Intelligent proportioning optimization and cost control method for blast furnace injection of carbon-containing hydrogen-rich fuel

Abstract

The present application relates to a kind of intelligent proportioning optimization and cost control method of carbon-containing hydrogen-rich fuel for blast furnace injection, specific steps are: setting parameters and constructing benchmark dataset to set process constraint boundary;Adopt moisture inert elimination algorithm to carry out dry basis uniform conversion and standardization treatment to fuel wet basis parameter;From three dimensions of heat value compensation, hydrogen reduction replacement, ash slag disturbance, simultaneously correct ton iron coke consumption;Respectively account fuel purchase and coke consumption cost to obtain ton iron comprehensive smelting cost by superposition;Based on mechanism accounting model, build iterative optimization algorithm, converge and output global optimal proportioning, and reverse calibrate fuel economic benchmark price.The present application solves the problem of inaccurate fuel proportioning and extensive cost control, realizes automatic proportioning optimization and accurate smelting cost accounting, and maximizes the reduction of ton iron comprehensive smelting cost under the premise of ensuring the stability of blast furnace.

Description

Technical Field

[0001] This invention relates to the field of blast furnace ironmaking technology, and more specifically to a method for intelligent proportioning optimization and cost control of carbon-rich hydrogen fuel for blast furnace injection. Background Technology

[0002] Replacing coke with pulverized coal for blast furnace injection is a core technology for reducing costs, increasing efficiency, and lowering carbon emissions in modern blast furnace ironmaking. Carbon-rich hydrogen fuel has the dual functions of exothermic combustion of carbon and decarbonization by hydrogen reduction, which can effectively reduce the coke ratio in the blast furnace and reduce carbon emissions in the ironmaking process, thus possessing significant technological and environmental advantages.

[0003] Currently, the selection and proportioning design of carbon-rich hydrogen fuels for blast furnaces in China still relies on the traditional, extensive model of manual calculation and unit price comparison. This model has many technical defects and seriously restricts the large-scale and economical application of low-carbon injection technology. The specific problems are as follows: 1. The current fuel ratio optimization is too simplistic and disconnected from actual furnace conditions. Existing fuel selection relies solely on simple comparisons of fuel market unit price and basic calorific value, failing to consider the unique hydrogen reduction and coke replacement effect of hydrogen-rich fuels, the slag quantity changes caused by ash composition fluctuations, and the interference of inert components (moisture) in materials on thermal performance and costs. It ignores the complex thermal and material linkage mechanisms within the blast furnace, resulting in significant deviations between theoretical ratios and actual production conditions. This easily leads to the mismatch problem of "low unit price but high overall smelting cost".

[0004] 2. Manual calculations are inefficient and inaccurate, and cannot achieve global optimization. Traditional proportioning schemes rely entirely on the professional experience of technical personnel. The number of proportioning samples that can be manually calculated is limited, and it is impossible to traverse all feasible proportioning ranges. It can only select locally better schemes, making it difficult to achieve global cost optimization under all working conditions and all parameters. At the same time, manual calculations are labor-intensive, highly subjective, and have a low error tolerance.

[0005] 3. Lack of multi-factor coupled accounting models and insufficient technical support. Existing technologies lack an integrated accounting system adapted to gas-solid composite carbon-rich hydrogen fuels, making it impossible to quantify the synergistic impact of the three core factors—calorific value differences, hydrogen reduction efficiency, and ash and slag quantity disturbances—on coke consumption and comprehensive cost per ton of iron. Fuel ratio adjustments and process parameter optimizations lack quantitative data support, resulting in highly subjective process decisions.

[0006] 4. Traditional accounting methods lack objectivity. Existing fuel economy comparison and proportioning screening methods rely solely on manually set comparison rules, pure mathematical numerical calculations, and experience-based judgments, lacking smelting process mechanisms, coupled optimization models, and automated iterative optimization technology.

[0007] In summary, there is an urgent need for an intelligent method for proportioning optimization and cost control that is based on the natural laws of blast furnace smelting, integrates multiple process constraints, and has automated iterative optimization capabilities. This method should abandon the traditional manual experience and simple numerical comparison mode, solve the industry problems of inaccurate carbon-rich hydrogen fuel proportioning, crude cost control, and lack of core technology, and provide reliable technical support for the large-scale and economical promotion of low-carbon injection technology in blast furnaces. Summary of the Invention

[0008] To address the aforementioned problems, this invention provides an intelligent method for optimizing the proportion and controlling the cost of carbon-rich hydrogen fuel for blast furnace injection. The aim is to solve the problems in existing technologies where the proportion of carbon-rich hydrogen fuel for blast furnaces relies on manual experience, has a single evaluation dimension, is not coupled with the mechanism of thermal and material changes in the furnace, and has low calculation accuracy. Under the premise of ensuring stable blast furnace conditions and meeting smelting quality standards, this method maximizes the reduction of the overall smelting cost per ton of iron.

[0009] To solve the above problems, the technical solution provided by the present invention is as follows: A method for intelligent proportioning optimization and cost control of carbon-rich hydrogen fuel for blast furnace injection includes the following steps: S100. Setting parameters and benchmark dataset: Setting the blending system of blast furnace injection fuel, wherein the blast furnace injection fuel includes a compound combination of traditional pure pulverized coal fuel and carbon-rich hydrogen fuel, and the blending scheme satisfies the following formula: Where: i is used to characterize the grouping of the proportioning scheme; A i Used to characterize the mass ratio of the traditional pure pulverized coal fuel; B i Used to characterize the mass ratio of the carbon-containing hydrogen-rich fuel; collect blast furnace production benchmark parameters and fuel physical property parameters, and set process constraint boundaries in combination with the actual production conditions of the blast furnace; S200. Perform dry-basis unified conversion of fuel wet-basis parameters: Using a moisture inertia elimination algorithm, the wet-basis delivered price and wet-basis calorific value of all fuels are uniformly converted into absolute dry-basis benchmark parameters; and based on the mass ratio of each group of blast furnace injection fuels, the weighted dry-basis lower heating value, weighted ash content and weighted dry-basis unit cost of the blast furnace injection fuels under different blending schemes are weighted and calculated to complete the standardization of the benchmark parameters of multiple blending schemes; S300. Correcting the coke consumption per ton of iron through multi-dimensional coupling: Based on the blast furnace smelting mechanism, the coke consumption per ton of iron is corrected in conjunction with three dimensions: calorific value compensation, hydrogen reduction replacement, and ash and slag quantity disturbance. Then, the actual coke consumption per ton of iron is calculated under the ratio scheme of each blast furnace injection fuel. S400. Coupled accounting of comprehensive smelting cost per ton of iron: For each group of blast furnace injection fuel ratio schemes, calculate the injection fuel purchase cost and coke consumption cost separately, and sum them to obtain the comprehensive smelting cost per ton of iron throughout the entire process. Also calculate the cost difference and cost reduction between each blast furnace injection fuel ratio scheme and the benchmark control group. S500. Calculate the optimal blending ratio and output parameters: Based on the mechanism accounting model, an iterative optimization algorithm is built to automatically traverse all feasible blending ratio schemes within the preset process constraint boundary, converge and output the globally optimal carbon-rich hydrogen fuel mass blending ratio, and calculate the economic benchmark price to the plant that is suitable for the current furnace condition through reverse calibration using the economic benchmark price; output the optimal fuel blending ratio, fuel economic benchmark price, cost reduction per ton of iron, and furnace condition suitability conclusion.

