Method for optimizing molten iron production ore blending under constraint of carbon emission reduction

Abstract

The invention relates to a molten iron production ore blending optimization method under the constraint of carbon emission reduction, and belongs to the technical field of steel production. The method comprises the steps that firstly, a non-linear programming model of the whole process before iron making is established, the non-linear programming model of the whole process before iron making takes daily molten iron profit maximization as a target function, and decision variables are daily dosage of iron ore, fuel and auxiliary materials; and then constructing constraint conditions of the pre-ironmaking full-process nonlinear programming model, then solving the pre-ironmaking full-process nonlinear programming model, outputting an optimal ore blending scheme, and then carrying out ore blending production according to the optimal ore blending scheme. Through the method, the profit of pig iron can be increased, the metallurgical performance of a furnace burden structure and the performance of furnace slag can be improved, smooth operation of the furnace condition of the blast furnace is promoted, and meanwhile the market risk caused by too high stock of concentrate and lump ore in the current period is reduced.

Description

Technical Field

[0001] This invention belongs to the field of iron and steel production technology, and specifically relates to an optimization method for iron production ore blending under carbon emission reduction constraints. Background Technology

[0002] Ironmaking costs account for over 80% of the total steel production cost for steel enterprises. Therefore, the key to reducing steel production costs lies in reducing ironmaking costs. Ironmaking costs mainly consist of ore costs, fuel costs, and processing costs. Among these, ore costs account for approximately 60% of the total production cost. The different purchase prices and quality compositions of different ores lead to variations in ironmaking production costs. Sintered ore accounts for over 70% of the blast furnace charge structure in China; therefore, reducing sintered ore costs and improving sintering efficiency are crucial to reducing ironmaking costs.

[0003] The steel industry's production process heavily relies on fossil fuels such as coal and coke. According to statistics from the Intergovernmental Panel on Climate Change (IPCC), CO2 emissions from the steel industry account for 4% to 5% of the world's total CO2 emissions. CO2 emissions from the ironmaking system account for over 85% of the entire steel industry's emissions. As China's second-largest CO2 emitter after the power industry, the steel industry accounts for approximately 15% of the country's total carbon emissions. Under the overall requirements of "dual carbon" (carbon reduction, carbon emission reduction, and carbon sequestration) and green manufacturing, the steel industry faces severe challenges beyond cost reduction; it will also face significant challenges related to carbon emission reduction. According to relevant research, smoke and dust, SO2, and NOx emissions from sintering production at 121 key large and medium-sized steel enterprises in China are the main sources of air pollutants in the industry, accounting for 42.83%, 65.75%, and 54.99% of the total, respectively. This analysis shows that, in addition to high carbon emissions and energy consumption, sintering production is also the largest source of air pollution in steel production. Given that China's steel production is dominated by long-process processes, reducing the proportion of sintered ore in the blast furnace and changing the blast furnace burden structure are important measures for carbon emission reduction and reducing industry air pollution.

[0004] In summary, past cost reduction measures in the ironmaking process have achieved certain results in different stages such as procurement, sintering production, and blast furnace smelting. Although current technologies have made progress in optimizing ironmaking costs, they still have the following shortcomings under current carbon emission reduction constraints:

[0005] 1. Local optimization, lack of global perspective: Existing ore blending optimization models are mostly limited to a single process (such as sintering batching or blast furnace burden structure), failing to couple and optimize the entire process and cost of sintering production, pelletizing production, blast furnace smelting, and even the iron-steel interface. This results in local optima but not global optima, and cannot achieve the lowest overall cost and maximum profit.

[0006] 2. Single objective, difficult to cope with multiple constraints: Existing models mostly take the lowest cost or highest output as the single objective, failing to incorporate multiple objectives such as "carbon emission reduction" (such as limiting the sintering ratio and controlling carbon emissions), "molten iron quality" and "economic profit" into a unified optimization framework, making it difficult to adapt to the complex production constraints under "dual carbon".

[0007] 3. Static models have poor dynamic adaptability: Most models are based on fixed parameters and linear assumptions, making it difficult to handle fluctuations in raw material market prices, dynamic changes in inventory, adjustments to equipment operating conditions, and highly nonlinear process relationships (such as the nonlinear relationship between coke ratio and furnace grade), resulting in a disconnect between optimization results and actual production.

[0008] 4. Experience-driven and low level of intelligence: Blast furnace ore blending schemes rely heavily on human experience and lack adaptive optimization capabilities based on data and mechanism models. They cannot quickly respond to market changes and policy requirements, and the decision-making is not scientific enough.

[0009] Therefore, overcoming the shortcomings of existing technologies is an urgent problem to be solved in the field of steel production technology. Summary of the Invention

[0010] The purpose of this invention is to address the shortcomings of existing technologies and provide an optimized method for iron production ore blending under carbon emission reduction constraints.

[0011] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0012] The method for optimizing ore blending in molten iron production under carbon emission reduction constraints includes the following steps:

[0013] Step (1): Establish a nonlinear programming model for the entire ironmaking process. The objective function of the nonlinear programming model for the entire ironmaking process is to maximize the daily profit of molten iron. The decision variables are the daily consumption of iron ore, fuel, and auxiliary materials. The objective function is as follows:

[0014] max Z = C × T Fe -f 烧 (x)-f 球 (x)-f 高 (x)

[0015] In the formula: C is the set price of molten iron, T Fe It is the output of molten iron, f 烧 (x) is the cost of sintered ore, f 球 (x) is the cost of pellet ore, f 高 (x) is the cost of the blast furnace;

[0016] Step (2) Construct the constraints of the nonlinear programming model for the entire ironmaking process. The constraints include: upper and lower limits of raw material inventory, chemical composition of sinter, chemical composition of pellets, blast furnace charge ratio, hot metal quality benchmark, and carbon emission reduction.

[0017] Step (3): Solve the nonlinear programming model of the entire ironmaking process, output the optimal ore blending scheme, and then carry out ore blending production according to the optimal ore blending scheme.

[0018] Furthermore, in step (1), the specific calculation method for the cost of sintered ore is as follows:

[0019]

[0020] In the formula, m is the number of types of ore, and P i x is the delivered price (yuan / ton) of the i-th type of ore. i P is the daily consumption (tons / day) of the i-th type of ore, k is the number of fuel types, and P is the daily consumption of the i-th type of ore. j This is the factory price (yuan / ton) of fuel type j, x j I represents the daily consumption (tons / day) of the j-th type of fuel, I is the number of auxiliary materials, and P is the daily consumption (tons / day) of the j-th type of fuel. n This is the delivered price (yuan / ton) of the nth auxiliary material, x n This is the daily usage (tons / day) of the nth auxiliary material, z i Z is the loss on ignition rate (tons / ton) for the i-th type of ore. j The burn loss rate (tons / ton) of the j-th fuel, z n P is the burn loss rate (tons / ton) of the nth auxiliary material. 费 These are the fixed costs of the sintering process, including labor, environmental protection, transportation, and water, electricity, and gas costs (yuan / ton of sinter). i S is the sulfur content (mass fraction, %) of the i-th ore. 烧 It is the sulfur content (mass fraction, %) retained in the sinter, T 烧 This is the output of sintered ore (tons / day), calculated using the following formula:

[0021] p 硫 This is the environmental cost (yuan / ton) for treating each kilogram of sulfur.

[0022] Furthermore, in step (1), the specific calculation method for the cost of pellet ore is as follows:

[0023]

[0024] In the formula, m is the number of types of ore, and P i x is the delivered price (yuan / ton) of the i-th type of ore. iI represents the daily usage (tons / day) of the i-th type of ore, where I is the number of types of auxiliary materials, and P is the number of auxiliary materials used. n This is the delivered price (yuan / ton) of the nth auxiliary material, x n This is the daily usage (tons / day) of the nth auxiliary material, z i Z is the loss on ignition rate (tons / ton) for the i-th type of ore. n P is the burn loss rate (tons / ton) of the nth auxiliary material. 费球 These are the fixed costs of the pelletizing process, including labor, environmental protection, transportation, and water, electricity, and gas costs (yuan / ton of pellets).

[0025] Furthermore, in step (1), the specific calculation method for blast furnace cost is as follows:

[0026]

[0027] In the formula, Q represents the number of types of purchased iron ore pellets, and P represents the number of types of purchased iron ore pellets. q This is the delivered price (yuan / ton) of the qth type of purchased iron ore pellets, x q This represents the daily usage (tons / day) of the qth type of purchased pellet ore, where T is the number of types of lump ore, and P... t This is the delivered price (yuan / ton) of type t lump ore, x t This is the daily usage (tons / day) of the t-th type of block ore, w c It is the coke ratio (tons of coke / tons of molten iron), T Fe It is the output of molten iron (tons / day), P c This is the price of coke (yuan / ton), w m It is the pulverized coal ratio (tons of pulverized coal / tons of molten iron), P m This is the price of pulverized coal (yuan / ton), P 费高 These are the fixed costs of the blast furnace process, including labor, environmental protection, transportation, and water, electricity, and gas costs (yuan / ton of molten iron).

[0028] Furthermore, in step (2), the specific constraints are as follows:

[0029] (1) Raw material inventory upper and lower limits constraints:

[0030] The ending inventory of each raw material must be between the safety stock and the maximum storage capacity at the end of the day to meet the following requirements.

[0031]

[0032] Where, x i : Daily consumption of the i-th raw material (iron ore, fuel, auxiliary materials) (unit: tons / day), t i，下 "Safety stock ÷ minimum inventory days", unit: tons / day, used to avoid inventory backlog; t i，上 S represents the maximum daily usage threshold for the i-th raw material. i，安全Safety stock level of raw material i (unit: tons), where T is the production planning cycle number of days;

[0033] (2) Constraints on the chemical composition of sintered ore:

[0034] Single ore proportion constraints:

[0035]

[0036] Where; x 矿，i x 燃，j x 辅,n These represent the daily usage (tons / day) of the i-th type of ore, the j-th type of fuel, and the n-th type of auxiliary material, respectively.

