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Article

Research on the Thermodynamic Simulation Model of Antimony–Lead Synergistic Side-Blown Oxidation Smelting Process Based on MetCal

1
School of Metallurgical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
2
School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Processes 2025, 13(4), 1244; https://doi.org/10.3390/pr13041244
Submission received: 8 March 2025 / Revised: 7 April 2025 / Accepted: 18 April 2025 / Published: 19 April 2025
(This article belongs to the Section Chemical Processes and Systems)

Abstract

:
On the basis of the theory of polyphase equilibrium and the utilization of the MetCal software platform (MetCal v7.81), we adopted the chemical equilibrium constant method and successfully constructed a multiphase equilibrium model and simulation system for the antimony–lead synergistic side-blown oxidation smelting process. In typical production conditions, which encompass factors such as the composition of raw material, the ratio of oxygen to material, and oxygen-enriched concentration, the equilibrium product composition and pivotal technical indices are modeled and computed. Calculation results indicated that, other than the trace elements in the smelting slag, the relative errors of the calculated values for the content of major elements in the antimony-rich crude lead and smelting slag were less than 10% of the measured value after average treatment in production. Therefore, our results showed that the developed model and system preferably embodied the practical production condition of the antimony–lead synergistic side-blown oxidation smelting process, which is capable of precisely forecasting the smelting outcomes and optimizing the process parameters, thereby offering effective guidance for the practical execution of the antimony–lead synergistic side-blown oxidation smelting process.

1. Introduction

Antimony is a vital strategic metal and has a wide range of uses in industry [1,2]. China is rich in antimony resources, with proven reserves of 640,000 tons of antimony in 2024, accounting for more than 30% of the global total [3]. At the same time, in 2023, the annual consumption of antimony resources in China reached approximately 150,000 tons, ranking it among the top in the world [4]. Antimony sulfide concentrate is one of the main raw materials for the production of metal antimony [5]; however, with increasing demands for antimony resources and the continuous shortage of single antimony ore resources, the extraction of antimony from complex resources containing antimony is the only means possible to sustainably develop the antimony industry [6,7]. Currently, more than 10 types of antimony-containing complex resources can meet the requirements of industrial production, including stibnite (Sb2S3), jamesonite (Pb4FeSb6S14), valentinite (Sb2O3, Sb 83.3%), senarmontite (Sb2O3, Sb 83.54%), and antimony–gold ore (Sb2S3, Sb 59.92%) [8,9,10]. Among these resources, antimony–gold symbiotic ore is a compound antimony ore with more economic value than single antimony ore [11]. As a result, this symbiotic ore is gradually replacing antimony sulfide concentrate and becoming one of the important raw materials for the production of metal antimony [12].
Currently, the treatment methods of antimony–gold symbiotic concentrate include the hydrometallurgical process and the pyrometallurgical process, with the latter method primarily used in enterprise production [13,14]. Volatilization smelting in a blast furnace is a typical pyrometallurgical process used for antimony-containing concentrates [15,16]. It faces issues, however, such as high energy consumption, difficulty in controlling environmental pollution, high production costs, and challenges in recovering precious metals [9]. The hydrometallurgical processes include the alkaline sulfide leach process [17,18], the acidic chloride leach process [19,20], and the pulp electrolysis process [21,22]. Although these processes do not produce sulfur dioxide (SO2) and have low energy consumption, they do encounter issues such as high consumption of water, large wastewater volumes, chlorine corrosion, and power consumption [23]. The oxygen-rich side-blown smelting process [24,25,26] has emerged as a novel technology in antimony smelting, characterized by its robust adaptability to raw materials, environmentally friendly approach, long furnace body life, high comprehensive metal recovery rate, and cost-effectiveness [27]. To address the issues of low metal recovery, high energy consumption, and high pollution associated with the existing treatment of antimony–gold concentrate, some scholars have proposed the adoption of a new synergistic oxygen-rich side-blown smelting process for lead concentrate and antimony–gold concentrate based on similar properties and its easy association with antimony and lead [28]. These scholars have initiated production practices. Zhang et al. [29] used the thermodynamic software FactSage 7.3 to calculate the reaction trend of various metal sulfides, the dominant region map of Me-S-O diagrams, and the distribution rule of phase equilibrium in the antimony–gold concentrate and lead concentrate synergistic smelting process and a verification test was carried out. However, the results were not quantitatively described in this research, and the yield of each product and the distribution behavior of each element in the product were not clear.
As advancements in computer hardware and software have been achieved, the quantitative analysis of the pyrometallurgical process has been performed using the multiphase equilibrium model [30] and leveraging computer-aided simulation techniques, which offer such benefits as high research efficiency, good reproducibility, low cost, and good security. Currently, scholars have performed thermodynamic simulation analysis and have conducted research on metallurgical processes, such as flash smelting [31,32], bottom-blown smelting [33,34], and side-blown smelting [35,36] of copper, lead, and other raw materials. These processes can better calculate and predict production output and reveal the distribution law of elements, providing theoretical guidance to optimize the production process.
Given these findings, in this study, we adopted the multiphase chemical equilibrium constant method [37,38], which was grounded in the mechanism of process and production practice of the antimony–lead synergistic side-blown oxidation smelting process and was based on the MetCal software platform (MetCal v7.81) [39]. We also researched and established a multiphase thermodynamic simulation model for the synergistic oxidation smelting of antimony and lead. Under typical production conditions, we conducted a thermodynamic simulation calculation for the antimony–lead synergistic side-blown oxidation smelting process while establishing a solid numerical modeling foundation for subsequent quantitative control and optimization of process parameters in this synergistic smelting process.

