Chalcopyrite Leaching in Alkaline Monosodium Glutamate Solutions: Process Optimization and Kinetic Study

Abstract

This study investigated the kinetics of chalcopyrite dissolution in an alkaline monosodium glutamate (MSG) solution using H₂O₂ and KMnO₄. The aims were to optimize process conditions for maximum copper dissolution and to study the kinetic mechanism of dissolution under varying conditions, such as particle size, oxidant type and concentration, temperature, and the presence of gangue minerals. Results showed that KMnO₄ exhibited better oxidative efficiency and stability than H₂O₂, yielding copper recoveries above 90% in most conditions while keeping the dissolution of some gangue metals, such as calcium, magnesium, and iron, lower, thereby reducing MSG consumption. Temperature and particle size were the most important factors in the effects on leaching kinetics; smaller particles allow higher initial reaction rates, while larger particles allow prolonged dissolution. The shrinking core model (SCM) was thus used to perform kinetic analysis and determine that diffusion controls the leaching process through the product layer. The calculated activation energies of 18.2 kJ/mol of MSG-H₂O₂ and 17.3 kJ/mol of MSG-KMnO₄ confirm the diffusional mechanism.

1. Introduction

Chalcopyrite (CuFeS₂) is recognized as the most abundant copper-bearing mineral globally, and it comprises more than 70% of the current global copper reserves and accounts for a significant portion of copper production [1,2,3]. Chalcopyrite ores are commonly treated through concentration using flotation and pyrometallurgy. This is associated with several problems, including the generation of flotation tailings (between 97% and 98% of the total mineral processed by weight), the generation of gases, such as SO₂, difficult management of by-products, and high water and energy consumption. Therefore, despite the important advances in pyrometallurgy to mitigate environmental concerns, it has been widely accepted that hydrometallurgical techniques can be efficient in the processing of complex, low-grade minerals with impurity contents such as arsenic, mercury, or antimony [3,4]. However, its refractory nature presents slow leaching kinetics, making there is no economically viable process on an industrial scale that allows copper to be extracted from this type of mineral by conventional hydrometallurgical processes such as sulfuric, hydrochloric, and nitric acids [5].

In addition, the depletion of copper oxides will lead to a reduction in copper production by hydrometallurgical means, from 30.8% of Chile’s total copper production in 2015 to 12% in 2027, according to Cochilco’s projections. This situation will lead to the underutilization of hydrometallurgical facilities, accounting for nearly 38.3% of Chile’s installed capacity for processing copper minerals [6,7,8]. That represents an opportunity to seek alternatives to treat copper sulfide minerals by hydrometallurgical means, using this potential idle productive capacity. Therefore, recent research on copper leaching from chalcopyrite has focused on various innovative approaches to enhance efficiency through alternative processes such as bioleaching [9,10], ionic liquids [2,11], ammonia [3,7,12,13,14,15], and amino acids [16] like glycine [17,18,19,20,21] and monosodium glutamate (MSG) [3,7,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26], addressing the mineral’s inherent refractory nature.

Over the past decade, alkaline leaching has come into the limelight with great potential to overcome some of these challenges. Alkaline reagents such as ammonia and glycine have been proven to show selectivity and less environmental impact than their acid counterparts. The implementation of ammonia faces several technical, operational, and environmental issues due to its high volatility, toxicity to aquatic organisms, and adverse effects on human health [7,21,27,28,29]. Glycine has emerged as a promising leaching agent due to its ability to form stable complexes with copper. However, its applicability to chalcopyrite leaching still needs to be improved because of slow kinetics and high reagent costs. This has driven the exploration of other organic reagents, including monosodium glutamate (MSG), as potential leaching agents. According to some preliminary studies, MSG, as a commonly used food additive, has several merits for copper extraction, including less environmental impact, consumption of fewer reagents, and suitability with mild operating conditions [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29].

The present work investigates chalcopyrite leaching in an alkaline MSG solution with hydrogen peroxide (H₂O₂) and potassium permanganate (KMnO₄) as oxidants, optimizes process parameters, and elucidates the kinetics of copper dissolution. The work addresses critical knowledge gaps regarding the roles of particle size, particle type, oxidant concentration, temperature, and gangue minerals in copper recovery. This work applies kinetic modeling and process optimization to study the dissolution mechanisms of chalcopyrite in MSG solutions. It evaluates the feasibility of this alternative approach to copper recovery from a sustainability point of view.