[0010] Preferably, the reference parameters for blast furnace production in step S100 include: the total amount of blast furnace pulverized coal fuel and carbon-rich hydrogen fuel used for injection per ton of iron, the lower heating value of coke on a dry basis, the market price of coke delivered to the plant, and a fixed industry-standard smelting reference coefficient; the fuel physical property parameters include: lower heating value on a dry basis, moisture content as received, ash content, combustion conversion rate, effective hydrogen content, and the price delivered to the plant on a wet basis; the smelting reference coefficient includes the hydrogen replacement coke reference coefficient and the standard density of hydrogen.

[0011] Preferably, the process constraint boundaries in step S100 include: a total pulverized coal injection ratio of 120~200 kg / tFe; a traditional pure pulverized coal fuel proportion of 60%~85%; a carbon-containing hydrogen-rich fuel proportion of 15%~40%; coke and slag accounting for 5%~12% of the total fuel fed into the furnace; a comprehensive blast furnace coke ratio of 330~480 kg / tFe; and an ore-coke ratio of 3.2~4.5. The ash content of the pulverized coal injection mixture is within ±1.5% of the benchmark value and does not exceed 14.0%; the comprehensive fuel ash content entering the furnace is maintained at 11.0%~16.0%, with short-term fluctuations not exceeding ±1.0%; the lower heating value range of the pulverized coal injection is 22.0MJ / kg~27.0MJ / kg, with single batch fluctuations ≤±0.8MJ / kg and 8-hour average fluctuations ≤±0.5MJ / kg; the comprehensive fuel calorific value range is 8.6~9.8GJ / tFe and hourly fluctuations ≤±0.25GJ / tFe; the theoretical combustion temperature range at the tuyeres is 2050~2250℃, with instantaneous fluctuations not exceeding ±30℃; the dry basis volatile matter of the pulverized coal is 18%~28%, and the comprehensive fuel sulfur content is ≤0.8%; the adjustment range of the fuel composition in a single instance does not exceed 5 percentage points.

[0012] Preferably, the weighted dry basis unit cost, the weighted dry basis lower heating value, and the weighted cost of the blast furnace injection fuel in step S200 are expressed by the following formula: Wherein: T a Used to characterize the dry basis price of conventional fuels; T b Used to characterize the dry-basis price of carbon-containing hydrogen-rich fuels; Q a Used to characterize the lower heating value (dry basis) of conventional fuels; Q b W is used to characterize the dry basis lower heating value of carbon-containing hydrogen-rich fuels; W is used to characterize the amount of fuel injected per ton of iron in a blast furnace.

[0013] Preferably, the correction method for calorific value compensation in step S300 is as follows: Using pure traditional pulverized coal fuel injection, compare the difference in the weighted dry basis lower calorific value of each group of compounded blast furnace injection fuels, and combine this with the equivalent exothermic efficiency of coke to calculate the increase or decrease in coke consumption corresponding to the calorific value fluctuation, expressed by the following formula: in: The value used to characterize the effect of calorific value on the consumption of coke per ton of iron under the i-th proportioning scheme; C ave,1 The amount of coke consumed per ton of iron under baseline operating conditions; Q ave,1 The weighted dry basis lower heating value is used to characterize the baseline operating condition; Q ave,i The weighted dry basis lower heating value is used to characterize the i-th proportioning scheme; W is used to characterize the amount of fuel injected per ton of iron in the blast furnace; Q c Used to characterize the dry basis lower heating value of coke.

[0014] Preferably, the correction method for hydrogen reduction replacement in step S300 is as follows: based on the effective hydrogen content and combustion conversion rate of the traditional pure pulverized coal fuel and the carbon-containing hydrogen-rich fuel, the total amount of hydrogen that can be released per unit mass of fuel is calculated. Combined with the hydrogen replacement coke benchmark coefficient, the amount of coke replaced by the hydrogen reduction reaction is calculated, expressed by the following formula: in: Used to characterize the absolute impact of hydrogen reduction on the consumption of ton iron coke under the i-th ratio scheme; Used to characterize the absolute impact of hydrogen reduction on the consumption of coke per ton of iron under benchmark operating conditions; The value is used to characterize the difference in the amount of coke consumed per ton of iron due to the change in the ratio; and The combustion conversion rates of the conventional pure pulverized coal fuel and the carbon-containing hydrogen-rich fuel are used to characterize the combustion conversion rates of the two fuels, respectively. and The effective hydrogen content is used to characterize the conventional pure pulverized coal fuel and the carbon-containing hydrogen-rich fuel, respectively. Used to characterize the standard density of hydrogen; Used to characterize the hydrogen-displaced coke reference coefficient.

[0015] Preferably, the correction method for the ash content disturbance in step S300 is as follows: based on the weighted ash content of the blast furnace injection fuel compounded under each group ratio, the amount of slag generated after fuel combustion is calculated, and then the coke consumption fluctuation caused by the change in slag content is calculated based on the blast furnace slag content-coke ratio linkage coefficient, expressed by the following formula: in: and These values ​​are used to characterize the influence of ash content on slag quantity under the i-th proportioning scheme and the baseline working condition, respectively. and The ash content of the conventional pure pulverized coal fuel and the carbon-rich hydrogen fuel are respectively used to characterize the ash content; γ is used to characterize the correlation coefficient between blast furnace slag amount and coke ratio. This value is used to characterize the fluctuation in coke consumption caused by the change in slag quantity.

[0016] Preferably, the actual coke consumption per ton of iron under each proportion scheme obtained by superimposing the three-dimensional correction quantities in step S300 is expressed by the following formula: in: The amount of coke consumed per ton of iron used to characterize the actual ratio of the i-th formulation. The value used to characterize the effect of calorific value on the consumption of coke per ton of iron under the i-th proportioning scheme; The value is used to characterize the difference in the amount of coke consumed per ton of iron due to the change in the ratio; This value is used to characterize the fluctuation in coke consumption caused by the change in slag quantity.

