Copper sulfide flotation under acidic conditions using a xanthogen formate compound as collector: Adsorption studies and experimental design approach

Colloids and Surfaces A 585 (2020) 124032

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Copper sulfide flotation under acidic conditions using a xanthogen formate compound as collector: Adsorption studies and experimental design approach

T

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D.M. Ávila-Márqueza, A. Blanco-Floresb,⁎, I.A. Reyes-Domíngueza,c, , H.P. Toledo-Jaldind, J. Aguilar-Carrilloa,c, R. Cruz-Gaonaa a Instituto de Metalurgia-Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Lomas 2a sección, 78210 San Luis Potosí, San Luis Potosí, Mexico b División de Ingeniería Mecánica, Tecnológico de Estudios Superiores de Tianguistenco, Carretera Tenango-Marquesa km 22, Santiago Tilapa, 52650 Santiago Tianguistenco, Estado de México, Mexico c Catedrático CONACyT - Consejo Nacional de Ciencia y Tecnología, Colonia Crédito Constructor Del. Benito Juárez, 03940 Ciudad de México, Mexico d Facultad de Química, Universidad Autónoma del Estado de México, Paseo Tollocan y Paseo Colon, 50110 Toluca, Estado de México, Mexico

G R A P H I C A L A B S T R A C T

A R T I C LE I N FO

A B S T R A C T

Keywords: Isobutyl xanthogen ethyl formate Microflotation tests XPS-mechanism Bornite mineral Experimental design

Under acidic conditions, isobutyl xanthogen ethyl formate was used as a collector compound in the flotation process for Cu recovery from the mineral bornite (Cu5FeS4). The mineral sample was characterized by X-Ray Diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), Scanning Electron Microscopy (SEM) and XRay Photoelectron Spectroscopy (XPS) techniques. Also found in lower proportions were chalcopyrite and quartz. The bornite presented a rough and porous surface. The adsorption study included kinetics, thermodynamics, and isotherm tests. The equilibrium time at pH 2 was 15 min. The kinetics model suggest the adsorption can be described by two processes, both related to the formation of a chelate. Thermodynamically, the collector adsorption process is favored at room temperature, whereas the process is not favored at lower temperatures. The adsorption capacity was 8.8 mg g−1 achieved by a combination of mechanisms on a heterogeneous surface. Under local optimized conditions, the highest Cu recovery (96.98 %) was determined from an experimental design for an initial collector concentration of 20 mg L−1, a flotation time of 3 min, and pH = 2.

⁎

Corresponding author. Corresponding author at: Instituto de Metalurgia-Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Lomas 2a sección, 78210 San Luis Potosí, San Luis Potosí, Mexico. E-mail addresses: [email protected] (A. Blanco-Flores), [email protected], [email protected] (I.A. Reyes-Domínguez). ⁎⁎

https://doi.org/10.1016/j.colsurfa.2019.124032 Received 28 June 2019; Received in revised form 24 September 2019; Accepted 26 September 2019 Available online 29 September 2019 0927-7757/ © 2019 Elsevier B.V. All rights reserved.

Colloids and Surfaces A 585 (2020) 124032

D.M. Ávila-Márquez, et al.

Contact time of the collector with the mineral surface represents one of the most important variables in the flotation process. In addition, the combinations between pH-tf, Ci-tf, and pH-Ci-tf variables have the most significant effect on Cu recovery percentage. The collector can be used satisfactorily at lower pH values. Although the xanthogen formate functional group acted on both the Fe and Cu ions in the mineral, it was observed that the collector has a higher affinity for Cu ions compared with Fe ions.

