Mine. References Ahmadi, A., Schaffie, M., Petersen, J., Schippers, A., Ranjbar, M., 2011. Conventional and
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Hydrometallurgy journal homepage: www.elsevier.com/locate/hydromet
Temperature control in copper heap bioleaching a,⁎
Wenying Liu , Giuseppe Granata a b
T
b
Department of Materials Engineering, University of British Columbia, 309-6350 Stores Road, Vancouver, BC V6T 1Z4, Canada Faculty of Science and Engineering, Department of Resources and Environmental Engineering, Waseda University, Tokyo, Japan
A R T I C L E I N F O
A B S T R A C T
Keywords: Heap temperature Raffinate flow rate Thermal cover Extent of pyrite oxidation Raffinate temperature
Heap leaching is being increasingly explored as a lower cost metallurgical technology to extract copper from sulfide ores of low grade and quality. Heap temperature is a critical factor in achieving economic copper extraction. The determination and control of heap temperature is challenging due to the intrinsic complexity of the intertwined fundamental processes occurring simultaneously inside a heap. In this study, we applied the HeapSim-2D model, calibrated using data provided by the Quebrada Blanca Mine, to study the response of heap temperature to variations of four key design parameters: raffinate flow rate, raffinate temperature, the extent of pyrite oxidation, and the application of a thermal cover. Note that the reported results have not been validated against experimental data and other possible rate-limiting factors are presently ignored. The modelling results showed that at a fixed raffinate temperature, the average heap temperature approached the raffinate temperature faster at a high flow rate than at a low flow rate. The heat generated by the oxidation of pyrite led to an increase in the heap temperature, but the magnitude of the increase was negligible at high raffinate flow rates, which would remove the generated heat via convection by the bulk movement of the leaching solution. The model predicted that the application of a thermal cover had a positive effect on maintaining the heap temperature, but the effect also depended on the raffinate flow rate. Understanding the effects of these design parameters on heap temperature is critical for achieving optimum copper extraction in heap leaching.
1. Introduction Temperature is among the key factors that control the kinetics of copper leaching from different copper-bearing sulfide minerals, such as chalcocite and chalcopyrite. Extraction of copper from chalcocite using bacterially-assisted heap leaching has been successfully practiced on a commercial scale (Petersen, 2016). In heap leaching, crushed copper sulfide ore is irrigated with leaching solution high in acid and low in dissolved copper (termed raffinate), generating an effluent solution low in acid and high in dissolved copper (termed pregnant leach solution or PLS). Chalcocite leaching is reported to occur in two stages, with the second stage that converts secondary covellite to copper sulfate and elemental sulfur being the rate-limiting step (Dutrizac and MacDonald, 1974; Niu et al., 2015). The kinetics of the second stage is sensitive to temperature as supported by a high activation energy on the order of 98 kJ/mol (Bolorunduro, 1999). A typical chalcocite heap bioleach pad, with temperature rarely exceeding 25 °C, operates between 8 months and 2 years to achieve approximately 80% of copper extraction (Petersen and Dixon, 2007). Dixon (2000) estimated that every 10 °C increase in temperature would theoretically lead to a threefold
⁎
increase in the intrinsic rate of the second-stage leaching. The application of heap leaching to treat chalcopyrite ore can potentially result in reduced cost and attractive environmental benefits in copper production (Pradhan et al., 2008; Robertson et al., 2012). However, heap leaching of chalcopyrite ore has not been successfully practiced on a commercial scale, mainly due to its slow rate of dissolution caused by passivation at low temperatures (Chen and Wen, 2013). It is known that the kinetics of copper leaching from chalcopyrite concentrate is greatly accelerated at elevated temperatures (Hackl et al., 1995; Olvera et al., 2016; Yue and Asselin, 2014). Agitation-based leaching of copper from chalcopyrite concentrate with the assistance of moderately (50 °C or below) and extremely (70–80 °C) thermophilic bacteria has been tested in laboratory, from pilot-scale to full-scale commercial demonstration, such as the BioCOP™ Process (Ahmadi et al., 2011; Batty and Rorke, 2006; Khoshkhoo, 2016). Even though in most cases the total sulfides in the ore possess enough energy to allow for high heap temperatures, poor heat management and lack of temperature control techniques are among the reasons for heap temperature failing to rise to the desired level (Chen and Wen, 2013). Therefore, it is essential to understand the key factors that control the heat balance of a heap if one is to create the conditions for a
Corresponding author. E-mail address: [email protected] (W. Liu).
https://doi.org/10.1016/j.hydromet.2018.01.001 Received 14 September 2017; Received in revised form 26 December 2017; Accepted 8 January 2018 Available online 10 January 2018 0304-386X/ © 2018 Elsevier B.V. All rights reserved.