[0017] Preferably, the comprehensive smelting cost per ton of iron in step S400 is expressed by the following formula: in: Used to characterize the cost of coke consumption; Used to characterize the price of coke delivered to the plant; Used to characterize the actual coke consumption per ton of iron; Used to characterize the procurement cost of the injected fuel; This is used to characterize the overall smelting cost of the entire process for each ton of iron.

[0018] Preferably, the specific calculation steps of the iterative optimization algorithm in step S500 are as follows: The real-time delivered prices of the traditional pure pulverized coal fuel and the carbon-rich hydrogen fuel are set. The lowest comprehensive smelting cost per ton of iron is taken as the iterative convergence objective. Under the constraints of the process boundary, the globally optimal mass ratio of the carbon-rich hydrogen fuel and its corresponding optimal fuel blending ratio are converged and output. The specific calculation steps for the reverse calibration of the economic benchmark price are as follows: Fix the globally optimal mass ratio of the carbon-rich hydrogen fuel, and iteratively adjust the input price of the carbon-rich hydrogen fuel until the comprehensive smelting cost per ton of iron under this compound scheme is equal to the benchmark comprehensive smelting cost under the pure traditional pure pulverized coal fuel injection condition. At this time, the calibrated carbon-rich hydrogen fuel price is the fuel economic benchmark price adapted to the current furnace condition.

[0019] Compared with the prior art, the present invention has the following advantages: 1. Because this invention adopts an automated iterative traversal mode, it fully covers all compliant proportioning conditions, thereby overcoming the shortcomings of traditional manual trial calculations, which have limited samples, low efficiency, and are prone to getting trapped in local optima. Through multi-dimensional coke ratio correction, it accurately matches the actual smelting conditions of the blast furnace, resulting in a significant improvement in the accuracy of proportioning optimization and cost accounting.

[0020] 2. Because this invention integrates three core influencing factors—calorific value exothermic, hydrogen reduction, and ash content disturbance—it comprehensively quantifies the positive and negative dual impacts of carbon-containing hydrogen-rich fuel on the blast furnace smelting process. This completely solves the problems of inaccurate proportioning and misjudgment of economic efficiency caused by traditional technologies that only compare purchase prices and ignore in-furnace process disturbances.

[0021] 3. Because this invention can simultaneously output the optimal injection ratio at the production end and the economic benchmark price of fuel at the procurement end, it achieves two-way empowerment of process production and raw material procurement. As a result, it can guide the blast furnace to adjust injection parameters and optimize fuel structure in real time, and provide quantitative technical basis for fuel bidding and pricing and raw material selection.

[0022] 4. Since the core model of this invention can be adapted to various carbon-rich and hydrogen-rich composite injection systems such as hydrogen-rich pulverized coal and biomass carbon fuel, and can be adapted to blast furnaces of different volumes and production conditions, it can be rapidly scaled up and applied without significant adjustment of model parameters. This results in the effective assistance of blast furnaces in achieving low-carbon and low-cost production. Attached Figure Description

[0023] Figure 1 This is a schematic diagram illustrating the workflow of the intelligent proportioning optimization and cost control method for carbon-rich hydrogen fuel used in blast furnace injection, as described in a specific embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the application scenario framework of blast furnace injection fuel ratio optimization according to a specific embodiment of the present invention. Figure 3 This is a flowchart of a blast furnace injection fuel ratio optimization method according to a specific embodiment of the present invention; Figure 4 This is a structural block diagram of a blast furnace injection fuel ratio optimization device according to a specific embodiment of the present invention. Detailed Implementation

[0024] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading the present invention, any modifications of the present invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0025] This invention application claims a method for intelligent proportioning optimization and cost control of carbon-rich hydrogen fuel for blast furnace injection, such as... Figure 1 , 2 As shown in 3 and 4, the steps include: S100. Set parameters and benchmark dataset: Set the proportioning system of blast furnace injection fuel. The blast furnace injection fuel includes a combination of traditional pure pulverized coal fuel and carbon-rich hydrogen fuel, and the proportioning scheme satisfies the following formula (1): (1) Where: i is used to characterize the grouping of the proportioning scheme; A i Used to characterize the mass ratio of the traditional pure pulverized coal fuel; B i Used to characterize the mass ratio of the carbon-containing hydrogen-rich fuel; collect blast furnace production benchmark parameters and fuel physical property parameters, and set process constraint boundaries in combination with the actual production conditions of the blast furnace.

[0026] S200. Perform dry-based unified conversion of wet-based fuel parameters: Using a moisture inertia elimination algorithm, the wet-based delivered price and wet-based calorific value of all fuels are uniformly converted into absolute dry-based benchmark parameters; and based on the mass ratio of each group of blast furnace injection fuels, the weighted dry-based lower heating value, weighted ash content and weighted dry-based unit cost of blast furnace injection fuels under different blending schemes are weighted and calculated to complete the standardization of benchmark parameters for multiple blending schemes.

[0027] S300. Correction of coke consumption per ton of iron through multi-dimensional coupling: Based on the blast furnace smelting mechanism, the coke consumption per ton of iron is corrected from three dimensions: calorific value compensation, hydrogen reduction and replacement, and ash and slag quantity disturbance. Then, the actual coke consumption per ton of iron is calculated under the fuel ratio scheme of each blast furnace injection.

[0028] S400. Coupled Calculation of Comprehensive Smelting Cost per Ton of Iron: For each group of blast furnaces, the fuel injection cost and coke consumption cost are calculated separately and then combined to obtain the comprehensive smelting cost per ton of iron. The cost difference and cost reduction between each blast furnace fuel injection ratio scheme and the benchmark control group are also calculated.

[0029] S500. Calculate the optimal blending ratio and output parameters: Based on the mechanism accounting model, an iterative optimization algorithm is built to automatically traverse all feasible blending ratio schemes within the preset process constraint boundary, converge and output the globally optimal carbon-rich hydrogen fuel mass blending ratio, and obtain the economic benchmark price to the plant that is suitable for the current furnace condition through reverse calibration using the economic benchmark price; output the optimal fuel blending ratio, fuel economic benchmark price, cost reduction per ton of iron, and furnace condition suitability conclusion.