1. Introduction

Several researchers have studied the effects of the substituents R and R1 on the interaction mechanism between xanthogen molecules and the mineral, and the increment of the hydrophobicity of the mineral when the xanthogen is adsorbed [19]. Yang et al. [20] described theoretically the selectivity in the flotation of chalcopyrite by xanthates and xanthate derivatives (xanthogens). The authors described that selectivity is associated with the strength of the coordinate covalent bonding between copper atom and the chalcopyrite surface. In addition, it was reported that selectivity of xanthogen molecules was higher than xanthate molecules for copper ion, owing to the stability of the ring formed between copper ion and the sulfur and oxygen atoms in the xanthogen structure. Others have studied the flotation process in chalcopyrite, molybdenite, and chalcocite using xanthates or other collector molecules different from xanthogens [21,22]. These copper sulfide mineral resources have a disadvantage in the presence of pyrite (undesired product) which must be suppressed. Alternatives to suppressing this phase under basic conditions have been investigated [6]. However, using xanthogen formates as collectors, there is no information as yet regarding the adsorption selectivity of copper in the presence of iron from a copper sulfide mineral to improve the flotation process without pyrite suppression. There are many factors that influence copper recovery through flotation process such as pH of flotation medium, contact time between collector and mineral, doses of mineral-collector volume ratio, and air flow to attach the air bubble with hydrophobic mineral surface. For these reasons, it is very important to optimize the experimental conditions taking into account the independent or combined action of these factors on the copper recovery percentage. It is also extremely important to determine the interactions among these factors since the recovery of copper could be increased and the industrial process could be improved by modifying the operational conditions of flotation. The aforementioned is possible by designing experiments where it is possible to control the variability of the flotation process owing to different factors and their interactions. The experimental design is a useful tool for analyzing the effect produced by two or more factors on the response variable, i.e., the recovery of Cu [23]. Several researchers have reported the use of factorial design to obtain the lowest number of experiments, and to simultaneously study the effects of all factors of interest in the flotation process. This enables the researchers to find an adequate mathematic model for the response variable. Akbari et al. applied a two level factorial design to determine the most significant factors on the recovery yields of Fenugreek seed [24]. Others have reported the optimization process of biogas production using a factorial design, combining the analyses of results obtained from factorial design with response surface methodology [25]. Venugopalan and Sathiyamoorthy employed a factorial design of 23 experiments. They analyzed three factors with a minimum number of eight experiments and obtained a mathematical equation for predicting the optimum values of the response variable [26]. Here, we evaluate the effect of xanthogen formates as an effective collector in the flotation of a copper sulfide mineral in the presence of iron mineral phases under acidic to neutral (pH 2–6) conditions. The xanthogen adsorption on copper sulfide mineral is supported by the kinetics and isotherm adsorption experiments. Improved flotation conditions to maximize copper recovery were obtained by a 23 experimental factorial design.

Globally over the last two centuries, there has been an increase in the demand for copper. This valuable metal has extensive uses owing to its thermal and electrical conductivity. Copper has a broad range of applications in the building materials and electric industry such as electrical equipment (generators, electrical cables) and valves to list but a few [1]. Approximately 80% of the metal is present in the Fe-Cu-S minerals (chalcopyrite, chalcocite, and bornite) and copper recovery is conducted by flotation. In the process, the sulfide minerals must have hydrophobic characteristics to attach to an air bubble [2]. One of the alternatives to achieving this requirement is surface mineral modification by adsorption of collectors. These collectors, i.e., organic compounds, often increase the hydrophobicity. For this reason, the adsorption process is critical in the flotation of copper minerals [3]. There are many collectors used in copper flotation such as oxime surfactants, fatty acids, phosphates/phosphonates, thiols, and sulfhydryl chelators, but the most common collectors for concentrated copper sulfide are xanthate compounds [4]. Although xanthate compounds have been extensively used, they are degraded in toxic environments when the process is carried out under acidic conditions [5–7]. This degradation impacts negatively the selectivity and the recovery processes. Although xanthates are highly reactive because they have two S atoms available to interact with the copper ion, they possess low Cu selectivity decreasing its recovery. An alternative to xanthates are the xanthogens, organic compounds that are also very effective as copper sulfide collectors in a wide range of pH (5.0–10.5). Xanthogens are more selective than xanthates owing to their low electron density in the S atom of the C]S bond. The main group in their chemical structure is the R−OC(S)SC(O)OR1, where R and R1 may be an alkyl or aryl group, and play a fundamental role in the collector properties. Subsequently, it has been shown that using the xanthogen formates, the selectivity of copper flotation is possible against the presence of pyrite-containing iron. Xiao et al., reported that interaction between copper and the collector is through C] S and C]O functional groups. The alkyl chains of these molecules function to alter the electronic density of the S and O atoms, which is an important factor in obtaining high copper recovery values [8]. Xanthogens are also stable in acidic pH conditions and can be used for recovery of copper from leaching wastes as produced by the hydrometallurgical industry [9]. These wastes are usually composed of oxides such as zinc ferrite and quartz, and sulfates such as anglesite, gypsum, and jarosite. copper is present as a sulfide, mainly chalcopyrite [10–15]. It has been emphasized that pH is an important aspect to consider in order to improve the efficiency of the flotation process. While it has been suggested that the metallurgical waste flotation stage should be carried out at a low pH [16,17], and to also consider the decrease of costs in floating minerals without the need to add neutralizing agents, there is little research on the flotation process for the recovery of valuable metals at acidic pH [10,18]. Also it is important to know the behavior of the collectors at acidic pH values in terms of chemical stability, the affinity to interact with the adsorption sites (copper), the maximum amount that is required to achieve the highest adsorption of the collector by copper, and the way in which the metal-collector interaction occurs. 2