Hydrometallurgy 176 (2018) 26–32
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“hot heap” leaching process. Mathematical modelling provides the possibility of assessing the effectiveness of key techniques in controlling heap temperature distribution. Leahy et al. (2005, 2007) used a onedimensional model to investigate heat balance in chalcocite bioleaching and found that leaching occurred in both top-down and bottom-up manners depending on the bacteria types being mesophiles or thermophiles. Dixon (2000) developed a one-dimensional HeapSim model to study how the heap temperature was affected by raffinate flow rate, solution and air heating, and the placing of an evaporation shield on top of the heap. The author concluded that heat loss occurred mainly by evaporation on the heap surface and by effluent solution flow. In this study, a two-dimensional model, referred to as the “HeapSim2D” to differentiate the two versions of the HeapSim model, was applied to simulate the heap temperature distribution in both the vertical and the radial directions under various operating conditions. Prior to its application, the model was calibrated using the historical heap leach monitoring data from the Quebrada Blanca Mine. An overview of the HeapSim-2D model was given elsewhere (Dixon and Afewu, 2011; Liu and Dixon, 2015; Liu and Hashemzadeh, 2017).
Table 1 Input and output parameters for the heat transfer component of the HeapSim-2D model. Description
Category
Unit
θs
Saturated moisture content (porosity) Irrigation rate Bulk density Density of water (solution) Latitude Number of the day Average annual minimum temperature Amplitude of annual minimum Diurnal temperature variation Phase lag Thermal conductivity of solids Specific heat capacity of solids Temperature of raffinate solution Heat transfer coefficient
Design
m3/m3
Design Constant Constant Empirical Empirical Empirical
m3/m2/s kg/m3 kg/m3 Degrees days K
Empirical Empirical Empirical Empirical Empirical Design Empirical, adjusted
°C °C days W/m/K J/kg/K K W/m2/K
Uw ρb ρw l N min T amb
ΔTambmin ΔTambday φambmin ks Cp, s Tw hconv
2. Methods 2.1. General heat balance A brief overview of the HeapSim-2D model is given here. The HeapSim-2D model comprises five components that simulate the five types of fundamental processes occurring in heap leaching: fluid flow, solute and gas species transport, chemical reaction kinetics, microbial activities, and heat transfer. The model divides a leach pad into multiple 2D-axisymmetric cylindrical leach units, the radius of which is equivalent to half drip emitter spacing and the height equivalent to the heap height. As with the heat transfer component, the general assumption is that the solid phase (ore particles), liquid phase (leaching solution), and gas phase (air) involved are in thermal equilibrium. The governing equation that describes the two-dimensional heat balance of a heap under leach is developed based on the conservation of energy (enthalpy) (Eq. (1)). Heat is transferred through the heap by conduction and convection via the flow of air and leaching solution. The heat flux term (q) associated with these two modes of heat transfer is expressed using the constitutive equation as Eq. (2). The heat source term (Q) inside the heap refers to the heat released from oxidation reactions. Even though ferric is the direct oxidant for sulfide oxidation, oxygen is the eventual electron acceptor, whose consumption is directly proportional to the extents of ferrous and sulfur oxidation. Thus, the heat released is estimated based on the total oxygen consumption in the heap, with 4.8 × 105 kJ of heat released per mole of oxygen consumed. The heat source term (Q) at the top boundary, i.e., heap surface, is the sum of three heat sources: (1) oxidation reactions; (2) heat carried by the incoming leaching solution or raffinate (qraff), described by Eq. (3); (3) heat transfer across the heap top via convection driven by the difference between the ambient temperature and the temperature at the top of the heap, described by (Eq. (4)). Even though the radiative heat transfer is combined into the general heat transfer coefficient (hconv) without being explicitly considered, the HeapSim-2D is capable of separately simulating the radiation heat transfer. The ambient temperature (Tamb) in Eq. (4) is simulated by Eq. (5), which is an extended version of the original equation developed by Dixon (2000). The inputs of Eq. (5) can be estimated from the local climatic data. Table 1 summarizes all model parameters.
