Copper smelting method and system based on co-processing of cyanidation tailings

Abstract

The invention provides a copper smelting method and system based on co-processing of cyanidation tailings. The method comprises the following steps: constructing a raw material component database comprising cyanidation tailings, copper concentrates and auxiliary materials, and setting a process target; performing iterative optimization calculation according to a set process target by taking the set amount of put cyanidation tailings as an initial condition, and outputting an optimal batching scheme; and smelting is conducted according to the obtained optimal batching scheme and the process characteristics of the target smelting furnace, cyanide is thoroughly decomposed, and valuable metal is recycled. The system provided based on the method comprises a database unit, a target and constraint setting unit, an iterative optimization calculation unit and a visualization output unit. According to the method, on the basis of the synergistic effect between the cyanidation tailings and copper smelting, the valuable metal in the tailings is effectively recovered while cyanide in the tailings is thoroughly decomposed in the copper smelting process; in addition, the cyanidation tailings can replace part of flux, so that the smelting cost is reduced.

Description

Technical Field

[0001] This invention relates to a method for the resource utilization of cyanide tailings, specifically to a copper smelting method and system based on the co-processing of cyanide tailings, belonging to the field of non-ferrous metal metallurgical technology. Background Technology

[0002] Cyanide tailings are solid waste generated after gold concentrate is extracted using the cyanidation process. They are reddish due to their high content of iron oxides (TFe content can reach 35%), hence the name "red slag." my country is the world's largest gold producer. According to the latest data, in 2024, China's refined gold production reached 534.1 tons, of which approximately 70% was refined using the cyanidation process. Based on this, it is estimated that the annual amount of cyanide tailings generated reached approximately 195 million tons, with historical stockpiles exceeding 150 million tons. Cyanide tailings have a complex composition, containing not only a certain amount of residual gold (usually 0.5~2 g / t), silver (10~50 g / t), and other precious metals, but also valuable metals such as copper, lead, and zinc. Furthermore, due to the presence of highly toxic residual cyanide (usually in the range of 200-1000 mg / kg) and various heavy metals (such as lead and arsenic), it is classified as hazardous waste (HW33, 092-003-33). Currently, the main method for treating cyanide tailings is landfilling. However, this method is costly (safe landfill disposal costs are typically over a thousand yuan per ton), occupies land resources, and poses long-term environmental risks, failing to completely solve the cyanide pollution problem. Furthermore, a certain amount of valuable metals cannot be recycled, resulting in resource waste.

[0003] On the other hand, modern copper smelting is a high-temperature, strong oxidation / reduction physicochemical process. Its furnace temperature can reach 1200℃. Therefore, in order to protect the inner wall and reduce the risk of ablation, the furnace charge must contain slag-forming components such as iron, calcium, and silicon. In the current technology, tens to hundreds of kilograms of iron-containing flux need to be purchased for each ton of copper concentrate smelted. This part of the cost expenditure is unavoidable. Summary of the Invention

[0004] To address the problems existing in the prior art, the first objective of this invention is to provide a copper smelting method based on the co-processing of cyanide tailings. This method utilizes the synergistic effect between cyanide tailings and copper smelting, taking advantage of the high temperature and high redox characteristics of copper smelting to thoroughly decompose cyanide in the tailings while effectively recovering valuable metals. Furthermore, the cyanide tailings also contain a large amount of silicon, calcium, and other slag-forming components essential for copper smelting, further replacing some traditional fluxes and thus reducing smelting costs.

[0005] The second objective of this invention is to provide a copper smelting system based on the co-processing of cyanide tailings. Compared with traditional experience-based batching or simple substitution methods, the system achieves precision, optimization and intelligence in the raw material batching process through iterative optimization of the calculation unit, and quickly responds to the impact of passive changes in the composition of cyanide tailings on the furnace condition.

[0006] To achieve the above-mentioned technical objectives, the present invention provides a copper smelting method based on the co-processing of cyanide tailings, comprising:

[0007] Step S1: Construct a raw material composition database including cyanide tailings, copper concentrate and auxiliary materials, and set process targets including matte grade and smelting temperature.