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2. Materials and methods

volume of treated solution (L), and m is the mass of copper mineral (g). A thermodynamic analysis as a function of temperature was carried out at 283, 298, and 313 K. The thermodynamic variables [27] were calculated from Eqs. 2 and 3:

2.1. Materials Isobutyl xanthogen ethyl formate (C-4940, PM = 222.3 g/mol, C4H9OCS2CO2C2H5), used as collector, was provided by SNF FloMin (Baytown, USA) with a purity higher than 90%. The copper sulfide mineral (Bornite) was supplied by Fresnillo plc (Zacatecas, Mexico). Crushing and grinding of the mineral samples were carried out using a cone mill and a pulverizer, respectively. Particles with diameters ranging from 37 to 74 μm were used for the adsorption and flotation studies.

ΔG∘ = −RT In K d In K d = −

(2)

ΔS ∘ ΔH ∘ + R RT

(3)

Where, ΔG° (J/mol), ΔH° (J/mol), and ΔS° (J/mol·K) are the Gibbs free energy change, adsorption enthalpy, and adsorption entropy, respectively. R is the gas constant (8.314 J/mol·K), T the temperature (K), and Kd the adsorption equilibrium constant. ΔH° and ΔS° are determined from the intercept and slope of plotting ln Kd vs. 1/T.

2.2. Mineral characterization

2.4. Adsorption isotherm studies

The copper mineral was characterized by scanning electron microscopy (SEM) using a JEOL JSM-6610LV SEM microscope (JEOL Ltd., Japan). It was operated at 11 kV to analyze the surface morphology taking images at magnifications of 3000X and 5000X using secondary electrons (SE). The sample was mounted in a specimen holder, placed on a carbon film, and sputter-coated with gold. Microanalyses by energy X-ray dispersive spectroscopy (EDS, X-Max Oxford Instruments, UK) were performed to obtain semi-quantitative chemical composition information. Mineralogical phases were identified by powder X-ray diffraction (XRD) in a Bruker D8 Advance X-ray diffractometer equipped with a CuKα radiation source and SOL-X solid-state detector. Analyses were performed in a 2Ɵ range from 10° to 90° including 2Ɵ steps at 0.3 s intervals. The copper mineral before and after the adsorption process was analyzed by Fourier-transform infrared spectroscopy (FT-IR) in the 4000–500 cm−1 range with a resolution of 4 cm−1 and 32 co-added scans. The FT-IR spectra were taken at room temperature using a Thermo Scientific Nicolet iS10 FTIR (Thermo Fisher Scientific, USA). KBr was added to the samples for added clarification. To understand the affinity of the collector by copper or other ions the X-ray photoelectron spectroscopy (XPS) narrow spectra were acquired using a JEOL JPS-9200 (JEOL Ltd., Japan). The operating conditions for all samples consisted of an Mg X-ray source (1253.6 eV) at 200 W, an area of analysis of 3 mm2, a pass energy of 15 eV, and a vacuum of 7.5 × 10−9 Torr. The spectra were analyzed using the Specsurf™ software and charge corrected by means of the adventitious carbon signal (C1s) at 284.5 eV. The Shirley and the Gauss-Lorentz methods were used for background and subtraction curves, respectively.