2. Process Mechanism and Model Establishment

2.1. Process Mechanism

The antimony–lead synergistic side-blown smelting process is based on a three-furnace segmented reaction design, including three stages of oxidation smelting, reduction smelting, and slag smelting [24]. In this study, we focused on the antimony–lead synergistic side-blown oxidation smelting process, which was completed in a side-blown oxidation furnace, as shown in Figure 1.
We added lead concentrate, antimony–gold concentrate, quartz sand, limestone, and other materials produced by belt ingredient mixing and granulation to a side-blown oxidation furnace. We added rich oxygen from both sides of the furnace body drum to the slag layer while strongly stirring the furnace high-temperature melt (1100–1300 °C). The antimony-containing and lead-containing materials oxidized rapidly, melted, and generated the primary antimony-rich crude lead, smelting slag, flue gas, and flue dust as well as other products in the furnace. The distribution rate of antimony and lead in the flue gas was low, while the distribution rate in the primary antimony-rich crude lead, smelting slag, and flue dust was high. Antimony and lead in the primary antimony-rich crude lead existed primarily in the form of monomers, and antimony and lead in the smelting slag and flue dust existed primarily in the form of oxides. Because of the large phase boundary area, the gas gave the molten pool a high stirring kinetic energy, the mass and heat transfer rate in the furnace was fast, and the phase composition of each product tended toward thermodynamic equilibrium quickly.
The products were precipitated and separated in the cylinder area of the furnace, the antimony-rich crude lead was discharged into the refining furnace from the siphon, and the smelting slag (containing approximately 30 wt% Pb, as analyzed using X-ray fluorescence (XRF)) was sent to the side-blown reduction furnace through the chute for further reduction smelting, which produced the secondary antimony-rich crude lead, reduction slag, flue gas, and flue dust. The gas generated during the oxidation and reduction smelting process was cooled in a waste heat boiler, and the flue dust was collected in an electric precipitator and then sent to the sulfuric acid system for acid production.

2.2. Model Assumption

In keeping with the mechanism of the antimony–lead synergistic side-blown oxidation smelting process, the products of this oxidative smelting process included antimony-rich crude lead, smelting slag, flue gas, and flue dust. In modeling the multiphase equilibrium model for this process, we assume that the equilibrium product phases encompassed three distinct phases: antimony-rich crude lead, smelting slag, and flue gas. The flue dust was composed of mixed mineral dust and product dust. Based on the reaction mechanism of the antimony–lead synergistic side-blown oxidation smelting process and literature reports, the composition of each equilibrium product is assumed to be as follows:
(1)
antimony-rich crude lead (ALd): Pb, PbS, Bi, Sb, Zn, Cu2S, FeS, As, Ag, and Other1;
(2)
smelting slag (Sl): PbO, PbSO4, PbS, ZnO, Cu2O, Cu2S, As2O3, Bi2O3, Sb2O3, Sb2O5, FeO, Fe3O4, FeS, CaO, Al2O3, MgO, SiO2, Ag, and Other2;
(3)
flue gas (gas): O2, Sb2O3, Sb2S3, Pb, PbO, PbS, As2O3, As2S3, Zn, ZnO, ZnS, SO2, S2, CO, CO2, N2, and H2O;
(4)
flue dust (Dt): Bi2S3, Sb2S3, As2S3, FeS, SiO2, Ag, Al2O3, H2O, FeS2, PbS, ZnS, Cu2S, Fe2O3, CaCO3, MgO, CaO, Pb, Bi, Zn, As, Sb, PbO, PbSO4, ZnO, Cu2O, Bi2O3, Sb2O3, Sb2O5, FeO, Fe3O4, As2O3, and Other3.
Among these products, “Other” denoted impurity elements that were not involved in the reactions but had the potential to influence the modeling of mass balance relationships.

2.3. Modeling Principles

We characterized the antimony–lead collaborative side-blown oxidation smelting process as a multiphase and multicomponent reactive system. The mathematical model for this process was developed using the chemical equilibrium constant method. In this method, at a constant temperature and pressure, we established a mathematical model based on the total moles of each element present and the independent chemical reactions occurring within the system when the system was at equilibrium. This model was expressed as a set of matrix nonlinear equations. By solving the mathematical model, we calculated the molar number of each phase and component in the system.
The multiphase multicomponent reaction system featured a set of linear independent molecular equation vectors, which were defined as independent components, and the rest were defined as subordinate components. Assuming that N e and N c , respectively, denoted the number of elements and the chemical composition in the antimony–lead synergistic side-blown oxidation smelting process, we determined the number of all components in the system by the reaction between the components, with the count of independent reactions denoted as N b , which was equal to N c   N e . These N b independent reactions were represented by the following matrix equation:
V j , i A i , k = B j , k
where V j , i denotes the matrix of stoichiometric coefficients, A i , k denotes the molecular equation matrix of independent components, B j , k   denotes the molecular equation matrix of subordinate components, and i ,   j , and   k denote, respectively, the independent component number, subordinate component number, and element type number.
According to the rules of matrix operation, V j , i can be calculated by the following equation:
V j , i = B j , k U k , i
where U k , i is the computable inverse matrix of V j , i .
For the antimony–lead synergistic side-blown oxidation smelting process, we assumed that 0.2% of the inert constituent “Other” was allocated to the antimony-rich crude lead, while 99.8% was directed toward the smelting slag. On the basis of product assumptions and the principle of phase equilibrium, the composition of the multiphase equilibrium products included 17 elements and 46 compounds. The 17 elements included 15 individual elements (Pb, Zn, Cu, Fe, S, As, Sb, Ag, Bi, Al, Mg, O, H, N, and C) and 2 pseudo-elements (SiO2 and CaO). The total number of compound types corresponded to the sum of the types of compounds present in the antimony-rich crude lead, smelting slag, and flue gas. Therefore, in Equations (1) and (2), i and k ranged from 1 to 17, and j ranged from 1 to 27. There were 17 independent components and 27 subordinate components, excluding “Other,” which were represented by the stoichiometric coefficient matrices shown in Table A1 and Table A2 of Appendix A, respectively.
Table 1 shows the equilibrium reactions of 27 subordinate components and their equilibrium constant K j . The equilibrium constant K j of the independent reaction can be expressed by the following equation:
K j = exp Δ G b j 0 V j i Δ G a i 0 R T
where R denotes the gas constant, T denotes the equilibrium temperature of the system, Δ G ai 0 denotes the standard Gibbs free energy of formation of i independent components, and Δ G bi 0 denotes the standard Gibbs free energy of formation of j subordinate components.
When the multiphase reaction system of antimony–lead synergistic side-blown oxidation smelting reached the chemistry balance state, we determined the connection between 17 independent components and 27 subordinate components as shown in the following equation:
Y j = Z m , j γ j K j i γ i X i Z m , i
where X i denotes the molar number of i independent components, γ i denotes the activity coefficient of i independent components, Y j denotes the molar number of j subordinate components, γ j denotes the activity coefficient of j subordinate components, Z m , i denotes the molar number of i independent components, Z m , j denotes the molar number of i subordinate components, and m denotes the product phase.
The total molar number Z m of each component in the m product phase in Equation (4) can be calculated by the following equation:
Z m = i m X i + j m Y j
where i(m) denotes that the sum is obtained only when the i independent component belongs to the m product phase, and j(m) denotes that the sum is obtained only when the j subordinate component pertains to the m product phase.
According to the law of conservation of mass, the mass of each element can be calculated by the following equation:
Q k = i A i , k X f + j B j , k Y f
where Q k denotes the molar number of the k element.
For a closed pyrometallurgical system, we assumed that the number of product phases was defined as N p when the temperature, pressure, and number of elements of the system were given and had reached equilibrium. From Equations (3), (4) and (6), we observed that there were N c + N p equations in the multiphase reaction system and that the number of variables to be solved was X i + Y j + Z m . The equations were matched in quantity to the variables that had to be resolved. The amount of each species in each phase at the equilibrium of this system can be obtained by solving the system of nonlinear equations consisting of Equations (4)–(6) according to the Newton–Raphson algorithm.