[0008] Step S2: Using the set amount of cyanide tailings to be added as the initial condition, perform iterative optimization calculations based on the process objectives set in Step S1, and output the optimal batching scheme.

[0009] Step S3: Smelt according to the optimal batching scheme obtained in step S2 and the process characteristics of the target smelting furnace to completely decompose the cyanide and recover valuable metals.

[0010] As a preferred embodiment, the raw materials include copper concentrate, flux, sulfiding agent, fuel and cyanide tailings, wherein the cyanide tailings account for 1 to 10% of the total dry weight of the raw materials.

[0011] As a preferred embodiment, the process objectives include matte grade, slag iron-silicon ratio, slag type, and smelting temperature.

[0012] As a preferred embodiment, the flux is limestone and / or quartz.

[0013] As a preferred option, the sulfiding agent includes pyrite.

[0014] As a preferred option, the fuel includes coke.

[0015] As a preferred embodiment, the iterative optimization calculation process is as follows: input the fixed component data of the raw materials, take the set process target as the objective, take the set amount of cyanide tailings to be added as the initial condition, dynamically adjust the proportion of other materials through iterative algorithms, perform target optimization calculation based on material balance and heat balance models, and output the globally optimal batching scheme.

[0016] One of the core steps of this invention is to use an iterative optimization algorithm based on a material balance and heat balance model to calculate the globally optimal batching scheme under the condition of fixed addition of cyanide tailings.

[0017] The iterative optimization calculation process is as follows:

[0018] 1. Input and initialization: Input the fixed composition data of each material and use the preset amount of cyanide tailings as the fixed initial condition; set multiple optimization objectives including copper matte grade and production cost, as well as multiple process constraints including slag iron-silicon ratio, copper matte grade target range, thermal equilibrium temperature range, and impurity volatilization rate threshold.

[0019] 2. Iterative optimization calculation: Starting from the initial conditions, the algorithm enters the iterative solution process:

[0020] 1) Scheme generation and simulation: Based on the current material ratios, and according to the material balance model and heat balance model, calculate and predict the matte grade, slag iron-silicon ratio, smelting temperature and impurity volatilization rate;

[0021] 2) Constraint compliance judgment: Compare the prediction results of step 1) with the set multiple constraints to determine whether the current allocation scheme meets all constraints;

[0022] 3) Objective function evaluation and scheme adjustment: If the current scheme satisfies all constraints, calculate its comprehensive objective function value for "maximizing the grade of matte" and "minimizing the production cost", and compare and update it with the historical best value;

[0023] 4) Subsequently, under the premise that the amount of cyanide tailings is fixed, the algorithm generates a new set of other material ratios according to a predetermined optimization strategy (such as gradient descent or the solution direction of linear programming), and returns to step 1) for the next round of calculation;

[0024] If the current solution does not meet all constraints, the algorithm first adjusts the proportions of relevant materials according to the type of unmet constraint (such as insufficient thermal balance or excessive iron-silicon ratio) to repair the constraints, and then returns to step 1).

[0025] Convergence and Output: Repeat steps 1) to 3) until one of the following convergence conditions is met: the improvement in the objective function value is less than a set threshold for a certain number of consecutive iterations, or the preset maximum number of iterations is reached. At this point, output the ingredient ratio scheme with the optimal comprehensive objective function value from the historical records;

[0026] 3. Output optimal ratio: The module outputs the suggested dry basis feed amount of each material after the above optimization calculation.

[0027] As a preferred embodiment, the cyanide tailings need to be pretreated before smelting. The process is as follows: the cyanide tailings are dried to a moisture content of ≤15%, and then crushed and granulated in sequence to obtain the final product.

[0028] As a preferred embodiment, the target smelting furnace is one of a flash furnace, a side-blown furnace, and a blast furnace.

[0029] As a preferred embodiment, the smelting process is carried out under oxygen-enriched conditions at 1100~1300℃.