[0037] Key component constraints:

[0038] 55%≤Fe 烧 ≤65%, S 烧 ≤0.08%, 1.8≤R 烧 ≤2.2

[0039] Among them, Fe 烧 It is the iron content (%) of the sinter, S 烧 It is the sulfur content (%) of the sinter, R 烧 It is the basicity of the sinter, i.e., the mass ratio of CaO to SiO2;

[0040] (3) Constraints of the chemical composition of pellets:

[0041] Production constraints:

[0042] 2000≤T 球 ≤3000 (tons / day)

[0043] Among them, T 球 For pellet production (tons / day);

[0044] Key component constraints:

[0045] 55%≤Fe 球 ≤65%, 0.02%≤S 球 ≤0.05%

[0046] Among them, Fe 球 It is the iron content (%) of the pellets, S 球 It is the sulfur content (%) of the pellets;

[0047] Bentonite consumption constraint:

[0048] 14 kg / t ≤ B ≤ 26 kg / t (B is the bentonite consumption per unit area)

[0049] (4) Blast furnace charge ratio constraints:

[0050] 50%≤R 烧 ≤70%, 20%≤R 球 ≤40%, 5%≤R 块 ≤15%, R 烧 +R 球 +R 块

[0051] =1

[0052] Among them, R 烧 It refers to the proportion of sintered ore in the blast furnace charge, R. 球 It refers to the proportion of pellets (including self-produced and purchased) in the blast furnace charge, R. 块 It refers to the proportion of lump ore in the blast furnace charge;

[0053] (5) Quality constraints for molten iron:

[0054] 0.3% ≤ [Si] 铁水 ≤0.6%, [S] 铁水 ≤0.035%

[0055] Among them, [Si] 铁水 It refers to the silicon content (%) in molten iron, [S]. 铁水 The sulfur content (%) in molten iron:

[0056] (6) Slag performance constraints:

[0057] MgO / Al2O3 ≥ 1.1

[0058] (7) Carbon emission reduction constraints:

[0059] Total CO2 = CO2 sintering + CO2 pelletizing + CO2 blast furnace ≤ CO2 limit

[0060] Wherein, CO2 is always the total carbon dioxide emissions (tons / day); CO2 sintering is the carbon dioxide emissions (tons / day) of the sintering process, calculated as CO2 sintering = sintering fuel consumption × 2.6; CO2 pelletizing is the carbon dioxide emissions (tons / day) of the pelletizing process, calculated as CO2 pelletizing = pelletizing fuel consumption × 2.6; CO2 blast furnace is the carbon dioxide emissions (tons / day) of the blast furnace process, calculated as CO2 blast furnace = coke consumption × 2.6 + pulverized coal consumption × 2.4; and CO2 quota is the carbon dioxide emission limit (tons / day).

[0061] Furthermore, in step (3), the gradient algorithm and interior point method are used to solve the problem.

[0062] Furthermore, step (3) specifically includes:

[0063] (3.1) Preprocessing: Setting decision variables and initial solutions, clarifying the model decision variables as: daily consumption of iron ore (sintering ore, pelletizing ore, lump ore) x 矿，i (i = 1, 2, ..., m), daily fuel consumption (sintering fuel, blast furnace coke) x 燃 j (j = 1, 2, ..., k), daily usage of auxiliary materials (sintering auxiliary materials, pelletized bentonite) x 辅 , n (n = 1, 2, ..., l), daily usage of purchased pellets x 外球，q (q = 1, 2, ..., Q), collectively denoted as x = (x 矿,i ,xfuelj,xauxiliaryn,xexternalq);The initial solution x^(0) is set based on the average production plan of the past 30 days (e.g., the initial consumption of a certain ore xore1^(0)=1000 tons / day), and must meet the relaxation range of all constraints (relaxation of raw material inventory constraints, relaxation of key component constraints, relaxation of blast furnace charge ratio constraints); The basis for limiting the relaxation range: All relaxation ranges are set based on the "production history data of the past 30 days": inventory relaxation refers to the maximum fluctuation of raw material inventory in the past 30 days (take 1 / 2 of the fluctuation value as the relaxation range), and component / ratio relaxation refers to the actual fluctuation range of process indicators in the past 30 days (to ensure the initial solution x (0) It should fall within the reasonable range of historical production, avoiding deviation from the actual production scenario.

[0064] (3.2) Gradient Algorithm: Preliminary Optimization and Initial Feasible Point Acquisition

[0065] Step (3.2.1), calculate the gradient of the objective function:

[0066] The objective function is max Z = C × T Fe -f 烧 (x)-f 球 (x)-f 高 (x), calculate its value at x. (0) gradient at The calculation of gradient components needs to be combined with the partial derivatives of the costs of each process, for example...

[0067] Step (3.2.2), determine the search direction and step size:

[0068] Set the search direction along the negative gradient direction (Ensure the objective function is monotonically increasing); use the golden section method for a one-dimensional search to find the optimal step size α. (0) : in the interval [α min α max (e.g., α) min =0, α max Within the range of 0.5, find f(x) (0) +αd (0)The largest α (0) The goal is to "maximize the profit increase in a single iteration in the direction of fastest profit growth." If α is too small, the adjustment range of decision variables (iron ore, fuel, etc.) will be insufficient, resulting in slow profit growth and low iteration efficiency. If α is too large, it may exceed the profit increase range, leading to a profit decrease and deviating from the core objective of "profit maximization." Where: d (0) The initial search direction is set along the negative gradient direction (i.e., Its function is to ensure that the daily profit from molten iron increases monotonically. The objective function is in the initial solution x (0) The gradient at a given point reflects the marginal change in profit when the daily usage (decision variable) of each raw material changes by one unit, and is used to define d. (0) x (0) This is the initial solution, set based on the average production plan over the past 30 days, including the initial daily usage of iron ore, fuel, auxiliary materials, and purchased pellets. α (0) It is the initial optimal step size, calculated using the golden section method in [α]. min α max Find the value within [the specified area] that maximizes the adjusted profit, and use it to [calculate x]. (0) Along d (0) Adjust to the new iteration point. [α] min α max [ ] represents the step size search range, limiting the step size to avoid excessive fluctuations in raw material usage. α min It is the lower bound of the step size, the minimum adjustment ratio of the decision variable in a single iteration. α max This is the upper limit of the step size, the maximum adjustment ratio of the decision variable in a single iteration (not exceeding 50% of the initial amount). f(x) (0) +αd (0) To find α, we need to adjust the daily profit of molten iron for the decision variables. (0) The core idea is to maximize this value.

[0069] Step (3.2.3), Iterative Update and Convergence Judgment: Calculate the new iteration point

[0070] x (1) =x (0) +α (0) d (0) Repeat steps (3.2.1) to (3.2.2) until the gradient magnitude satisfies the condition. At this time x (k) As the initial feasible point for the interior point method; where x (1) It is the new iteration point, determined by x. (0) +α (0) d (0) Calculation, including daily usage updates for each raw material, x (k)The decision variable value in the k-th gradient iteration is used as the initial feasible point in the interior-point method after convergence. ε is the gradient magnitude of the objective function in the k-th iteration, which measures the marginal impact on profit, and ε1 is the convergence threshold of the gradient algorithm.

[0071] (3.3) Interior point method: Constraint satisfaction and optimal solution convergence

[0072] Step (3.3.1), construct the barrier function:

[0073] Transform all inequality constraints into g t Construct a logarithmic barrier function of the form (x)>0:

[0074] Where μ>0 is the barrier parameter (initial value is μ0=1), T is the total number of inequality constraints, the barrier term ensures that the iteration point is always within the feasible region, and g t (x) is transformed into "g" t Inequality constraint functions of the form "(x)>0" (such as inventory and composition constraints), where F(x,μ) is a logarithmic barrier function. Transform the constrained optimization into an unconstrained one, ln(g) t (x) is the core logarithmic term of the obstacle term, which prevents the iteration point from exceeding the feasible region.

[0075] Step (3.3.2), unconstrained optimization of the barrier function:

[0076] x obtained by gradient algorithm (k) Starting with Newton's method, we perform unconstrained optimization of F(x, μ0): Calculate the Hessian matrix. Solving Newton's equations

[0077] The search direction d is obtained, the step size is determined through line search, and the iteration points are updated until F(x, μ0) converges. Where x (k) The interior-point method is the starting point of the interior-point method iteration, i.e., the decision variable value after the gradient algorithm converges. F(x, μ0) is the initial barrier function constructed with μ0 = 1. The object of Newton's method optimization is... It is the Hessian matrix of F(x, μ0), reflecting the curvature of the function. The gradient of F(x, μ0) reflects the trend of the function's change, and d is the search direction of Newton's method. Please provide a solution.

[0078] Step (3.3.3), barrier parameter decay and iteration termination:

[0079] Press μ (k+1) =ρ·μ (k)For the attenuation barrier parameter (ρ = 0.1, typical attenuation coefficient), repeat steps (3.3.1) to (3.3.2); when μ (k) <ε2(ε2=10) -6 And the difference in the objective function between two adjacent iterations |f(x) (k+1) )-f(x (k) )|<10 -3 When the daily value reaches 10,000 yuan, the iteration stops; at this point, x... (k+1) This is the optimal solution of the original model (specifically, the "nonlinear programming model for the entire ironmaking process" established in step (1)). Wherein, μ (k) μ is the barrier parameter in the k-th interior point method iteration. (k+1) ρ is the obstacle parameter in the (k+1)th interior-point method iteration, ρ is the obstacle parameter decay coefficient, and ε2 is the obstacle parameter convergence threshold.

[0080] f(x (k) f(x) is the daily profit of molten iron (ten thousand yuan / day) in the k-th interior point method iteration. (k+1) ) is the daily profit of molten iron (ten thousand yuan / day) in the (k+1)th iteration of the interior point method, |f(x( k+1) )-f(x (k) )|<10 -3 This is the profit difference convergence threshold; if it is less than 0.001 million yuan / day, the profit is stable. (k+1) It is the optimal solution of the original model (nonlinear programming model of the entire ironmaking process), including the optimal daily usage of each raw material.

