1. Introduction
The Chilean mining industry leads global copper production, primarily through flotation concentration processes. In the year 2022, copper production obtained through flotation concentration accounted for 53.4% of the total, and it is projected to increase to 69.9% by 2034 [
1]. However, a significant percentage of concentrate production is exported, with only a minor portion being processed domestically through pyrometallurgical methods. One of the main reasons is the increasing challenge of capturing a higher amount of environmentally harmful gases, such as sulfur dioxide (SO
2) and arsenic trioxide (As
2O
3) [
2].
Arsenic can be found in tennantite (Cu
12As
4S
13) or enargite (Cu
3AsS
4), and through flotation concentration, the presence of these species in copper concentrates can lead to the presence of arsenic. In northern Chile, it has been documented that excessive exposure to arsenic is associated with skin lesions and an increase in mortality from bladder and lung cancer [
3]. As a consequence, metallurgical plants are penalized due to impurities that the copper concentrate may carry. Arsenic is an impurity that is mostly penalized when its content exceeds 0.2%, and when its concentration is above 0.5%, the concentrate is referred to as “complex” [
4].
Due to the aforementioned factors, it becomes necessary to explore new alternatives that allow for sustainable and environmentally friendly treatments. Researchers Olvera and Dixon [
5] propose hydrometallurgy as a treatment alternative since it keeps arsenic species stable in solution. Additionally, authors such as Cerda et al. [
6], Hernández et al. [
7], Taboada et al. [
8], Liu et al. [
9], and Vargas et al. [
10] suggest that hydrometallurgical plants could harness new capabilities to process sulfide minerals as a result of the depletion of oxidized minerals. According to the Chilean Copper Commission (Cochilco), copper production through hydrometallurgical processes represented 25.1% of the total in the year 2021, and this figure is projected to decrease to 6.3% by the year 2033 [
2].
However, the treatment of copper sulfide minerals through hydrometallurgical processes poses a challenge for both researchers and the Chilean mining industry. This is due to the formation of a layer on the mineral surface that inhibits the reaction and thus prevents the dissolution of the mineral. In response to this, several authors are studying the dissolution of sulfide minerals in ammoniacal media (Aracena et al. [
11]), acids with the presence of chloride (Phuong et al. [
12]), activated carbon (Fazel et al. [
13]), oxygen (Yang et al. [
14]), and pyrite (Ruiz et al. [
15]), among others. On an industrial scale, the primary alternatives focus on pressure leaching (utilized in Chile by Codelco) and chloride media methods (such as Cuprochlor, Outotec Hydrocopper, and the Intex Process). The pressure leaching process yields rapid results but comes with the disadvantage of high costs associated with autoclave development. On the other hand, chloride media forms a porous product layer and enhances reduction–oxidation properties [
6,
13].
Authors such as Cerda et al. [
6], Hernández et al. [
7], Taboada et al. [
8], and Quezada et al. [
16,
17] propose a pretreatment (acid curing) before the leaching stage. This pretreatment involves the sulfation of the mineral through the addition of sulfuric acid and other agents under investigation, such as sodium chloride and ferric salts. Neira et al. [
18] mention that pretreatment of copper minerals or concentrates through hydrometallurgical means shortens leaching times and leads to an increase in copper extraction. While the compiled studies suggest a benefit to copper extraction through pretreatment, there are no published documents evaluating this effect on complex concentrates (enargite, as a contributor of copper and arsenic).
Cerda et al. [
6] highlight that curing time and temperature are the most relevant variables in the pretreatment, while the addition of chloride seems not to exert a significant effect on copper extraction. Cerda et al. [
5] indicate that with a pretreatment involving the addition of 90 kg/t of chloride, 40 days of curing, and a temperature of 20 °C, a copper extraction of 84.76% is achieved from a mineral primarily composed of chalcopyrite. However, it is essential to mention that copper extraction varies according to the method used, achieving an extraction of 84.76% when carried out in reactors and 49% when using mini-columns.
Hernández et al. [
7] conducted a pretreatment on a mineral primarily composed of chalcopyrite, achieving a copper extraction of 58.6% through the addition of 23.3 kg/t of sodium nitrate, 19.8 kg/t of sodium chloride, and a curing period of 30 days. The study reveals that the addition of sodium chloride increases copper extraction, in contrast to the findings presented by Cerda et al. [
6], where it is mentioned that this additive does not have a significant impact. On the other hand, the addition of sodium nitrate contributes to the incorporation of oxidizing ions during the curing stage, which, in turn, facilitates the subsequent dissolution of copper. Furthermore, an increase in the curing time leads to an increment in copper extraction, highlighting the positive effect of this variable in the process.