[0030] As a preferred embodiment, when the target smelting furnace is a flash furnace or a side-blown furnace, the method of feeding the raw materials into the furnace during smelting is as follows: the raw materials are weighed according to the optimal batching scheme, fully mixed, and then granulated or directly injected into the furnace through a spray gun.

[0031] As a preferred embodiment, when the target smelting furnace is a blast furnace, the method of feeding the raw materials into the furnace during smelting is as follows: after weighing the raw materials according to the optimal batching scheme, they are added in layers in the order of coke-flux-lump material.

[0032] As a preferred embodiment, the process of complete cyanide decomposition is as follows: the cyanide in the cyanide tailings is completely oxidized and decomposed into metal oxides and non-toxic small molecule gases, and the valuable metal components in the tailings, including copper, lead and zinc, are oxidized and enter the slag phase or volatile phase.

[0033] Precious metals such as copper, gold, and silver in the tailings are mainly reduced and enriched in the matte phase, and are recovered along with the matte; metals such as iron participate in the slag-forming reaction in the form of oxides and enter the slag phase; volatile metals such as lead and zinc volatilize into the flue gas, and are collected by the subsequent tail gas treatment system for centralized recovery and treatment.

[0034] The present invention also provides a copper smelting system based on the co-processing of cyanide tailings, for implementing the copper smelting method described in any of the above-mentioned methods; the copper smelting system includes a database unit, a target and constraint setting unit, an iterative optimization calculation unit, and a visualization output unit.

[0035] Compared with the prior art, the beneficial technical effects of the technical solution provided by the present invention are as follows:

[0036] 1) The method provided by this invention utilizes the synergistic effect between cyanide tailings and copper smelting. Taking advantage of the high temperature and strong oxidizing properties in the copper smelting process, it can effectively recover valuable metals while completely decomposing cyanide in the tailings. In addition, the cyanide tailings also contain a large amount of silicon, calcium and other slag-forming components that are necessary in the copper smelting process, which can further replace some of the traditional flux, thereby reducing smelting costs.

[0037] 2) The system provided by this invention, through iterative optimization of the calculation unit, achieves precision, optimization, and intelligence in the raw material batching process compared to traditional experience-based batching or simple substitution methods, and quickly responds to the impact of passive changes in the composition of cyanide tailings on the furnace condition. Furthermore, by constructing a multi-objective optimization model, this invention can simultaneously consider multiple dimensions such as elemental balance, thermal balance, cost control, and environmental constraints. Through iterative calculation, it dynamically solves the globally optimal ratio, which not only significantly reduces the dependence on operator experience, but also ensures that key process indicators such as matte grade and slag properties remain stable and optimal after the addition of cyanide tailings, thereby greatly improving the stability and economic benefits of the production process. Attached Figure Description

[0038] Figure 1 The above are process flow diagrams used in embodiments 1 to 3 of the present invention. Detailed Implementation

[0039] The principles and features of the present invention are described below with reference to embodiments. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0040] Example 1

[0041] This invention provides a copper smelting method based on the co-processing of cyanide tailings. The method utilizes a copper smelter employing a closed blast furnace smelting process to treat cyanide tailings generated by a gold smelter. The main chemical components (dry basis) of the cyanide tailings are: Fe 38%, SiO2 28%, CaO 6%, Cu 1.2%, Au 0.9 g / t, Ag 52 g / t, S 1.8%, As 0.15%, CN - 180 mg / kg. The specific procedure is as follows:

[0042] Step S1: Construct a raw material composition database including cyanide tailings, copper concentrate and auxiliary materials, and set process targets including matte grade and smelting temperature.

[0043] Based on 100kg of copper concentrate, the process targets are set as follows: matte grade: 33%, slag iron-silicon ratio: 0.9, slag type: FeO-SiO2-CaO system, smelting temperature: 1250℃; the raw materials for copper smelting include cyanide tailings, copper concentrate, quartz, limestone, pyrite and coke.