[0081] (3.4) Optimal solution verification and scheme output:

[0082] The optimal solution x * =x (k+1) Substituting all constraints into the calculation, although the constraints are used as the iteration boundary, numerical iteration errors exist, which may cause slight deviations in the theoretically satisfied solution. Verification is required to confirm the following: 1. The interior point method relies on the "obstacle parameter μ decay" (in the file, μ decays from 1 to ε2 = 10 according to ρ = 0.1). -6 If μ does not completely decay to near 0, the obstacle term still has a slight "penalty effect," and the optimal solution obtained through iteration may be close to but not completely fit the constraint boundary (e.g., the theoretically calculated sulfur content of sinter is 0.08%, but the actual result calculated by substituting it into the original constraint formula is 0.081%, slightly exceeding the upper limit of 0.08%); 2. There are floating-point precision errors in the gradient calculation and Newton equation solution process (e.g., when the amount of decision variables is a decimal, the cumulative calculation of components may produce a deviation of 0.01%-0.1%), which needs to be eliminated by verification to ensure that the solution can be directly used for actual production (avoiding process substandardness due to small deviations). If satisfied, the "optimal ore blending scheme" (including the daily usage of each raw material and the blast furnace charge ratio) is output, where the daily usage of each raw material (decision variable, clarified in step 3.1): x矿,i This represents the daily consumption of the i-th type of iron ore (including sintering ore, pelletizing ore, and lump ore, unit: tons / day); x 燃，j This refers to the daily consumption of fuel type j (including sintering fuel and blast furnace coke, unit: tons / day); x 辅，n This is the daily usage of the nth type of auxiliary material (including sintering auxiliary materials and pelletized bentonite, unit: tons / day); x 外球,q This is the daily usage of the qth type of purchased pellets (unit: tons / day). Blast furnace burden ratio: R 烧 It is the mass proportion (%) of sinter in the blast furnace charge; R 球 It is the mass proportion (%) of ore pellets (including self-produced and purchased) in the blast furnace feed; R 块 It is the mass proportion (%) of lump ore in the blast furnace charge; if it is not satisfied (e.g., the constraint deviation of a certain component is >1%), then adjust the obstacle parameter attenuation coefficient ρ (e.g., change it to 0.05) and iterate again to solve.

[0083] In the cost calculation of sintered ore in this invention, the sulfur treatment cost includes S i and S 烧 Since the formula is a percentage, it needs to be converted to a unit of mass for actual calculations, so the formula is adjusted as follows: To ensure consistency of units.

[0084] The technical problem to be solved by this invention is how to construct a global, multi-objective coupled nonlinear programming model that, under the premise of satisfying carbon emission reduction constraints, replacing sinter with pellets, meeting the hot metal quality benchmark, and various inventory and process constraints, maximizes the daily hot metal profit through the coordinated optimization of sintering production, pellet production, and blast furnace smelting in the entire ironmaking process, and outputs specific ore blending schemes and furnace charge structures that can guide production.

[0085] The system of this invention adopts a B / S architecture and consists of three parts: front-end, back-end service, and ingredient model. The front-end is developed using technologies such as Vue 3.0, axios, promise, IView, and Element-ui. The back-end service is developed using technologies such as .Net Core 3.1, EF Core 3.1 / 6, JWT, Dapper, Autofac, Oracle, and Redis. The ingredient model is built using Python, and the optimization and solution part is developed using C++ to ensure the running efficiency of the iterative calculation.

[0086] After establishing the nonlinear programming model, the decision variables (daily iron ore consumption, fuel consumption, auxiliary material consumption, and purchased pellet consumption) are solved. The solved decision variables are then substituted into the model to calculate the overall profit, molten iron production, molten iron process parameters, and slag process parameters. Since the model is a highly nonlinear optimization problem and the objective function is non-convex, a combination of gradient descent and interior point methods is used to avoid the gradient getting trapped in local optima.

[0087] The optimization of the model requires analyzing and summarizing a large amount of production data, taking into account the actual production conditions within the plant, to calculate the internal and external return ratios in sintering, the coke and coal ratios in blast furnace fuel, the iron recovery rate, and the iron content in molten iron. The quality retention rates and adjustment coefficients for each component in the sintering, pelletizing, and blast furnace processes all need to be calculated, summarized, and verified based on actual production data. Especially for newly introduced minerals, actual production must rely on the results of sintering cup experiments for correction. Only by organically combining the results of the ore blending model optimization with sintering cup experiments can the production and operation goals of optimizing ore usage costs and minimizing ironmaking costs be achieved.

[0088] The concept of this invention is as follows: Under the constraints of carbon emission reduction and the substitution of sinter with pellets, ironmaking production needs to consider not only the inventory conditions of various raw materials, but also their corresponding chemical composition, metallurgical properties, and the impact of various iron-containing raw materials on the parameters, quality, and utilization coefficients of sintering and pelletizing processes. Furthermore, it must consider the rational combination of blast furnace burden structure. Given the availability of multiple iron-containing burdens, and based on an understanding of their characteristics, it is necessary to ensure smooth blast furnace production, minimize pig iron costs, and further consider the quality requirements of steelmaking for blast furnace iron products. Therefore, cost reduction cannot only consider intermediate processes such as procurement, sintering, and pelletizing; it must comprehensively consider the entire process from raw material procurement to blast furnace production. Only by integrating the composition, performance, and price of various raw materials with sintering, pelletizing, and ironmaking production, and implementing full-process batching, with the ultimate goal of optimizing the overall cost of ironmaking, can the effect of cost reduction and efficiency improvement be truly achieved.

[0089] Compared with the prior art, the beneficial effects of this invention are as follows:

[0090] This invention effectively overcomes the shortcomings of existing technologies by constructing a nonlinear programming model covering the entire ironmaking process and employing a solution strategy combining gradient algorithms and interior point methods. On the one hand, it unifies carbon emission reduction constraints, molten iron quality requirements, and economic benefit objectives within a global optimization framework, achieving collaborative optimization and multi-objective balance across sintering, pelletizing, and blast furnace processes. It introduces multiple constraints such as raw material inventory, chemical composition, and process indicators to accurately describe the nonlinear relationship between coke ratio and furnace feed grade, improving the model's dynamic adaptability and decision-making accuracy. On the other hand, based on the natural laws of metallurgy, carbon cycle, and material balance, and according to the "furnace feed" principle... The "correlation law between grade and coke ratio" stabilizes the blast furnace hearth temperature and improves the smooth operation of the furnace. Based on the "quantitative relationship between fossil fuel combustion and CO2 emissions," it reduces CO2 emissions per ton of molten iron and sintering pollutants. Following the "performance law determined by the composition of furnace charge / slag," it improves the metallurgical properties of furnace charge and the fluidity of slag. Relying on the "material balance law," it optimizes inventory to avoid raw material deterioration or shortage. Ultimately, while reducing reliance on manual experience and outputting the globally optimal ore blending scheme, it not only increases pig iron profits and promotes smooth blast furnace operation, but also reduces the market risk of excessive concentrate and lump ore inventory, achieving a synergistic improvement in technical and economic effects.

[0091] Table 1 shows the results of the model before and after optimization after one year of application. Figure 3 Hot metal cost composition: This section visually presents the cost structure of the entire hot metal production process in a blast furnace, showcasing ore costs, coke and pulverized coal costs, and blast furnace processing fees. It also displays the hot metal cost before and after optimization (2383 yuan / ton before optimization, 2313 yuan / ton after optimization) and the changes in each cost item. The effects before and after optimization are also shown. Figure 4 The cost of ore consists of: the cost of dismantled sinter, self-produced pellets, purchased pellets, lump ore, and deductions for gas recovery, reflecting the change in total ore cost before and after optimization (1237 yuan / ton of molten iron before optimization, 1172 yuan / ton of molten iron after optimization).

[0092] Table 1

[0093] Project Name Before optimization After optimization change Total profit from molten iron (ten thousand yuan / day) 326 374 48 Iron production (tons / day) 7078 7050 -28 Cost of molten iron (RMB / ton of molten iron) 2383 2313 -70 Overall furnace grade 57.01 56.78 -0.23 Ore cost (RMB / ton of molten iron) 1237 1172 -65 Ore quantity (tons / day) 11833 11833 0 Ore price (RMB / ton of product) 837 795 -42 Attached Figure Description

[0094] Figure 1 Structure diagram of the full cost optimization model for ironmaking;

[0095] Figure 2 The relationship between changes in blast furnace feed grade and coke ratio;

[0096] Figure 3 This constitutes the cost of molten iron;

[0097] Figure 4 This is a component of ore cost. Detailed Implementation

[0098] The present invention will now be described in further detail with reference to the embodiments.

[0099] Those skilled in the art will understand that the following embodiments are for illustrative purposes only and should not be construed as limiting the scope of the invention. Where specific techniques or conditions are not specified in the embodiments, they are performed in accordance with the techniques or conditions described in the literature in the field or according to the product instructions. Materials or equipment whose manufacturers are not specified are all conventional products that can be obtained by purchase.

[0100] Example 1

[0101] The method for optimizing ore blending in molten iron production under carbon emission reduction constraints includes the following steps:

[0102] Step (1): Establish a nonlinear programming model for the entire ironmaking process. The objective function of the nonlinear programming model for the entire ironmaking process is to maximize the daily profit of molten iron. The decision variables are the daily consumption of iron ore, fuel, and auxiliary materials. The objective function is as follows:

[0103] max Z = C × T Fe -f 烧 (x)-f 球 (x)-f 高 (x)

[0104] In the formula: C is the set price of molten iron, T Fe It refers to the output of molten iron, f 烧 (x) is the cost of sintered ore, f 球 (x) is the cost of pellet ore, f 高 (x) is the cost of the blast furnace;

[0105] Step (2) Construct the constraints of the nonlinear programming model for the entire ironmaking process. The constraints include: upper and lower limits of raw material inventory, chemical composition of sinter, chemical composition of pellets, blast furnace charge ratio, hot metal quality benchmark, and carbon emission reduction.

[0106] Step (3): Solve the nonlinear programming model of the entire ironmaking process, output the optimal ore blending scheme, and then carry out ore blending production according to the optimal ore blending scheme.

