A multi-component copper smelting slag activity calculation method and process optimization system

Abstract

This invention belongs to the field of copper pyrometallurgical technology, specifically relating to a method for calculating the activity of multi-component copper smelting slag and a process optimization system. The method obtains the mole fraction of slag components, operating temperature T, and molten pool oxygen potential, and utilizes a function including binary and ternary interaction coefficients and an oxygen potential-temperature correction function f(,T)·δ. i The mathematical model accurately calculates the component activity α. i The system includes modules for data acquisition, activity calculation, and optimization recommendation. Based on a comparison of the activity calculation results with a preset target range, it can automatically recommend optimized schemes for process parameters such as slag-forming agent ratio and operating temperature. This invention is applicable to copper smelting conditions ranging from 1150℃ to 1350℃, with an activity prediction error of less than 8%. It can effectively guide production control, achieving the effects of reducing copper content in slag and increasing direct copper recovery.

Description

Technical Field

[0001] This invention belongs to the field of copper pyrometallurgical technology, and particularly relates to a method for calculating the activity of multi-component slag suitable for high oxygen-enriched molten pool smelting conditions and a process optimization system based on this method. Background Technology

[0002] In modern copper smelting, oxygen-enriched smelting technology has become the mainstream process due to its high efficiency and energy saving. However, while high oxygen enrichment increases production capacity and matte grade, it also easily leads to localized over-oxidation of the molten pool, generating high-melting-point Fe3O4. This, in turn, causes increased slag viscosity, deteriorated fluidity, and difficulty in slag-gold separation, ultimately resulting in a series of production problems such as increased copper loss in the slag and intensified furnace lining erosion. The physicochemical properties of the slag, especially the activity of its key components (such as SiO2 and FeO), are the core thermodynamic parameters that determine its melting point, viscosity, and slag-copper partitioning behavior. Therefore, accurately and quickly obtaining slag activity data under specific operating conditions and using this data to guide process adjustments is of decisive significance for achieving efficient, low-consumption, and stable copper smelting production.

[0003] Currently, obtaining the activity of slag components mainly relies on high-temperature equilibrium experiments (such as the chemical equilibrium method and the double crucible reference method) or calculations based on commercial thermodynamic software. While experimental methods are accurate, they are time-consuming and costly, failing to meet the needs of real-time production control. Commercial software, on the other hand, is typically a "black box" tool; its internal model is not transparent and it fails to incorporate key process parameters that can be directly sensed and controlled at the smelting site—such as the oxygen potential of the molten pool. Activity is introduced as an independent, explicit variable in the activity calculation model. Most existing theoretical models are designed for simple slag systems or are based on ideal solution assumptions. For complex multi-component slag systems in actual smelting, such as CaO-MgO-SiO2-Al2O3-FeO, especially under high oxygen-enriched conditions, their prediction accuracy is often insufficient. Furthermore, existing process control relies heavily on operator experience and lacks effective tools to quantitatively correlate microscopic thermodynamic parameters such as activity with macroscopic process parameters (such as slag-forming agent ratio, oxygen enrichment concentration, and operating temperature) and achieve closed-loop optimization. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method and process optimization system for calculating the activity of multi-component copper smelting slag that is highly accurate, adaptable, and directly applicable to production optimization. This method achieves rapid and accurate calculation of the activity of complex slag systems by constructing a thermodynamic model that explicitly includes a correction term for the oxygen potential of the molten pool. The system combines the calculation method with process logic, automatically recommending optimized operation schemes based on the activity calculation results.

[0005] To achieve the above objectives, the present invention first provides a method for calculating the slag activity in multi-element copper smelting, comprising the following steps:

[0006] Obtain the molar fraction of components, operating temperature T, and molten pool oxygen potential of the copper smelting slag to be evaluated. The activity α of component i in the slag is calculated according to the following mathematical model. i :lnα i = ln x i + Σ j ε ij x j + Σ j , k ρ ijk x j x k + f( T)· δ i , where x i ε is the mole fraction of component i. ij The binary activity interaction coefficient ρ is used to characterize the interaction between component i and component j. ijk The ternary activity interaction coefficient, f(, is used to characterize the interaction between components i, j, and k.) T) is the oxygen potential of the molten pool The correction function related to the operating temperature T, δ i For component i, the correction function f( The sensitivity coefficient of , T).