[0044] Step S2: Using the set amount of cyanide tailings to be added as the initial condition, perform iterative optimization calculations based on the process objectives set in Step S1, and output the optimal batching scheme.

[0045] The iterative optimization calculation process is as follows: input the fixed composition data of the raw materials, take the set process target as the goal, take the set amount of cyanide tailings to be added as the initial condition, dynamically adjust the proportion of other materials through iterative algorithm, perform target optimization calculation based on material balance and heat balance model, and output the global optimal batching scheme.

[0046] The iterative optimization calculation process is as follows:

[0047] 1. Input and initialization: Input the fixed composition data of each material and use the preset amount of cyanide tailings as the fixed initial condition; set multiple optimization objectives including copper matte grade and production cost, as well as multiple process constraints including slag iron-silicon ratio, copper matte grade target range, thermal equilibrium temperature range, and impurity volatilization rate threshold.

[0048] 2. Iterative optimization calculation: Starting from the initial conditions, the algorithm enters the iterative solution process:

[0049] 1) Scheme generation and simulation: Based on the current material ratios, and according to the material balance model and heat balance model, calculate and predict the matte grade, slag iron-silicon ratio, smelting temperature and impurity volatilization rate;

[0050] 2) Constraint compliance judgment: Compare the prediction results of step 1) with the set multiple constraints to determine whether the current allocation scheme meets all constraints;

[0051] 3) Objective function evaluation and scheme adjustment: If the current scheme satisfies all constraints, calculate its comprehensive objective function value for "maximizing the grade of matte" and "minimizing the production cost", and compare and update it with the historical best value;

[0052] 4) Subsequently, under the premise that the amount of cyanide tailings is fixed, the algorithm generates a new set of other material ratios according to a predetermined solution direction such as gradient descent method, and returns to step 1) to perform the next round of calculation;

[0053] Convergence and Output: Repeat steps 1) to 3) until one of the following convergence conditions is met: the improvement of the objective function value is less than the set threshold for several consecutive iterations, or the preset maximum number of iterations is reached; at this time, output the ingredient scheme with the best comprehensive objective function value in the history record.

[0054] 3. Output optimal ratio: The module outputs the suggested dry basis feed amount of each material after the above optimization calculation.

[0055] After the above iterative process, the optimal batching scheme and the main component content of each raw material are shown in Table 1.

[0056]

[0057] Step S3: Smelt according to the optimal batching scheme obtained in step S2 and the process characteristics of the target smelting furnace to completely decompose the cyanide and recover valuable metals.

[0058] Based on the aforementioned optimal batching scheme, dry cyanide tailings, copper concentrate, and other raw materials are accurately weighed, and 4% coke powder and coal powder are added as additives and 6% bentonite as binder for dry mixing. After adjusting the moisture content of the mixture to 6-8%, it is fed into a roller mill for grinding 2-4 times to improve the agglomeration and agglomerate strength of the material. Subsequently, the ground material is placed in a high-pressure double-roller briquetting machine and pressed into shape under a linear pressure of 20-50 MPa. Finally, the raw agglomerates are dried to a moisture content of 1-2% to obtain hard agglomerates with compressive strength that meet the requirements for furnace entry.

[0059] The granulated agglomerates, coke, and flux are added to the blast furnace in layers according to a predetermined weight ratio and sequence. A typical charging sequence is: bottom layer coke, middle layer flux, and top layer agglomerates. The charge column height is controlled at 3.5m, and the air volume at 800m³ / h. 3 / h, air temperature 500℃, furnace top temperature 250~350℃, hearth temperature 1150~1250℃. Continuous feeding and intermittent copper and slag discharge are implemented.

[0060] The present invention tested various indicators of the product obtained from the above process, and the results were as follows: the grade of matte was 32.3%, the iron-silicon ratio of the slag was 0.91, the copper content in the slag was 0.5%, and the coke yield was 9%. The results show that all indicators of the obtained product met the expected targets. Furthermore, after secondary combustion, the SO2 concentration in the smelting flue gas was approximately 5.1%, and the CN content was... - Concentration <0.5mg / m³ 3 The produced water-quenched slag was tested according to the "Identification Standard for Hazardous Waste - Leaching Toxicity Identification" (GB5085.3-2007). The cyanide leaching concentration was <0.1mg / L and the arsenic leaching concentration was <0.1mg / L. The test results were lower than the standard limits and met the environmental protection requirements.