2.4. Mathematical Models and Computing Systems

On the basis of the mechanism of the antimony–lead synergistic side-blown oxidation smelting process and the modeling principle described in Section 2.3, we constructed the multiphase equilibrium model for this process using the chemical equilibrium constant method. The model can be solved by the calculation flowchart shown in Figure 2. After considering the heat balance connection between the input material and the output material within the smelting system, we developed the multiphase equilibrium calculation system in Figure 3 using the following equation, which was based on the MetCal software development platform (MetCal v7.81):
i n A Δ H 298 , A i + i n A 298 T i C p A i d T = j n B Δ H 298 , B j + j n B 298 T j C p B j d T + Q Loss
where Ai denotes the reactant; Ti denotes the initial temperature of the reactant Ai; Bj denotes the product; Tj denotes the temperature of the product Bj; nA denotes the number of reactants; nB denotes the number of products; H denotes the enthalpy value; Cp denotes the heat capacity; and QLoss denotes the amount of heat loss.

3. Materials and Methods

3.1. Raw Material Composition

The raw materials for the side-blowing oxidation smelting process of an antimony and lead smelting enterprise in China included lead concentrate, antimony–gold concentrate, quartz sand, limestone, air, and industrial oxygen (oxygen volume concentration of 95%). We conducted XRF analysis, and the technical parameters of the equipment were as follows: in the linear range of less than 1%, the streaming gas detector counted 3000 kcps per second, and the scintillation detector counted 1500 kcps per second; the angular positioning accuracy of the scanning channel was less than 0.0001 degrees; and the range of analyzed elemental concentration was xppm—100%. We also used a spectrometer to analyze its elemental composition. According to the XRF analysis, we obtained the average value of the elemental composition of the raw materials used by the enterprise in December 2023, as shown in Table 2, Table 3, Table 4 and Table 5.

3.2. Calculation Condition and Mixed Ore Composition

Based on typical production data from a domestic antimony–lead smelting enterprise’s side-blown oxidative smelting process, the total raw material input was 50 t/h, with lead concentrate being 85.2%, antimony–gold concentrate 10.6%, quartz 0.2%, and limestone 4%. The oxygen–material ratio was set at 130 Nm3/t, and the oxygen enrichment concentration was 90%. We calculated the melting temperature according to the heat balance. We assumed that the temperature of the antimony-rich crude lead was 300 °C lower than that of the smelting slag and that the temperature of the flue gas and dust was 20 °C higher than that of the smelting slag. The elemental composition of each raw material is detailed in Table 2, Table 3, Table 4 and Table 5. The outer wall area was 400 m2, the furnace mouth area was 8 m2, and the furnace lining thickness was 1 m. The ambient temperature was 30 °C, and the liner material was made of magnesio-chrome tiles with a coefficient of blackness of 0.8 and a convective heat transfer coefficient of 3.5 W/(m2·K).
The mixed ore is obtained by batching and physical mixing of lead concentrate, antimony–gold concentrate, quartz, and lime melt, and its physical phase composition is shown in Table 6. The physical phase composition of the mixed ore was obtained by summing the physical phase compositions of the four solid raw materials. Among them, the physical phase compositions of antimony–gold concentrate and lead concentrate were obtained from the elemental analysis results obtained from XRF analysis of samples taken under typical working conditions and, based on the characteristics of the two types of concentrates belonging to sulfide ores and the principle of the minimum free energy [42], they were calculated by the constructed physical phase computation model assuming their physical phase types (i.e., compound compositions).

3.3. Thermodynamic Data

By combining Equation (8), the Kirchhoff formula, with Equation (9), which details the connection between the temperature and the standard molar entropy change in reactions, we computed the Gibbs free energy of the components in the antimony–lead synergistic side-blown oxidation smelting process using Equation (10):
Δ H T θ = Δ H 298 θ + 298 T C p   d T
Δ S T θ = Δ S 298 θ + 298 T C p T   d T
Δ G T θ = Δ H 298 θ T · Δ S 298 θ + 298 T C p   d T T 298 T C p T d T
We extracted the standard thermodynamic data for the products from the MetCal software (MetCal v7.81)’s database, as shown in Appendix A, Table A3. To negate the effects of reaction kinetics, we considered both production analysis and literature references [43,44,45,46] and adjusted the activity coefficients for certain products, as noted in Appendix A, Table A4. MQC indicates that the activity of the component is determined using MetCal v7.81’s modified quasi-chemical solution model for activity calculations. We treated the flue gas as an ideal gas, which suggested that its component activity coefficient should be set to 1.

4. Result and Discussion

4.1. Calculated Result

According to the calculation conditions and the phase composition of mixed ore of Section 3.2, we used the constructed multiphase equilibrium calculation system for the antimony–lead synergistic side-blown oxidation smelting process to calculate the composition and yield of equilibrium products. Table 7 and Table 8, respectively, show the main technical indices and heat balance calculation results of the antimony–lead synergistic oxidation smelting process. And the allocation behavior of the accessory elements in the products is illustrated in Figure 4.

4.2. Results Comparison

On the basis of production report data from a domestic antimony–lead smelting enterprise’s side-blown oxidation smelting section released in mid-December 2023, we compared the average analysis results of samples of the antimony-rich crude lead and smelting slag with the values calculated using our model, as shown in Table 9.