The results will contribute to improving environmentally friendly leaching technologies for refractory copper ores, generally corresponding to the increasing demands within sustainable resource extraction methods in the mining industry.

2. Materials and Methods

2.1. Materials

The sample used in this study was obtained from an Australian mining company. The mineral was dry, ground, and sieved to obtain four different size fractions: +38–53 µm, +53–75 µm, +75–106 µm, and +106–150 µm. X-ray diffraction (XRD) of the sample showed that the sample contains pyrrhotite, pentlandite, chalcopyrite, and magnetite. The percentage of the elemental composition of the various size fractions was determined by XRF analysis (

Table 1. Percentages of the elemental composition in each particle size fraction of the sample determined by XRF. wt% element concentration.

Size Fraction, µm	Al2O3	CaO	Co	Cr	Cu	Fe	K2O	MgO	MnO	Ni	SiO2	S
38–53	8.2	4.4	0.06	0.11	0.82	17.96	0.26	6.99	0.16	1.7	25.40	8.3
53–75	7.9	4.4	0.04	0.09	0.44	12.96	0.21	5.55	0.16	1.07	21.58	5.1
75–106	8.5	4.4	0.04	0.1	0.45	14.23	0.23	6.1	0.17	1.1	24.7	6.3
106–150	8.7	4.4	0.04	0.1	0.35	13.86	0.23	6.2	0.18	0.96	25.2	6.07

).

The main chemicals used in the experiments were monosodium glutamate (C₅H₈NO₄Na), hydrogen peroxide (H₂O₂), potassium permanganate (KMnO₄), and sodium hydroxide (NaOH). Monosodium glutamate was of commercial grade (Ajinomoto) and was used as a leaching reagent. H₂O₂ and KMnO₄ analytical-grade levels were obtained from Merck (Australia) for use as oxidizing agents in the process. NaOH analytical grade obtained from Merck (Australia) was used to adjust pH because no extraneous cations such as calcium or magnesium would enter the solution. The solvent used was Deionized water to maintain the purity of the solution.

2.2. Procedure

Each experiment was conducted in a thermally controlled LABWIT ZWY-211B incubator shaker to quantify the effect of different factors on the copper leaching kinetic from the ore sample in alkaline MSG solutions. In each test, 500 mL conical flasks were filled with 300 mL 0.5 M MSG solution and desired oxidant concentrations at pH 9.4 using sodium hydroxide (NaOH). The required solution temperature was achieved and maintained with a digitally controlled heated system integrated into the incubator shaker. At the desired temperature, 30 g of the sample was added to the solution, and the shaker was started. The leaching test was carried out for 24 h. After a specific leaching time, the shaker was stopped, the slurry was allowed to stand for one minute for the particles to settle, and then 3 mL solution was extracted and filtered through a 0.45-µm diameter syringe-membrane filter to determine copper concentration using Inductively Coupled Plasma Optical Emission Spectroscopy ICP-OES. The percentage of copper extraction was calculated based on the amount of copper released in the solution, normalized to the total amount. At the end of the experiment, the solution was filtered, and the leach residue was rinsed with deionized water to remove any adsorbed species. Finally, the leach residue was dried in an oven at 50 °C and analyzed through XRD.

2.3. Kinetic SCM Modelling

The shrinking core model (SCM) has been developed to study the reaction kinetics of such a heterogeneous leaching process. For the leaching kinetics of most minerals, the shrinking core model has been widely employed and is thought to depict reality accurately in a wide range of leaching scenarios. According to the SCM, the solid–liquid reactions during the leaching process occur in five successive stages [30]:

• Diffusion of the reactant through the liquid film surrounding the particle to the particle surface
• Diffusion of the liquid reactant through the solid product layer to the unreacted core surface
• The reaction of the liquid reactant with the solid at the reaction surface
• Diffusion of the formed liquid products through the solid product layer to the outer surface of the solid, and finally
• Diffusion of the liquid products through the liquid film back to the main body of the fluid