[0061] For the recovery of valuable metals, the results were as follows: gold recovery rate 94.2%, silver recovery rate 95.5%, and copper recovery rate 97.0%. Furthermore, the overall process resulted in good permeability of the furnace charge after granulation, stable furnace conditions, no suspended or sprayed material, and good slag flowability, making it easy to separate. The main components and recovery rates of cyanide tailings, mixed furnace charge, slag, and matte are shown in Table 2.

[0062]

[0063] Example 2

[0064] This invention provides a copper smelting method based on the co-processing of cyanide tailings. The method utilizes an oxygen-enriched side-blown furnace smelting process in a copper smelter to treat cyanide tailings generated by a gold smelter. The main chemical components (dry basis) of the cyanide tailings are: Fe 35.5%, SiO2 24.5%, CaO 5.5%, Cu 0.9%, Au 1.2 g / t, Ag 45 g / t, S 1.6%, As 0.12%, CN - 220 mg / kg. The specific procedure is as follows:

[0065] Step S1: Construct a raw material composition database including cyanide tailings, copper concentrate and auxiliary materials, and set process targets including matte grade and smelting temperature.

[0066] Based on 100kg of copper concentrate, the process targets are set as follows: matte grade: 36%, slag iron-silicon ratio: 0.9, slag type: FeO-SiO2-CaO system, smelting temperature: 1250℃; the raw materials for copper smelting include cyanide tailings, copper concentrate, quartz, limestone and coal.

[0067] Step S2: Using the set amount of cyanide tailings to be added as the initial condition, perform iterative optimization calculations based on the process objectives set in Step S1, and output the optimal batching scheme.

[0068] The iterative optimization calculation process is as follows: input the fixed composition data of the raw materials, take the set process target as the goal, take the set amount of cyanide tailings to be added as the initial condition, dynamically adjust the proportion of other materials through iterative algorithm, perform target optimization calculation based on material balance and heat balance model, and output the global optimal batching scheme.

[0069] The optimal ingredient ratio and the content of the main components of each raw material are shown in Table 3.

[0070]

[0071] Step S3: Smelt according to the optimal batching scheme obtained in step S2 and the process characteristics of the target smelting furnace to completely decompose the cyanide and recover valuable metals.

[0072] After the cyanide tailings are dehydrated by pressure filtration to a water content of 18%, they are dried to a water content of less than 8% by a steam dryer. The dried cyanide tailings are then precisely weighed with copper concentrate, quartz, limestone and other materials according to the aforementioned optimal batching scheme and fed into a high-efficiency mixer for thorough mixing. The uniformly mixed furnace charge is then fed into the hopper of the oxygen-enriched side-blown furnace through a closed conveying system.

[0073] The mixed furnace charge is sprayed into the molten pool at a speed of 60~80m / s through a special spray gun, with the oxygen concentration controlled at 75% and the molten pool temperature at 1180~1220℃; the operation system of continuous feeding, continuous matte discharge, and intermittent slag discharge is adopted.

[0074] The present invention tested various indicators of the product obtained from the above process, and the results were as follows: the grade of copper matte was 35.7%, the iron-silicon ratio of the slag was 0.92, the copper content in the slag was 0.5%, the copper recovery rate was 98.8%, and the coal combustion rate was 9%. The results show that all indicators of the obtained product met the expected targets. Furthermore, after secondary combustion, the SO2 concentration in the smelting flue gas was approximately 11.2%, and the CN content was... - Concentration <0.5mg / m³ 3 The produced water-quenched slag was tested according to the "Identification Standard for Hazardous Waste - Leaching Toxicity Identification" (GB5085.3-2007). The cyanide leaching concentration was <0.02mg / L and the arsenic leaching concentration was <0.08mg / L. The test results were lower than the standard limits and met the environmental protection requirements.