[0030] It should be noted that the benchmark parameters for blast furnace production in step S100 include: the total fuel consumption per ton of iron injected by the blast furnace using traditional pure pulverized coal fuel and carbon-rich hydrogen fuel, the lower heating value of coke on a dry basis, the market price of coke delivered to the plant, and a fixed industry-standard smelting benchmark coefficient; the fuel physical property parameters include: lower heating value on a dry basis, moisture content as received, ash content, combustion conversion rate, effective hydrogen content, and the price delivered to the plant on a wet basis; the smelting benchmark coefficients include the hydrogen-replacement coke benchmark coefficient and the standard hydrogen density. In the specific application of this embodiment, the fixed industry-standard smelting benchmark coefficient is set as follows: the hydrogen-replacement coke benchmark coefficient is 0.201 kg / Nm³. 3 The standard density of hydrogen is 0.0899 kg / m³. 3 The conventional fuel has a dry basis net calorific value of 31.7 MJ / kg, an as-received moisture content of 8%, a wet basis price of RMB 1776 / ton, a dry basis price of RMB 1930 / ton, and an ash content of 10.4%. The hydrogen-rich carbon fuel has a dry basis net calorific value of 27.82 MJ / kg, an as-received moisture content of 10%, a wet basis price of RMB 1465 / ton, a dry basis price of RMB 1628 / ton, and an ash content of 10%. The blast furnace pulverized iron injection fuel quantity is 165 kg / tp. The coke has a dry basis net calorific value of 31 MJ / kg and a wet basis price of RMB 2814 / ton. In the first blending scheme, the conventional fuel accounts for 100% of the mass, and the hydrogen-rich carbon fuel accounts for 0%. In the second blending scheme, the conventional fuel accounts for 85% of the mass, and the hydrogen-rich carbon fuel accounts for 15%.

[0031] It should be further explained that the process constraints in step S100 include: the total pulverized coal injection ratio is 120~200 kg / tFe; the proportion of traditional pure pulverized coal fuel is 60%~85%; the proportion of carbon-containing hydrogen-rich fuel is 15%~40%; coke and slag account for 5%~12% of the total fuel fed into the furnace; the overall coke ratio of the blast furnace is 330~480 kg / tFe; the ore-coke ratio is 3.2~4.5; the ash content of the injected mixed pulverized coal is within ±1.5% of the benchmark value and does not exceed 14.0%; the overall ash content of the fuel fed into the furnace is maintained at 11.0%~16.0%, with short-term fluctuations. The fluctuation should not exceed ±1.0%; the lower heating value range of pulverized coal is 22.0 MJ / kg to 27.0 MJ / kg, with a single batch fluctuation ≤ ±0.8 MJ / kg and an 8-hour average fluctuation ≤ ±0.5 MJ / kg; the comprehensive fuel calorific value range is 8.6 to 9.8 GJ / tFe and the hourly fluctuation ≤ ±0.25 GJ / tFe; the theoretical combustion temperature range at the tuyeres is 2050 to 2250℃, with an instantaneous fluctuation not exceeding ±30℃; the dry basis volatile matter of pulverized coal is 18% to 28%, and the comprehensive fuel sulfur content is ≤0.8%; the adjustment range of fuel composition in a single instance shall not exceed 5 percentage points.

[0032] By setting the above boundary conditions, we can effectively ensure that the proportioning scheme is adapted to actual production and avoid abnormal fluctuations in furnace conditions.

[0033] It should be noted that, since the moisture content of different fuels varies, and moisture, as an inert component, does not participate in the combustion heat release and reduction reaction, it directly affects the accuracy of cost accounting. Therefore, a moisture inert elimination algorithm is adopted to uniformly convert the wet basis delivered price and wet basis calorific value of all fuels into the absolute dry basis benchmark parameters, thereby eliminating the accounting deviation caused by moisture differences.

[0034] In step S200, the weighted dry basis unit cost, weighted dry basis lower heating value, and weighted cost of the blast furnace injection fuel are expressed by the following formula (2): (2) Wherein: T a Used to characterize the dry basis price of conventional fuels; T b Used to characterize the dry-basis price of carbon-containing hydrogen-rich fuels; Q a Used to characterize the lower heating value (dry basis) of conventional fuels; Q b W is used to characterize the dry basis lower heating value of carbon-containing hydrogen-rich fuels; W is used to characterize the amount of fuel injected per ton of iron in a blast furnace.

[0035] Based on the proportion parameter A of each group i B iThe weighted average lower heating value, weighted average ash content, and weighted average unit cost of the mixed injection fuel under different blending schemes were calculated to standardize the benchmark parameters of multiple blending schemes. For example, the first blending scheme (85% and 15% blending ratio) for benchmark comparison and the second blending scheme (60% and 40% blending ratio) for calculation were used. The weighted dry basis price T of the first formulation ave,1 =T a 85%+T b 15% = 1930 85%+1628 15% = 1885 yuan / ton; The weighted dry basis price T of the second formulation ave,2 =T a 60%+T b 40% = 1930 60% + 1628 40% = 1809 yuan / ton; The weighted dry basis calorific value Q of the first formulation ave,1 =Q a 85%+Q b 15% = 31.7 85% + 27.82 15% = 31.12 MJ / kg; The weighted dry basis calorific value Q of the second formulation ave,2 =Q a 60%+Q b 40% = 31.7 60% + 27.82 40% = 30.15 MJ / kg.

[0036] When the blast furnace pulverized iron injection fuel quantity is W=165kg / tp: The weighted cost of injection fuel for formulation 1, S ave,1 =T ave,1 W=1885 0.165 = 311 yuan / ton of iron; The weighted average cost S of the injection fuel for the second formulation ave,2 =T ave,2 W=1809 0.165 = 299 yuan / ton of iron.

[0037] It should be noted that the correction method for calorific value compensation in step S300 is as follows: using pure traditional pulverized coal fuel injection, the difference in the weighted dry basis lower heating value of each group of compounded blast furnace injection fuels is compared, and combined with the equivalent exothermic efficiency of coke, the increase or decrease in coke consumption corresponding to the calorific value fluctuation is calculated, and the influence of fuel calorific value difference on coke ratio is quantified and expressed by the following formula (3): (3) in: The value used to characterize the effect of calorific value on the consumption of coke per ton of iron under the i-th proportioning scheme; C ave,1 The amount of coke consumed per ton of iron under baseline operating conditions; Q ave,1 The weighted dry basis lower heating value is used to characterize the baseline operating condition; Q ave,i The weighted dry basis lower heating value is used to characterize the i-th proportioning scheme; W is used to characterize the amount of fuel injected per ton of iron in the blast furnace; Q c Used to characterize the dry basis lower heating value of coke.

[0038] In this embodiment, under the first proportioning scheme, the coke consumption per ton of iron is C. ave,1 =350kg / tp is used as the statistical benchmark value, and the lower heating value of coke on a dry basis, Q c The value is 31 MJ / kg. Based on the above formula, the impact C on the coke consumption per ton of iron due to changes in calorific value under the second proportioning scheme is... ave,2 =350-(30.15 165-31.12 165) / 31=355.16kg / tp.