These stages are described by rate equations that relate the extent of reaction $x$ to time $t$ [30]:

Film Diffusion Controls:

$$k_{l}t=x$$

(1)

Product layer diffusion controls:

$$k_{d}t=1-3(1-x{)}^{{\displaystyle \raisebox{1ex}{$2$}\!\left/ \!\raisebox{-1ex}{$3$}\right.}}+2(1-x)$$

(2)

Chemical reaction controls:

$$k_{r}t=1-(1-x{)}^{{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$3$}\right.}}$$

(3)

where $x$ represents the solid particle conversion fraction, $k_l$, $k_d$, and $k_r$ represent the apparent rate constants for each controlling step, and t represents the reaction time. For many of these reactions, the leaching kinetics could not be satisfactorily explained purely by either a chemical or a diffusional step. For such complex situations, several workers have suggested mixed control models; for example, [31,32]. These models show that interfacial mass transfer and solid-film diffusion add to the reaction kinetics simultaneously, providing a more realistic representation of the leaching process. Thus, concluded that. The following equation gives the mixed control model:

$$kt={\displaystyle \frac{1}{3}}ln\left(1-x\right)-\left[1-(1-x{)}^{{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$3$}\right.}}\right]$$

(4)

Experimental data are plotted using these equations to identify the rate-controlling step, and the model equation with a correlation coefficient closest to one is deemed to represent the dominant mechanism.

The energy activation describes the relationship between temperature and reaction rates. Hence, by using the Arrhenius formula given in Equation (5), activation energy is another important quantity that can support the rate-determining component in hydrometallurgical processes as follows [33]:

$$k=Aexp\left({\displaystyle \frac{{-E}_a}{RT}}\right)$$

(5)

where k is the reaction rate constant (min⁻¹), A is the exponential factor (min⁻¹), E_a is the activation energy (J mol⁻¹), R is the gas constant (8.314 J mol⁻¹ K⁻¹), and T is the leaching temperature (Kelvin).

To understand whether the mechanism of copper–glutamate dissolution is either diffusion-controlled or chemically controlled, the activation energy for copper dissolution was calculated by a plot of rate constants vs. 1/T. A straight line is obtained from the leaching experiments conducted at 15, 30, 45, and 60 °C. The activation energy can be calculated using the slope of this line and the following equation:

$$s={-E}_a/RT$$

(6)

3. Results and Discussions

3.1. Effect of the Type and Concentration of the Oxidizing Reagent

The influence of oxidizing reagent type and concentration on leaching efficiency was assessed using hydrogen peroxide (H₂O₂) at 1%, 3%, and 5% (v/v) and potassium permanganate (KMnO₄) at 1 g/L, 2 g/L, and 3 g/L. A particle size range of +53–75 µm was used. The stirring speed, temperature, initial MSG concentration, and solution pH were maintained constant at 300 rpm, 30 °C, 0.5 M MSG, and 9.4, respectively. The results in Figure 1 indicate that higher H₂O₂ concentrations (3% and 5%) produced faster copper extraction during the initial 10 h than 1% H₂O₂. However, after this period, the dissolution rates tended to converge, and after 24 h, similar copper recoveries were obtained for all peroxide concentrations. This behavior suggests that although higher H₂O₂ concentrations initially enhance sulfide oxidation, the overall leaching process may become controlled by diffusion limitations or surface passivation phenomena at longer reaction times. A possible explanation for this behavior is the formation of highly reactive hydroxyl radicals (OH•) generated during peroxide decomposition, which can promote sulfide oxidation and the formation of elemental sulfur species on the mineral surface [27,34]. The accumulation of sulfur-containing species may partially hinder reagent diffusion and reduce the accessibility of the reactive surface, affecting the overall dissolution kinetics. Similar passivation behavior associated with sulfur formation during sulfide oxidation has been previously reported in alkaline and glycine-based leaching systems [27,34]. The following reactions can represent the formation of elemental sulfur and its subsequent oxidation to sulfate ions:

$${2HO}^{•}+{2S}^{2-}\to {2S}^0+H_{2}O+{0.5O}_2$$

(7)

$${2S}^0+{2H}_{2}O+{3O}_2\to {2SO}_4^{2-}+{4H}^{+}$$

(8)

