Method for dynamically adjusting copper concentrate ingredients to control antimony content in matte

Abstract

The invention discloses a method for dynamically adjusting copper concentrate ingredients to control the antimony content in matte. The method comprises the steps of data collection and calculation, target control and stage division, state transition equation calculation and a dynamic adjustment mechanism. Production data shows that when the antimony content in the matte is controlled not to exceed 0.4%, the antimony content in the anode copper of the product does not exceed the standard, and therefore the method for dynamically adjusting the copper concentrate ingredients to control the antimony content in the matte is provided, the antimony content in the matte is controlled in real time, and the antimony content of the anode copper of the product is prevented from being accumulated to exceed the standard. Based on the antimony content feedback in the matte smelted in each stage, the proportion of copper concentrate, smoke dust and cold charge is dynamically adjusted, so that the antimony content in the matte is kept within a safe range, and the subsequent process is prevented from being influenced.

Description

Technical Field

[0001] The invention relates to the field of metal smelting, and in particular to a method for dynamically adjusting copper concentrate ingredients to control the antimony content in matte. Background Art

[0002] In recent years, the structure of copper concentrate raw materials has changed, and the impurities (especially antimony) entering the furnace have increased year by year. In addition, the anode slag, smoke and other cold materials will also be returned to the furnace for smelting, resulting in the circulation and enrichment of antimony impurities in the smelting system. If high antimony ore impurities are directly added to the raw materials according to the traditional batching process, the antimony content in the raw materials is high. Even if the copper liquid is blown, refined and impurities are removed, the anode copper product is likely to cause the antimony impurity content to exceed the standard, thus affecting the product quality.

[0003] Traditional batching processes often rely on empirical judgment, making it difficult to achieve precise control of antimony content and lacking a dynamic adjustment mechanism. Therefore, it is urgent to develop a method that can solve the above technical problems. Summary of the invention

[0004] The object of the present invention is to provide a method for dynamically adjusting the copper concentrate batching to control the antimony content in copper matte.

[0005] The object of the present invention is achieved in that the method for dynamically adjusting the copper concentrate batching to control the antimony content in matte comprises data collection and calculation, control target and stage division, state transfer equation calculation and dynamic adjustment mechanism, wherein: Data collection and calculation: real-time collection of antimony content and quality data in materials at each stage; Control objectives and stage division: The target is to control the antimony content in matte to not exceed the set upper limit Sb max =0.4%; each round of batching and smelting feedback is regarded as a stage, and the input of each stage is the feedback of the antimony content in the material of the previous stage; the cumulative amount of antimony in the matte of the smelting system is defined as the state variable, which represents the cumulative amount of antimony in the matte at the current stage. The cumulative amount of antimony in the matte at stage t can be expressed as Sb t The goal of dynamic programming is to adjust the proportion of copper concentrate according to the current state variables at each stage so that the antimony content does not exceed the preset upper limit; State transfer equation calculation: The state transfer equation of each stage defines how the antimony accumulation in the current stage affects the next stage. In each stage, the system uses the antimony accumulation in the current stage as the state variable Sb t , and the antimony content Sb in the next stage is predicted by the following state transfer equation t+1 : Where m t , Sb tis the mass and antimony content of the copper liquid in the system at the previous stage. α is the contribution coefficient of the mixed copper concentrate to the antimony accumulation, with a coefficient range of 0.2-0.4; β is the contribution coefficient of the smoke batching to the antimony accumulation, with a coefficient range of 0.25-0.4; Sb yc is the antimony content of the smoke batch; γ is the contribution coefficient of the cold batch to the antimony accumulation, the coefficient range is 0.04-0.1, Sb cm is the antimony content in the cold material batch; η is the enrichment rate of antimony in the slag, ranging from 0.35-0.45, Sb slag is the antimony content in the smelting slag; Dynamic adjustment mechanism: In order to effectively adjust the ratio of high-antimony copper concentrate, cold material and smoke to ensure that the antimony content of matte in the smelting system is close to the target value, a genetic algorithm is used to search for the optimal ingredient combination in each stage.

[0006] Production data shows that when the antimony content in matte is controlled not to exceed 0.4%, the antimony content in the product anode copper can be kept within the standard. Therefore, the present invention proposes a method for dynamically adjusting the copper concentrate ingredients to control the antimony content in matte, and to control the antimony content in matte in real time to avoid the cumulative antimony content in the product anode copper exceeding the standard. Based on the feedback of the antimony content in matte after each stage of smelting, the proportions of copper concentrate, smoke and cold materials are dynamically adjusted to keep the antimony content in matte within a safe range to avoid affecting the subsequent process. The steps for establishing the dynamic programming model can be as follows: 1. Data collection and calculation Data collection: Real-time collection of antimony content and quality data in copper concentrate, cold material, smoke, matte, and smelting slag at each stage; Calculation of antimony content: Assume that there are n types of copper concentrates in the batching system, and their antimony content Sb i and mass m i , the antimony content Sb of the mixed copper concentrate is calculated by the following formula total .