[0039] It should be further explained that the correction method for hydrogen reduction and replacement in step S300 is as follows: based on the effective hydrogen content and combustion conversion rate of traditional pure pulverized coal fuel and carbon-rich hydrogen fuel, the total amount of hydrogen that can be released per unit mass of fuel is calculated. Combined with the hydrogen replacement coke benchmark coefficient, the amount of coke replaced by hydrogen reduction reaction is calculated, and the core advantages of low carbon and reduced coke of hydrogen-rich fuel are quantified and expressed by the following formula (4): (4) in: This is used to characterize the absolute impact of hydrogen reduction on the consumption of ton iron coke under the i-th ratio scheme. Used to characterize the absolute impact of hydrogen reduction on the consumption of coke per ton of iron under benchmark operating conditions; The value is used to characterize the difference in the amount of coke consumed per ton of iron due to the change in the ratio; and The combustion conversion rates of the conventional pure pulverized coal fuel and the carbon-containing hydrogen-rich fuel are used to characterize the combustion conversion rates of the two fuels, respectively. and The effective hydrogen content is used to characterize the conventional pure pulverized coal fuel and the carbon-containing hydrogen-rich fuel, respectively. Used to characterize the standard density of hydrogen; Used to characterize the hydrogen-displaced coke reference coefficient.

[0040] In this embodiment, the combustion rate of conventional fuel is 60%, and the hydrogen content is 3%; the combustion rate of hydrogen-rich carbon fuel is 80%, and the hydrogen content is 6%. Under the first mixing ratio scheme (A1=85%, B1=15%), the absolute impact of hydrogen reduction on the consumption of tonnes of iron coke is... =[(165 85% 60% 3%+165 15% 80% 6%) / 2 / 0.0899] 0.201 = 4.15 kg / tp; Under the second formulation (A2 = 60%, B2 = 40%), =[(165 60% 60% 3%+165 40% 80% 6%) / 2 / 0.0899] 0.201 = 5.53 kg / tp; the difference in coke consumption per ton of iron. =5.53-4.15=1.38kg / tp.

[0041] It should be further explained that the correction method for the ash content disturbance in step S300 is as follows: based on the weighted ash content of the compounded blast furnace injection fuel under each group ratio, the amount of slag generated after fuel combustion is calculated, and then the coke consumption fluctuation caused by the change in slag content is calculated based on the linkage coefficient of blast furnace slag content-coke ratio, to make up for the technical defect of the traditional calculation ignoring the ash content disturbance, and expressed by the following formula (5): (5) in: and These values ​​are used to characterize the influence of ash content on slag quantity under the i-th proportioning scheme and the baseline working condition, respectively. and The ash content of the conventional pure pulverized coal fuel and the carbon-rich hydrogen fuel are respectively used to characterize the ash content; γ is used to characterize the correlation coefficient between blast furnace slag amount and coke ratio. This value is used to characterize the fluctuation in coke consumption caused by the change in slag quantity.

[0042] In this embodiment, the correlation coefficient between blast furnace slag quantity and coke ratio is γ = 0.23, where 100 kg / t of slag ratio affects the coke ratio by 23 kg / t. Traditional fuel ash content A a =10.4%, ash content of hydrogen-rich carbon-containing fuel A b =6%. The impact of ash content on slag quantity under the first proportioning scheme. =165 85% 60% 10.4% + 165 15% 80% 6% = 9.9396 kg / t; Influence value under the second proportioning scheme =165 60% 60% 10.4% + 165 40% 80% 6% = 9.3456 kg / t; Fluctuation in coke consumption caused by changes in slag quantity =(9.3456-9.9396) 0.23 = -0.13662 kg / tp.

[0043] It should be further explained that the actual coke consumption per ton of iron under each proportion scheme obtained by superimposing the three-dimensional correction quantities in step S300 is expressed by the following formula (6): (6) in: The amount of coke consumed per ton of iron used to characterize the actual ratio of the i-th formulation. The value used to characterize the effect of calorific value on the consumption of coke per ton of iron under the i-th proportioning scheme; The value is used to characterize the difference in the amount of coke consumed per ton of iron due to the change in the ratio; This value is used to characterize the fluctuation in coke consumption caused by the change in slag quantity. In this embodiment, under the second proportioning scheme, the actual coke consumption per ton of iron is... =355.16-1.38-(-0.13662)=353.9kg / tp.

[0044] It should be noted that the comprehensive smelting cost per ton of iron in step S400 is expressed by the following formula (7): (7) in: Used to characterize the cost of coke consumption; Used to characterize the price of coke delivered to the plant; Used to characterize the actual coke consumption per ton of iron; Used to characterize the procurement cost of pulverized fuel; This is used to characterize the overall smelting cost per ton of iron throughout the entire process. In this embodiment, the wet-basis delivered price C of coke is used. c The cost is 2814 yuan / ton. Under the first blending scheme, the coke cost is F. ave,1 =2814 0.35 = 984.9 yuan / ton of iron; the comprehensive cost is K1 = 984.9 + 311 = 1296 yuan / ton of iron. Under the second blending scheme, the coke cost is F. ave,2 =2814 0.3539 = 995.9 yuan / ton of iron; the comprehensive cost is K2 = 995.9 + 299 = 1294 yuan / ton of iron. By comparing the calculation results, the comprehensive cost K2 of compound scheme 2 is less than the comprehensive cost K1 of the benchmark compound scheme 1, which directly reflects the significant economic benefits brought about by blending hydrogen-rich fuel after adjusting the ratio.

[0045] It should be noted that the specific calculation steps of the iterative optimization algorithm in step S500 are as follows: The real-time delivered prices of traditional pure pulverized coal fuel and carbon-rich hydrogen fuel are set. The goal of iterative convergence is to minimize the comprehensive smelting cost per ton of iron. Under the constraints of the process boundary, the globally optimal carbon-rich hydrogen fuel mass ratio and its corresponding optimal fuel blending ratio are converged and output. The specific calculation steps for reverse calibration of the economic benchmark price are as follows: Fix the globally optimal carbon-rich hydrogen fuel mass ratio, and iteratively adjust the input price of the carbon-rich hydrogen fuel until the comprehensive smelting cost per ton of iron under this compound scheme is equal to the benchmark comprehensive smelting cost under the pure traditional pure pulverized coal fuel injection condition. At this time, the calibrated carbon-rich hydrogen fuel price is the fuel economic benchmark price adapted to the current furnace condition.

[0046] The final output includes four core parameters: optimal fuel blending ratio, fuel economic benchmark price, cost reduction per ton of iron, and furnace condition adaptability conclusions. These parameters directly guide the adjustment of blast furnace on-site injection process and fuel procurement pricing.