Intermediate
Description
Formula
Unit
a d z Q kw
Local hour angle Solar declination Zenith angle of the sun Volumetric heat generation rate Thermal conductivity of water (solution) Thermal conductivity of air Specific heat capacity of water (solution) Specific heat capacity of air Heat carried by raffinate solution Heat loss via convection Ambient temperature
Eq. (5) Eq. (5) Eq. (5) Function of T
– – – W/m3 W/m/K
Function of T Function of T
W/m/K J/kg/K
Function of T Eq. (3) Eq. (4) Eq. (5)
J/kg/K W/m2 W/m2 K
kg Cp,
w
Cp, g qraff qconv Tamb
Output
Description
Formula
Unit
T θ vw ρg vg q qz qr
Heap temperature Heap moisture content Solution velocity Density of air Air velocity Heat flux Heat flux in the vertical direction Heat flux in the radial direction
From solution transport From solution transport From gas transport From gas transport Calculated from T Calculated from T
K m3/m3 m/s kg/m3 m/s W/m2 W/m2 W/m2
q2 = q z2 + qr 2
q = ρw vw Cp,w T + ρg vg Cp,g T − [θkw + (θs − θ)k g + (1 − θs )k s] ∇T (2)
qraff = ρw Uw Cp,w Tw
(3)
q conv = h conv (Tamb − Ttop)
(4)
⎛ N + 284.5 − min min + ∆Tamb Tamb = T amb sin ⎜2π 365.25 ⎝ day ⎧ + ∆T amb ⎨0 ⎩
min φamb ⎞
⎟ ⎠
(cos(z) > 0) (cos(z) ≤ 0)
cos(z)
(5)
(
where cos(z) = sin (l) sin (d) + cos (l) cos (d) cos (a); a = 2π N +
d = 0.40928 sin
(
N + 284.5 2π 365.25
1 2
);
).
2.2. Model calibration The HeapSim-2D was calibrated using the historical monitoring data at the Quebrada Blanca (QB) Mine. QB is a copper mine located in the Chilean Andes, about 240 km southeast of Iquique, at an elevation of approximately 4400 m. At this elevation, the atmospheric pressure was calculated to be 0.58 atm using the barometric formula, with the
∂q 1 ∂rqr ∂ − ∇∙q = − z − = [ρ Cp, s T + θρw C p,w T + (θs − θ)ρg C p,g T] ∂z r ∂r ∂t b −Q
Input
(1)
27
Hydrometallurgy 176 (2018) 26–32
W. Liu, G. Granata
Table 2 Particle size distribution of the ore. Material size, mm Cumulative pass, %
25.4 100
19 99.6
12.7 79.7
9.51 64.1
4.76 44.0
Daily ambient temperature, ˚C
mole fractions of oxygen and carbon dioxide set at 0.21 and 0.004, respectively. The simulations were done assuming that all air applied flowed through the entire heap and that there were no preferential flow paths causing air loss through sideways. The operation uses heap and dump leaching, solvent extraction (SX), and electrowinning (EW), to produce copper cathodes. The heap studied had a total copper grade of 1.48%, of which 8% was soluble copper (oxides), 74% was cyanide soluble copper (chalcocite), and 18% was insoluble copper (chalcopyrite). The crushed ore, whose particle size distribution is shown in Table 2, was transported by conveyor belt 1.4 km to agglomeration. The heap, as a dynamic pad, had a height of 8 m and a surface area of 66,018 m2. The whole heap was divided into 19 leach modules that began irrigation sequentially, with full heap irrigation being achieved by day 37. Irrigation, provided by drip emitters arranged in a 55 by 55 cm grid, was applied at an average rate of 12 L/ m2/h for the first 100 days, 9 L/m2/h from day 101 to day 245, and 1.8 L/m2/h from day 246 to day 444, the end of the leach cycle. Aeration was provided at an average aeration rate of 0.23 m3/m2/h, but only for the first 250 days of irrigation.