The influence of KMnO₄ concentration on copper leaching from chalcopyrite ore is represented in Figure 2. A positive correlation between oxidant concentration and copper extraction was evident from the results obtained. Higher concentrations of KMnO₄ accelerated the leaching process, especially during the first 10 h, where 3 g/L showed the fastest dissolution rates. After 24 h, the extractions reached about 90% for 3 g/L and about 70% for 1 g/L, thus showing improved recoveries with the increase in oxidant availability. However, the gap between 2 g/L and 3 g/L narrowed after 10 h, showing a case of diminishing returns at higher concentrations.

Among the two oxidants used, potassium permanganate (KMnO₄) exhibited better performance in comparison to hydrogen peroxide (H₂O₂), both in the kinetic and general aspects of copper leaching. Indeed, differences between these two oxidants became greater after some periods of leaching, at which stage the oxidative efficiency of KMnO₄ provided continued dissolution of copper, whereas H₂O₂ tended to plateau, most probably because it decomposed, or its oxidative potential decreased under the operating conditions used in this test. These results indicate that KMnO₄ is more appropriate for long-lasting leaching processes that require continuous oxidation, while H₂O₂ could be more effective for fast and short-term leaching. KMnO₄ is effective due to its high oxidative potential and stability, which ensure consistent copper dissolution.

3.2. Effects of Particle Size

The effect of particle size on copper dissolution rate was investigated using the four fractions of 38–53, 53–75, 75–106, and 106–150 µm. Figure 3 presents the trends for kinetics and the level of copper dissolution in the MSG-H₂O₂ system with varying particle sizes. The copper dissolution profiles exhibit a general increase in leaching efficiency with time for all size ranges, although distinct behaviors are observed depending on particle size. In the early stage (up to 6 h), the 53–75 µm fraction shows the fastest initial dissolution, reaching over 30% extraction. However, its performance plateaus and shows fluctuations, indicating possible surface passivation or experimental variability. In contrast, the coarser fractions (75–106 µm and 106–150 µm) exhibit slower initial kinetics but achieve significantly higher overall dissolution at 24 h, reaching over 50% and 60%, respectively. The finest size fraction (38–53 µm) presents a more gradual and consistent leaching profile, ultimately achieving around 47% copper dissolution after 24 h. The copper dissolution profiles exhibit a general increase in leaching efficiency with time for all size ranges, although distinct behaviors are observed depending on particle size. This might be due to its lower initial passivation rate, which allows more sustained reaction rates. Agglomeration effects could also occur in smaller particles and limit reagent diffusion, thus reducing their overall recovery.

Figure 4 presents the kinetics of copper dissolution in the MSG-KMnO₄ system for different particle size fractions for 24 h. Four particle size fractions were evaluated: 38–53 µm, 53–75 µm, 75–106 µm, and 106–150 µm. Larger particle size fractions, 106–150 µm, exhibit the highest dissolution rates of copper and recovery, especially beyond 12 h. This is explicable for a couple of reasons. Coarser particles may have higher internal porosity, which allows the leachant to penetrate deeper and enhances copper dissolution. In addition, mineralogical differences may favor the presence of easily leachable copper oxides in larger particles. The behavior of intermediate-size fractions (53–75 µm and 75–106 µm) is transitional between these extremes, where dissolution rates and recovery improve as the particle size is reduced. On the other hand, finer fractions, for example, 38–53 µm, could be affected by passivation effects through the formation of precipitate layers, such as native sulfur, that prevent further leachant penetration. It may also reduce the effective surface area and leachant accessibility by agglomerating fine particles during leaching.