0.2 g of copper mineral was mixed with a fixed solution volume using different initial concentrations of collector (from 1 to 50 mg/L) and stirring. Equilibrium time was determined by adsorption kinetic experiments. The experiment was performed in duplicate at 298 K and pH = 2 (others have shown that the collectors of the xanthogen formate family are stable at pH = 2). The amount of total collector adsorbed on copper mineral was calculated from Eq. 1. 2.5. Experimental design Estimation of variables was achieved by a two-level factorial design with central point, using Minitab program. 23 experimental designs consisting of 17 experiments (three variables and two levels: low (-) and high (+), and one central point (0)). All of the experiments were replicated. Operation parameters were pH, initial concentration (Ci), and microflotation time (tf) (Table 1). The independent variable was copper recovery percentage (%R). Table 1 shows the experimental design used to determine the variables that have the highest impact on copper recovery during microflotation tests under acidic or neutral conditions. The common flotation parameters reported by others include pH, collector addition or dosage and type, flotation time, and depressants. These are important factors that can increase the percent recovery of Cu, regardless of the types of copper sulfide used in the flotation process [28]. A range of parameters were selected for experimental design with regard to previous work where the common flotation parameters pH and collector concentration are variables that correlate with adsorption studies of hydrophobic molecules on the sulphide minerals. The typical range of these parameters are pH values between 2 and 10, flotation

2.3. Adsorption kinetic experiments Table 1 Experimental design with levels, design matrix, and the response variables.

The equilibrium time was determined by adsorption kinetic experiments. The equilibrium time is very important in flotation studies as it indicates the maximum adsorption to achieve the hydrophobisation of the mineral surface. During experiments, 0.2 g of copper were placed in contact with a fixed collector volume at 20 mg/L. The mixtures were agitated at 120 rpm, and samples were separated by filtration after certain intervals of time. The solution pH was 2 and temperature was maintained at 298 K. Adsorption kinetic experiments were carried out in duplicate. The amount of non-adsorbed collector was determined by UV–vis spectroscopy at λ = 274 nm with a UV–vis Genesys 10S spectrophotometer (Thermo Fisher Scientific, USA). The adsorbed amounts, q, were calculated from Eq. (1).

q=

(C0 − Ct ) ∙V m

(1)

Where, Co is the initial collector concentration (mg/L), Ct is the collector concentration of the solution (mg/L) at time t (min), V is the 3

Run

pH

Ci (mg/L)

tf (min)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

2 6 2 6 2 6 2 6 2 6 2 6 2 6 2 6 4

20 20 60 60 20 20 60 60 20 20 60 60 20 20 60 60 40

1 1 1 1 3 3 3 3 1 1 1 1 3 3 3 3 2

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times between 1–4 min, and 20–80 mg/L of initial collector concentration. These values have been reported for the flotation of several copper sulfide minerals [28–30]. The best experimental conditions for Cu flotation tests were identified using several tools: a Pareto chart, ANOVA analysis, mathematical regression analysis, normal probability plot, residues normal probability plot, main effects plot, and surface response. Alipanahpour et al. argues that these statistical parameters are very important for the correct interpretation of the results [31]. Microflotation tests were carried out in a 100 mL-Hallimond tube flotation cell. The mineral was mixed with a solution of C-4940 collector at a fixed initial concentration for each experiment (Table 1). The equilibrium time was obtained from adsorption kinetic experiments. The biphasic system was transferred to the Hallimod tube. The mixture was stirred for a time tf, and the solution pH was adjusted according to the experimental design matrix presented in Table 1. The nitrogen flow rate was kept at 20 mL/min. The foams and concentrates were collected, dried, and weighed. The copper and iron recovery percentages were calculated using both dry weights of final products and copper percentages obtained from elemental analyses by Atomic Absorption Spectroscopy (AAE) in a PerkinElmer Analyst 200 (PerkinElmer Inc., USA). Concentrates were previously digested with a 3:1 HCl-HNO3 solution, and the final volume was fixed to 100 mL. 3. Results and discussion 3.1. Mineral characterization The investigated copper mineral was characterized by a rough surface of solid particles (aggregates) and porous structure. The difference in color contrast is related to the different mineralogical phases (Fig. 1a and b). When the collector is adsorbed, the mineral surface is smoother and the mineral particles’ edges disappear as the surface is covered by the collector molecules (Fig. 1c and d). This behavior is important for the flotation test because a smooth surface after adsorption of collector could improve the floatability of mineral [32]. The presence of several metallic species may be related to other mineral phases, considering the mineralogical composition of the copper mineral (Fig. 1e). Fe, Cu, and S were the main elements present in the EDS analysis. Analysis by atomic absorption spectroscopy indicates that the amount of Cu (40.4 wt.%) was higher than Fe (12.3 wt.%), which is consistent with the results of SEM-EDS. These values were employed for calculation of Cu and Fe recovery percentages in the microflotation studies. The results of the powder XRD show the presence of bornite and chalcopyrite (Fig. 2). The first phase is predominant which suggests that the mineral sample consists mainly of bornite (Cu5FeS4). This result is consistent with the presence of Fe, Cu, and S observed by SEM-EDS (Fig. 1e). Zanin et al., [33] demonstrated that a crystalline mineral could achieve a fast flotation, and bornite is the sulfide mineral with the highest flotation rate and copper recovery (above 80%). The FT-IR spectra show the presence of bands in the range of 800–1400 cm−1 that correspond to chalcopyrite vibrations [34]. Bands in the range 1800–2400 cm−1 correspond to bornite [35] while the one observed at 806 cm−1 is quartz [36]. When collector C-4940 is adsorbed on the mineral, the FT-IR spectra show characteristic bands of collector at 1839, 1546, and 1087 cm−1 for C]O, CH3 symmetric vibrations, and C]S, respectively. The new bands observed at lower frequencies are consistent with Fe-O and Cu-O bonds [37]. The displacement of some bands between 1800 and 2700 cm−1 is also related to adsorption of collector on the mineral surface (Fig. 3).