Unit
θs
Saturated moisture content Irrigation rate Bulk density Density of water (solution) Latitude Number of the day Average annual minimum Amplitude of annual minimum Diurnal temperature variation Phase lag Thermal conductivity of solids Specific heat capacity of solids Temperature of raffinate solution Heat transfer coefficient
Design
0.37
m3/m3
Design Constant Constant
12 1600 1000
L/m2/h kg/m3 kg/m3
Empirical Empirical Empirical
− 21 1 January = 1 273.15
°S days K
Empirical
2
°C
Empirical
6
°C
Empirical Empirical
220 1
days W/m/K
Empirical
1000
J/kg/K
Design
299
K
Empirical, adjusted
0.5
W/m2/K
Uw ρb ρw l N min T amb
ΔTambmin ΔTambday φambmin ks Cp,
s
Tw hconv
0.074 9.1
0.037 7.5
14 A
2005 - 2010 10 6 2 -2 -6
Average daily ambient temperature, ˚C
simulated
B
10 6 2 -2 -6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Fig. 1. (A) monitored daily ambient temperature between 2005 and 2010 at the Quebrada Blanca Mine; (B) simulated average daily ambient temperature compared with the monitored average ambient temperature.
temperature (Tamb) was used for the subsequent calculation of the convective heat transfer flux over the heap surface by Eq. (4). The HeapSim-2D model was then calibrated by fitting multiple model outputs with the corresponding monitoring data. The model outputs include heap temperature, PLS copper concentration, PLS acid concentration, and PLS redox potential (Fig. 2). The equations and procedures for simulating the solute transport and reaction kinetics are not given here and can be found in Liu and Hashemzadeh (2017) and Dixon and Afewu (2011). The model could be successfully calibrated to simulate the PLS copper concentration (Fig. 2B), PLS acid concentration (Fig. 2C), and PLS redox potential (Fig. 2D). The simulation results have been reported in Liu and Hashemzadeh (2016). As with the heat transfer component (Fig. 2A), the model calibration was done by adjusting the heat transfer coefficient (hconv) to simulate the average heap temperature. The rest of the parameters, as shown in Table 3, were fixed. Since there were no direct monitoring data on the heap temperature profile, the monitoring data on the temperatures of the PLS and the raffinate solution were used as the benchmarks in the calibration process. Another benchmark was that the temperatures at the top and the bottom of the heap, measured by Lizama (2001), were 16 °C and 27 °C, respectively. Fig. 2A shows that once irrigation was initiated, the heap average temperature increased from the initial temperature of 6 °C to approximately the same as the raffinate temperature over a period of 50 days. Between 50 and 250 days, the average heap temperature closely followed the raffinate temperature at around 27 °C, during which period
Table 3 Values of model input parameters. Value
0.149 11.7
monitored
Prior to the model calibration, the ambient temperature (Tamb) at which the heap leach pad was operated was simulated by Eq. (5). The estimated input parameters associated with this equation are given in Table 3. The estimation was done using the monitoring data on the daily ambient temperatures over a six-year period from 2005 to 2010 (Fig. 1A). The simulated daily ambient temperature was then compared with the monitored average ambient temperature over the period (Fig. 1B). Fig. 1B shows that Eq. (5) could satisfactorily simulate the seasonal trend of the daily ambient temperature, with the summer maxima from December to February, and the winter minima from June to August in the southern hemisphere. The simulated ambient
Category
0.42 16.0
14
3.1. Model calibration
Description
0.841 22.4
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
3. Results and discussion
Input
2 30.7
28
Hydrometallurgy 176 (2018) 26–32
W. Liu, G. Granata
30
Temperature, ºC
raffinate
A
simulated Cu concentration, g/L
40
30
20 heap average
PLS
10
B
monitored
20 15 10 5 0
0 0
100
200 300 Leaching time, days
0
400
100
200 300 Leaching time, days
850
8 simulated
C
simulated 800 Eh (vs SHE), mV
monitored
6 Acid, g/L
25
4
2
400
D
monitored
750 700 650 600
0 0
100
200 300 Leaching time, days
0
400
100
200 300 Leaching time, days
400
Fig. 2. Model calibration using the historical monitoring data at the QB Mine: (A) monitored raffinate and PLS (pregnant leach solution) temperature compared with the simulated heap average temperature; (B) monitored and simulated copper concentration in PLS; (C) monitored and simulated acid concentration in PLS; (D) monitored and simulated redox potential of PLS.