[0007] Based on actual smelting production data, constrain Sb total ≤1.2%.

[0008] 2. Control objectives and stage division Objective: To control the antimony content in copper matte to not exceed the set upper limit Sb max =0.4%; Stage division: Each round of batching and smelting feedback is regarded as a stage. The input of each stage is the copper concentrate, cold material, smoke and dust of the previous stage, as well as the feedback of antimony content in matte and slag after each round of smelting.

[0009] State variable: The cumulative amount of antimony in the matte of the smelting system is defined as the state variable, which represents the cumulative amount of antimony in the matte at the current stage. The cumulative amount of antimony in the matte at stage t can be expressed as Sb t The goal of dynamic programming is to adjust the proportion of copper concentrate according to the current state variables at each stage so that the antimony content does not exceed the preset upper limit.

[0010] 3. State transfer equation calculation State transition equation: The state transition equation of each stage defines how the antimony accumulation in the current stage affects the next stage. In each stage, the system uses the antimony accumulation in the current stage as the state variable Sb t , and the antimony content Sb in the next stage is predicted by the following state transfer equation t+1 :

[0011] Where m t , Sb t is the mass and antimony content of the copper liquid in the system at the previous stage. α is the contribution coefficient of the mixed copper concentrate to the antimony accumulation, with a coefficient range of 0.2-0.4; β is the contribution coefficient of the smoke batching to the antimony accumulation, with a coefficient range of 0.25-0.4; Sb yc is the antimony content of the smoke batch; γ is the contribution coefficient of the cold batch to the antimony accumulation, the coefficient range is 0.04-0.1, Sb cm is the antimony content in the cold material batch; η is the enrichment rate of antimony in the slag, ranging from 0.35-0.45, Sb slag It is the antimony content in the smelting slag. (These contribution coefficients are based on actual smelting production data, and the contribution coefficients can be updated every six months according to the distribution trend of antimony in the smelting process) 4. Dynamic adjustment mechanism In order to effectively adjust the ratio of high antimony copper concentrate, cold material and smoke to ensure that the antimony content of matte in the smelting system is close to the target value, a genetic algorithm (GA) is used to search for the optimal ingredient combination in each stage.

[0012] Specific steps of genetic algorithm (GA): (1) Initial population generation: A set of batching schemes is randomly generated, including different combinations of copper concentrate, cold material, and soot. For example, high antimony, medium antimony, and low antimony copper concentrate, soot, and cold material are initialized to a certain ratio, and the total amount is limited to not exceed the allowable range.

[0013] (2) The antimony content of each solution in the next stage is calculated using the state transfer equation, i.e., Sb t+1 ; Set the fitness function f =│Sb t+1 -Sb max │, the goal is to find fMinimal combination.

[0014] (3) Select the ingredient scheme with high fitness from the random schemes to enter the next generation. The scheme with higher fitness is more likely to be selected.

[0015] (4) Crossover and mutation: In the selected scheme, the ingredient ratios of different schemes are exchanged with a set crossover probability (such as exchanging the ratio of high antimony and medium antimony copper concentrates), and certain ratios are randomly adjusted with a certain probability (for example, increasing the proportion of cold materials and reducing the proportion of smoke).

[0016] (5) Iteration: Repeatedly perform operations such as selection, crossover, and mutation to generate multiple generations of ingredient plans and ultimately find the optimal ingredient combination that is close to the target value.

[0017] Constraints Decision variable x t represents the copper concentrate ratio selected in stage t. The ratio decision at each stage will affect the state variable Sb t+1 During the optimization process, it is necessary to impose constraints on the copper concentrate proportion at each stage to ensure that the process is actually feasible. The sum of the proportions equal to 1 represents that the amount of material allocated to the smelting system is full load.

[0018] Element content constraints: To ensure the normal operation of the copper smelting process, other main elements of the mixed ingredients need to be constrained. The sulfur content is between 21%-29%, the iron content is between 22%-28%, the quartz solvent is between 13%-18%, the coal blending rate is 1.5%-2%, and the copper grade is between 18%-22%.