[0047] It should be noted that the entire mechanistic calculation model is the underlying computational kernel supporting ratio optimization, cost calculation, and price reverse calibration. It consists of four sub-models connected in series: the S100 benchmark constraint model, the S200 fuel parameter standardization conversion model, the S300 three-dimensional coke ratio coupling correction model, and the S400 comprehensive smelting cost accounting model per ton of iron. All iterative optimization and economic price calibration repeatedly call this mechanistic model to complete quantitative calculations. The specific mechanisms of each sub-module are as follows: I. Sub-model 1: S100 process constraint boundary determination mechanism model core role For any combination of pulverized coal and carbon-rich hydrogen fuel, compliance verification is performed, and infeasible ratios that exceed the safety threshold for blast furnace production are screened out. Only the solutions that meet all process boundaries are retained for subsequent cost calculations. This is the basis for the constraint judgment of the optimization algorithm.

[0048] Core Mechanism Content It includes all hard production constraint thresholds, and the verification dimensions include: 1. Mixing ratio constraints: pure coal powder 60%~85%, hydrogen-rich fuel 15%~40%, with a single adjustment range of ≤5 percentage points; 2. Total pulverized coal injection and fuel constraints for furnace: Total pulverized coal ratio 120~200 kg / tFe, coke-butane ratio 5%~12%, overall coke ratio 330~480 kg / tFe, ore-coke ratio 3.2~4.5; 3. Ash content control constraints: Ash content of mixed pulverized coal ≤14%, comprehensive ash content of coal entering the furnace 11%~16%, with corresponding fluctuation limits; 4. Calorific value and heat balance constraints: The lower heating value of the mixed fuel on a dry basis is 22.0~27.0 MJ / kg, and the comprehensive fuel calorific value per ton of iron is 8.6~9.8 GJ / tFe; 5. Furnace temperature and environmental constraints: Theoretical combustion temperature at the tuyere is 2050~2250℃, volatile matter in pulverized coal is 18%~28%, and total sulfur content in the fuel is ≤0.8%. Only when all indicators meet the threshold ratio is the solution deemed feasible and sent to the next stage of mechanism calculation.

[0049] II. Sub-model 2: S200 fuel wet-to-dry standardization conversion mechanism model core role Eliminating interference from differences in external moisture content among different fuels, unifying the calculation benchmark, and achieving a fair weighted comparison of calorific value, cost, and ash content among different blends and fuels, thus providing a unified standard input for coke ratio correction.

[0050] Core Mechanism Content 1. Moisture Inertia Removal and Conversion Logic: Remove the influence of moisture from the wet basis delivered price and wet basis calorific value of each fuel, and convert them into an absolute dry basis unit price and a dry basis lower heating value. 2. Weighted Calculation Theory of Compounding: Based on the ratio and weighted calculation, the three core mixing indicators of each ratio are output: 1) weighted dry basis lower heating value of the mixed fuel; 2) weighted ash content of the mixed fuel; 3) weighted dry basis unit purchase cost of the mixed fuel.

[0051] III. Sub-model 3: S300 multi-dimensional coupled focal ratio correction mechanism model (core smelting mechanism) core role Abandoning the traditional simplified algorithm of estimating coke consumption based on a single calorific value, this model combines the physicochemical reaction laws of blast furnace ironmaking with three independent metallurgical mechanisms to jointly correct the coke consumption per ton of iron. It accurately quantifies the coke reduction / increase effect brought about by blending carbon-rich and hydrogen-rich fuels, which is the core of the metallurgical mechanism of the entire model.

[0052] It includes three parallel correction mechanisms, which are ultimately superimposed to obtain the actual focal ratio: 1. The calorific value compensation and correction mechanism takes 100% pure pulverized coal injection as the benchmark operating condition and compares the weighted dry basis calorific value difference between the blended fuel and the benchmark fuel. Combined with the coke exothermic equivalent efficiency, the quantification shows that a higher calorific value can reduce the consumption fluctuation of coke, while a lower calorific value requires additional coke, thus eliminating the impact of fuel calorific value differences on the heat balance.

[0053] 2. Hydrogen Reduction and Replacement Correction Mechanism (Unique Core Benefit Mechanism of Hydrogen-Rich Fuel): Based on the effective hydrogen content and combustion conversion rate of the two fuels, the total amount of hydrogen released per unit fuel ratio is calculated; by using the hydrogen replacement coke benchmark coefficient, the amount of coke saved by hydrogen directly reducing iron ore instead of carbon reduction is quantified, which is the core source of reducing the coke ratio of hydrogen-rich fuel.

[0054] 3. The mechanism for correcting ash content and slag quantity disturbances: Increased ash content in mixed fuels will increase slag production. Slag formation heat absorption and decreased permeability in the furnace will increase coke consumption. Based on the correlation coefficient between slag quantity and coke ratio, the increase in coke consumption caused by changes in ash content is calculated.

[0055] 4. Coupling and superposition mechanism: The three types of corrections, namely calorific value, hydrogen replacement and slag quantity, are superimposed to output the actual coke consumption per ton of iron under this ratio.

[0056] IV. Sub-model 4: Coupled accounting mechanism model of S400-ton iron comprehensive smelting cost core role The cost of the entire process is calculated by combining the two main smelting consumables, pulverized fuel and coke, and quantifying the economic differences of different proportion schemes to provide an objective function (minimum cost per ton of iron) for the optimization algorithm.

[0057] Core Mechanism Content 1. Itemized cost calculation: 2. Purchasing cost of pulverized iron fuel: Total pulverized iron consumption per ton × Weighted average dry basis unit cost of mixed fuel; 3. Coke consumption cost: Actual iron-coke ratio per ton after three-dimensional correction × Coke dry basis market price delivered to the plant; 4. Comprehensive Cost Synthesis: The sum of the two costs yields the comprehensive smelting cost per ton of iron per single-ratio process. 5. Economic comparison mechanism: The difference between the cost and the benchmark pure pulverized coal operating condition is calculated to output the cost reduction per ton of iron, which directly reflects the economic benefits of blending hydrogen-rich fuel.

[0058] V. Calling Logic of Mechanism Calculation Model in S500 Iterative Optimization 1. Optimization cycle process: Given a set of hydrogen-rich fuel ratios → send to the constraint model to verify feasibility → if feasible, call the dry basis conversion model → three-dimensional coke ratio correction model to calculate actual coke consumption → cost accounting model outputs the comprehensive cost per ton of iron. 2. Iterative convergence rule: Taking the minimum comprehensive smelting cost per ton of iron as the optimization objective, the feasible blending ratio is iterated within all process constraint boundaries, and the blending ratio scheme with lower cost is continuously updated until the cost value converges, and the globally optimal hydrogen-rich fuel blending ratio is output. 3. Economic benchmark price reverse calibration logic: Fix the optimal ratio, repeatedly adjust the input price of carbon-rich hydrogen fuel, and repeatedly run the whole mechanism accounting model until the comprehensive cost of the compound scheme is equal to the benchmark cost of pure coal powder. At this time, the corresponding price is the economic benchmark price delivered to the plant that is suitable for the current furnace conditions.