Fig. 1. SEM images of copper mineral at different voltages and magnifications, before adsorption: a) 11 kV 5000X, b) 20 kV at 5000X, and after adsorption: c) 12 kV at 2000X, d) 11 kV at 5000X; e) EDS map.

3.2. Adsorption kinetic experiments Equilibrium time is an important parameter in microflotation 4

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pseudo first-order model (Table 2). This means that the process took place through physical interaction, considering that the experimentally obtained q (qexp) is similar to the calculated one (qecal). The good correlation obtained with the pseudo second-order model suggests that the process could also be described by chemisorption, possibly due to sharing or exchanging electrons between the mineral (adsorbent) and the molecule of the collector C-4940 (adsorbate). These results suggest that the adsorption can be described by two processes that are both related to the formation of a chelate-a more stable structure due to its better electronic density distribution [39,40]. As industrial mining processes are usually performed at a range of different temperatures, we also investigated the effect of temperature variations on the collector adsorption capacity (Fig. 5). The collector adsorption process was favored at room temperature (298 K) and less so at 313 K and 283 K. Especially at lower temperatures, the process is not favored due to the molecules' reduced kinetic energy and mobility through the surface material. This observation is confirmed by the equilibrium times (Fig. 5) and rate constants of the pseudo second-order model (k283K = 3.62⋅10−3 g/ mg min and k298K = 0.517 g/mg min). The thermodynamic variables obtained from Eqs. 2 and 3 are summarized in Table 3. The three processes were spontaneous and thermodynamically favorable, with the level of spontaneity decreasing from 298 K, over 313 K, to 283 K. The difference in Gibbs free energy shows the same temperature dependence (Table 3). It has been suggested that for values of ΔG° between 0 and −20 kJ/ mol a physisorption process occurs [41]. In addition, the process is endothermic (adsorption enthalpy greater than zero). The positive value of ΔS° indicates that randomness increases at the solid-liquid interface which is characteristic of all system that tend to equilibrium. For this reason, this system is dominated by entropic rather than enthalpic changes (|ΔS°T| > |ΔH°|) [42].

Fig. 2. Powder XRD pattern corresponding to the copper mineral.

3.3. Adsorption isotherms

Fig. 3. FT-IR spectra corresponding to the collector (C-4940), copper mineral (MN), and copper mineral with collector adsorbed (MN + C-4940).

The adsorption isotherm was obtained under fixed experimental conditions. At lower concentrations, the adsorption of collector rapidly increased until the material was saturated at higher concentrations (results not shown). The experimental data were compared to adsorption isotherm nonlinear models such as the Langmuir, Freundlich, Sips, and Temkin models [38]. Langmuir models indicate that adsorption takes place on a homogeneous surface and that adsorption continues until the monolayer is complete. This model can be expressed as:

studies because it achieves complete mineral hydrophobisation due to collector adsorption on the mineral surface. Before achieving system equilibrium at te = 15 min (Fig. 4) adsorption increased very rapidly until a nearly constant value of qe = 3.3958 mg g−1 was reached. This is consistent with reports of a fast flotation processes by Zanin et al., [33]. Pseudo first- and pseudo second-order models were used with the experimental data to gain some insights about the nature and type of the adsorption process. The pseudo-first order model is based on the physisorption process on a homogeneous surface and it is described by Eq. 4.