the raffinate flow rates were maintained at high levels (9 L/m2/h). The heap average temperature started to drop on day 245 when the raffinate flow rate was reduced to 1.8 L/m2/h. The simulation of the heap temperature was considered valid for two reasons: the model output fell within the measured 17 °C and 27 °C; the model output fell within the measured PLS temperature and the raffinate temperature. The latter was predicted because the other major heat contributor, the oxidation of pyrite originally present in the ore, was expected to occur to a minimal extent. The model output corresponded to a heat transfer coefficient (hconv) of 0.5 W/m2/K. This seemingly small value could be explained by the application of a thermal cover over the leach pad at the QB mine.
than the heap bottom did, which was more pronounced in the case of a lower flow rate. Another key factor simulated is the extent of pyrite oxidation. Note that the pyrite here refers to the hypothetical blending of finely ground pyrite concentrate from flotation with the agglomerated ore. The percentage of pyrite blended in the agglomerated ore was set at 2% by weight. Due to its fine particle size and large surface area, the blended pyrite concentrate was expected to react to a much greater extent than the pyrite associated with the copper ore resulting from its coarse grain size and low degree of liberation (Bouffard and Dixon, 2002). However, the oxidation of pyrite associated with the copper ore can still play an important role if it can be easily accessed by the leaching solution. Fig. 4A shows the effect of blended pyrite oxidation on the heap average temperature under three raffinate flow rates and at a fixed raffinate temperature of 20 °C. The contribution of the pyrite oxidation to the heap temperature was much more significant at lower raffinate flow rates than at higher ones. Even though the lowest raffinate flow rate of 1 L/m2/h corresponded to the lowest extent of pyrite oxidation of 51%, the heap temperature rose to the highest among the three raffinate flow rates tested. In contrast, there was almost negligible effect of pyrite oxidation on the heap temperature at the two higher flow rates, even though the extents of pyrite oxidation were higher in both cases. This negligible effect was attributed to the heat generated by the pyrite oxidation at higher flow rates being transferred away by the bulk movement of the raffinate solution. The significant contribution of pyrite oxidation at a low raffinate flow rate was also seen at the raffinate temperatures of 30 °C (Fig. 4B). Not only the heap average temperature but also the heap temperature distribution was vastly different at different flow rates. In the case of the high flow rate (Fig. 4C), the temperature at different depths quickly approached the raffinate temperature within 50 days and remained constant for the rest of the leaching cycle. Conversely, in the case of the low flow rate (Fig. 4D), the heap temperature at the bottom (8 m) increased more slowly than at the top; the contribution of the pyrite oxidation was more pronounced at increasing depth and only the temperature on the very top of the heap (2 m) was controlled by the
3.2. Heap temperature distribution in response to variations of key design factors The calibrated model was used for the sensitivity study that assessed the response of the heap temperature distribution to the variations of the critical design factors. In the sensitivity studies, all empirical parameters were fixed and only controllable design parameters were varied. The design parameters under investigation are raffinate temperature, raffinate flow rate (irrigation rate), the extent of pyrite oxidation, and the application of a thermal cover. The heap initial temperature was set at 6 °C, lower than the raffinate temperature. Fig. 3A and B show the simulation results of the effect of raffinate temperature and flow rate on the heap temperature. The simulation results were obtained under the assumption that no chemical reactions occurred and that no heat loss via convection over the heap top (hconv = 0). At a fixed raffinate temperature, the average heap temperature gradually approached the raffinate temperature, but the time required for the heap to reach this temperature was controlled by the raffinate flow rate. As the raffinate flow rate was decreased from 12 to 1 L/m2/h (Fig. 3A), the time required increased from 22 days to 300 days. As shown by Fig. 3C and D, the time required to reach the raffinate temperature increased with increasing depth. In other words, the temperature at the heap top approached that of the raffinate faster 29
Hydrometallurgy 176 (2018) 26–32
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A
Heap average temperature, ºC
Heap average temperature, ºC
30
20
10 12 L/m²/h 6 L/m²/h 1 L/m²/h
raffinate: 20 ˚C 0 0
50
100 150 200 Leaching time, days
250
B
20
10 raffinate: 20 °C irrigation: 12 L/m2/h
raffinate: 30 °C
0
300
0
20
40 60 Leaching time, days
80
30
30
C
raffinate: 20 ˚C irrigation: 6 L/m2/h
Heap temperature, ºC
Heap temperature, ºC
30
20 increasing depth 10 0.5 m: heap top
100
D
raffinate: 20 ˚C irrigation: 1 L/m2/h 20 increasing depth 10 0.5 m: heap top
8 m: heap bottom
8 m: heap bottom 0
0 0
20
40 60 Leaching time, days
80
0
100
50
100
150
200
250
300
Time, days
Fig. 3. (A) Simulated heap average temperature at different raffinate flow rates and a fixed raffinate temperature; (B) simulated heap average temperature at different raffinate temperatures and a fixed raffinate flow rate; (C) simulated heap temperature profile at a fixed raffinate temperature and a higher raffinate flow rate; (D) simulated heap temperature profile at a fixed raffinate temperature and a lower raffinate flow rate. All simulations were done under the assumption that no chemical reactions occurred, and no heat loss via convection over the heap top (hconv = 0).