[0059] It should be noted that the blast furnace smelting process verification relies on on-site industrial testing and online monitoring systems. It conducts full-cycle process verification of the aforementioned optimal proportioning scheme and fuel usage conditions corresponding to the economic benchmark price. The verification of furnace stability and smelting indicators completes the effect verification, forming a closed-loop verification and scheme optimization basis. The specific content is as follows: Medium- and long-term smelting index benchmarking: Select 3 to 7 consecutive production shifts, using the original pure pulverized coal injection condition as a reference, and statistically compare the core smelting indicators such as total fuel consumption per ton of iron, utilization coefficient, ore-coke ratio, and slag quantity under the two sets of conditions. Verify the deviation between the coke reduction and efficiency improvement effect of hydrogen-rich fuel and the theoretical calculation value. If the deviation exceeds the allowable range (fuel ratio <1%, utilization coefficient <1%, ore-coke ratio <5%, slag ratio <3%), backtrack the parameter conversion, couple the correction model, and fine-tune the benchmark coefficient and related parameters.

[0060] A process verification report will be issued: If all indicators meet the standards, the furnace condition is stable, and the cost and coke reduction effect are consistent with the theoretical calculation, the solution can be put into normal use. If there are problems such as indicators exceeding the limit, furnace condition fluctuations, or cost deviations, the abnormal data will be sent back to the parameter dataset, the iterative optimization process will be restarted, the proportioning range and accounting model will be corrected, until a final implementation solution suitable for the site is formed.

[0061] In this specific embodiment, test parameters were selected for a three-day continuous test. Using the original pure pulverized coal injection condition as a reference, the core smelting indicators such as comprehensive coke ratio, total fuel consumption per ton of iron, utilization coefficient, ore-coke ratio, and slag quantity were statistically compared between the two sets of conditions. The deviation between the coke reduction and efficiency improvement effect of hydrogen-rich fuel and the theoretical calculation value was verified. The changes in the main blast furnace parameters before and after the industrial test are shown in Table 1. Table 1. Changes in main parameters of the blast furnace before and after the industrial test. As shown in Table 1, all indicators are within a safe and reasonable threshold range, the furnace operation is stable, and the cost and coke reduction effect are highly consistent with the theoretical calculation. Therefore, the scheme is deemed to meet the standards for normalized operation.

[0062] In the above detailed description, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features of the single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, wherein each claim stands alone as a preferred embodiment of the invention.

[0063] The disclosed embodiments have been described above to enable any person skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be applied to other embodiments without departing from the spirit and scope of this disclosure. Therefore, this disclosure is not limited to the embodiments given herein, but is consistent with the broadest scope of the principles and novel features disclosed in this application.

[0064] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

[0065] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent proportioning optimization and cost control of carbon-rich hydrogen fuel for blast furnace injection, characterized in that: Includes the following steps: S100. Setting parameters and benchmark dataset: Setting the blending system of blast furnace injection fuel, wherein the blast furnace injection fuel includes a compound combination of traditional pure pulverized coal fuel and carbon-rich hydrogen fuel, and the blending scheme satisfies the following formula: Where: i is used to characterize the grouping of the proportioning scheme; A i Used to characterize the mass ratio of the traditional pure pulverized coal fuel; B i Used to characterize the mass ratio of the carbon-containing hydrogen-rich fuel; Collect blast furnace production baseline parameters and fuel physical property parameters, and set process constraint boundaries in combination with the actual blast furnace production conditions; S200. Perform dry-basis unified conversion of fuel wet-basis parameters: Using a moisture inertia elimination algorithm, the wet-basis delivered price and wet-basis calorific value of all fuels are uniformly converted into absolute dry-basis benchmark parameters; and based on the mass ratio of each group of blast furnace injection fuels, the weighted dry-basis lower heating value, weighted ash content and weighted dry-basis unit cost of the blast furnace injection fuels under different blending schemes are weighted and calculated to complete the standardization of the benchmark parameters of multiple blending schemes; S300. Correcting the coke consumption per ton of iron through multi-dimensional coupling: Based on the blast furnace smelting mechanism, the coke consumption per ton of iron is corrected in conjunction with three dimensions: calorific value compensation, hydrogen reduction replacement, and ash and slag quantity disturbance. Then, the actual coke consumption per ton of iron is calculated under the ratio scheme of each blast furnace injection fuel. S400. Coupled accounting of comprehensive smelting cost per ton of iron: For each group of blast furnace injection fuel ratio schemes, calculate the injection fuel purchase cost and coke consumption cost separately, and sum them to obtain the comprehensive smelting cost per ton of iron throughout the entire process. Also calculate the cost difference and cost reduction between each blast furnace injection fuel ratio scheme and the benchmark control group. S500. Calculate the optimal blending ratio and output parameters: Based on the mechanism accounting model, an iterative optimization algorithm is built to automatically traverse all feasible blending ratio schemes within the preset process constraint boundary, converge and output the globally optimal carbon-rich hydrogen fuel mass blending ratio, and calculate the economic benchmark price to the plant that is suitable for the current furnace condition through reverse calibration using the economic benchmark price; output the optimal fuel blending ratio, fuel economic benchmark price, cost reduction per ton of iron, and furnace condition suitability conclusion.

2. The intelligent proportioning optimization and cost control method for carbon-rich hydrogen fuel used in blast furnace injection according to claim 1, characterized in that: The benchmark parameters for blast furnace production in step S100 include: the total amount of blast furnace pulverized coal fuel and carbon-rich hydrogen fuel used for injection per ton of iron, the lower heating value of coke on a dry basis, the market price of coke delivered to the plant, and the fixed industry-standard smelting benchmark coefficient; the fuel physical property parameters include: lower heating value on a dry basis, moisture content as received, ash content, combustion conversion rate, effective hydrogen content, and the price delivered to the plant on a wet basis; the smelting benchmark coefficient includes the hydrogen replacement coke benchmark coefficient and the standard density of hydrogen.

3. The intelligent proportioning optimization and cost control method for carbon-rich hydrogen fuel used in blast furnace injection according to claim 1, characterized in that: The process constraints in step S100 include: a total pulverized coal ratio range of 120~200 kg / tFe; a traditional pure pulverized coal fuel proportion range of 60%~85%; a carbon-containing hydrogen-rich fuel proportion range of 15%~40%; coke and slag accounting for 5%~12% of the total fuel fed into the furnace; a comprehensive blast furnace coke ratio range of 330~480 kg / tFe; a coke-ore ratio range of 3.2~4.5; ash content of the injected mixed pulverized coal within ±1.5% of the baseline value and not exceeding 14.0%; comprehensive fuel ash content in the furnace maintained at 11.0%~16.0%, with short-term fluctuations not exceeding ±1.0%; a lower heating value range of pulverized coal of 22.0 MJ / kg~27.0 MJ / kg, with single-batch fluctuations ≤±0.8 MJ / kg and 8-hour average fluctuations ≤±0.5 MJ / kg; and a comprehensive fuel calorific value range of 8.6~9.8 GJ / tFe with hourly fluctuations ≤±0.