[0007] Furthermore, the component i is selected from at least one of CaO, MgO, SiO2, Al2O3 and FeO, and the slag is a multi-component slag system containing CaO, MgO, SiO2, Al2O3 and FeO.

[0008] Furthermore, the correction function f( (, T) satisfy the following relation: f( T) = log 10 ( ) + A / T + C, where A ranges from 14000 to 16000, and C is a constant.

[0009] Furthermore, the binary activity interaction coefficient ε ij and the ternary activity interaction coefficient ρ ijk The following steps are used to obtain: A reference dataset of component activities of the multi-component slag system under different compositions, temperatures, and oxygen potentials is acquired, wherein the reference dataset is derived from thermodynamic software calculations and / or experimental measurements; Multiple regression analysis is used to analyze the ε in the formula based on the reference dataset. ijand ρ ijk The fitting process is used to determine the result.

[0010] This invention also provides a process optimization system for multi-group copper smelting, specifically including:

[0011] The data acquisition module is used to obtain the molar fraction of components in the slag, the operating temperature T, and the oxygen potential of the molten pool. ;

[0012] The activity calculation module, which pre-stores the aforementioned mathematical model and its parameters, is used to calculate the activity α of the target component in the slag under the current or predetermined operating conditions. i ;

[0013] The optimization recommendation module is connected to the activity calculation module and is used to receive the activity α. i The optimization recommendation module has a preset target SiO2 activity range and is configured to: compare the calculated SiO2 activity with the target SiO2 activity range; when the calculated SiO2 activity is not within the target SiO2 activity range, adjust at least one parameter input to the activity calculation module based on the mathematical model, and generate an optimized parameter recommendation value based on the recalculated activity result.

[0014] Furthermore, the target SiO2 activity ranges from 0.15 to 0.20.

[0015] Furthermore, the optimized recommendation module is specifically configured to execute at least one of the following rules:

[0016] (a) If the calculated SiO2 activity is less than 0.15, a recommendation is made to increase the proportion of CaO and / or decrease the proportion of Al2O3.

[0017] (b) If the calculated SiO2 activity is below 0.15, a recommendation to increase the operating temperature is generated;

[0018] (c) If the calculated FeO activity is higher than 0.80, a recommendation to reduce the oxygen enrichment concentration is generated.

[0019] Furthermore, the slag has an iron-silicon ratio of 1.8 to 2.0, a CaO content of 2.5 wt% to 3.2 wt%, a MgO content of 1.4 wt% to 1.6 wt%, and an Al2O3 content of 4.0 wt% to 4.5 wt%.

[0020] Furthermore, the operating temperature T is between 1150°C and 1350°C.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] (1) High prediction accuracy: By introducing the oxygen potential of the molten pool ( By incorporating correction functions and interaction coefficients directly related to temperature, the activity calculation model constructed in this invention can accurately reflect the complex physicochemical behavior of multi-component slag under high oxygen-enriched conditions. Its prediction error for the activity of key components is less than 8%, effectively solving the problem of insufficient accuracy of traditional models under such complex conditions.

[0023] (2) Strong practicality and adaptability: The model and system are specifically developed for the CaO-MgO-SiO2-Al2O3-FeO multi-element slag system and high oxygen enrichment conditions commonly used in modern copper smelting. Its effective parameter range covers typical production conditions (such as 1150-1350℃), providing a reliable tool for activity assessment and process analysis of complex industrial systems.