[0019] Through the dynamic programming optimization algorithm, the system will calculate the optimal batching decision x at each stage based on the current state (antimony accumulation) and the antimony content of various copper concentrates. t , while meeting all constraints to ensure that the antimony content in matte is within a reasonable range. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] The method for dynamically adjusting the copper concentrate batching to control the antimony content in matte of the present invention includes data collection and calculation, control target and stage division, state transfer equation calculation and dynamic adjustment mechanism, wherein: Data collection and calculation: real-time collection of antimony content and quality data in materials at each stage; Control objectives and stage division: The target is to control the antimony content in matte to not exceed the set upper limit Sb max =0.4%; each round of batching and smelting feedback is regarded as a stage, and the input of each stage is the feedback of the antimony content in the material of the previous stage; the cumulative amount of antimony in the matte of the smelting system is defined as the state variable, which represents the cumulative amount of antimony in the matte at the current stage. The cumulative amount of antimony in the matte at stage t can be expressed as Sb t The goal of dynamic programming is to adjust the proportion of copper concentrate according to the current state variables at each stage so that the antimony content does not exceed the preset upper limit; State transfer equation calculation: The state transfer equation of each stage defines how the antimony accumulation in the current stage affects the next stage. In each stage, the system uses the antimony accumulation in the current stage as the state variable Sb t , and the antimony content Sb in the next stage is predicted by the following state transfer equation t+1 ;

[0022] Where m t , Sb t is the mass and antimony content of the copper liquid in the system at the previous stage. α is the contribution coefficient of the mixed copper concentrate to the antimony accumulation, with a coefficient range of 0.2-0.4; β is the contribution coefficient of the smoke batching to the antimony accumulation, with a coefficient range of 0.25-0.4; Sb yc is the antimony content of the smoke batch; γ is the contribution coefficient of the cold batch to the antimony accumulation, the coefficient range is 0.04-0.1, Sb cm is the antimony content in the cold material batch; η is the enrichment rate of antimony in the slag, ranging from 0.35-0.45, Sb slag is the antimony content in the smelting slag; Dynamic adjustment mechanism: In order to effectively adjust the ratio of high-antimony copper concentrate, cold material and smoke to ensure that the antimony content of matte in the smelting system is close to the target value, a genetic algorithm is used to search for the optimal ingredient combination in each stage.

[0023] The materials at each stage described in the data collection and calculation are copper concentrate, cold material, fume, matte and smelting slag at each stage.

[0024] The antimony content described in the data collection and calculation is calculated as follows: specifically, assuming that there are n types of copper concentrates in the batching system, and their antimony content Sb i and mass m i , the antimony content Sb of the mixed copper concentrate is calculated by the following formula total .

[0025]

[0026] Based on actual smelting production data, constrain Sb total≤1.2%.

[0027] The specific steps of the genetic algorithm described in the dynamic adjustment mechanism are: (1) Initial population generation: randomly generate a set of batching schemes, including different ratio combinations of copper concentrate, cold material, and soot; for example, initialize high antimony, medium antimony, and low antimony copper concentrate, soot, and cold material to a certain ratio, and limit the total amount to not exceed the allowable range; (2) The antimony content of each solution in the next stage is calculated using the state transfer equation, i.e., Sb t+1 ; Set the fitness function f =│Sb t+1 -Sb max │, the goal is to find f The smallest combination; (3) Select the ingredient scheme with high fitness from the random schemes to enter the next generation. The scheme with higher fitness is more likely to be selected; (4) Crossover and mutation: In the selected scheme, the proportions of ingredients in different schemes are exchanged with a set crossover probability, such as exchanging the ratio of high antimony and medium antimony copper concentrates; certain proportions are randomly adjusted with a certain probability, such as increasing the proportion of cold materials and reducing the proportion of smoke; (5) Iteration: Repeated selection, crossover, and mutation operations to generate multiple generations of ingredient plans and ultimately find the optimal ingredient combination that is close to the target value.

[0028] The present invention is further described below with specific implementation cases: Example 1 The antimony content of matte in the current smelting system is 0.25%, and there are 8 tons of matte left. The current batching is as follows: High antimony copper concentrate 1: Antimony content 2%, 20 tons (0.29%) Medium antimony copper concentrate 2: antimony content 0.2%, 28 tons Low antimony copper concentrate 3: Antimony content 0.05%, 42 tons Smoke dust: Antimony content 3%, add 6 tons Cold material: Antimony content 0.1%, 4 tons Slag: Antimony content 0.18%, slag volume 40 tons The above production data are substituted into the state transfer equation, the coefficient α is set to 0.3, β is set to 0.35, γ is set to 0.05, and η is 0.4.

[0029] Then Sb t+1 =0.29%, which is lower than the target of 0.4%. The ingredients can be adjusted according to the dynamic adjustment mechanism.

[0030] Specific steps for applying genetic algorithm (GA): (1) Initial population generation: The total amount of the generated batching solution is set to no more than 100 tons. The batching ratio of the initial population is randomly generated.

[0031] Plan 1: 25 tons of high antimony copper concentrate, 20 tons of medium antimony, 30 tons of low antimony, 7 tons of smoke, and 5 tons of cold material; Plan 2: 15 tons of high antimony copper concentrate, 30 tons of medium antimony, 35 tons of low antimony, 10 tons of smoke, and 3 tons of cold material; Plan 3: 20 tons of high antimony copper concentrate, 25 tons of medium antimony, 30 tons of low antimony, 8 tons of smoke and dust, and 5 tons of cold material.