4.3. Discussion

From the results of the data presented in Figure 3, it is evident that elements such as Pb, Sb, Zn, As, Cu, and Fe were mainly enriched in the smelting slag during the antimony–lead synergistic side-blown oxidation smelting process, whereas Ag and Bi were mainly distributed in the antimony-rich crude lead. With the exception of S, the distribution rate of the other associated elements in the flue gas was not significant. The results show that the elements Pb, Sb, Zn, As, Cu, and Fe are more easily oxidized into slag, while Ag and Bi are less easily oxidized, so they are more distributed in antimony-rich crude lead.
Reviewing the data presented in Table 9, we observed that the calculated values for most elements in the products closely matched the average production measurements, except for certain elements that were not detected during production. Specifically, the relative errors for Pb, Sb, Cu, As, Bi, and Ag in the antimony-rich crude lead were 4.83%, 3.92%, 0.08%, 0.36%, 1.88%, and 1.23%, respectively. For the smelting slag, the relative errors for Pb, Sb, Zn, Cu, Fe, CaO, SiO2, S, As, Bi, and Ag were 1.43%, 9.29%, 7.96%, 8.29%, 2.64%, 0.55%, 2.03%, 4.57%, 0.10%, 0%, and 5.17%, respectively. Notably, the discrepancies between simulated and measured values for Zn, Cu, Sb, and As in the smelting slag were substantial, which we attributed to detection errors associated with the quantification of trace elements in the production analysis, as well as inherent errors in the model itself.
In order to further improve the accuracy and reliability, it is necessary to continue to optimize the model and to carry out more precise detection in the future. Although there are certain discrepancies between the production data and the calculated values of the model, these errors are currently in a controllable range, with relative errors remaining below 10%. These comparison results demonstrated that the model developed in this research was able to capture the multiphase reaction characteristics of the antimony–lead synergistic side-blown oxidation smelting process. This process had the capacity to precisely forecast the smelting production process and to refine process parameters, thus serving as an effective instrument for the subsequent systematic thermodynamic analysis of the process.

5. Conclusions

  • On the basis of the multiphase reaction mechanism and features of the antimony–lead synergistic side-blown oxidation smelting process, we constructed a thermodynamic simulation model and calculation system of the antimony–lead synergistic side-blown oxidation smelting process using the chemical equilibrium constant approach and MetCal software platform (MetCal v7.81), which provided a software-based tool for the thermodynamic calculation and analysis of the smelting process. The model is based on the MetCal platform (MetCal v7.81), which has high accuracy, low error predictions and fast computation speed.
  • Using the established calculation system, we conducted an instance validation under the typical production conditions of a domestic enterprise. The outcomes from the product composition closely matched the actual manufacturing outcomes, demonstrating that the constructed model effectively embodied the multiphase reaction features of the antimony–lead synergistic side-blown oxidation smelting process and possessed the ability to accurately forecast the output of this process.
  • Through verification and contrast experiments, we found that the calculated values of the main technical index for the antimony–lead synergistic side-blown oxidation smelting process had a small relative error compared with the average measured values from industrial production during the same period. The relative errors of the calculated mass fractions of Pb, Sb, Zn, Cu, Fe, CaO, SiO2, S, As, Bi, and Ag in antimony-rich crude lead and smelting slag are less than 10%. Although there is an error margin, it is acceptable. In the future, the constructed model and calculation system can be used to carry out conditional experiments to optimize and regulate different process parameters, so as to provide a model basis for the subsequent on-line optimization and regulation of the antimony and lead synergistic side-blowing oxidation smelting process.

Author Contributions

Conceptualization, M.L. and B.M.; Formal analysis, Z.Z. (Zhenquan Zhong) and M.L.; Investigation, Y.F. and X.C.; Resources, Z.Z. (Zhenquan Zhong) and Z.Z. (Zhongtang Zhang); Visualization, Z.Z. (Zhenquan Zhong) and M.L.; Writing—original draft, Z.Z. (Zhenquan Zhong), M.L. and Y.F.; and Writing—review and editing, Z.Z. (Zhenquan Zhong) and M.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Project No. 2022YFC2904201, supported by the National Key R&D Program of China; Project No. 52364047, supported by the National Natural Science Foundation of China; Project No. 20212BAB204026, supported by the Natural Science Foundation of Jiangxi Province of China; Project No. 2019M662268, supported by the China Postdoctoral Science Foundation; and Project No. 2018KY15, supported by the Postdoctoral Funding program of Jiangxi Province, China.

Data Availability Statement

The original contributions presented in this study are included in the article material. Further inquiries can be directed to the corresponding author.