[0024] (3) Forming a closed-loop optimization with significant economic benefits: This invention combines precise activity calculation with process parameter adjustment logic to construct an intelligent system capable of real-time diagnosis and reverse optimization. Applying this system, the adjustment of process parameters such as slag-forming agent ratio and operating temperature can be directly guided based on the activity results, thereby achieving optimized control of slag properties. Practical application shows that this method helps reduce the copper content in the slag by approximately 0.15-0.2%, increases the direct copper recovery rate by approximately 1.2-1.8%, and improves slag fluidity, thus bringing significant comprehensive economic benefits.

[0025] (4) Convenient and efficient application: This invention transforms the complex thermodynamic calculation of slag into a mathematical model and software system that can be executed quickly, replacing expensive and time-consuming experimental determination, and providing core algorithms and practical tools for intelligent monitoring and refined management of the copper smelting process. Detailed Implementation

[0026] The present invention will be described in detail below through specific embodiments. These embodiments are intended to clearly demonstrate the specific implementation process of the technical solutions claimed in the claims, and are not intended to limit the present invention. Those skilled in the art can make various modifications based on the inventive concept, and all such modifications fall within the protection scope of the present invention.

[0027] Example 1

[0028] First, the target slag system was determined to be a typical CaO-MgO-SiO2-Al2O3-FeO pentagonal system used in copper smelting. To obtain a reference dataset for model parameter fitting, two approaches were adopted: one was to use FactSage 8.2 thermodynamic software and its FToxid database to systematically calculate the oxygen potential of this slag system at three temperature points: 1150℃, 1250℃, and 1350℃. From 10 - 10 atm to 10 -6Atm, as well as the equilibrium activity of each component within a wide range of compositional variations, generated more than 2,000 sets of calculation data; secondly, in the laboratory, through a high-temperature double crucible reference equilibrium experiment at 1350℃, the SiO2 and FeO activities of several key component points were measured, and about 50 sets of experimental data were obtained for verification and calibration.

[0029] After obtaining the above reference dataset, multiple regression analysis was used to analyze the core mathematical model lnα. i = ln x i +Σ j ε ij x j + Σ j , k ρ ijk x j x k + f( , T) · δ i Perform global parameter fitting. The specific form of the correction function f(PO2, T) is f( , T) = log 10 ( The fitting process aims to minimize the overall error between the model's predicted values ​​and the reference dataset, optimizing the solution of all binary activity interaction coefficients ε. ij The key ternary coefficient ρ ijk The sensitivity coefficient δ of each component to oxygen potential i And constants A and C. Through iterative calculations, an optimal set of parameters was finally determined. In particular, the fitted value of constant A is 15237.

[0030] To verify the model's accuracy, a subset of independent data not involved in the fitting was used for testing. The results show that for the prediction of SiO2 and FeO activities, the average relative error between the model's calculations and the values ​​calculated by FactSage software and experimental measurements is less than 8%, especially at high oxygen potentials (…). >10 -8 Under the condition of f(atm), the predicted stability is significantly better than the traditional model that ignores the oxygen potential correction term. This fully demonstrates the effectiveness of introducing f(atm). The correction function (T) plays a crucial role in improving the accuracy of activity prediction under complex oxidation conditions.

[0031] Example 2

[0032] This embodiment demonstrates the real-time control application of a process optimization system on a production line. In a double-sided blowing pool smelting operation at a copper smelter, the system's data acquisition module collected a set of operating parameters in real time: the slag chemical composition, after analysis, was SiO2 22.1 wt%, CaO 2.7 wt%, MgO 1.5 wt%, Al2O3 4.3 wt%, FeO 69.4 wt%; the operating temperature T was 1310℃; and the oxygen potential of the molten pool was measured by online sensors. 1.2×10 -7 These data are transmitted to the system's activity calculation module, which pre-stores the complete set of model parameters determined in Example 1. The mathematical model is immediately invoked for calculation. The instantaneous output results are: SiO2 activity α(SiO2) = 0.124, FeO activity α(FeO) = 0.83.