[0107] Example 2

[0108] The method for optimizing ore blending in molten iron production under carbon emission reduction constraints includes the following steps:

[0109] Step (1): Establish a nonlinear programming model for the entire ironmaking process. The objective function of the nonlinear programming model for the entire ironmaking process is to maximize the daily profit of molten iron. The decision variables are the daily consumption of iron ore, fuel, and auxiliary materials. The objective function is as follows:

[0110] max Z = C × T Fe -f烧 (x)-f 球 (x)-f 高 (x)

[0111] In the formula: C is the set price of molten iron, T Fe It refers to the output of molten iron, f 烧 (x) is the cost of sintered ore, f 球 (x) is the cost of pellet ore, f 高 (x) is the cost of the blast furnace;

[0112] Step (2) Construct the constraints of the nonlinear programming model for the entire ironmaking process. The constraints include: upper and lower limits of raw material inventory, chemical composition of sinter, chemical composition of pellets, blast furnace charge ratio, hot metal quality benchmark, and carbon emission reduction.

[0113] Step (3): Solve the nonlinear programming model of the entire ironmaking process, output the optimal ore blending scheme, and then carry out ore blending production according to the optimal ore blending scheme.

[0114] In step (1), the specific calculation method for the cost of sintered ore is as follows:

[0115]

[0116] In the formula, m is the number of types of ore, and P i x is the delivered price (yuan / ton) of the i-th type of ore. i P is the daily consumption (tons / day) of the i-th type of ore, k is the number of fuel types, and P is the daily consumption of the i-th type of ore. j This is the factory price (yuan / ton) of fuel type j, x j I represents the daily consumption (tons / day) of the j-th type of fuel, I is the number of auxiliary materials, and P is the daily consumption (tons / day) of the j-th type of fuel. n This is the delivered price (yuan / ton) of the nth auxiliary material, x n This is the daily usage (tons / day) of the nth auxiliary material, z i Z is the loss on ignition rate (tons / ton) for the i-th type of ore. j The burn loss rate (tons / ton) of the j-th fuel, z n P is the burn loss rate (tons / ton) of the nth auxiliary material. 费 These are the fixed costs of the sintering process, including labor, environmental protection, transportation, and water, electricity, and gas costs (yuan / ton of sinter). i S is the sulfur content (mass fraction, %) of the i-th ore. 烧 It is the sulfur content (mass fraction, %) retained in the sinter, T 烧 This is the output of sintered ore (tons / day), calculated using the following formula: p 硫 This is the environmental cost (yuan / ton) for treating each kilogram of sulfur.

[0117] In step (1), the specific calculation method for the cost of pellet ore is as follows:

[0118]

[0119] In the formula, m is the number of types of ore, and P i x is the delivered price (yuan / ton) of the i-th type of ore. i I represents the daily usage (tons / day) of the i-th type of ore, where I is the number of types of auxiliary materials, and P is the number of auxiliary materials used. n This is the delivered price (yuan / ton) of the nth auxiliary material, x n This is the daily usage (tons / day) of the nth auxiliary material, z i Z is the loss on ignition rate (tons / ton) for the i-th type of ore. n P is the burn loss rate (tons / ton) of the nth auxiliary material. 费球 These are the fixed costs of the pelletizing process, including labor, environmental protection, transportation, and water, electricity, and gas costs (yuan / ton of pellets).

[0120] In step (1), the specific calculation method for blast furnace cost is as follows:

[0121]

[0122] In the formula, Q represents the number of types of purchased iron ore pellets, and P represents the number of types of purchased iron ore pellets. q This is the delivered price (yuan / ton) of the qth type of purchased iron ore pellets, x q This represents the daily usage (tons / day) of the qth type of purchased pellet ore, where T is the number of types of lump ore, and P... t This is the delivered price (yuan / ton) of type t lump ore, x t This is the daily usage (tons / day) of the t-th type of block ore, w c It is the coke ratio (tons of coke / tons of molten iron), T Fe It is the output of molten iron (tons / day), P c This is the price of coke (yuan / ton), w m It is the pulverized coal ratio (tons of pulverized coal / tons of molten iron), P m This is the price of pulverized coal (yuan / ton), P 费高 These are the fixed costs of the blast furnace process, including labor, environmental protection, transportation, and water, electricity, and gas costs (yuan / ton of molten iron).

[0123] In step (2), the specific constraints are as follows:

[0124] (1) Raw material inventory upper and lower limits constraints:

[0125] The ending inventory of each raw material must be between the safety stock and the maximum storage capacity at the end of the day to meet the following requirements.

[0126]

[0127] Where, xi : Daily consumption of the i-th raw material (iron ore, fuel, auxiliary materials) (unit: tons / day), t i，下 "Safety stock ÷ minimum inventory days", unit: tons / day, used to avoid inventory backlog; t i，上 S represents the maximum daily usage threshold for the i-th raw material. i，安全 Safety stock level of raw material i (unit: tons), where T is the production planning cycle number of days;

[0128] (2) Constraints on the chemical composition of sintered ore:

[0129] Single ore proportion constraints:

[0130]

[0131] Where; x 矿，i x 燃，j x 辅，n These represent the daily usage (tons / day) of the i-th type of ore, the j-th type of fuel, and the n-th type of auxiliary material, respectively.

[0132] Key component constraints:

[0133] 55%≤Fe 烧 ≤65%, S 烧 ≤0.08%, 1.8≤R 烧 ≤2.2

[0134] Among them, Fe 烧 It is the iron content (%) of the sinter, S 烧 It is the sulfur content (%) of the sinter, R 烧 It is the basicity of the sinter, i.e., the mass ratio of CaO to SiO2;

[0135] (3) Constraints of the chemical composition of pellets:

[0136] Production constraints:

[0137] 2000≤T 球 ≤3000 (tons / day)

[0138] Among them, T 球 For pellet production (tons / day);

[0139] Key component constraints:

[0140] 55%≤Fe 球 ≤65%, 0.02%≤S 球 ≤0.05%

[0141] Among them, Fe 球 It is the iron content (%) of the pellets, S 球 It is the sulfur content (%) of the pellets;

[0142] Bentonite consumption constraint:

[0143] 14 kg / t ≤ B ≤ 26 kg / t (B is the bentonite consumption per unit area)

[0144] (4) Blast furnace charge ratio constraints:

[0145] 50%≤R 烧 ≤70%, 20%≤R 球 ≤40%, 5%≤R 块 ≤15%, R 烧 +R 球 +R 块

[0146] =1

[0147] Among them, R 烧 It refers to the proportion of sintered ore in the blast furnace charge, R. 球 It refers to the proportion of pellets (including self-produced and purchased) in the blast furnace charge, R. 块 It refers to the proportion of lump ore in the blast furnace charge;

[0148] (5) Quality constraints for molten iron:

[0149] 0.3% ≤ [Si] 铁水 ≤0.6%, [S] 铁水 ≤0.035%

[0150] Among them, [Si] 铁水 It refers to the silicon content (%) in molten iron, [S]. 铁水 The sulfur content (%) in molten iron:

[0151] (6) Slag performance constraints:

[0152] MgO / Al2O3 ≥ 1.1

[0153] (7) Carbon emission reduction constraints:

[0154] Total CO2 = CO2 sintering + CO2 pelletizing + CO2 blast furnace ≤ CO2 limit

[0155] Wherein, CO2 is always the total carbon dioxide emissions (tons / day); CO2 sintering is the carbon dioxide emissions (tons / day) of the sintering process, calculated as CO2 sintering = sintering fuel consumption × 2.6; CO2 pelletizing is the carbon dioxide emissions (tons / day) of the pelletizing process, calculated as CO2 pelletizing = pelletizing fuel consumption × 2.6; CO2 blast furnace is the carbon dioxide emissions (tons / day) of the blast furnace process, calculated as CO2 blast furnace = coke consumption × 2.6 + pulverized coal consumption × 2.4; and CO2 quota is the carbon dioxide emission limit (tons / day).

[0156] In step (3), the gradient algorithm and interior point method are used to solve the problem.

[0157] Step (3) specifically includes:

[0158] (3.1) Preprocessing: Setting decision variables and initial solutions, clarifying the model decision variables as: daily consumption of iron ore (sintering ore, pelletizing ore, lump ore) x 矿，i (i = 1, 2, ..., m), daily fuel consumption (sintering fuel, blast furnace coke) x 燃 j (j = 1, 2, ..., k), daily usage of auxiliary materials (sintering auxiliary materials, pelletized bentonite) x 辅 , n (n = 1, 2, ..., l), daily usage of purchased pellets x 外球，q (q = 1, 2, ..., Q), collectively denoted as x = (x 矿，i ,xfuelj,xauxiliaryn,xexternalq);The initial solution x^(0) is set based on the average production plan of the past 30 days (e.g., the initial consumption of a certain ore xore1^(0)=1000 tons / day), and must meet the relaxation range of all constraints (relaxation of raw material inventory constraints, relaxation of key component constraints, relaxation of blast furnace charge ratio constraints); The basis for limiting the relaxation range: All relaxation ranges are set based on the "production history data of the past 30 days": inventory relaxation refers to the maximum fluctuation of raw material inventory in the past 30 days (take 1 / 2 of the fluctuation value as the relaxation range), and component / ratio relaxation refers to the actual fluctuation range of process indicators in the past 30 days (to ensure the initial solution x (0) It should fall within the reasonable range of historical production, avoiding deviation from the actual production scenario.

[0159] (3.2) Gradient Algorithm: Preliminary Optimization and Initial Feasible Point Acquisition

[0160] Step (3.2.1), calculate the gradient of the objective function:

[0161] The objective function is max Z = C × T Fe -f 烧 (x)-f 球 (x)-f 高 (x), calculate its value at x. (0) gradient at The calculation of gradient components needs to be combined with the partial derivatives of the costs of each process, for example...