Acknowledgments

We would like to thank Huang Jindi for his suggestions on this article. Thanks to Tong Changren for his guidance and support in obtaining the license to use MetCal v7.81. Special thanks to Guangxi Nandan Nanfang Metal Co., Ltd. for providing the production data for this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Stoichiometry matrix for 17 independent species.
Table A1. Stoichiometry matrix for 17 independent species.
ComponentPhasePbZnCuFeSAsSbMgAlBiAgSiO2CaOOCHN
PbLd10000000000000000
PbSLd10001000000000000
ZnLd01000000000000000
Cu2SLd00201000000000000
FeSLd00011000000000000
AsLd00000100000000000
SbLd00000010000000000
BiLd00000000010000000
AgLd00000000001000000
Al2O3Sl00000000200003000
MgOSl00000001000001000
SiO2Sl00000000000100000
CaOSl00000000000010000
O2Gas00000000000002000
COGas00000000000001100
H2OGas00000000000001020
N2Gas00000000000000002
Table A2. Stoichiometry matrix for 27 subordinate species.
Table A2. Stoichiometry matrix for 27 subordinate species.
ComponentPhasePbZnCuFeSAsSbBiAgSiO2CaOOCHN
PbOSl100000000001000
PbSO4Sl100010000004000
PbSSl100010000000000
ZnOSl010000000001000
Cu2OSl002000000001000
Cu2SSl002010000000000
FeOSl000100000001000
Fe3O4Sl000300000004000
FeSSl000110000000000
As2O3Sl000002000003000
Sb2O3Sl000000200003000
Bi2O3Sl000000020003000
Sb2O5Sl000000200005000
AgSl000000001000000
PbGas100000000000000
PbOGas100000000001000
PbSGas100010000000000
ZnGas010000000000000
ZnOGas010000000001000
ZnSGas010010000000000
As2O3Gas000002000003000
As2S3Gas000032000000000
Sb2O3Gas000000200003000
Sb2S3Gas000030200000000
SO2Gas000010000002000
S2Gas000020000000000
CO2Gas000000000002100
Table A3. Standard thermodynamic parameters of product components.
Table A3. Standard thermodynamic parameters of product components.
ComponentState Δ H 298 Θ /
(kJ·mol−1)
Δ S 298 Θ
(J·K−1·mol−1)
cp = a + b × 10−3T + c × 105T−2 + d × 10−6T2
abcd
PbLiquid3.87370.50627.1591.02900
PbSLiquid−93.14384.12966.946000
BiLiquid9.27171.98027.197000
SbLiquid17.53162.71231.381000
ZnLiquid5.72748.54931.381000
Cu2SLiquid−68.100132.46289.665000
FeSLiquid−64.63191.20862.552000
AsLiquid21.56853.28428.833000
AgLiquid6.39343.22033.473000
PbOLiquid−202.24973.37965.000000
PbSO4Liquid−923.159148.494186.004000
ZnOLiquid−309.54247.92060.669000
Cu2OLiquid−130.22496.40299.916000
As2O3Liquid−643.439128.125152.720000
Sb2O3Liquid−675.490143.628156.904000
Sb2O5Liquid−971.925125.105141.331−3.732−20.1130
Bi2O3Liquid−578.024149.814202.005000
FeOLiquid−257.27657.59168.201000
Fe3O4Liquid−993.334198.385213.389000
SiO2Liquid−927.5489.31085.7740.0000.0000.000
CaOLiquid−572.90840.98062.7620.0000.0000.000
MgOLiquid−561.01812.83366.9460.0000.0000.000
Al2O3Liquid−595.56845.145144.8660.0000.0000.000
PbGas195.205175.37728.063−11.029−9.3104.728
PbOGas68.139240.04841.612−3.526−20.1361.014
PbSGas127.959251.41637.3500.194−2.0960.140
ZnGas130.40316.99220.898−0.133−0.0670.034
ZnOGas136.518242.81137.671−0.286−1.9850.735
ZnSGas204.322236.404166.350−85.742−166.12521.952
As2O3Gas−322.845371.92582.1346.444−5.3560
As2S3Gas27.042314.28996.2011.071−8.2130
Sb2O3Gas−708.564129.903180.004000
Sb2S3Gas119.661409.820107.6360.209−7.2550
O2Gas0205.15434.8601.312−14.1410.163
SO2Gas−296.820248.22654.7813.350−24.745−0.241
S2Gas128.603228.16934.6723.286−2.816−0.312
COGas−110.544197.66529.9325.415−10.814−1.054
CO2Gas−393.515213.77454.4375.116−43.579−0.806
N2Gas0191.61523.52912.1171.210−3.076
H2OGas−241.832188.83731.43814.106−24.952−1.832
Table A4. Activity coefficients of product components.
Table A4. Activity coefficients of product components.
ComponentPhaseActivity Coefficient
PbOSl1
PbSO4Sl0.8
PbSSl0.5
ZnOSl0.1
Cu2OSl0.002
Cu2SSl50
FeOSl0.0001
Fe3O4Sl0.1
FeSSl0.0001
As2O3Sl0.003
Sb2O3Sl0.002
Sb2O5SlMQC
Bi2O3Sl1.64
CaOSl0.1
MgOSl1
Al2O3Sl1
SiO2Sl0.1
AgSl1.351
PbALd0.35
PbSALd20
SbALd0.0078
CuSALd0.028
FeSALd100
ZnALd0.066
AsALd0.058
BiALd18
AgALd0.045