Heap average temperature, ºC
Heap average temperature, ºC
50
50
A
raffinate: 20 ˚C 40 30 20
12 L/m²/h, 69% reacted 6 L/m²/h, 61% reacted 1 L/m²/h, 51% reacted
10 0 0
100
200 300 400 Leaching time, days
30 20 raffinate: 20 °C, 51% reacted
10
raffinate: 30 °C, 51% reacted
0 0
500
100
200 300 400 Leaching time, days
50
50
C
raffinate: 20 ˚C irrigation: 6 L/m2/h 61% reacted
40
Heap temperature, ºC
Heap temperature, ºC
B
irrigation: 1 L/m2/h 40
30 20 0.5 m: heap top
10
D
raffinate: 20 ˚C irrigation: 1 L/m2/h 51% reacted
40
500
30 20 0.5 m: heap top
10
8 m: heap bottom
8 m: heap bottom 0
0 0
100
200
300
400
0
500
Leaching time, days
100
200
300
400
500
Time, days
Fig. 4. (A) Simulated heap average temperature at different raffinate flow rates and a fixed raffinate temperature; (B) simulated heap average temperature at two raffinate temperatures and a fixed flow rate; (C) simulated heap temperature profile at a high flow rate; (D) simulated heap temperature profile at a low flow rate. The simulations were performed under the assumption that no other sulfide reactions other than the blended pyrite oxidation occurred, and no heat loss via convection over the heap top (hconv = 0). The percentages reacted represent the extents of the oxidation of the added pyrite.
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Fig. 5. Effect of applying a thermal cover over the top of the heap on the heap temperature at a fixed raffinate temperature of 20 °C: (A) a lower raffinate flow rate of 1 L/m2/h and heat transfer coefficient of 1 W/m2/K representing the case without a thermal cover; (B) a higher raffinate flow rate of 6 L/m2/h and heat transfer coefficient of 1 W/m2/K representing the case without a thermal cover; (C) a lower raffinate flow rate of 1 L/m2/h and heat transfer coefficient of 0.5 W/m2/K representing the case with a thermal cover; (D) higher raffinate flow rate of 6 L/m2/h and heat transfer coefficient of 0.5 W/m2/K representing the case with a thermal cover; 51% and 62% reacted referring to the extents of the blended pyrite oxidation.
arbitrary. It is extremely challenging to accurately estimate this value because of its dependence on the local climatic conditions. However, the choice of 1 W/m2/K served the purpose of comparing the effect of thermal cover application. The simulation results show that the decrease in the heat transfer coefficient due to the application of a thermal cover led to an increase in the heap temperature, reflecting the reduced heat loss across the warmer heap top by convection to the cooler ambient. However, the impact of the heat transfer coefficient depended on the raffinate flow rate. In the case of a higher heat transfer coefficient (without thermal cover) and a lower raffinate flow rate (Fig. 5A), the heap top temperature exhibited oscillation, which was in sync with the seasonal change of the ambient temperature. This was attributed to the heap top temperature being more responsive to the ambient temperature at the low flow rate than at the high flow rate.