[0032] (2) Fitness calculation: Substitute the state transfer equation to calculate the Sb of each solution t+1 , and calculate the difference between it and the target 0.4% as the fitness.

[0033] (3) Selection, crossover and mutation: After multiple generations of iterations, find the t+1 ≈0.4 batching scheme. Scheme: 22 tons of high antimony copper concentrate, 24 tons of medium antimony copper concentrate, 36 tons of low antimony copper concentrate, 6 tons of smoke and 6 tons of cold material.

[0034] (4) Verification: Substitute the optimized ratio into the state transfer equation, Sb t+1 ≈0.395.

[0035] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for dynamically adjusting copper concentrate ingredients to control the antimony content in copper matte, characterized in that: The method for dynamically adjusting the copper concentrate ingredients to control the antimony content in the matte includes data collection and calculation, control target and stage division, state transfer equation calculation and dynamic adjustment mechanism, wherein: Data collection and calculation: real-time collection of antimony content and quality data in materials at each stage; Control objectives and stage division: The target is to control the antimony content in matte to not exceed the set upper limit Sb max =0.4%; each round of batching and smelting feedback is regarded as a stage, and the input of each stage is the feedback of the antimony content in the material of the previous stage; the cumulative amount of antimony in the matte of the smelting system is defined as the state variable, which represents the cumulative amount of antimony in the matte at the current stage. The cumulative amount of antimony in the matte at stage t can be expressed as Sb t The goal of dynamic programming is to adjust the proportion of copper concentrate according to the current state variables at each stage so that the antimony content does not exceed the preset upper limit; State transfer equation calculation: The state transfer equation of each stage defines how the antimony accumulation in the current stage affects the next stage. In each stage, the system uses the antimony accumulation in the current stage as the state variable Sb t , and the antimony content Sb in the next stage is predicted by the following state transfer equation t+1 : Where m t , Sb t is the mass and antimony content of the copper liquid in the system at the previous stage. α is the contribution coefficient of the mixed copper concentrate to the antimony accumulation, with a coefficient range of 0.2-0.4; β is the contribution coefficient of the smoke batching to the antimony accumulation, with a coefficient range of 0.25-0.4; Sb yc is the antimony content of the smoke batch; γ is the contribution coefficient of the cold batch to the antimony accumulation, the coefficient range is 0.04-0.1, Sb cm is the antimony content in the cold material batch; η is the enrichment rate of antimony in the slag, ranging from 0.35-0.45, Sb slag is the antimony content in the smelting slag; Dynamic adjustment mechanism: In order to effectively adjust the ratio of high-antimony copper concentrate, cold material and smoke to ensure that the antimony content of matte in the smelting system is close to the target value, a genetic algorithm is used to search for the optimal ingredient combination in each stage.

2. The method for dynamically adjusting copper concentrate ingredients to control antimony content in matte according to claim 1, characterized in that: The materials at each stage described in the data collection and calculation are copper concentrate, cold material, fume, matte and smelting slag at each stage.

3. The method for dynamically adjusting copper concentrate ingredients to control antimony content in matte according to claim 1, characterized in that: The antimony content described in the data collection and calculation is calculated as follows: specifically, assuming that there are n types of copper concentrates in the batching system, and their antimony content Sb i and mass m i , the antimony content Sb of the mixed copper concentrate is calculated by the following formula total : Based on actual smelting production data, constrain Sb total ≤1.2%.

4. The method for dynamically adjusting copper concentrate batching to control antimony content in matte according to claim 1, characterized in that: The specific steps of the genetic algorithm described in the dynamic adjustment mechanism are: (1) Initial population generation: randomly generate a set of batching schemes, including different ratio combinations of copper concentrate, cold material, and soot; for example, initialize high antimony, medium antimony, and low antimony copper concentrate, soot, and cold material to a certain ratio, and limit the total amount to not exceed the allowable range; (2) The antimony content of each solution in the next stage is calculated using the state transfer equation, i.e., Sb t+1 ; Set the fitness function f =│Sb t+1 -Sb max │, the goal is to find f The smallest combination; (3) Select the ingredient scheme with high fitness from the random schemes to enter the next generation. The scheme with higher fitness is more likely to be selected; (4) Crossover and mutation: In the selected scheme, the proportions of ingredients in different schemes are exchanged with a set crossover probability, such as exchanging the ratio of high antimony and medium antimony copper concentrates; certain proportions are randomly adjusted with a certain probability, such as increasing the proportion of cold materials and reducing the proportion of smoke; (5) Iteration: Repeated selection, crossover, and mutation operations to generate multiple generations of ingredient plans and ultimately find the optimal ingredient combination that is close to the target value.

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