References

  1. Li, J.; Xu, D.; Zhu, Y. Global antimony supply risk assessment through the industry chain. Front. Energy Res. 2022, 10, 1007260. [Google Scholar] [CrossRef]
  2. Zhao, G.; Li, W.; Geng, Y.; Bleischwitz, R. Uncovering the features of global antimony resource trade network. Resour. Policy 2023, 85, 103815. [Google Scholar] [CrossRef]
  3. U.S. Geological Survey. Mineral Commodity Summaries 2024; U.S. Geological Survey: Reston, VA, USA, 2024; 212p.
  4. Tang, M.T.; Tang, C.B.; Yang, J.M.; Chen, Y.M.; Yang, S.H. Development Trend of Antimony Industry in China under Dual Carbon Strategy. Nonferrous Met. 2024, 11, 110–116. [Google Scholar]
  5. Multani, R.S.; Feldmann, T.; Demopoulos, G.P. Antimony in the metallurgical industry: A review of its chemistry and environmental stabilization options. Hydrometallurgy 2016, 164, 141–153. [Google Scholar] [CrossRef]
  6. Wu, Q.J.; Lv, Z.; Cao, J.C. Distribution and supply of antimony resources in China and abroad and development status of antimony industry Chain. Multipurp. Util. Miner. Resour. 2022, 43, 77–82. [Google Scholar]
  7. Zhong, D.P.; Li, L.; Tan, C. Recovery of antimony from antimony-bearing dusts through reduction roasting process under CO—CO2 mixture gas atmosphere after firstly oxidation roasted. J. Cent. South Univ. 2018, 25, 1904–1913. [Google Scholar] [CrossRef]
  8. Dembele, S.; Akcil, A.; Panda, S. Technological trends, emerging applications and metallurgical strategies in antimony recovery from stibnite. Miner. Eng. 2022, 175, 107304. [Google Scholar] [CrossRef]
  9. Ding, J.; Zhang, Y.; Ma, Y.; Wang, Y.; Zhang, J.; Zhang, T. Metallogenic characteristics and resource potential of antimony in China. J. Geochem. Explor. 2021, 230, 106834. [Google Scholar] [CrossRef]
  10. Kanellopoulos, C.; Sboras, S.; Voudouris, P.; Soukis, K.; Moritz, R. Antimony’s Significance as a Critical Metal: The Global Perspective and the Greek Deposits. Minerals 2024, 14, 121. [Google Scholar] [CrossRef]
  11. Huang, M.; Li, Z.; Wang, Q.; Guo, X.; Li, W. Antimony and gold substance flows analysis of pyrometallurgical process for antimony-gold concentrates. J. Clean. Prod. 2023, 420, 138385. [Google Scholar] [CrossRef]
  12. Ding, L.F.; Liu, Y.C.; Fu, J.G.; Zhang, Y.R.; Lin, Y.; Zhao, Y.C.; Hua, X.Y. Sulfur Reduction and Upgrading of High-Sulfur Antimony-Gold Bulk Concentrate from Russia. Min. Metall. Eng. 2023, 43, 77–79. [Google Scholar]
  13. Ma, D.; Li, D.B.; Chen, X.G.; Pei, Z.Y. Research progress of antimony concentrate smelting technology. China Nonferrous Metall. 2020, 49, 49–54. [Google Scholar]
  14. Zhu, Q.; Yang, J.G.; Tang, S.Y.; Man, T.X.; Liu, J.; Ye, W.L.; Tang, C.B. Current Development Status of Clean Metallurgical Technologiesfor Antimony. Nonferrous Met. (Extr. Metall.) 2025, 04, 30–38. [Google Scholar]
  15. Yu, Z.; Wang, L.; Zheng, Q.; Che, X.; Cui, X.; Wei, S.; Li, H.; Shi, X. Present Situation and Research Progress of Comprehensive Utilization of Antimony Tailings and Smelting Slag. Sustainable 2023, 15, 13947. [Google Scholar] [CrossRef]
  16. Wang, K.; Wang, Q.M.; Chen, Y.L.; Li, Z.C.; Guo, X.Y. Antimony and arsenic substance flow analysis in antimony pyrometallurgical process. Trans. Nonferrous Met. Soc. China 2023, 33, 2216–2230. [Google Scholar] [CrossRef]
  17. Ling, H.; Malfliet, A.; Blanpain, B.; Guo, M. A review of the technologies for antimony recovery from refractory ores and metallurgical residues. Miner. Process. Extr. Metall. Rev. 2024, 45, 200–224. [Google Scholar] [CrossRef]
  18. Celep, O.; Alp, İ.; Deveci, H. Improved gold and silver extraction from a refractory antimony ore by pretreatment with alkaline sulphide leach. Hydrometallurgy 2011, 105, 234–239. [Google Scholar] [CrossRef]
  19. Krenev, V.; Dergacheva, N.; Fomichev, S. Hydrometallurgical processes of antimony extraction from ores and concentrates. Theor. Found. Chem. Eng. 2016, 50, 613–619. [Google Scholar] [CrossRef]
  20. Yang, T.; Rao, S.; Liu, W.; Zhang, D.; Chen, L. A selective process for extracting antimony from refractory gold ore. Hydrometallurgy 2017, 169, 571–575. [Google Scholar] [CrossRef]
  21. Solozhenkin, P.M.; Alekseev, A.N. Innovative Processing and Hydrometallurgical Treatment Methods for Complex Antimony Ores and Concentrates. Part II: Hydrometallurgy of Complex Antimony Ores. J. Min. Sci. 2010, 46, 446–452. [Google Scholar] [CrossRef]
  22. Zhang, Y.; Wang, C.; Ma, B.; Jie, X.; Xing, P. Extracting antimony from high arsenic and gold-containing stibnite ore using slurry electrolysis. Hydrometallurgy 2019, 186, 284–291. [Google Scholar] [CrossRef]
  23. Wang, Y.; Liu, C.; Li, Y.; Ye, Y.; Xu, F.; Li, Y. Metallic antimony preparation by carbothermic reduction of stibnite concentrates: Strategies, mechanisms, and comparison of microwave and conventional roasting. Miner. Eng. 2024, 208, 108584. [Google Scholar] [CrossRef]
  24. Zhou, A.; Zhang, L.; Zhou, Y.; Li, Y.; Wu, X.; Xia, L.; Liu, Z. Co-Smelting Process of Pb Concentrate and Zn Leaching Residues with Oxygen-Rich Side Blowing Furnaces: Industrial Application and Material Balance. JOM 2023, 75, 5833–5846. [Google Scholar] [CrossRef]
  25. Mao, Q.H.; Gang, Y.; Chong, Y.; Long, H. Dynamic soft sensor modeling of matte grade in copper oxygen-rich side blow bath smelting process. Measurement 2023, 223, 113792. [Google Scholar]