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100 80 60 monitored simulated aeration raffinate 37 °C raffinate 37 °C & aeration
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3.3. Effect of different temperature control techniques on copper extraction at QB
Fig. 6. The impact of three hypothetical scenarios on the current chalcocite leach operation at the QB Mine: (1) maintain aeration; (2) increase raffinate temperature by 10 °C, and (3) apply both scenarios at the same time
The findings of this work have implications on the current chalcocite leaching operation at the Quebrada Blanca Mine. Since blending pyrite with the agglomerated ore was not a feasible option, three whatif scenarios were tested to improve the current operation: the first scenario maintained the aeration for the entire leach cycle as opposed to terminating it on day 250; the second scenario increased the raffinate temperature by 10 °C from the current 27 °C to 37 °C; the third scenario was a combination of the two previous ones. Fig. 6 shows that all three scenarios had positive effects on the leaching recovery to different extents. The recovery slightly increased by approximately 3% by maintaining aeration for the entire leach cycle. Increasing raffinate temperature by 10 °C had a pronounced positive effect on the copper recovery, with an increase of 17% in copper extraction. The combination of both scenarios resulted in a further 3% increase in the extraction rate, because more oxygen would be required by the bacterial activities.
raffinate temperature. It was therefore concluded that with increasing raffinate flow rate, the raffinate temperature played an increasingly dominant role in controlling the heap temperature in a top-down manner until to a certain flow rate at which the temperature of the entire heap was fully controlled by the raffinate temperature. Fig. 5 shows the simulation results of the effect of applying a thermal cover over the top of the heap on the heap temperature at low and high raffinate flow rates. The effect of thermal cover application was reflected in the change in the heat transfer coefficient. The case of applying a thermal cover was represented by a heat transfer coefficient of 0.5 W/m2/K (Fig. 5C and D) as opposed to 1 W/m2/K (Fig. 5A and B) representing the case without a thermal cover. The choice of 1 W/m2/K to represent the case without a thermal cover is, to a certain extent, 31
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electrochemical bioleaching of chalcopyrite concentrates by moderately thermophilic bacteria at high pulp density. Hydrometallurgy 106, 84–92. Batty, J.D., Rorke, G.V., 2006. Development and commercial demonstration of the BioCOP™ thermophile process. Hydrometallurgy 83, 83–89. Bolorunduro, S., 1999. Kinetics of Leaching of Chalcocite in Acid Ferric Sulphate Media: Chemical and Bacterial Leaching. M.A.Sc. Thesis. University of British Columbia. Bouffard, S.C., Dixon, D.G., 2002. On the rate-limiting steps of pyritic refractory gold ore heap leaching: results from small and large column tests. Miner. Eng. 15, 859–870. Chen, B.-W., Wen, J.-K., 2013. Feasibility study on heap bioleaching of chalcopyrite. Rare Metals 32, 524–531. Dixon, D.G., 2000. Analysis of heat conservation during copper sulphide heap leaching. Hydrometallurgy 58, 27–41. Dixon, D.G., Afewu, K.I., 2011. Mathematical modelling of heap leaching under drip irrigation. In: Percolation Leaching: The Status Globally and in Southern Africa. The Southern African Institute of Mining and Metallurgy, pp. 1–29. Dixon, D.G., Petersen, J., 2003. Comprehensive modeling study of chalcocite column and heap bioleaching. In: Riveros, P.A., Dixon, D.G., Dreisinger, D.B., Menacho, J. (Eds.), Copper 2003 - Cobre 2003, Santiago, Chile, pp. 493–515. Dutrizac, J.E., MacDonald, R.J.C., 1974. The kinetics of dissolution of covellite in acidified ferric sulphate solutions. Can. Metall. Q. 13, 423–433. Hackl, R.P., Dreisinger, D.B., Peters, E., King, J.A., 1995. Passivation of chalcopyrite during oxidative leaching in sulfate media. Hydrometallurgy 39, 25–48. Khoshkhoo, M., 2016. Chalcopyrite (bio)leaching in Sulphate Solutions: An Investigation Into Hindered Dissolution With a Focus on Solution Redox Potential. PhD Thesis. Luleå University of Technology. Leahy, M.J., Davidson, M.R., Schwarz, M.P., 2005. A model for heap bioleaching of chalcocite with heat balance: bacterial temperature dependence. Miner. Eng. 18, 1239–1252. Leahy, M.J., Davidson, M.R., Schwarz, M.P., 2007. A model for heap bioleaching of chalcocite with heat balance: Mesophiles and moderate thermophiles. Hydrometallurgy 