[0162] Step (3.2.2), determine the search direction and step size:

[0163] Set the search direction along the negative gradient direction (Ensure the objective function is monotonically increasing); use the golden section method for a one-dimensional search to find the optimal step size α. (0) In the interval

[0164] [α min a max (e.g., α) min =0, α max Within the range of 0.5, find f(x) (0) +αd (0) The largest α (0) The goal is to "maximize the profit increase in a single iteration in the direction of fastest profit growth." If α is too small, the adjustment range of decision variables (iron ore, fuel, etc.) will be insufficient, resulting in slow profit growth and low iteration efficiency. If α is too large, it may exceed the profit increase range, leading to a profit decrease and deviating from the core objective of "profit maximization." Where: d (0) The initial search direction is set along the negative gradient direction (i.e., Its function is to ensure that the daily profit from molten iron increases monotonically. The objective function is in the initial solution x (0) The gradient at a given point reflects the marginal change in profit when the daily usage (decision variable) of each raw material changes by one unit, and is used to define d. (0) x (0) This is the initial solution, set based on the average production plan over the past 30 days, including the initial daily usage of iron ore, fuel, auxiliary materials, and purchased pellets. α (0) It is the initial optimal step size, calculated using the golden section method in [α]. min α max Find the value within [the specified area] that maximizes the adjusted profit, and use it to [calculate x]. (0) Along d (0) Adjust to the new iteration point. [α] min α max [ ] represents the step size search range, limiting the step size to avoid excessive fluctuations in raw material usage. α min It is the lower bound of the step size, the minimum adjustment ratio of the decision variable in a single iteration. α max This is the upper limit of the step size, the maximum adjustment ratio of the decision variable in a single iteration (not exceeding 50% of the initial amount). f(x) (0) +αd (0) To find α, we need to adjust the daily profit of molten iron for the decision variables. (0) The core idea is to maximize this value.

[0165] Step (3.2.3), Iterative Update and Convergence Judgment: Calculate the new iteration point x (1) =x (0) +α (0) d (0) Repeat steps (3.2.1) to (3.2.2) until the gradient magnitude satisfies the condition. At this time x (k) As the initial feasible point for the interior point method; where x (1) It is the new iteration point, determined by x.(0) +α (0) d (0) Calculation, including daily usage updates for each raw material, x (k) The decision variable value in the k-th gradient iteration is used as the initial feasible point in the interior-point method after convergence. ε is the gradient magnitude of the objective function in the k-th iteration, which measures the marginal impact on profit, and ε1 is the convergence threshold of the gradient algorithm.

[0166] (3.3) Interior point method: Constraint satisfaction and optimal solution convergence

[0167] Step (3.3.1), construct the barrier function:

[0168] Transform all inequality constraints into g t Construct a logarithmic barrier function of the form (x)>0:

[0169] Where μ>0 is the barrier parameter (initial value is μ0=1), T is the total number of inequality constraints, the barrier term ensures that the iteration point is always within the feasible region, and g t (x) is transformed into "g" t Inequality constraint functions of the form "(x)>0" (such as inventory and composition constraints), where F(x,μ) is a logarithmic barrier function. Transform the constrained optimization into an unconstrained one, ln(g) t (x) is the core logarithmic term of the obstacle term, which prevents the iteration point from exceeding the feasible region.

[0170] Step (3.3.2), unconstrained optimization of the barrier function:

[0171] x obtained by gradient algorithm (k) Starting with Newton's method, we perform unconstrained optimization of F(x, μ0): Calculate the Hessian matrix. Solving Newton's equations

[0172] The search direction d is obtained, the step size is determined through line search, and the iteration points are updated until F(x, μ0) converges. Where x (k) The interior-point method is the starting point of the interior-point method iteration, i.e., the decision variable value after the gradient algorithm converges. F(x, μ0) is the initial barrier function constructed with μ0 = 1. The object of Newton's method optimization is... It is the Hessian matrix of F(x, μ0), reflecting the curvature of the function.

[0173] The gradient of F(x, μ0) reflects the trend of the function's change, and d is the search direction of Newton's method. Please provide a solution.

[0174] Step (3.3.3), barrier parameter decay and iteration termination:

[0175] Press μ (k+1) =ρ·μ (k) For the attenuation barrier parameter (ρ = 0.1, typical attenuation coefficient), repeat steps (3.3.1) to (3.3.2); when μ (k) <ε2(ε2=10) -6 And the difference in the objective function between two adjacent iterations |f(x) (k+1) )-f(x (k) )|<10 -3 When the daily value reaches 10,000 yuan, the iteration stops; at this point, x... (k+1) This is the optimal solution of the original model (specifically, the "nonlinear programming model for the entire ironmaking process" established in step (1)). Wherein, μ (k) μ is the barrier parameter in the k-th interior point method iteration. (k+1) Here, ρ is the obstacle parameter in the (k+1)th interior point method iteration, ε2 is the obstacle parameter decay coefficient, and f(x) is the obstacle parameter convergence threshold. (k) f(x) is the daily profit of molten iron (ten thousand yuan / day) in the k-th interior point method iteration. (k+1) ) is the daily profit of molten iron (ten thousand yuan / day) in the (k+1)th iteration of the interior point method.

[0176] |f(x (k+1) )-f(x (k) )|<10 -3 This is the profit difference convergence threshold; if it is less than 0.001 million yuan / day, the profit is stable. (k+1) It is the optimal solution of the original model (nonlinear programming model of the entire ironmaking process), including the optimal daily usage of each raw material.

[0177] (3.4) Optimal solution verification and scheme output:

[0178] The optimal solution x * =x (k+1) Substituting all constraints into the calculation, although the constraints are used as the iteration boundary, numerical iteration errors exist, which may cause slight deviations in the theoretically satisfied solution. Verification is required to confirm the following: 1. The interior point method relies on the "obstacle parameter μ decay" (in the file, μ decays from 1 to ε2 = 10 according to ρ = 0.1). -6If μ does not completely decay to near 0, the obstacle term still has a slight "penalty effect," and the optimal solution obtained through iteration may be close to but not completely fit the constraint boundary (e.g., the theoretically calculated sulfur content of sinter is 0.08%, but the actual result calculated by substituting it into the original constraint formula is 0.081%, slightly exceeding the upper limit of 0.08%); 2. There are floating-point precision errors in the gradient calculation and Newton equation solution process (e.g., when the amount of decision variables is a decimal, the cumulative calculation of components may produce a deviation of 0.01%-0.1%), which needs to be eliminated by verification to ensure that the solution can be directly used for actual production (avoiding process substandardness due to small deviations). If satisfied, the "optimal ore blending scheme" (including the daily usage of each raw material and the blast furnace charge ratio) is output, where the daily usage of each raw material (decision variable, clarified in step 3.1): x 矿,i This represents the daily consumption of the i-th type of iron ore (including sintering ore, pelletizing ore, and lump ore, unit: tons / day); x 燃，j This refers to the daily consumption of fuel type j (including sintering fuel and blast furnace coke, unit: tons / day); x 辅，n This is the daily usage of the nth type of auxiliary material (including sintering auxiliary materials and pelletized bentonite, unit: tons / day); x 外球，q This is the daily usage of the qth type of purchased pellets (unit: tons / day). Blast furnace burden ratio: R 烧 It is the mass proportion (%) of sinter in the blast furnace charge; R 球 It is the mass proportion (%) of ore pellets (including self-produced and purchased) in the blast furnace feed; R 块 It is the mass proportion (%) of lump ore in the blast furnace charge; if it is not satisfied (e.g., the constraint deviation of a certain component is >1%), then adjust the obstacle parameter attenuation coefficient ρ (e.g., change it to 0.05) and iterate again to solve.

[0179] Application Example 1

[0180] With the core objective of maximizing daily molten iron profit, a nonlinear programming model covering the entire ironmaking process is constructed. By integrating material flow, energy flow, and cost flow, carbon emission reduction constraints (i.e., limiting the proportion of high-carbon-emission sinter) are incorporated into the model. Raw material inventory, process indicators, and quality requirements are transformed into the model. The model is solved using an optimization algorithm, and the final output is the daily usage of each raw material that maximizes total profit, i.e., the optimal ore blending scheme and blast furnace burden structure. This scheme is then directly used to guide actual production.

[0181] This invention is achieved through the following technical solution: a method for obtaining a low-cost blast furnace iron ore batching scheme based on a nonlinear programming model, comprising the following steps:

[0182] 1. Model Establishment

[0183] A mechanistic model is established by comprehensively considering the flow of matter, energy, and information. The overall structure of the model is shown below. Figure 1The system adopts a B / S architecture, and the backend service is developed using the Python language.

[0184] 2. Model Research

[0185] The ironmaking full-cost optimization model is a nonlinear programming model in operations research. The decision variables of the model are the daily consumption of iron ore, fuel, and auxiliary materials. The model aims to maximize the daily profit of molten iron and is a single-objective nonlinear optimization model.

[0186] The model's input data includes information such as the price, composition, and availability of iron ore. The model output includes: 1. the solved decision variable values, namely iron ore, fuel, usage, and proportions; 2. daily cost and profit details; and 3. information on the quality and composition of molten iron and slag.

[0187] max Z = C × T Fe -f 烧 (x)-f 球 (x)-f 高 (x)

[0188] In the formula: C is the set price of molten iron, T Fe It refers to the output of molten iron, f 烧 (x) is the cost of sintered ore, f 球 (x) is the cost of pellet ore, f 高 (x) is the cost of the blast furnace;

[0189] 2.1 Sintering Cost Calculation

[0190] (1) Objective function: Calculation of sinter cost, including the costs of ore, fuel, auxiliary materials, labor, environmental protection, and transportation. The specific calculation method is as follows:

[0191]

[0192] In the formula, m is the number of types of ore, and P i x is the factory price of the i-th type of ore. i P represents the daily consumption of the i-th type of ore, k is the number of fuel types, and P is the daily consumption of the i-th type of ore. j x is the factory price of the j-th type of fuel. j Let I be the daily consumption of the j-th type of fuel, I be the number of types of auxiliary materials, Pn be the factory price of the n-th type of auxiliary material, and x be the daily consumption of the j-th type of fuel. n This is the daily dosage of the nth excipient, z i Z is the burn-off rate of the i-th ore. j Z is the burn rate of the j-th fuel. n P is the burn-off rate of the nth auxiliary material; 费 These are the fixed costs of the sintering process, including labor, environmental protection, transportation, and water, electricity, and gas costs; S i S is the sulfur content of the i-th ore.烧 It refers to the sulfur content retained in the sinter, T 烧 This refers to the yield of sintered ore, calculated using the following formula: p 硫 It is the environmental cost of treating each kilogram of sulfur.