25. GJ / tFe; The theoretical combustion temperature range of the tuyeres is 2050~2250℃, with instantaneous fluctuations not exceeding ±30℃; the volatile matter content of pulverized coal is 18%~28% on a dry basis, and the comprehensive fuel sulfur content is ≤0.8%; the adjustment range of fuel composition in a single instance shall not exceed 5 percentage points.

4. The intelligent proportioning optimization and cost control method for carbon-rich hydrogen fuel used in blast furnace injection according to claim 1, characterized in that: The weighted dry basis unit cost, the weighted dry basis lower heating value, and the weighted cost of the blast furnace injection fuel in step S200 are expressed by the following formula: Wherein: T a Used to characterize the dry basis price of conventional fuels; T b Used to characterize the dry-basis price of carbon-containing hydrogen-rich fuels; Q a Used to characterize the lower heating value (dry basis) of conventional fuels; Q b W is used to characterize the dry basis lower heating value of carbon-containing hydrogen-rich fuels; W is used to characterize the amount of fuel injected per ton of iron in a blast furnace.

5. The intelligent proportioning optimization and cost control method for carbon-rich hydrogen fuel used in blast furnace injection according to claim 4, characterized in that: The correction method for calorific value compensation in step S300 is as follows: Using pure traditional pulverized coal fuel injection, compare the difference in the weighted dry basis lower calorific value of each group of compounded blast furnace injection fuels, and combine this with the equivalent exothermic efficiency of coke to calculate the increase or decrease in coke consumption corresponding to the calorific value fluctuation, expressed as follows: in: The value used to characterize the effect of calorific value on the consumption of coke per ton of iron under the i-th proportioning scheme; C ave,1 The amount of coke consumed per ton of iron under baseline operating conditions; Q ave,1 The weighted dry basis lower heating value is used to characterize the baseline operating condition; Q ave,i The weighted dry basis lower heating value is used to characterize the i-th proportioning scheme; W is used to characterize the amount of fuel injected per ton of iron in the blast furnace; Q c Used to characterize the dry basis lower heating value of coke.

6. The intelligent proportioning optimization and cost control method for carbon-rich hydrogen fuel used in blast furnace injection according to claim 5, characterized in that: The correction method for hydrogen reduction replacement in step S300 is as follows: Based on the effective hydrogen content and combustion conversion rate of the traditional pure pulverized coal fuel and the carbon-rich hydrogen fuel, the total amount of hydrogen that can be released per unit mass of fuel is calculated. Combined with the hydrogen replacement coke benchmark coefficient, the amount of coke replaced by the hydrogen reduction reaction is calculated, expressed by the following formula: in: Used to characterize the absolute impact of hydrogen reduction on the consumption of ton iron coke under the i-th ratio scheme; Used to characterize the absolute impact of hydrogen reduction on the consumption of coke per ton of iron under benchmark operating conditions; The value is used to characterize the difference in the amount of coke consumed per ton of iron due to the change in the ratio; and The combustion conversion rates of the conventional pure pulverized coal fuel and the carbon-containing hydrogen-rich fuel are used to characterize the combustion conversion rates of the two fuels, respectively. and The effective hydrogen content is used to characterize the conventional pure pulverized coal fuel and the carbon-containing hydrogen-rich fuel, respectively. Used to characterize the standard density of hydrogen; Used to characterize the hydrogen-displaced coke reference coefficient.

7. The intelligent proportioning optimization and cost control method for carbon-rich hydrogen fuel used in blast furnace injection according to claim 6, characterized in that: The correction method for the ash content disturbance in step S300 is as follows: Based on the weighted ash content of the blast furnace injection fuels compounded under each blend ratio, the amount of slag generated after fuel combustion is calculated, and then the coke consumption fluctuation caused by the change in slag content is calculated based on the blast furnace slag content-coke ratio linkage coefficient, expressed by the following formula: in: and These values ​​are used to characterize the influence of ash content on slag quantity under the i-th proportioning scheme and the baseline working condition, respectively. and The ash content of the conventional pure pulverized coal fuel and the carbon-rich hydrogen fuel are respectively used to characterize the ash content; γ is used to characterize the correlation coefficient between blast furnace slag amount and coke ratio. This value is used to characterize the fluctuation in coke consumption caused by the change in slag quantity.

8. The intelligent proportioning optimization and cost control method for carbon-rich hydrogen fuel used in blast furnace injection according to claim 7, characterized in that: In step S300, the actual coke consumption per ton of iron under each proportion scheme is obtained by superimposing the three-dimensional correction quantities and expressed by the following formula: in: The amount of coke consumed per ton of iron used to characterize the actual ratio of the i-th formulation. The value used to characterize the effect of calorific value on the consumption of coke per ton of iron under the i-th proportioning scheme; The value is used to characterize the difference in the amount of coke consumed per ton of iron due to the change in the ratio; This value is used to characterize the fluctuation in coke consumption caused by the change in slag quantity.

9. The intelligent proportioning optimization and cost control method for carbon-rich hydrogen fuel used in blast furnace injection according to claim 8, characterized in that: The comprehensive smelting cost per ton of iron in step S400 is expressed by the following formula: in: Used to characterize the cost of coke consumption; Used to characterize the price of coke delivered to the plant; Used to characterize the actual coke consumption per ton of iron; Used to characterize the procurement cost of the injected fuel; This is used to characterize the overall smelting cost of the entire process for each ton of iron.

10. The intelligent proportioning optimization and cost control method for carbon-rich hydrogen fuel used in blast furnace injection according to claim 9, characterized in that: The specific calculation steps of the iterative optimization algorithm described in step S500 are as follows: The real-time delivered prices of the traditional pure pulverized coal fuel and the carbon-rich hydrogen fuel are set. The lowest comprehensive smelting cost per ton of iron is taken as the iterative convergence objective. Under the constraints of the process boundary, the globally optimal mass ratio of the carbon-rich hydrogen fuel and its corresponding optimal fuel blending ratio are converged and output. The specific calculation steps for the reverse calibration of the economic benchmark price are as follows: Fix the globally optimal mass ratio of the carbon-rich hydrogen fuel, and iteratively adjust the input price of the carbon-rich hydrogen fuel until the comprehensive smelting cost per ton of iron under this compound scheme is equal to the benchmark comprehensive smelting cost under the pure traditional pure pulverized coal fuel injection condition. At this time, the calibrated carbon-rich hydrogen fuel price is the fuel economic benchmark price adapted to the current furnace condition.

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