Taboada et al. [
7] increased copper extraction from a mixed mineral from 62.5% to 67.9% by adding ferric ions (Fe
2(SO
4)
3·9.2H
2O) and ferrous ions (FeSO
4·7H
2O) during the pretreatment, using 25 kg/t of H
2SO
4, 50 kg/t of NaCl, and a curing period of 30 days. In this context, it is important to highlight that the addition of sulfuric acid did not show a significant impact, as it only resulted in a 3% improvement in copper extraction. In contrast, a proportional relationship was observed between the increase in sodium chloride addition and curing days, with the increase in copper extraction. Quezada et al. [
16,
17] demonstrated that the curing time, in combination with the temperature during leaching, increases copper extraction. In these studies [
15,
16], the studied pretreatment conditions included 15 kg/t of H
2SO
4, 25 kg/t of NaCl, and a curing period of 15 days. Following the pretreatment, leaching at 70 °C achieved a copper extraction of 92%, compared to the 87% extraction obtained in the sample without pretreatment [
16]. At a temperature of 90 °C, a copper extraction of 94% was achieved with pretreatment, while without pretreatment, the copper extraction was 90% [
17]. In both studies, a copper extraction difference of 4.0% to 5.0% was achieved by conducting a pretreatment on the sample.
This document evaluates the effects of sulfuric acid, sodium chloride, curing time, and temperature in the pretreatment of a copper concentrate with a high arsenic content prior to leaching. The copper concentrate was characterized using various techniques. The pretreatment’s effect was assessed by measuring the copper in solution (copper extraction) resulting from tests with and without pretreatment. All experiments were conducted in duplicate. The obtained results were analyzed through statistical analysis, which generates an empirical model that enables the determination of the interaction between variables, predicting copper extraction.
2. Materials and Methods
2.1. Copper Concentrate Sample
The copper concentrate used comes from a mining company in the Antofagasta Region, Chile. It was characterized using a micro-rotary separator (Quantachrome instruments, Boynton Beach, FL, USA), and a representative sample of 5 g was obtained. The chemical composition of the concentrate was determined through atomic absorption spectrometry (SpectrAA-50/55, Varian, Santa Clara, CA, USA). The mineralogical characterization was carried out using 3 techniques: Qemscan using a Model Zeiss EVO 50 (Zeiss, Oberkochen, Germany) with Bruker AXS XFlash 4010 detectors (Bruker, Billerica, MA, USA) and iDiscover 5.3.2.501 software (FEI Company, Brisbane, Australia); a scanning electron microscope (SEM) (JEOL J-7100F, Tokyo, Japan) operating at 20 kV under high vacuum conditions (Emitech K-950X, Lohmar, Germany) coupled with an energy-dispersive X-ray spectroscopy (EDS) microanalysis system (Oxford Instruments INCA, Oxfordshire, UK); and X-ray diffraction (XRD) analysis using a diffractometer (PANalytical, X’Pert PRO MPD Alpha1, Malvern, UK) operating from 4° to 100° (2θ), 45 kV, 40 mA, and Kα 1.54 Å, with a step size 0.017° and a time per step of 150 s. X-ray diffractograms were interpreted using the X’pert HighScore Plus v.3.0e software (PANalytical, Almelo, The Netherlands). And finally, the particle size distribution was determined using a Microtrac S3500 (Microtrac Inc., York, PA, USA).
2.2. Experimental Design
In this study, an experimental design using the Taguchi methodology is employed for the combination of controllable factor levels. This methodology is a statistical approach used in the design of experiments that aims to optimize the quality of processes. It employs orthogonal arrays, which are structured matrices that ensure experiments are balanced and efficient. This balance is achieved because the factor levels are weighted equally, allowing for a fair assessment of each factor’s impact on the outcome. The use of orthogonal arrays in the Taguchi method allows for a significant reduction in the number of experimental runs needed compared to full factorial designs, which is both cost-effective and time-saving [
19]. The parameters under analysis, along with their respective levels, are listed in
Table 1. The response variable in all tests is copper extraction (copper in solution). The construction of this Taguchi orthogonal design was carried out using the Minitab software (17.1.0, LLC, State College, PA, USA). Among the different interfaces to develop the Taguchi method, the present study used Minitab due to its comprehensive suite of tools that facilitate the design, modification, analysis, and prediction of experiments using Taguchi’s approach. Through this methodology, an L
16 (4
4) matrix is generated that incorporates 4 parameters and 4 levels for each of them (see
Table 2).
For the results analysis, the response surface methodology (RSM) is employed, allowing the assessment of the influence of these variables on copper extraction. In this case, RSM is a statistical technique used to model and analyze the relationships between the parameters and the copper extraction by fitting polynomial equations to experimental data. This methodology allows the identification of optimum process conditions for maximum copper extraction efficiency. RSM also facilitates the assessment of interactions between different variables, providing insights into complex system behavior. To do this, the experimentally calculated copper extractions based on the parameters and specific levels in
Table 1 must be considered. Then, using the experimental combinations defined through the Taguchi design, as specified in
Table 2, an empirical polynomial model is fitted using the Matlab software (ver. R2022a, MathWorks Inc., Natick, MA, USA). This model is a second-order polynomial (Equation (1)), which considers the 4 input variables detailed above that influence copper extraction.