[0193] (2) Constraints: Optimizing the ore usage structure is not an unbounded optimization, but rather an optimization based on constraints set according to the actual production process. Within the upper and lower limits of the constraints, the optimal decision variables (the amount of each ore used) and the optimal solution (maximum profit) are found. The main constraints include the ratio of each ore usage, process index constraints, ore inventory and output constraints, as well as the ratio of pelletizing and blast furnace processes and carbon emission reduction constraints.

[0194] The lower limit expression for the decision-making ratio of ore is:

[0195]

[0196] Among them, t i下 It is the minimum proportion threshold set based on the inventory in the material yard.

[0197] The upper limit expression for the decision-making ratio of ore is:

[0198]

[0199] Among them, t i上 It is the maximum ratio threshold set based on the inventory in the material yard.

[0200] 2.2 Pellet Cost Calculation

[0201] (1) Objective function: Cost calculation for iron ore pellets, including costs for labor, environmental protection, transportation, and utilities (water, electricity, gas). The specific formula is shown below:

[0202]

[0203] In the formula, m is the number of types of ore, and P i x is the factory price of the i-th type of ore. i P represents the daily usage of the i-th type of ore, l represents the number of types of auxiliary materials, and P represents the daily usage of the i-th type of ore. n x is the factory price of the nth auxiliary material. n This is the daily dosage of the nth excipient, z i Z is the burn-off rate of the i-th ore. n P is the burn loss rate of the nth auxiliary material. 费球 These are the fixed costs of the pelletizing process, including labor, environmental protection, transportation, and water, electricity, and gas costs.

[0204] (2) Constraints:

[0205] The constraints on pellet production include: self-produced pellet output, self-produced pellet grade, pellet SiO2 content, pellet Al2O3 content, pellet S content, pellet P content, and bentonite consumption per unit area.

[0206] For example:

[0207] constraint name unit lower limit value Upper limit Self-produced pellets ton Determined based on enterprise output. Determined based on enterprise output. Quality of self-produced pellets ％ 55 65 <![CDATA[Pellet SiO2 content]]> ％ 0 15 <![CDATA[Pellet Al2O3 content]]> ％ 0 23 S content in pellets ％ 0.02 23 P content in pellets ％ 0 0.52 Bentonite consumption per unit kg / t 14 26

[0208] The constraint formulas are as follows:

[0209] 1. Constraints on self-produced pellet output:

[0210] Qmin≤Q≤Qmax

[0211] Qmin and Qmax are determined based on the company's output.

[0212] 2. Grade constraints of self-produced pellets:

[0213] 55% ≤ G ≤ 65%

[0214] 3. Constraints on SiO2 content in pellets:

[0215] 0% ≤ SiO2 ≤ 15%

[0216] 4. Constraints on Al2O3 content in pellets:

[0217] 0% ≤ Al2O3 ≤ 23% 5. S content constraints in pellets:

[0218] 0.02% ≤ S ≤ 0.05%

[0219] 6. Powder content constraint:

[0220] 0% ≤ P ≤ 0.52%

[0221] 7. Bentonite consumption constraint:

[0222] 14kg / t≤B≤26kg / t

[0223] 2.3 Blast Furnace Cost Calculation

[0224] (1) Objective function: The blast furnace feed includes sinter, self-produced pellets, purchased pellets, lump ore, coke, and pulverized coal. The model calculates the iron production, and the coke ratio formula is fitted based on the data regression model. The blast furnace cost calculation formula is as follows:

[0225]

[0226] In the formula, Q represents the number of types of purchased iron ore pellets, and P represents the number of types of purchased iron ore pellets. q This is the delivered price of the qth type of purchased pellet ore, x. q This represents the daily usage of the qth type of purchased pellet ore, where T is the number of different types of block ore, and P is...t x is the price delivered to the factory for the t-th type of ore. t w is the daily consumption of the t-th type of ore. c It's the focal ratio, T. Fe It refers to the output of molten iron, P. c It's the price of coke, w m It is the pulverized coal injection ratio, P m It refers to the price of pulverized coal, P. 费高 These are the fixed costs of the blast furnace process, including labor, environmental protection, transportation, and water, electricity, and gas costs. The coke ratio multiplied by the iron production equals the coke consumption. The coke ratio equals w 基 Reference focal ratio and T Fe To summarize the total impact of changes in lens quality, we selected data from 11 months and calculated the baseline focal ratio w. 基， Then, the change in focal ratio is fitted by the change in chroma, and the fitted curve is as follows: Figure 2 visible.

[0227] Table 1. Coke ratio and output corresponding to different furnace feed grades.

[0228] time Grade of furnace feed (%) Coke ratio (kg / t molten iron) Iron production (t / day) January 2018 54.8 510 6889 February 2018 56.3 495 6908 March 2018 55.6 502 7013 April 2018 55.7 500 7021 May 2018 56.0 498 7050 June 2018 55.2 505 6980 July 2018 55.9 499 7030 August 2018 56.1 496 7045 September 2018 55.5 503 6995 October 2018 55.8 500 7025 November 2018 55.3 504 6905

[0229] The formula for calculating the focal ratio is:

[0230]

[0231] In the formula, T Fe计 It is the calculated blast furnace feed grade, T Fe计 The calculation method is to divide the total mass of Fe element entering the blast furnace by the sum of the total mass of sinter, pellets, lump ore, and auxiliary materials (referring to the total mass of fuel (coke, pulverized coal) and auxiliary materials (such as dolomite, limestone, etc., used to adjust slag composition) entering the blast furnace). This total mass is calculated as "total mass of iron-containing materials + total mass of auxiliary materials," where "iron-containing materials" includes sinter, self-produced pellets, purchased pellets, and lump ore; the unit for "total" is "tons (t)." Fe计 (Blast furnace feed grade) calculation formula: T Fe计 = Total mass of iron-containing materials + Total mass of auxiliary fuels + Total mass of Fe elements fed into the furnace × 100%; Where, "Total mass of Fe elements fed into the furnace" = Sintered ore usage × Sintered ore Fe content + Self-produced pellet usage × Pellet Fe content + Purchased pellet usage xq × Purchased pellet Fe content + Lump ore usage xt × Lump ore Fe content (unit: t).

[0232] Since both the numerator and denominator have decision variables, when substituted into the exponential equation, this model is a highly nonlinear programming model.

[0233] The specific constraints are as follows:

[0234] (1) Raw material inventory upper and lower limits constraints:

[0235] The ending inventory of each raw material must be between the safety stock and the maximum storage capacity at the end of the day to meet the following requirements.

[0236]

[0237] Where, x i : Daily usage of the i-th raw material, t i,下 "Safety stock ÷ minimum inventory days"; t i，上 S represents the maximum daily usage threshold for the i-th raw material. i，安全 The safety stock level of the i-th raw material, where T is the number of days in the production planning cycle;

[0238] (2) Constraints on the chemical composition of sintered ore:

[0239] Single ore proportion constraints:

[0240]

[0241] Where; x 矿，i x 燃，j x 辅,n These represent the daily usage of the i-th type of ore, the j-th type of fuel, and the n-th type of auxiliary material, respectively.

[0242] Key component constraints:

[0243] 55%≤Fe 烧 ≤65%, S 烧 ≤0.08%, 1.8≤R 烧 ≤2.2

[0244] Among them, Fe 烧 It refers to the iron content of sinter, S. 烧 It refers to the sulfur content of sintered ore, R 烧 It is the basicity of the sinter, i.e., the mass ratio of CaO to SiO2;

[0245] (3) Constraints of the chemical composition of pellets:

[0246] Production constraints:

[0247] 2000≤T 球 ≤3000

[0248] Among them, T 球 For pellet production;

[0249] Key component constraints:

[0250] 55%≤Fe 球 ≤65%, 0.02%≤S 球 ≤0.05%

[0251] Among them, Fe 球It refers to the iron content of the pellets, S 球 It refers to the sulfur content of the pellets;

[0252] Bentonite consumption constraint:

[0253] 14kg / t≤B≤26kg / t

[0254] Where B represents the unit consumption of bentonite;

[0255] (4) Blast furnace charge ratio constraints:

[0256] 50%≤R 烧 ≤70%, 20%≤R 球 ≤40%, 5%≤R 块 ≤15%, R 烧 +R 球 +R 块

[0257] =1

[0258] Among them, R 烧 It refers to the proportion of sintered ore in the blast furnace charge, R. 球 It refers to the proportion of ore pellets in the blast furnace charge, R. 块 It refers to the proportion of lump ore in the blast furnace charge;

[0259] (5) Quality constraints for molten iron:

[0260] 0.3% ≤ [Si] 铁水 ≤0.6%, [S] 铁水 ≤0.035%

[0261] Among them, [Si] 铁水 It refers to the silicon content in molten iron, [S]. 铁水 It refers to the sulfur content in molten iron;

[0262] (6) Slag performance constraints:

[0263] MgO / Al2O3 ≥ 1.1

[0264] (7) Carbon emission reduction constraints:

[0265] Total CO2 = CO2 sintering + CO2 pelletizing + CO2 blast furnace ≤ CO2 limit

[0266] Wherein, CO2 is always the total carbon dioxide emissions; CO2 sintering is the carbon dioxide emissions from the sintering process, calculated as CO2 sintering = sintering fuel consumption × 2.6; CO2 pelletizing is the carbon dioxide emissions from the pelletizing process, calculated as CO2 pelletizing = pelletizing fuel consumption × 2.6; CO2 blast furnace is the carbon dioxide emissions from the blast furnace process, calculated as CO2 blast furnace = coke consumption × 2.6 + pulverized coal consumption × 2.4; and CO2 quota is the carbon dioxide emission limit.

[0267] 3. Model Solving and Solution Output

[0268] With the above calculated values ​​and results, the complete technical solution of this invention enters the final execution stage:

[0269] Step 1: Model Solving. A strategy combining gradient descent and interior point methods is employed to solve the constructed highly nonlinear programming model. This process is performed automatically and iteratively within the software to find the objective function (max f(x) = C × T). Fe -f 烧 (x)-f 球 (x)-f 高 (x) is a set of decision variables x that achieve the maximum value (i.e., the optimal daily consumption of various raw materials).