3.3. Effects of Temperature

The leaching rate experiments of copper versus temperature dependence were carried out in the temperature range from 15 to 60 °C in solutions containing 0.5 M MSG and an H₂O₂ concentration of 3% for 24 h. Figure 5 illustrates a plot of temperature effects on copper dissolution. The leaching increases with the increase in temperature, but among these tested temperatures, it reaches the highest dissolution at about 45 °C. The trend indicates that temperature enhances both the kinetics and overall effectiveness of the leaching process, probably because of increased reactivity between copper and the MSG. At lower temperatures of 15 °C and 30 °C, dissolution is slower, and the copper recovery remains moderate, indicating that these temperatures do not provide enough energy to drive the reaction efficiently. Surprisingly, at 60 °C, though dissolution occurs, the recovery is lower than that at 30 and 45 °C, possibly due to the thermal decomposition of H₂O₂ or other side reactions, which reduces its oxidizing efficiency.

Figure 6 shows the effect of temperature on copper dissolution in the MSG-KMnO₄ system at different temperatures. The results show that copper dissolution increases with increased temperature, giving 47.3, 98.3, 92.9, and 99.2% final extraction values for 15, 30, 45, and 60 °C, respectively. If the test was performed at 15 °C, it is much slower, with about 35% dissolved after 24 h. Contrasting with this, in the case of 60 °C, more than 90% copper dissolution is attained in 10 h, while the rate of reaction improves significantly with an increase in temperature. Intermediate temperatures of 30 and 45 °C increase copper recovery rates and dissolution extents proportionally, reaching almost 80% dissolution in 10 h at 45 °C. This trend reflects the temperature-dependent nature of the leaching reaction, probably due to increased rates of chemical reactions and better mass transfer at higher temperatures.

Both systems show temperature-dependent kinetics, with increased dissolution of copper at higher temperatures. However, the MSG-KMnO₄ system realizes appreciably higher dissolution rates and efficiencies due to the stronger oxidizing power of KMnO₄. For example, MSG-KMnO₄ realizes 90% dissolution of copper in 10 h at 60 °C, while MSG-H₂O₂ realizes only about 50% after 24 h at the same temperature. At 15 °C, MSG-KMnO₄ exhibits about 35% dissolution, whereas it is about ~25% in MSG-H₂O₂. It indicates that under similar conditions, KMnO₄ gave a better performance in the promotion of copper leaching, which means it was a more efficient oxidant in this system.

3.4. Effects of Other Element Ions

The solutions were analyzed to determine if as much MSG would dissolve other metals present in the sample. Figure 7 presents the extraction of calcium (Ca), magnesium (Mg), iron (Fe), copper (Cu), nickel (Ni), and cobalt (Co) in an MSG-H₂O₂ leaching system. Notably, these were the only elements detected in the leachate under the experimental conditions. Ca shows fast leaching kinetics, with about 70% extraction in the first half hour, probably connected to the high solubility of calcium salts under the leaching conditions. Mg is almost constant, indicating low leaching, probably due to low reactivity or formation of stable, insoluble compounds. The Fe shows quasi-null extraction during the entire test. Cu, Ni, and Co present different behaviors indicative of selective leaching. These metal extractions increase steadily, with Co and Ni showing a significantly higher final recovery (98% and 86%, respectively) than Cu (50%), demonstrating that Ni and Co favorably interact with the MSG-H₂O₂ system. The interplay between these metals highlights the selective nature of the leaching system, where dissolution kinetics and thermodynamic stability of metal complexes dictate the extraction behavior. These trends align with studies demonstrating the selective dissolution of metals in hydrogen peroxide-based systems [22].

The effect of other metal ions on copper leaching in the MSG-KMnO₄ system is illustrated in Figure 8. It is observed that the leaching of Ca and Mg remains very low and relatively invariant, indicating limited interaction with the KMnO₄ oxidant. Similarly, Fe does not exhibit any detectable dissolution, as in the case of the MSG-H₂O₂ system, thus confirming that Fe is not reactive under these conditions. On the contrary, Cu, Ni, and Co exhibit marked progressive leaching behavior, with final recoveries of 98.3%, 96%, and 93.5%, respectively. The better extraction performance of these metals by KMnO₄ indicates their strong oxidative potential, which is effective in driving the dissolution of these metals with stability in the solution. High selectivity of the MSG-KMnO₄ system was observed, attributed to the priority of Cu, Ni, and Co extraction while suppressing the dissolution of Ca, Mg, and Fe. Overall, KMnO₄ proved to be an excellent oxidant with excellent recoveries and selectivity for copper and associated metals, therefore becoming a valuable alternative for efficient leaching.