qt = qe (1 − e K Lt )

(4)

Where qt and qe are the amounts of collector adsorbed (mg g−1) at times t and te (min), respectively. KL is the adsorption rate constant (min−1) of the pseudo first-order model. The pseudo-second order model (Eq. 5) assumes chemical adsorption through sharing or exchanging electrons between solid adsorbent and liquid adsorbate molecules [38]:

qt =

qe2 kt 1 + qe t

(5)

Here, qt and qe are again the amounts of collector adsorbed (mg g−1) at times t and te (min), respectively, and k is the adsorption rate constant (g mg−1 min−1) of the pseudo second-order model. The results show that the adsorption process is well described by the

Fig. 4. Adsorption kinetics of collector on the mineral surface. 5

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Table 2 Kinetic parameters for adsorption of C-4940 collector. KL and k are the adsorption rate constants for each model while qexp and qcal are the experimentally obtained and calculated values of q, respectively. Kinetic parameters qexp = 3.3871 mg/g

Table 5 Results for microflotation tests using the experimental design from Table 1. Run

pH

Initial concentration (mg/L)

Flotation contact time (min)

Cu recovery (%)

Fe recovery (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

2 6 2 6 2 6 2 6 2 6 2 6 2 6 2 6 4

20 20 60 60 20 20 60 60 20 20 60 60 20 20 60 60 40

1 1 1 1 3 3 3 3 1 1 1 1 3 3 3 3 2

55.19 59.38 86.41 65.62 96.98 91.01 57.27 94.06 50.35 61.41 93.75 69.89 95.85 83.10 65.34 87.20 73.70

47.22 29.09 72.54 51.35 72.86 67.79 51.01 78.10 43.81 42.42 69.54 51.28 75.15 63.36 46.69 67.38 58.11

Kinetic models for C-4940 adsorption Pseudo first-order model

Pseudo second-order model

qcal (mg/g) Rate constant

3.4062 KL (min−1) = 0.6760

R2 RRS χ2

0.9923 0.0710 0.0089

3.5229 k (g/mg· min) = 0.5175 0.9986 0.0127 0.0016

Fig. 5. Adsorption of collector C-4940 on copper sulfide mineral as a function of temperature.

Table 3 Thermodynamic results for adsorption of C-4940 on the copper mineral. Temperature (K)

ΔG° (kJ/ mol)

ΔH° (kJ/ mol)

ΔS° (kJ/ mol·K)

|ΔS°T| > |ΔH°|

283 298 313

−2.170 −8.888 −7.829

51.414

0.189

53.49 56.32 59.16

Fig. 6. Pareto diagram for experimental design, were AB, AC, BC, and ABC are the interactions between variables. Table 6 Analysis of variance (ANOVA) for microflotation tests.

Table 4 Parameters of adsorption of collector C-4940 determined from the isotherm models. Models

Parameters pH = 2

Langmuir model qm (mg/g) KL (L/mg) R2

8.89 0.49 0.9894

Freundlich model KF (mg/g·(mg/L)n) 1/n R2

2.81 0.54 0.9650

Sips model qm (mg/g) KS (L/mg)n 1/n R2

7.09 0.73 0.75 0.9923

Temkin model a (J/mol) b (L/g) R2

4.42 2.04 0.9976

qe =

KL qm Ce 1 + KL Ce

Variables

P-value

A: pH B: Ci C: tf AB AC BC ABC

0.543 0.153 0 0.325 0.003 0 0

(6)

where Ce is the equilibrium adsorption concentration in the aqueous solution in mg/L, qe and qm are the equilibrium and maximum adsorption capacity (mg/g), respectively, and KL is the affinity constant between adsorbate and adsorbent (L/mg). The Freundlich model describes a chemical adsorption process whose heterogeneous surface is not energetically uniform (Eq. 7).

qe = KF Ce1/ n

(7)

Here, KF is a variable related to the adsorption capacity of the adsorbent (mg/g (mg L−1)n), and 1/n indicates the heterogeneity of the material adsorbent. The closer the value is to zero the more heterogeneous the 6

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Fig. 7. a) Normal probability plots, b) normal probability plots of residues were AB, AC, BC and ABC are the interaction between variables, c) plot of residuals against predicted (fitted) values and d) main effects plot where the horizontal dotted line at 75.80% indicates the average CU recovery of the 17 experiments.