[0270] Step Two: Scheme Generation. The optimal decision variable value x obtained from the solution is substituted into the various sub-cost functions and constraints in the model for verification, thereby outputting a complete "Optimal Ore Blending and Production Scheme" that can guide daily production. This scheme specifically includes:

[0271] 1. Blast furnace charge structure: the optimal ratio of sinter, self-produced pellets, purchased pellets, and lump ore (e.g., sinter: pellets: lump ore = 55%: 25%: 20%).

[0272] 2. Sintering feedstock plan: Specific daily usage (tons / day) of 20 types of iron ore, 2 types of fuel, and 3 types of auxiliary materials.

[0273] 3. Pelletizing feedstock plan: Specific daily usage (tons / day) of 10 types of iron ore and 1 type of bentonite.

[0274] 4. Economic and technical indicators: Calculation results of predicted maximum daily profit, molten iron production, molten iron cost, and key process indicators (such as furnace grade, slag basicity, molten iron sulfur content, etc.).

[0275] Step 3: Application of the Solution. Based on the "Optimal Ore Blending and Production Scheme" output by the model, the production planning department formulates and issues production instructions to guide the sintering, pelletizing, and blast furnace workshops in precise ore blending and production operations, thereby achieving the invention's objectives of cost reduction, efficiency improvement, and carbon emission reduction.

[0276] Application Example 2

[0277] 1. Data Collection and Standardization

[0278] Key data list:

[0279] Raw material data: Prices (delivered to factory) and chemical composition (T) of iron ore (20 types), fuel (2 types), and auxiliary materials (3 types of sintered ore, 1 type of pelletized ore). Fe (S, P, SiO2, Al2O3), available quantity (inventory upper and lower limit constraint formula: t) i，下 ≤x i ≤t i，上 , where: x i Daily consumption of the i-th raw material (iron ore, fuel, auxiliary materials) (unit: tons / day) t i，下 "Safety stock ÷ minimum inventory days", unit: tons / day, used to avoid inventory backlog. i，上 The daily maximum usage threshold for the i-th raw material (calculated from the maximum inventory, i.e., "maximum inventory of the i-th raw material ÷ maximum inventory days", unit: tons / day, used to avoid inventory depletion) and burn-off rate z i (Obtained through testing or sintering cup experiments).

[0280] For example:

[0281] PB iron ore fines: Price 890 yuan / ton, TFe 61.5%, SiO2 4.2%, Al2O3 2.1%, S 0.01%, minimum inventory 500 tons, maximum inventory 8000 tons, burn loss rate 1.5%.

[0282] Mac Iron Ore Powder: Price 870 yuan / ton, TFe 61.0%, SiO2 3.8%, Al2O3 2.5%, S 0.02%, inventory minimum 300 tons, maximum 6000 tons, burn loss rate 2.0%.

[0283] Coke powder: Price 1500 yuan / ton, fixed carbon 85%, sulfur 0.7%.

[0284] Bentonite: Price 450 yuan / ton, MgO 21%, consumption constraint 14-26 kg / t pellet.

[0285] Process data: Sinter and pellet composition constraints (e.g., pellet SiO2≤15%, Al2O3≤23%), blast furnace burden structure ratio (proportion of sinter, pellets, and lump ore), and hot metal quality standards (Si and S content, constraints).

[0286] For example:

[0287] Sinter basicity (CaO / SiO2): Constrained range 1.8-2.2.

[0288] Blast furnace burden structure: sinter 50-70%, pellets 20-35%, lump ore 5-15%.

[0289] Molten iron quality: Si content 0.3%-0.6%, S content ≤0.035%.

[0290] Cost data: fixed costs for labor, environmental protection, transportation, water, electricity, and gas; sulfur treatment tiered pricing, coke, and pulverized coal prices.

[0291] For example:

[0292] Fixed sintering cost: 15 yuan / ton.

[0293] Fixed cost for pellets: 18 yuan / ton.

[0294] Fixed cost of blast furnace: 35 yuan / ton.

[0295] Price of secondary metallurgical coke: 2450 yuan / ton.

[0296] Pulverized coal price: 1050 yuan / ton.

[0297] Data processing requirements:

[0298] Use standardized units (e.g., components are expressed as "%" and unit consumption as "kg / t");

[0299] Missing values ​​are interpolated using data from adjacent batches (e.g., if the sulfur content of a certain ore is missing, the average of the last 3 batches is taken).

[0300] 2. Model parameter configuration

[0301] Objective function parameters:

[0302] Iron price C: Based on the company's current sales price (e.g., 3200 yuan / ton);

[0303] Cost function coefficients:

[0304] Sintering cost f_sinter(x): The prices of ore, fuel, auxiliary materials, and sulfur treatment costs need to be specified.

[0305] For example: S 烧 This is the sulfur content retained in the sinter, taken as 0.05%.

[0306] Pelletizing cost: Bentonite consumption is constrained at 14-26 kg / t;

[0307] Blast furnace cost: The coke ratio is fitted by the grade of the coke entering the furnace, which requires more than 11 months of historical data for fitting.

[0308] Constraint parameters:

[0309] Inventory constraints: set to t based on yard capacity.i，下 (e.g., a minimum daily inventory of 500 tons for a certain ore) and;

[0310] t i，上 (e.g., maximum inventory of 2000 tons / day);

[0311] Compositional constraints: S ≤ 0.08% for sintered ore, grade ≥ 55% for blast furnace feed, etc.

[0312] 3. Model Building and Solution

[0313] System architecture deployment:

[0314] The B / S architecture is set up as follows: the front end (Vue 3.0 + Element-UI) is used for data entry and result display, the back end (.NET Core 3.1 + Python modeling + C++ solving) implements model calculations, and the database (Oracle + Redis) stores real-time data.

[0315] Solution process:

[0316] With the goal of "maximizing daily molten iron profits",

[0317] maxZ=C×T Fe -f 烧 (x)-f 球 (x)-f 高 (x);

[0318] Substitute the decision variables (daily usage of each raw material) and constraints, and solve the problem using a combination of gradient algorithm and interior point method;

[0319] Iteration termination condition: The difference in profit between two consecutive calculations is less than 0.1 million yuan / day (ensuring convergence to the optimal solution).

[0320] 4. Implementation and Verification of the Solution

[0321] Application of output scheme:

[0322] Adjust the production plan according to the "blast furnace charge structure" and "sintering / pelleting batching scheme" output by the model (e.g., 55% sinter, 25% pellets, and 20% lump ore).

[0323] Track the deviation between actual indicators (molten iron production, cost, composition) and model predictions (must be <5%, otherwise parameters such as zi should be corrected through sintering cup experiments).

[0324] Application Example 3

[0325] Northern steel mills (deepening pellet substitution under winter carbon emission reduction pressure)

[0326] Company Background: A steel mill in northern China (with an annual capacity of 8 million tons) faces production restrictions and emission reduction requirements during the winter (carbon emissions from the sintering process need to be reduced by 15%). At the same time, it has low-priced iron ore pellets in stock (TFe 62%, priced 120 yuan / ton lower than sintered ore). It needs to replace sintered ore with pellets to achieve the dual goals of emission reduction and cost reduction.

[0327] The method for optimizing ore blending in molten iron production under carbon emission reduction constraints includes the following steps:

[0328] Step (1): Establish a nonlinear programming model for the entire ironmaking process. The objective function of the nonlinear programming model for the entire ironmaking process is to maximize the daily profit of molten iron. The decision variables are the daily consumption of iron ore, fuel, and auxiliary materials. The objective function is as follows:

[0329] max Z = C × T Fe -f 烧 (x)-f 球 (x)-f 高 (x)

[0330] In the formula: C is the set price of molten iron, T Fe It refers to the output of molten iron, f 烧 (x) is the cost of sintered ore, f 球 (x) is the cost of pellet ore, f 高 (x) is the cost of the blast furnace;

[0331] Step (2) Construct the constraints of the nonlinear programming model for the entire ironmaking process. The constraints include: upper and lower limits of raw material inventory, chemical composition of sinter, chemical composition of pellets, blast furnace charge ratio, hot metal quality benchmark, and carbon emission reduction.

[0332] Step (3): Solve the nonlinear programming model of the entire ironmaking process, output the optimal ore blending scheme, and then carry out ore blending production according to the optimal ore blending scheme.

[0333] Implementation process:

[0334] Data input:

[0335] Raw materials: 18 kinds of iron ore (including 3 kinds of high-grade pellet raw materials), sintering fuel (coke powder, anthracite), bentonite (consumption 22kg / t);

[0336] Constraints: The proportion of sintered ore is reduced from 60% to 45% (still ≤70%), the proportion of pellets is increased to 35% (still ≤35% upper limit), and the proportion of lump ore is 20%; carbon emissions per ton of iron are ≤1.6 tons of CO2, and sulfur emissions per ton of iron are ≤0.03%.

[0337] Model optimization strategy:

[0338] Pelletization instead of sintering: Increase the proportion of pellet ore to 35% and select low-sulfur pellet raw materials (S 0.02%) to reduce environmental protection costs;

[0339] Cost balance: Utilize the strategy of "complementing high-silicon low-alumina ore with high-alumina low-silicon ore", and combine two types of domestic ore (SiO2 7% + Al2O3 3% and SiO2 3% + Al2O3 7% in a 1:1 ratio) to control the slag Al2O3 ≤ 16%.

[0340] Implementation results:

[0341] Emission reduction: Carbon emissions from the sintering process were reduced by 18% (exceeding the 15% target), and production successfully met standards during the winter.

[0342] Costs: The cost of molten iron decreased from 2,420 yuan / ton to 2,350 yuan / ton (an average daily cost reduction of 560,000 yuan), and the substitution of pellets reduced fuel consumption by 8 kg / ton;

[0343] Quality: The Si content in the molten iron is stable at 0.4-0.5%, meeting the requirements for steelmaking.