Equation (1) represents a polynomial function describing the copper extraction process in terms of various parameters. In this equation,
Xn denotes each parameter (
Table 1), and
βn represents the coefficients determined through response surface methodology (RSM). This second-order equation accounts for both individual coefficients for each parameter and the interactions among different parameters. The coefficients derived from the fitted empirical model serve to formulate a prediction function, which estimates the percentage of copper extraction in experimental tests. The obtained coefficients from the fitted empirical model are used to formulate the prediction function, which estimates copper extraction in the experimental tests. The model’s confidence level is determined by calculating the coefficient of determination R
2 and the root mean square error (RMSE) using Matlab. The R
2 coefficient indicates the proportion of the total variance of the variable explained by the regression. However, RMSE is calculated by using the average square root of the square absolute error between the obtained copper extraction and the predicted copper extraction.
The closer this value is to 1, the better the fit of the model for predicting copper extraction; otherwise, values far from 1 indicate a higher error. Minimizing the error is crucial to accurately predicting copper extraction and generating the response surface graph, which is based on independent parameters, and provides a true visualization of the effect of the variables under investigation on copper extraction. The visual representation of the response surface is generated from prediction points and is subject to evaluation in the predictor model.
2.3. Curing Experiments
In this research, 16 curing experiments were conducted, all in duplicate. The tests were carried out with the addition of sulfuric acid and sodium chloride, as well as different curing times and temperature conditions (
Table 1). For the curing experiments, a representative sample of 5 g of the concentrate was used, which was placed in a watch glass. Then, separately, sodium chloride and sulfuric acid were added (in the corresponding amounts for each experiment). Subsequently, water was added to achieve gradual adhesion between particles until the formation of stable agglomerates was achieved. Once the agglomerates were formed, the samples were covered with another watch glass to prevent evaporation, and they were subjected to specific temperature and time conditions in an oven (Memmert GmbH, H110N230/115, Schwabach, Germany).
Once the curing time was completed, the samples were washed with a diluted solution of sulfuric acid at a concentration of 0.5 g/L (for the dissolution of any sulfates or soluble phases formed in the pretreatment). The washing was carried out using mechanical agitation, using an RW 20 digital overhead stirrer (IKA, Staufen im Breisgau, Germany) at 200 min−1. The solid/liquid ratio was maintained at 1/100 and at room temperature for a period of 5 min. Sample solutions (50 mL aliquots) were periodically withdrawn for chemical analysis of copper in solution. The samples were filtered (0.2 µm), and the copper concentrations in the filtrate were determined by atomic absorption spectrometry (SpectrAA-50/55, Varian, Santa Clara, CA, USA).
4. Conclusions
The sample under study is a complex copper concentrate with a content of 35.57% total copper, 1.58% soluble copper, and 5.91% arsenic. It is composed of 35.93% enargite and 16.55% digenite as the main copper contributors. Scanning electron microscopy analysis identifies that enargite particles are both liberated and occluded by silicate minerals.
The highest copper extraction in the pretreatment is achieved by adding 210 kg/t of sulfuric acid and maintaining a temperature of 50 °C for 15 days of curing, achieving a copper extraction of 26.71%. Eventually, the dissolution of copper is associated with the chalcocite/digenite present.
The interaction between the increased addition of sulfuric acid and the curing time is beneficial for copper extraction, especially when the addition of sulfuric acid exceeds 70 kg/t. This is associated with the moisture content of the agglomerate; if the amount of sulfuric acid is less than 70 kg/t, it will have lower moisture. Consequently, the solution tends to evaporate faster, limiting the contact between the solution and the mineral.
The influence of sodium chloride is conditioned by the addition of sulfuric acid. For elevated concentrations of sulfuric acid (210 kg/t), an increase in the addition of sodium chloride will not have a significant impact on copper extraction. However, in the absence of sulfuric acid, sodium chloride will have a significant impact on achieving higher copper extractions. This is due to its ability to increase the acidity of the system and satisfy sulfation reactions.
When carrying out the curing of the mineral at a higher temperature, it is important to maintain an adequate level of moisture to ensure the uniform distribution of the solution. The increase in temperature leads to greater evaporation of the solution, causing the agglomerate to dry, thereby hindering its reactivity (generation of copper sulfate or other more soluble phases).
This study uses response surface methodology (RSM) to model copper extraction based on factors such as curing time, temperature, and sulfuric acid and sodium chloride concentrations. The predictive model shows high reliability with an R2 of 0.9895 and a low root mean square error (RMSE) of 0.6742, confirming its precision in predicting copper extraction under different conditions. RSM serves as a valuable complement to the Taguchi method, offering a robust statistical representation of experimental assays. The fitting of the model with RSM yields a reliable estimation of copper extraction rates. Notably, the high R-squared value provides valuable insight into the behavior of copper extraction with respect to the parameters under investigation.