3.5. Kinetic Analysis

The rate constants and correlation coefficients for each process variable were determined from plots of the various shrinking core kinetic models.

Table 2. Correlation coefficient values of the shrinking core kinetic model for copper leaching in the MSG-H₂O₂ system.

Coefficient of Variation for the Evaluated Models: R2
Variables	Chemical Reaction Controls	Diffusion Through Product Layer Controls	Mixed Control
	$1-(1-\mathit{x}{)}^{1/3}$	$1-3(1-\mathit{x}{)}^{2/3}+2(1-\mathit{x})$	$\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$3$}\right.\mathit{l}\mathit{n}\left(1-\mathit{x}\right)-\left[1-(1-\mathit{x}{)}^{-1/3}\right]$
H2O2concentration, %
1	0.9645	0.9999	0.9948
3	0.7496	0.8796	0.8939
5	0.9906	0.9873	0.9592
Particle size, µm
38–53	0.9927	0.9882	0.9642
53–75	0.7496	0.8796	0.8939
75–106	0.9197	0.9018	0.8778
106–150	0.9751	0.8897	0.8350
Temperature, °C
15	0.9772	0.9874	0.9847
30	0.7294	0.8796	0.8939
45	0.7522	0.8643	0.8965
60	0.8836	0.8844	0.8839

and

Table 3. Correlation coefficient values of the shrinking core kinetic model for copper leaching in the MSG-KMnO₄ system.

Coefficient of Variation for the Evaluated Models: R2
Variables	Chemical Reaction Controls	Diffusion Through Product Layer Controls	Mixed Control
	$1-(1-\mathit{x}{)}^{1/3}$	$1-3(1-\mathit{x}{)}^{2/3}+2(1-\mathit{x})$	$\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$3$}\right.\mathit{l}\mathit{n}\left(1-\mathit{x}\right)-\left[1-(1-\mathit{x}{)}^{-1/3}\right]$
KMnO4concentration, g/L
1	0.9857	0.9852	0.9040
2	0.9981	0.9640	0.7707
3	0.9992	0.9761	0.7888
Particle size, µm
38–53	0.9864	0.9882	0.8564
53–75	0.9985	0.9640	0.7707
75–106	0.9982	0.9811	0.7929
106–150	0.9373	0.9216	0.8748
Temperature, °C
15	0.9896	0.9902	0.9671
30	0.9985	0.9640	0.7707
45	0.9916	0.9903	0.8557
60	0.8931	0.9282	0.9575

show determined correlation coefficients (R²) for the models and variables in the MSG-H₂O₂ and MSG-KMnO₄ systems, respectively. The values of regression coefficients obtained after fitting the experimental data in the shrinking core models indicate the dynamic interaction of chemical reactions and diffusion controls. At low H₂O₂ concentrations (1%) and low temperatures (15 °C), the diffusion through the product layer was dominant, as shown by the highest R². Increasing the H₂O₂ concentration and temperature improved reaction kinetics, shifting the rate control towards chemical reactions; under moderate conditions, the models of Mixed Control fitted well. Smaller particles (38–53 μm) favored chemical control because of the higher surface area, while for intermediate sizes, 53–106 μm, the process was controlled by diffusion. At a higher temperature of 60 °C and a higher oxidant concentration of 5%, the system became controlled by chemical reaction, which resulted from the enhancement of the reaction rate and minimum diffusion resistance. These results reveal that in the MSG-H₂O₂ system, copper dissolution is controlled by chemical reaction and diffusion through the product layer. However, the dominant rate-limiting step is diffusion through the product layer since estimated time contributions at different temperatures are far higher than those of the chemical reaction model.

On the other hand, the determined R² for the various models and variables in the MSG-KMnO₄ system (Table 3) indicated that chemical reaction and diffusion through the product layer controlled the copper leaching kinetics. At low KMnO₄ concentrations of 1 g/L, there was a dynamic interplay between chemical reaction and diffusion product layer dominance. However, increasing the concentration of KMnO₄ resulted in the development of reaction kinetics and shifted the rate control towards chemical reaction, just like in the MSG-H₂O₂ system. At temperatures of 15 and 45 °C, the leaching process was controlled by both chemical reaction and diffusion through the product layer. In contrast, the contribution from the chemical reaction was almost null when the temperature was raised to 60 °C.