[0033] The calculation results are then sent to the optimization recommendation module. This module has a preset target SiO2 activity range of 0.15 to 0.20. The current α(SiO2) = 0.124 is below the target lower limit. According to the module's built-in configuration rules: (a) if the calculated SiO2 activity is below 0.15, a recommendation to increase the CaO addition ratio and / or decrease the Al2O3 addition ratio is generated; (b) if the calculated SiO2 activity is below 0.15, a recommendation to increase the operating temperature is generated; (c) if the calculated FeO activity is above 0.80, a recommendation to decrease the oxygen enrichment concentration is generated.

[0034] The system triggers the judgment logic: the conditions of rules (a) and (b) are met (SiO2 activity is below 0.15), and the condition of rule (c) is not met (FeO activity is above 0.80, 0.83). Therefore, the optimization recommendation module generates a preliminary diagnosis and optimization suggestion: "The current SiO2 activity of the slag (0.124) is too low, and there is a risk of over-oxidation. The system suggests performing at least one of the following operations: (1) increase the CaO addition ratio; (2) decrease the Al2O3 addition ratio; (3) increase the operating temperature."

[0035] To provide further quantitative guidance, the system initiated an iterative simulation. Assuming the operator prioritized adjusting the slag-forming agent ratio, the system, based on the model, automatically increased the CaO content in the simulation input parameters while keeping temperature, oxygen potential, and other components constant. When the simulation increased the CaO content to 3.1 wt% and fine-tuned the Al2O3 content to 4.1 wt%, the recalculated α(SiO2) was 0.152, successfully entering the target range. At this point, the optimization recommendation module output the final quantitative optimization recommendation value: "It is recommended to adjust the batching to increase the CaO content in the slag to approximately 3.1 wt% and control the Al2O3 content at approximately 4.1 wt%." After production personnel made adjustments based on this recommendation, on-site observation confirmed that the slag fluidity improved.

[0036] Example 3

[0037] This example demonstrates the application of a process optimization system in the design phase of a new process scheme, specifically determining the optimal slag type through reverse calculation. The plant plans to implement a process enhancement scheme: increasing the matte grade from 62% to 66%, and simultaneously increasing the oxygen enrichment concentration in the blower from 68% to 72%. Preliminary heat balance calculations indicate that under this enhanced operating condition, the molten pool operating temperature needs to be maintained at 1300℃, corresponding to a certain molten pool oxygen potential. It will rise to approximately 6.0 × 10 -7 atm.

[0038] To pre-design a slag profile capable of stable operation under these high-intensity oxidation conditions, technicians used this optimization system for reverse engineering. Constraints were set in the system: fixed temperature T = 1300℃, oxygen potential... =6.0×10 -7 atm; The optimization objective is to stabilize the predicted SiO2 activity α (SiO2) within the ideal range of 0.15-0.20; At the same time, based on the characteristics of the raw materials and the refractoriness requirements of the furnace lining, the search range is limited to the advantageous region defined in claim 8: iron-silicon ratio 1.8-2.0, CaO content 2.5-3.2 wt%, MgO content 1.4-1.6 wt%, and Al2O3 content 4.0-4.5 wt%.

[0039] After system startup, the optimization and recommendation module generates a large number of candidate slag mix proportions within a given composition range using optimization algorithms (such as genetic algorithms and gradient descent methods). It then iteratively calls the activity calculation module to quickly evaluate the predicted α(SiO2) and α(FeO) for each mix proportion. After multiple rounds of iterative screening and convergence calculations, the system finally outputs an optimal recommended slag mix: iron-silicon ratio 2.0, CaO content 3.0 wt%, MgO content 1.5 wt%, and Al2O3 content 4.0 wt%. The system report shows that using this slag mix, under the target operating conditions, the model predicts α(SiO2) to be 0.161 and α(FeO) to be 0.77, fully meeting the preset targets, and the FeO activity is far from the high-limit warning value.