  26. Bian, Z.; Chen, D.; Sun, L.; Wang, L.; Zhao, H.; Zhen, Y.; Qi, T. Numerical Simulation and Experimental Investigation of Multiphase Flow in an Oxygen-Rich Side-Blown Bath Smelting Furnace. JOM 2023, 75, 3962–3974. [Google Scholar] [CrossRef]
  27. Jiang, X.; Cui, Z.; Chen, M.; Zhao, B. Mixing behaviors in the horizontal bath smelting furnaces. Metall. Mater. Trans. B 2019, 50, 173–180. [Google Scholar] [CrossRef]
  28. Boyle, R.; Jonasson, I. The geochemistry of antimony and its use as an indicator element in geochemical prospecting. J. Geochem. Explor. 1984, 20, 223–302. [Google Scholar] [CrossRef]
  29. Zhang, Z.T.; Liu, L.J.; Li, Y.H.; Nie, H.P.; Wang, R.X.; Xu, Z.F. Thermodynamic Study on Synergistic Smelting Process of Complex Antimony Gold Concentrate and Lead Concentrate. Nonferrous Met. (Extr. Metall.) 2023, 12, 9–17. [Google Scholar]
  30. Leal, A.M.M.; Kulik, D.A.; Kosakowski, G. Computational methods for reactive transport modeling: A Gibbs energy minimization approach for multiphase equilibrium calculations. Adv. Water Resour. 2016, 88, 231–240. [Google Scholar] [CrossRef]
  31. Wang, J.; Chen, Y.; Zhang, W.; Zhang, C. Furnace structure analysis for copper flash continuous smelting based on numerical simulation. Trans. Nonferrous Met. Soc. China 2013, 23, 3799–3807. [Google Scholar] [CrossRef]
  32. Feng, Y.C.; Li, M.Z.; Dao, Y.Q.; Huang, J.D.; Xie, J.C.; Li, J.B. Thermodynamic Simulation Analysis of Copper Flash Smelting Process with Oxidized Ore Addition. Nonferrous Met. (Extr. Metall.) 2025, 3, 10–20. [Google Scholar]
  33. Zhang, Z.K.; Li, M.; LLiu, K.; LI, X.X. Analysis on the Influence Law of Key Parameters for Double Bottom Blowing Continuous Copper Smelting Process Based on MetCal Calculation Model. Sci. Technol. Eng. 2022, 22, 8652–8659. [Google Scholar]
  34. Wang, B.R.; Yang, H.Y.; Jin, Z.N.; Tong, L.L.; Ma, Z.Y. Arsenic distribution and phase structure in oxygen-enriched bottom blown copper smelting process. Chin. J. Nonferrous Met. 2024, 34, 908–922. [Google Scholar]
  35. Wang, J.S.; Qin, J.; Tao, J.; Huang, T.; Shi, X.X.; Cao, Z.M. Thermodynamic simulation and optimization of lead side blowing oxidation smelting process. Nonferrous Met. Sci. Eng. 2020, 11, 7–15. [Google Scholar]
  36. Wu, X.Y.; Chen, F.Y.; Chen, Z.H.; He, E.; Zhang, X.X.; Hou, Y.Q.; Xie, G. Optimization of copper slag type in double-furnace continuous smelting with top and side blowing. Chin. J. Nonferrous Met. 2024, 34, 3476–3489. [Google Scholar]
  37. Shen, Z.; Li, Y.; Xu, N.; Sun, B.; Du, W.; Xu, M.; Chang, L. Investigation on the chemical equilibrium products for CnHmOlNk type fuels using equilibrium constants database. Fuel 2022, 310, 122325. [Google Scholar] [CrossRef]
  38. Crerar, D.A. A method for computing multicomponent chemical equilibria based on equilibrium constants. Geochim. Cosmochim. Acta 1975, 39, 1375–1384. [Google Scholar] [CrossRef]
  39. Li, M.Z.; Zhou, J.M.; Tong, C.R.; Zhang, W.H.; Chen, Z.; Wang, J.L. Thermodynamic Modeling and Optimization of the Copper Flash Converting Process Using the Equilibrium Constant Method. Metall. Mater. Trans. B-Process Metall. Mater. Process. Sci. 2018, 49, 1794–1807. [Google Scholar] [CrossRef]
  40. Li, M.; Feng, Y.; Chen, X. Thermodynamic Simulation Model of Copper Side-Blown Smelting Process. Metals 2024, 14, 840. [Google Scholar] [CrossRef]
  41. Chen, X.Z.; Li, M.Z.; Liu, F.P.; Huang, J.D.; Yang, M.H. Multi-Phase Equilibrium Model of Oxygen-Enriched Lead Oxidation Smelting Process Based on Chemical Equilibrium Constant Method. Processes 2023, 11, 3043. [Google Scholar] [CrossRef]
  42. Chen, S.; Zhang, J.; Wang, Y.; Wang, T.; Li, Y.; Liu, Z. Thermodynamic Study of H2-FeO Based on the Principle of Minimum Gibbs Free Energy. Metals 2023, 13, 225. [Google Scholar] [CrossRef]
  43. Wang, J.; Wen, X.; Zhang, C. Thermodynamic model of lead oxide activity in PbO–CaO–SiO2–FeO–Fe2O3 slag system. Trans. Nonferrous Met. Soc. China 2015, 25, 1633–1639. [Google Scholar] [CrossRef]
  44. Maruoka, N.; Ueda, S.; Shibata, H.; Yamaguchi, K.; Kitamura, S.-y. Thermodynamic properties of lead oxide in a mixture of stainless steelmaking and nonferrous smelting slags. High Temp. Mater. Process. 2012, 31, 273–279. [Google Scholar] [CrossRef]
  45. Tan, P.; Zhang, C.; Zhang, R. Computer model of QSL lead smelting process. J. Cent. South Univ. Technol. 1996, 27, 543–546. [Google Scholar]
  46. Wang, J.; Zhang, C.; Zhang, W. Multi-phase equilibrium model of lead flash smelting process. J. Cent. South Univ. Sci. Technol 2012, 43, 429–434. [Google Scholar]
Figure 1. Oxygen-enriched side-blown furnace [40]: 1—molten pool; 2—feed opening; 3—furnace stack; 4—flue gas; 5—furnace slag; 6—slag basin; 7—fire bricklayer; 8—blast main; 9—side-wall water jacket; 10—tuyere; 11—antimony-rich lead layer; and 12—antimony-rich lead pool.
Figure 1. Oxygen-enriched side-blown furnace [40]: 1—molten pool; 2—feed opening; 3—furnace stack; 4—flue gas; 5—furnace slag; 6—slag basin; 7—fire bricklayer; 8—blast main; 9—side-wall water jacket; 10—tuyere; 11—antimony-rich lead layer; and 12—antimony-rich lead pool.
Processes 13 01244 g001
Figure 2. Calculation flowchart of thermodynamic model [41].
Figure 2. Calculation flowchart of thermodynamic model [41].
Processes 13 01244 g002
Figure 3. Multiphase equilibrium calculation system for the antimony–lead synergistic side-blown oxidation smelting process.