85, 24–41. Liu, W., Dixon, D.G., 2015. Modelling solution flow in copper heap bioleaching – a case study. In: Proceedings of the 3rd International Heap Leach Solutions Conference, InfoMine, September 12–16, Reno, Nevada, USA, pp. 63–72. Liu, W., Hashemzadeh, M., 2016. Modelling copper recovery, total iron concentration and acid balance in chalcocite heap bioleaching. In: Proceedings of the 4th International Heap Leach Solutions Conference, InfoMine, October 18–20, Lima, Peru. Liu, W., Hashemzadeh, M., 2017. Solution flow behavior in response to key operating parameters in heap leaching. Hydrometallurgy 169, 183–191. Lizama, H.M., 2001. Copper bioleaching behaviour in an aerated heap. Int. J. Miner. Process. 62, 257–269. Niu, X., Ruan, R., Tan, Q., Jia, Y., Sun, H., 2015. Study on the second stage of chalcocite leaching in column with redox potential control and its implications. Hydrometallurgy 155, 141–152. Olvera, O.G., Rebolledo, M., Asselin, E., 2016. Atmospheric ferric sulfate leaching of chalcopyrite: thermodynamics, kinetics and electrochemistry. Hydrometallurgy 165, 148–158. Petersen, J., 2016. Heap leaching as a key technology for recovery of values from lowgrade ores – a brief overview. Hydrometallurgy 165 (Part 1), 206–212. Petersen, J., Dixon, D.G., 2007. Principles, mechanisms and dynamics of chalcocite heap bioleaching. In: Microbial Processing of Metal Sulfides. Springer, pp. 193–218. Pradhan, N., Nathsarma, K.C., Srinivasa Rao, K., Sukla, L.B., Mishra, B.K., 2008. Heap bioleaching of chalcopyrite: a review. Miner. Eng. 21, 355–365. Robertson, S.W., Van-Staden, P.J., Seyedbagheri, A., 2012. Advances in High-temperature Heap Leaching of Refractory Copper Sulphide Ores. Vol. 112. The Southern African Institute of Mining and Metallurgy, pp. 1045–1050. Yue, G., Asselin, E., 2014. Kinetics of ferric ion reduction on chalcopyrite and its influence on leaching up to 150°C. Electrochim. Acta 146, 307–321.
The HeapSim-2D model, calibrated using the historical data of the Quebrada Blanca Mine, was applied to assess the effect of four temperature control techniques on the heap temperature distribution: raffinate temperature, raffinate flow rate, the extent of pyrite oxidation, and the application of a thermal cover. The simulations were done under the assumption that other factors, such as acid supply, air permeability and aeration were not rate-limiting. It is understood that these factor could become rate-limiting at elevated temperatures (Bouffard and Dixon, 2002; Dixon and Petersen, 2003). Note that the results reported here were only model predictions and not validated against experimental data. The heap temperature was found to be controlled by raffinate temperature at high raffinate flow rates. The contribution of pyrite oxidation to raising heap temperature was significant only at low raffinate flow rates. The application of a thermal cover led to an increase in the heap temperature, but the magnitude of the increase was also controlled by raffinate flow rate. It was therefore concluded that with increasing raffinate flow rate, the raffinate temperature played an increasingly dominant role in controlling heap temperature in a top-down manner until to a certain extent at which the temperature of the entire heap was fully controlled by the raffinate temperature. Using the knowledge gained from the modelling study, three hypothetical scenarios were tested to assess the response of the QB operation to three temperature control techniques. Maintaining aeration for the entire leach cycle in combination with increasing raffinate temperature by 10 °C could potentially lead to a 20% increase in the copper extraction from the current 66%. This conclusion is of particular importance because both techniques are very simple to implement from an operational standpoint. Portable low-pressure air blowers are low cost and simple to operate. Likewise, diesel heat exchangers for solution heating are easy to install. Understanding the effects of these temperature control techniques on heap temperature distribution is critical for achieving optimum copper extraction from low-grade sulfide ores using heap leaching. Given that the reported results are only model predictions, further work is required to validate the modelling results against experimental data. Acknowledgement The authors would like to thank Teck Metals Applied Research and Technology for providing the heap leach data at the Quebrada Blanca Mine. References Ahmadi, A., Schaffie, M., Petersen, J., Schippers, A., Ranjbar, M., 2011. Conventional and
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