[0075] For the recovery of valuable metals, the results were as follows: gold recovery rate 95.6%, silver recovery rate 95.9%, and copper recovery rate 97.1%. Furthermore, the overall process resulted in good permeability of the furnace charge after granulation, stable furnace conditions, no suspended or sprayed material, and good slag flowability, making it easy to separate. The main components and recovery rates of cyanide tailings, mixed furnace charge, slag, and matte are shown in Table 4.

[0076]

[0077] Example 3

[0078] This invention provides a copper smelting method based on the co-processing of cyanide tailings. The method utilizes an oxygen-enriched side-blown furnace smelting process in a copper smelter to treat cyanide tailings generated by a gold smelter. The main chemical components (dry basis) of the cyanide tailings are: Fe 30.8%, SiO2 35.2%, CaO 3.5%, Cu 0.7%, Au 1.1 g / t, Ag 48 g / t, S 1.3%, CN... - 200 mg / kg. The specific procedure is as follows:

[0079] Step S1: Construct a raw material composition database including cyanide tailings, copper concentrate and auxiliary materials, and set process targets including matte grade and smelting temperature.

[0080] Based on 100kg of copper concentrate, the process targets are set as follows: matte grade: 40%, slag iron-silicon ratio: 1.1, slag type: FeO-SiO2-CaO system, smelting temperature: 1250℃; the raw materials for copper smelting include cyanide tailings, copper concentrate, quartz, limestone and coal.

[0081] Step S2: Using the set amount of cyanide tailings to be added as the initial condition, perform iterative optimization calculations based on the process objectives set in Step S1, and output the optimal batching scheme.

[0082] Using the set amount of cyanide tailings as the initial condition, iterative optimization calculations are performed based on the process objectives set in step S1, and the optimal batching scheme is output.

[0083] The iterative optimization calculation process is as follows: input the fixed composition data of the raw materials, take the set process target as the goal, take the set amount of cyanide tailings to be added as the initial condition, dynamically adjust the proportion of other materials through iterative algorithm, perform target optimization calculation based on material balance and heat balance model, and output the global optimal batching scheme.

[0084] The optimal ingredient ratio and the content of the main components of each raw material are shown in Table 5.

[0085]

[0086] Step S3: Smelt according to the optimal batching scheme obtained in step S2 and the process characteristics of the target smelting furnace to completely decompose the cyanide and recover valuable metals.

[0087] After the cyanide tailings are dehydrated by pressure filtration to a water content of 18%, they are dried to a water content of less than 8% by a steam dryer. The dried cyanide tailings are then precisely weighed with copper concentrate, quartz, limestone and other materials according to the aforementioned optimal batching scheme and fed into a high-efficiency mixer for thorough mixing. The uniformly mixed furnace charge is then fed into the hopper of the oxygen-enriched side-blown furnace through a closed conveying system.

[0088] The mixed furnace charge is sprayed into the molten pool at a speed of 60~80m / s through a special spray gun, with the oxygen concentration controlled at 78% and the molten pool temperature at 1200~1250℃; the operation system of continuous feeding, continuous matte discharge, and intermittent slag discharge is adopted.

[0089] The present invention tested various indicators of the product obtained from the above process, and the results were as follows: the grade of the produced matte was 39.8%, the iron-silicon ratio of the slag was 1.09, the copper content in the slag was 0.6%, the copper recovery rate was 97.2%, and the coal combustion rate was 7.4%. The results show that all indicators of the obtained product met the expected targets. Furthermore, after secondary combustion, the SO2 concentration in the smelting flue gas was approximately 12.5%, and the CN content was... - Concentration <0.5mg / m³ 3 The produced water-quenched slag was tested according to the "Identification Standard for Hazardous Waste - Leaching Toxicity Identification" (GB5085.3-2007). The cyanide leaching concentration was <0.02mg / L and the arsenic leaching concentration was <0.1mg / L. The test results were lower than the standard limits and met the environmental protection requirements.