3.4. Experimental design

material. The Sips model describes adsorption by a combination of mechanisms such as multilayer and monolayer adsorption and surface precipitation:

qe = qm

(K S ) Cens 1 + (K S Ce )ns

Table 5 shows the results based on the experimental design from Table 1 to find the best copper recovery conditions. In all cases, the values of Cu recovery percentages are higher than 50% for microflotation tests carried out at pH = 2. The combination of an acidic pH = 2 with the lowest initial concentration (Ci = 20 mg/L) and a microflotation contact time of tf = 1 min yielded the lowest Cu recovery percentage (run 1). By increasing tf to 3 min (runs 5 and 13) and pH to 6 (runs 6 and 14), Cu recovery increased to 96% and 91%, respectively. This means that the collector C-4940 has a good response over a wide range of acidic pH values. On the other hand, microflotation contact time represents one of the most important variables considering that its increase leads to a proportional increase of the Cu recovery percentage. The Pareto diagram is a useful tool to determine the variable that has the highest effect on the response variable [43]. If an effect is above the Durbin-Watson statistical value (2.306), then the factor is considered as statistically significant (Fig. 6). In this context, the microflotation contact time is the only independent factor that shows a significant effect on Cu recovery (Table 5). The interaction between two or three variables (pH-tf, Ci-tf, and pH-Ci-tf) also has a significant influence on the experimental results. Although pH and initial concentration of collector are important process variables in flotation, these effects were not significant based on results from Pareto Diagram. Therefore, it is crucial to consider the combined effects of processing variables since their individual contributions do not need to be statistically significant to achieve a higher percentage of copper recovery. The criteria for significance and adequacy of the model were based on a variance analysis (ANOVA) for each variable [44]. Four variables (tf, pH-tf, Ci-tf, and pH-Ci-tf) had P-values below 5% (95% confidence interval), which is a good indicator of significance (Table 6) and

(8)

Where, KS is the adsorption affinity and nS (dimensionless) the coefficient of heterogeneity. Based on the value of nS, Eq. (8) can be reduced to the Langmuir or Freundlich equations. The Temkin model (Eq. 9) relates to a chemisorption process.

qe =

RT In(bCe ) a

(9)

Here, a (J/mol) relates to the heat of adsorption and b (L/g) is a binding constant. The adsorption of the collector on copper mineral can be well described by the Temkin and the Sips adsorption isotherm models (Table 4), which suggests that several mechanisms that could favor the subsequent flotation process may be involved. The maximum adsorption capacity determined by the Langmuir model was 8.89 mg/g. Due to the lack of adsorption studies for this collector, this result could not be compared to the others. Nevertheless, the fact that the mineral surface can adsorb 8.89 mg/g of collector represents a good Cu recovery percentage. In addition, the 1/n parameter suggested a favorable adsorption process. As a > b in the Temkin model, the affinity of adsorbate molecules is stronger than the surface adsorbed molecules.

7

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Fig. 9. XPS spectra corresponding to copper mineral after collector (C-4940) adsorption: a) Cu 2p3/2 and b) Fe 2p3/2.

(Fig. 7b) we conclude that the results are homogeneously distributed. The plot of residual versus predicted values (Fig. 7c) shows that residuals are randomly distributed and that the variance is not constant (since the residual values are different from zero) [45]. These results are consistent with our starting hypothesis that if three initial factors (pH, Ci, and tf) were modified, the response variable (Cu recovery) would change considerably - especially for changes in pH and tf (important parameters to consider in the flotation of copper sulfides). The main effects for each factor are plotted in Fig. 7d. pH did not turn out to have a significant effect because changing pH from 2 to 6 only resulted in a minor increase in Cu recovery from 75.14 to 76.46%. A slightly more pronounced increase from 74.16 to 77.4% was observed by increasing the initial concentration of collector from 20 to 60 mg/L. Nevertheless, Bulatovic reported the formation of micelles when the initial collector concentration increased because the interaction between hydrocarbons chain formed aggregates of colloidal size collector molecules [46]. Varying tf resulted in a much more notable increase in Cu recovery percentage from 61% at tf = 1 min to 84% at tf = 3 min. Eq. 10 describes the relationship between predictor variables and response variables:

Fig. 8. Response surfaces for Cu recovery as a function of variable interactions: a) Ci and pH, b) tf and pH, and c) tf and Ci.