The apparent rate constants obtained from the plots of $1-3(1-x{)}^{2/3}+2(1-x)$ versus time at investigated temperatures were used to create Arrhenius plots for MSG-H₂O₂ and MSG-KMnO₄ systems. From the slope of the fitted equation in Figure 9, the apparent activation energies were calculated to be 18.2 kJ/mol for the MSG-H₂O₂ system and 17.3 kJ/mol for the MSG-KMnO₄ system. Therefore, the observed activation energies indicate that the dissolution mechanism of copper in both systems is diffusional product layer controlled.

Using $k$ values determined for each process variable for copper leaching, an empirical power law model was established using plots of lnk versus ln [H₂O₂], ln [KMnO₄], ln [PS] (Equation (9)). The straight-line slope in each plot shows the calculated reaction order with respect to the variable.

$$1-3(1-x{)}^{2/3}+2(1-x)=\left[k_m(C_{oxidant}{)}^a(PS{)}^{b}exp\left({\displaystyle \frac{{-E}_a}{RT}}\right)\right]t$$

(9)

The values of the constants Km, a, and b for the copper leaching in the MSG-H₂O₂ system were 2.93, 0.35, and −0.62, respectively. The obtained reaction orders to the MSG-KMnO₄ system can be empirically represented by Equation (10):

$$1-3(1-x{)}^{2/3}+2(1-x)=\left[2.93(C_{{KMnO}_4}{)}^{0.35}(PS{)}^{-0.62}exp\left({\displaystyle \frac{-17328}{RT}}\right)\right]t$$

(10)

The empirical kinetic expression obtained for the MSG–KMnO₄ system indicates that the leaching rate is positively influenced by KMnO₄ concentration, with a lower dependence on monosodium glutamate concentration. Direct comparison of these parameters with kinetic constants reported for conventional H₂SO₄–KMnO₄ and H₂SO₄–H₂O₂ systems is difficult because different kinetic models, experimental conditions, particle sizes, and mineralogical characteristics have been employed in previous studies. Li et al. reported that chalcopyrite leaching kinetics are strongly influenced by oxidant type, redox potential, temperature, and passivation phenomena; moreover, diffusion control, chemical reaction control, and mixed control mechanisms have been reported under different leaching conditions [35]. Therefore, the kinetic parameters obtained in the present study should be interpreted as specific to the alkaline MSG–KMnO₄ system and are not directly comparable to the apparent kinetic constants reported for acidic leaching systems.

4. Conclusions

The effects of oxidant type and concentration, temperature, particle size, and gangue minerals on copper leaching in alkaline MSG solutions were investigated. It was observed that copper dissolution rates are increased by using KMnO₄ as an oxidant; indeed, KMnO₄ exhibits high efficiency, especially in continuous leaching, for its high oxidative potential and stability. KMnO₄ also depresses the dissolution of gangue metals like Ca, Mg, and Fe while increasing the recovery of interest metals like Cu, Ni, and Co compared with H₂O₂. Recoveries of almost 100% of these metals in MSG-KMnO₄ systems were realized at a temperature of 30 °C in 24 h. Of all the parameters evaluated, temperature and particle size were considered critical factors influencing the kinetics and extent of copper dissolution. Thus, optimum conditions must be balanced between higher reaction rates and sustained dissolution. Kinetic modeling thus confirmed that, in both systems, the rate-determining step is the diffusion of reagents through the product layer. Calculated apparent activation energies were 18.2 kJ/mol and 17.3 kJ/mol for MSG-H₂O₂ and MSG-KMnO₄ systems. These results present a bright outlook for using MSG as a benign substitute for conventional leaching reagents and give further impetus toward developing green resource-extraction technologies for refractory copper ores. Its use in hydrometallurgy could further be examined to optimize its industrial adoption and extend its usefulness into metal recovery processes.