[0344] Application Example 4

[0345] Southern steel mills (high-alumina ore inventory digestion and slag optimization)

[0346] Company Background: A steel plant in southern China (annual capacity of 6 million tons) has a backlog of high-alumina ore (Al2O3 8-10%, accounting for 30% of the inventory), resulting in excessively high Al2O3 content (18%) in the blast furnace slag, poor fluidity (MgO / Al2O3 = 0.8), and a coke ratio as high as 570 kg / t. The furnace condition needs to be improved through ore blending optimization.

[0347] The method for optimizing ore blending in molten iron production under carbon emission reduction constraints includes the following steps:

[0348] Step (1): Establish a nonlinear programming model for the entire ironmaking process. The objective function of the nonlinear programming model for the entire ironmaking process is to maximize the daily profit of molten iron. The decision variables are the daily consumption of iron ore, fuel, and auxiliary materials. The objective function is as follows:

[0349] max Z = C × T Fe -f 烧 (x)-f 球 (x)-f 高 (x)

[0350] In the formula: C is the set price of molten iron, T Fe It is the output of molten iron, f 烧 (x) is the cost of sintered ore, f 球 (x) is the cost of pellet ore, f 高 (x) is the cost of the blast furnace;

[0351] Step (2) Construct the constraints of the nonlinear programming model for the entire ironmaking process. The constraints include: upper and lower limits of raw material inventory, chemical composition of sinter, chemical composition of pellets, blast furnace charge ratio, hot metal quality benchmark, and carbon emission reduction.

[0352] Step (3): Solve the nonlinear programming model of the entire ironmaking process, output the optimal ore blending scheme, and then carry out ore blending production according to the optimal ore blending scheme.

[0353] Implementation process:

[0354] Data input:

[0355] Raw materials: 12 kinds of domestic minerals (including 5 kinds of high-alumina minerals), 2 kinds of high-magnesium lump minerals (MgO 9%), dolomite (MgO 21%);

[0356] Constraints: Slag MgO / Al2O3 ≥ 1.1, coke ratio ≤ 550 kg / t, pellet Al2O3 ≤ 23%.

[0357] Model optimization strategy:

[0358] High-alumina ore digestion: Following the strategy of "adding dolomite blocks to balance slag MgO / Al2O3", high-alumina ore and high-magnesia ore are mixed at a ratio of 4:1, and dolomite (5 kg / t of molten iron) is added to raise the slag MgO / Al2O3 ratio to 1.2.

[0359] Focal ratio optimization: By fitting the focal ratio (w) 基 =560, k=1.0), the grade of the furnace feed increased from 55% to 55.5%, and the coke ratio decreased by 15 kg / t.

[0360] Implementation results:

[0361] Furnace conditions: Blast furnace pressure differential decreased from 190 kPa to 165 kPa, slag fluidity improved, and shutdown rate decreased by 40%;

[0362] Costs: The cost of molten iron decreased from 2,500 yuan / ton to 2,430 yuan / ton (an average daily cost reduction of 420,000 yuan), and the high-alumina ore inventory was depleted within 3 months;

[0363] Indicators: Overall furnace feed grade increased by 0.5%, and coke ratio decreased to 555 kg / t (in line with the "coke ratio and grade correlation" rule).

[0364] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing ore blending in molten iron production under carbon emission reduction constraints, characterized in that, Includes the following steps: Step (1): Establish a nonlinear programming model for the entire ironmaking process. The objective function of the nonlinear programming model for the entire ironmaking process is to maximize the daily profit of molten iron. The decision variables are the daily consumption of iron ore, fuel, and auxiliary materials. The objective function is as follows: max Z=C×T Fe -f 烧 (x)-f 球 (x)-f 高 (x) In the formula: C is the set price of molten iron, T Fe It refers to the output of molten iron, f 烧 (x) is the cost of sintered ore, f 球 (x) is the cost of pellet ore, f 高 (x) is the cost of the blast furnace; Step (2) Construct the constraints of the nonlinear programming model for the entire ironmaking process. The constraints include: upper and lower limits of raw material inventory, chemical composition of sinter, chemical composition of pellets, blast furnace charge ratio, hot metal quality benchmark, and carbon emission reduction. Step (3): Solve the nonlinear programming model of the entire ironmaking process, output the optimal ore blending scheme, and then carry out ore blending production according to the optimal ore blending scheme.

2. The method for optimizing iron ore blending under carbon emission reduction constraints according to claim 1, characterized in that, In step (1), the specific calculation method for the cost of sintered ore is as follows: In the formula, m is the number of types of ore, and P i x is the factory price of the i-th type of ore. i P represents the daily consumption of the i-th type of ore, k is the number of fuel types, and P is the daily consumption of the i-th type of ore. j x is the factory price of the j-th type of fuel. j P represents the daily consumption of the j-th type of fuel, l is the number of types of auxiliary materials, and P n x is the factory price of the nth auxiliary material. n This is the daily dosage of the nth excipient, z i Z is the burn-off rate of the i-th ore. j It is the burn rate of the j-th fuel, z n P is the burn-off rate of the nth auxiliary material; 费 These are the fixed costs of the sintering process, including labor, environmental protection, transportation, and water, electricity, and gas costs; S i S is the sulfur content of the i-th ore. 烧 It refers to the sulfur content retained in the sinter, T 烧 This refers to the yield of sintered ore, calculated using the following formula: p 硫 It is the environmental cost of treating each kilogram of sulfur.

3. The method for optimizing iron ore blending under carbon emission reduction constraints according to claim 1, characterized in that, In step (1), the specific calculation method for the cost of pellet ore is as follows: In the formula, m is the number of types of ore, and P i x is the factory price of the i-th type of ore. i P represents the daily usage of the i-th type of ore, l represents the number of types of auxiliary materials, and P represents the daily usage of the i-th type of ore. n x is the factory price of the nth auxiliary material. n This is the daily dosage of the nth excipient, z i Z is the burn-off rate of the i-th ore. n P is the burn loss rate of the nth auxiliary material. 费球 These are the fixed costs of the pelletizing process, including labor, environmental protection, transportation, and water, electricity, and gas costs.

4. The method for optimizing iron ore blending under carbon emission reduction constraints according to claim 1, characterized in that, In step (1), the specific calculation method for blast furnace cost is as follows: In the formula, Q represents the number of types of purchased iron ore pellets, and P represents the number of types of purchased iron ore pellets. q This is the delivered price of the qth type of purchased pellet ore, x. q This represents the daily usage of the qth type of purchased pellet ore, where T is the number of different types of block ore, and P is... t x is the price delivered to the factory for the t-th type of ore. t w is the daily consumption of the t-th type of ore. c It's the focal ratio, T. Fe It refers to the output of molten iron, P. c It's the price of coke, w m It is the pulverized coal injection ratio, P m It refers to the price of pulverized coal, P. 费高 These are the fixed costs of the blast furnace process, including labor, environmental protection, transportation, and water, electricity, and gas costs.

5. The method for optimizing iron ore blending under carbon emission reduction constraints according to claim 1, characterized in that, In step (2), the specific constraints are as follows: (1) Raw material inventory upper and lower limits constraints: The ending inventory of each raw material must be between the safety stock and the maximum storage capacity at the end of the day, satisfying the t i,下 ≤x i ≤t i，上 , Where, x i : Daily usage of the i-th raw material, t i，下 "Safety stock ÷ minimum inventory days"; t i，上 S represents the maximum daily usage threshold for the i-th raw material. i，安全 The safety stock level of the i-th raw material, where T is the number of days in the production planning cycle; (2) Constraints on the chemical composition of sintered ore: Single ore proportion constraints: Where; x 矿，i x 燃，j x 辅，n These represent the daily usage of the i-th type of ore, the j-th type of fuel, and the n-th type of auxiliary material, respectively. Key component constraints: 55%≤Fe 烧 ≤65%,S 烧 ≤0.08%,1.8≤R 烧 ≤2.2 Among them, Fe 烧 It refers to the iron content of sinter, S 烧 It refers to the sulfur content of sintered ore, R 烧 It is the basicity of the sinter, i.e., the mass ratio of CaO to SiO2; (3) Constraints of the chemical composition of pellets: Production constraints: 2000≤T 球 ≤3000 Among them, T 球 For pellet production; Key component constraints: 55%≤Fe 球 ≤65%,0.02%≤S 球 ≤0.05% Among them, Fe 球 It refers to the iron content of the pellets, S 球 It refers to the sulfur content of the pellets; Bentonite consumption constraint: 14kg / t≤B≤26kg / t Where B represents the unit consumption of bentonite; (4) Blast furnace charge ratio constraints: 50%≤R 烧 ≤70%,20%≤R 球 ≤40%,5%≤R 块 ≤15%,R 烧 +R 球 +R 块 =1 Among them, R 烧 It refers to the proportion of sintered ore in the blast furnace charge, R. 球 It refers to the proportion of ore pellets in the blast furnace charge, R. 块 It refers to the proportion of lump ore in the blast furnace charge; (5) Quality constraints for molten iron: 0.3%≤[Si] 铁水 ≤0.6%,[S] 铁水 ≤0.035% Among them, [Si] 铁水 It refers to the silicon content in molten iron, [S]. 铁水 It refers to the sulfur content in molten iron; (6) Slag performance constraints: MgO / Al2O3 ≥ 1.1 (7) Carbon emission reduction constraints: Total CO2 = CO2 sintering + CO2 pelletizing + CO2 blast furnace ≤ CO2 limit Wherein, CO2 is always the total carbon dioxide emissions; CO2 sintering is the carbon dioxide emissions from the sintering process, calculated as CO2 sintering = sintering fuel consumption × 2.6; CO2 pelletizing is the carbon dioxide emissions from the pelletizing process, calculated as CO2 pelletizing = pelletizing fuel consumption × 2.6; CO2 blast furnace is the carbon dioxide emissions from the blast furnace process, calculated as CO2 blast furnace = coke consumption × 2.6 + pulverized coal consumption × 2.4; and CO2 quota is the carbon dioxide emission limit.

6. The method for optimizing iron ore blending under carbon emission reduction constraints according to claim 1, characterized in that, In step (3), the gradient algorithm and interior point method are used to solve the problem.

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