consistent with the results observed in the Pareto diagram. Combinations of three variables had the most significant effect on Cu recovery percentage. By plotting the normal probability, we can identify those parameters that are close to a straight line (i.e., residues are normally distributed and the parameters are therefore not significant). Fig. 7a shows that the individual factor, tf, and combined factors {pH-tf, Ci-tf, and pHCi-tf} are far from the straight line and therefore have significant effects. These results match our conclusion from the Pareto diagram. In terms of experimental design, the residue is defined as the difference between the observed and adjusted values. It is therefore important to know the statistical effect of residues on the overall analyses, including the generosity of the regression adjustment. As the normal probability plot of the residues is similar to an inverted S-type curve

% Cu recovery = -29.9+12.06pH+2.641Ci +52.75t f -0.4017pH*Ci -6.41pH*t f -1.334Ci*t f +0.2145pH*Ci*t f -2.10central point R2 (10)

= 96.70, SD = 4.1524 2

The coefficient of determination (R ) indicates that 96.70% of the variability of % Cu recovery is explained by the independent variables and could therefore be predicted by the model [44]. Since the standard deviation is lower than 5%, it can be assumed that the response is welldescribed by the model. 8

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San Luis Potosi (UASLP) for providing access to their facilities to carry out the experimental work. Delia Ávila would like to thank CONACYT for funding her postgraduate scholarship. We would also like to acknowledge the valuable time and technical assistance provided by Rosa Lina Tovar and Izanami Lopez from the Institute of Metallurgy at UASLP.

The response surface graphs (Fig. 8) show that the best experimental conditions to maximize Cu recovery are: tf = 3 min, Ci = 20 mg L−1, and pH = 2. Higher initial concentrations were not selected because the collector could cause the formation of micelles and thereby affect Cu recovery [46]. 3.5. Effect of Fe and Cu phases of bornite mineral on Cu recovery during microflotation tests

References

Generally, Cu recovery was higher for pH = 2 compared to pH = 6 and about 1.3 times higher than Fe recovery. The collector C-4940 thus allows recovery of both Fe and Cu, with a slightly higher affinity for the latter. Fe recovery decreased for increasing pH, possibly due to the fact that Fe is suppressed at a more basic pH which favors Cu recovery [47]. XPS results indicate that the collector shows an affinity for both metallic species. The mineral bonds the collector through the characteristic functional groups (eC]S and OeC]O). The two peaks in the Cu 2p3/2 spectrum near 934.8 and 937.6 eV are related to the binding of Cu-O and Cu-S (Fig. 9a). Fig. 9b shows signals of Fe 2p3/2 close to 707.8 and 710.3 eV that can be attributed to Fe-S2 and Fe-O2 interactions as suggested by the signals obtained for the O and S species [31] (results not shown). Hence, we have established that adsorption of collector C-4940 on copper mineral takes places and is achieved due to an interaction between Cu and Fe with the functional groups of the collector. Furthermore, the affinity of C-4940 is higher for Cu than Fe species. Kinetics experiments confirmed this indicating that the presence of Fe phases allowed for electrostatic and chemisorption interactions between collector and surface mineral. Ávila-Marquez et al. [27] reported that if the analyzed mineral is pure copper sulfide (covellite) the kinetics of the adsorption process can be described by chemisorption alone.

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4. Conclusions The adsorption of the collector C-4940 on a copper sulfide mineral surface (bornite) takes place through physical interactions with a low equilibrium time. A change in temperature does not promote collector adsorption, although this process is spontaneous, endothermic, and entropic for the three temperatures investigated. The interaction of collector molecules with the mineral surface was demonstrated by XPS analysis and showed that the functional groups of the collector bind to the Cu and Fe elements on the mineral surface forming a six-membered ring. The results from the microflotation tests showed a higher collector affinity for Cu than Fe ions. Cu recovery was maximal in our study when using a flotation process that used a xanthogen formate compound as collector across a wide range of operation conditions and even at a low pH of 2. Under certain conditions xanthate compounds decompose and lose their properties as collector. These results have important implications since the flotation process with a xanthogen formate collector compound can be performed at acidic conditions, thereby avoiding the neutralization stage and minimizing the use of water and neutralizing agents. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors would like to thank the National Council of Sciences and Technology of Mexico (CONACYT) for the financial support through project CB-254952-2016. A special acknowledgment is also extended to the Institute of Metallurgy of the Autonomous University of 9

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