[0090] For the recovery of valuable metals, the results were as follows: gold recovery rate 95.9%, silver recovery rate 95.8%, and copper recovery rate 97.2%. Furthermore, the overall process resulted in good permeability of the furnace charge after granulation, stable furnace conditions, no suspended or sprayed material, and good slag flowability, making it easy to separate. The main components and recovery rates of cyanide tailings, mixed furnace charge, slag, and matte are shown in Table 6.

[0091]

[0092] The methods described above in Examples 1-3 are implemented using the following copper smelting system, which includes a database unit, a target and constraint setting unit, an iterative optimization calculation unit, and a visualization output unit.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A copper smelting method based on the co-processing of cyanide tailings, characterized in that, include: Step S1: Construct a raw material composition database including cyanide tailings, copper concentrate and auxiliary materials, and set process targets including matte grade and smelting temperature. Step S2: Using the set amount of cyanide tailings to be added as the initial condition, perform iterative optimization calculations based on the process objectives set in Step S1, and output the optimal batching scheme. Step S3: Smelt according to the optimal batching scheme obtained in step S2 and the process characteristics of the target smelting furnace to completely decompose the cyanide and recover valuable metals.

2. The copper smelting method based on the co-processing of cyanide tailings according to claim 1, characterized in that: The raw materials include copper concentrate, flux, sulfiding agent, fuel and cyanide tailings, wherein the cyanide tailings account for 1-10% of the total dry weight of the raw materials; the process objectives include matte grade, slag iron-silicon ratio, slag type and smelting temperature.

3. The copper smelting method based on the co-processing of cyanide tailings according to claim 1, characterized in that: The flux is limestone and / or quartz; the sulfiding agent includes pyrite; and the fuel includes coke.

4. The copper smelting method based on the co-processing of cyanide tailings according to claim 3, characterized in that: The iterative optimization calculation process is as follows: input the fixed composition data of the raw materials, take the set process target as the objective, take the set amount of cyanide tailings to be added as the initial condition, dynamically adjust the proportion of other materials through iterative algorithm, perform target optimization calculation based on material balance and heat balance model, and output the global optimal batching scheme.

5. A copper smelting method based on the co-processing of cyanide tailings according to claim 1, characterized in that: The cyanide tailings need to be pretreated before smelting. The process is as follows: the cyanide tailings are dried to a moisture content of ≤15%, and then crushed and granulated in sequence to obtain the final product.

6. The copper smelting method based on the co-processing of cyanide tailings according to claim 1, characterized in that: The target smelting furnace is one of a flash furnace, a side-blown furnace, and a blast furnace; the smelting process is carried out under oxygen-enriched conditions at 1100~1300℃.

7. A copper smelting method based on the co-processing of cyanide tailings according to claim 6, characterized in that: When the target smelting furnace is a flash furnace or a side-blown furnace, the method of feeding the raw materials into the furnace during smelting is as follows: after weighing the raw materials according to the optimal batching scheme, they are fully mixed and then granulated or directly injected into the furnace through a spray gun; when the target smelting furnace is a blast furnace, the method of feeding the raw materials into the furnace during smelting is as follows: after weighing the raw materials according to the optimal batching scheme, they are added in layers in the order of coke-flux-lump material.

8. A copper smelting method based on the co-processing of cyanide tailings according to claim 6, characterized in that: The process of complete cyanide decomposition is as follows: the cyanide in the cyanide tailings is completely oxidized and decomposed into metal oxides and non-toxic small molecule gases. Valuable metal components, including copper, lead and zinc, in the tailings are oxidized and enter the slag phase or volatile phase.

9. A copper smelting system based on the co-processing of cyanide tailings, characterized in that: The system is used to implement the copper smelting method according to any one of claims 1 to 8; the copper smelting system includes a database unit, a target and constraint setting unit, an iterative optimization calculation unit, and a visualization output unit.

Images