[0040] The plant adopted the recommended slag type for process adjustments and put it into trial production. Actual operating data shows that, under conditions of achieving high matte grade (66%) and high oxygen concentration (72%), the average copper content in the slag remained stable at 0.59%, the direct copper recovery rate increased by 1.8% compared to the original process, and the furnace conditions were stable with no acceleration in the furnace lining erosion rate. This fully verifies the effectiveness and reliability of the present invention's system for reverse engineering and optimizing process parameters based on a thermodynamic model.

[0041] The above description is a preferred embodiment of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for calculating the activity of slag in multi-component copper smelting, characterized in that, Includes the following steps: Obtain the molar fraction of components, operating temperature T, and molten pool oxygen potential of the copper smelting slag to be evaluated. ; The activity α of component i in the slag is calculated based on the following mathematical model. i : lnα i = ln x i + S j e ij x j + S j , k r ijk x j x k + f( , T) · d i Where, x i ε is the mole fraction of component i. ij The binary activity interaction coefficient ρ is used to characterize the interaction between component i and component j. ijk The ternary activity interaction coefficient, f(, is used to characterize the interaction between components i, j, and k.) T) is the oxygen potential of the molten pool The correction function related to the operating temperature T, δ i For component i, the correction function f( The sensitivity coefficient of , T).

2. The method for calculating the activity of slag according to claim 1, characterized in that, The component i is selected from at least one of CaO, MgO, SiO2, Al2O3 and FeO, and the slag is a multi-component slag system containing CaO, MgO, SiO2, Al2O3 and FeO.

3. The method for calculating the activity of slag according to claim 2, characterized in that, The correction function f(P) O2 (T) satisfies the following relationship: f( , T) = log 10 ( ) + A / T + C Where A ranges from 14000 to 16000, and C is a constant.

4. The method for calculating the activity of slag according to claim 1, characterized in that, The binary activity interaction coefficient ε ij and the ternary activity interaction coefficient ρ ijk Obtained through the following steps: Obtain a reference dataset of component activity of the multi-component slag system under different compositions, temperatures and oxygen potentials, wherein the reference dataset is derived from thermodynamic software calculation data and / or experimental measurement data; Using multiple regression analysis, based on the reference dataset, the ε in the formula is... ij and ρ ijk The fitting process is used to determine the result.

5. A process optimization system for multi-group copper smelting, characterized in that, include: The data acquisition module is used to obtain the molar fraction of components in the slag, the operating temperature T, and the oxygen potential of the molten pool. ; The activity calculation module, which pre-stores the mathematical model and its parameters as described in any one of claims 1 to 4, is used to calculate the activity α of the target component in the slag under the current or predetermined working conditions. i ; The optimization recommendation module is connected to the activity calculation module and is used to receive the activity α. i The optimization recommendation module has a preset target SiO2 activity range and is configured to: compare the calculated SiO2 activity with the target SiO2 activity range; when the calculated SiO2 activity is not within the target SiO2 activity range, adjust at least one parameter input to the activity calculation module based on the mathematical model, and generate an optimized parameter recommendation value based on the recalculated activity result.

6. The process optimization system according to claim 5, characterized in that, The target SiO2 activity range is 0.15 to 0.

20.

7. The process optimization system according to claim 6, characterized in that, The optimized recommendation module is specifically configured to execute at least one of the following rules: (a) If the calculated SiO2 activity is less than 0.15, a recommendation is made to increase the proportion of CaO and / or decrease the proportion of Al2O3. (b) If the calculated SiO2 activity is below 0.15, a recommendation to increase the operating temperature is generated; (c) If the calculated FeO activity is higher than 0.80, a recommendation to reduce the oxygen enrichment concentration is generated.

8. The process optimization system according to claim 5, characterized in that, The slag has an iron-silicon ratio of 1.8 to 2.0, a CaO content of 2.5 wt% to 3.2 wt%, a MgO content of 1.4 wt% to 1.6 wt%, and an Al2O3 content of 4.0 wt% to 4.5 wt%.

9. The process optimization system according to claim 5, characterized in that, The operating temperature T is 1150°C to 1350°C.

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