Figure 3. Multiphase equilibrium calculation system for the antimony–lead synergistic side-blown oxidation smelting process.
Processes 13 01244 g003
Figure 4. Distribution rate of accessory elements in the product: (a) Pb, (b) Sb, (c) Zn, (d) As, (e) Cu, (f) Fe, (g) Ag, (h) S, and (i) Bi.
Figure 4. Distribution rate of accessory elements in the product: (a) Pb, (b) Sb, (c) Zn, (d) As, (e) Cu, (f) Fe, (g) Ag, (h) S, and (i) Bi.
Processes 13 01244 g004
Table 1. Equilibrium reactions and equilibrium constants of the antimony–lead synergistic side-blown oxidation melting system.
Table 1. Equilibrium reactions and equilibrium constants of the antimony–lead synergistic side-blown oxidation melting system.
Equilibrium ReactionKjEquilibrium ReactionKj
2Pb(ALd) + O2(Gas) = 2PbO(Sl)K1As2O3(Sl) = As2O3(Gas)K15
PbS(ALd) + O2(Gas) = Pb(Sl) + SO2(Gas)K2Sb2O3(Sl) = Sb2O3(Gas)K16
2Zn(ALd) + O2(Gas) = 2ZnO(Sl)K36FeO(Sl) + O2(Gas) = 2Fe3O4(Sl)K17
4Sb(ALd) + 3O2(Gas) = 2Sb2O3(Sl)K42FeS(Sl) + 3O2(Gas) = 2FeO(Sl) + 2SO2(Gas)K18
4As(ALd) + 3O2(Gas) = 2As2O3(Sl)K52Cu2S(Sl) + 3O2(Gas) = 2Cu2O(Sl) + 2SO2(Gas)K19
Pb(ALd) = Pb(Gas)K62PbO(Sl) + O2(Gas) + 2SO2(Gas) = 2PbSO4(Sl)K20
Ag(ALd) = Ag(Sl)K72ZnS(Gas) + 3O2(Gas) = 2ZnO(Gas) + 2SO2(Gas)K21
2Cu2S(Sl) + 3O2(Gas) = 2Cu2O(Sl) + 2SO2(Gas)K82Zn(Gas) + O2(Gas) = 2ZnO(Gas)K22
2FeS(ALd) + 3O2(Gas) = 2FeO(Sl) + 2SO2(Gas)K92As2S3(Gas) + 9O2(Gas) = 2As2O3(Gas) + 6SO2(Gas)K23
2Pb(Gas) + O2(Gas) = 2PbO(Gas)K102Sb2S3(Gas) + 9O2(Gas) = 2Sb2O3(Gas) + 6SO2(Gas)K24
PbS(Sl) + 2PbO(Sl) = 3Pb(ALd) + SO2(Gas)K11S2(Gas) + 2O2(Gas) = 2SO2(Gas)K25
PbS(Sl) = PbS(Gas)K122CO(Gas) + O2(Gas) = 2CO2(Gas)K26
ZnO(Sl) = ZnO(Gas)K134Sb(ALd) + 5O2(Gas) = 2Sb2O5(Sl)K27
4Bi(ALd) + 3O2(Gas) = 2Bi2O3(Sl)K14
Table 2. Chemical composition of lead concentrate (wt.%).
Table 2. Chemical composition of lead concentrate (wt.%).
PbZnBiCuFeCaSiO2
40.765.900.540.6514.802.18.58
SMgAlCOOther
16.301.140.260.637.311.03
Table 3. Chemical composition of antimony–gold concentrate (wt.%).
Table 3. Chemical composition of antimony–gold concentrate (wt.%).
SbFeSSiO2AsAlAgOOther
40.553.5027.0012.3413.820.361.220.320.90
Table 4. Chemical composition of quartz (wt.%).
Table 4. Chemical composition of quartz (wt.%).
SiO2CaOFeOOOther
85.005.002.640.297.07
Table 5. Chemical composition of limestone (wt.%).
Table 5. Chemical composition of limestone (wt.%).
FeOCaOSiO2OOther
0.4953.001.040.4345.04
Table 6. Phase composition of mixed ore (wt.%).
Table 6. Phase composition of mixed ore (wt.%).
PbSZnSCu2SFe2O3FeSCaCO3SiO2AgAl2O3
37.236.960.6410.050.534.158.180.120.46
As2S3Sb2S3Bi2S3MgOH2OFeS2CaOOther
2.305.730.534.156.9510.121.982.60
Table 7. Calculation results of the main technical index.
Table 7. Calculation results of the main technical index.
NameUnitValueNameUnitValue
Pb content of ALd%89.29Pb content of dust%38.03
Sb content of ALd%3.24Sb content of dust%5.05
Direct yield of Pb%20.62Temperature of ALd°C829
Direct yield of Sb%5.87Temperature of slag°C1129
FeO/SiO2 in slag 2.30Temperature of gas°C1149
CaO/SiO2 in slag 0.53Yield of dust%13.88
CaO/FeO in slag 0.28Yield of ALd%6.18
Pb content of slag%32.88Yield of slag%48.60
Sb content of slag%5.34Yield of gas%33.69
Table 8. Calculation results of heat balance.
Table 8. Calculation results of heat balance.
TypeHeat TypeMaterial NameTemperature (°C)Heat Quantity (MJ/h)Ratio of Heat
(%)
Heat incomePhysical heatMixed ore250.000.00
Industrial oxygen250.000.00
Air250.000.00
Chemical heat 2559,250.37100.00
Exchange heatCooling inlet water37
Total 55,250.37100.00
Heat outcomePhysical heatAntimony-rich crude lead829480.120.81
Smelting slag112923,199.9239.16
Flue gas114924,207.0340.86
Dust11495186.278.75
Exchange heatCooling outlet water38836.391.41
Natural heat
dissipation
605340.649.01
Total 59,250.37100.00
Table 9. Calculation results and industrial data (wt.%).
Table 9. Calculation results and industrial data (wt.%).
ValuePhasePbSbZnCuFeCaOSiO2SAsBiAg
CalculatedALd89.2923.2380.0021.3190--0.3480.5554.2840.903
Measured85.1763.370-1.318----0.5534.3660.892
CalculatedSl32.8755.2756.5900.56217.0626.08311.5581.2822.060.070.061
Measured33.3515.8157.1600.51917.5256.05011.7971.2262.0640.0700.058
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Zhong, Z.; Li, M.; Feng, Y.; Ma, B.; Chen, X.; Zhang, Z. Research on the Thermodynamic Simulation Model of Antimony–Lead Synergistic Side-Blown Oxidation Smelting Process Based on MetCal. Processes 2025, 13, 1244. https://doi.org/10.3390/pr13041244

AMA Style

Zhong Z, Li M, Feng Y, Ma B, Chen X, Zhang Z. Research on the Thermodynamic Simulation Model of Antimony–Lead Synergistic Side-Blown Oxidation Smelting Process Based on MetCal. Processes. 2025; 13(4):1244. https://doi.org/10.3390/pr13041244

Chicago/Turabian Style

Zhong, Zhenquan, Mingzhou Li, Yuchen Feng, Baozhong Ma, Xinzhou Chen, and Zhongtang Zhang. 2025. "Research on the Thermodynamic Simulation Model of Antimony–Lead Synergistic Side-Blown Oxidation Smelting Process Based on MetCal" Processes 13, no. 4: 1244. https://doi.org/10.3390/pr13041244

APA Style

Zhong, Z., Li, M., Feng, Y., Ma, B., Chen, X., & Zhang, Z. (2025). Research on the Thermodynamic Simulation Model of Antimony–Lead Synergistic Side-Blown Oxidation Smelting Process Based on MetCal. Processes, 13(4), 1244. https://doi.org/10.3390/pr13041244

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