Blast furnace batching optimization system, method and equipment and computer storage medium

Abstract

The invention discloses a blast furnace batching optimization system, method and equipment and a computer storage medium, and relates to the technical field of blast furnace ironmaking automation. The system comprises a data acquisition module, a data processing module, a computational logic module and a front-end presentation module. The method comprises the following steps: automatically collecting real-time component data of crude fuel; carrying out batch average processing on the data to obtain a calculation condition; based on a material balance and thermodynamic principle, performing batching calculation to obtain blast furnace production parameters and cost indexes; and displaying a result through a front-end interface. Through automatic data acquisition and integrated calculation, the defects that traditional manual batching calculation is low in efficiency, large in error and lagged in cost control are overcome, accurate, rapid and optimized blast furnace batching is achieved, production decisions are effectively guided, and the production cost and the labor intensity of operators are reduced.

Description

Technical Field

[0001] This invention relates to the field of blast furnace ironmaking automation technology, and in particular to a blast furnace batching optimization system, method, equipment, and computer storage medium. Background Technology

[0002] Blast furnace ironmaking is one of the most critical technological processes in the steel industry, involving complex physicochemical reactions. During smelting, the furnace charge (including iron ore, coke, and flux) is introduced from the top of the furnace in a specific ratio, reacting with the high-temperature hot blast blown in from the bottom to ultimately produce molten iron and slag. Blast furnace smelting uses a wide variety of raw materials, primarily including iron ore (such as sinter, pellets, and lump ore), fuel (such as coke and pulverized coal), and flux (such as limestone and dolomite). Long-term production practice has shown that using a single ore raw material is insufficient to achieve ideal technical and economic indicators. A rational blending of multiple raw materials is necessary to optimize the furnace charge structure, effectively ensuring slag basicity, stabilizing furnace conditions, reducing fuel consumption, and ensuring molten iron quality. Therefore, blast furnace charge preparation is a crucial aspect of blast furnace production management.

[0003] Currently, many steel companies still heavily rely on traditional manual, offline calculation methods in the blast furnace feeding process. This traditional method has several significant drawbacks: 1. Low computational efficiency and poor real-time performance: Manual calculation is cumbersome, time-consuming, and labor-intensive, making it unsuitable for the fast-paced production and frequent changes in raw materials required in blast furnaces. When raw material conditions or smelting processes change, it is impossible to quickly provide new batching schemes, resulting in delayed production guidance.

[0004] 2. Difficulty in guaranteeing calculation accuracy: Manual calculations are prone to errors and are often based on idealized and simplified models, failing to comprehensively and accurately consider the complex chemical composition of various raw materials and fuels (such as Fe, etc.). CaO The composition of raw materials (such as MgO, S, P, K, Na, Zn, etc.) and their behavior during the smelting process (such as the distribution of elements in molten iron and slag) can lead to discrepancies between the batching scheme and actual production needs.

[0005] 3. Lagging and isolated data foundation: Traditional offline batching methods are difficult to integrate effectively with the company's real-time database system. Operators often cannot obtain the latest and most accurate raw material composition data and price information. Poor communication between various production units (such as raw material yards, laboratories, and blast furnace control rooms) results in untimely updates to the basic data on which calculations are based, and even human error in transmission.

[0006] 4. Lack of systematic analysis and cost control: Manual calculations usually focus on meeting basic process requirements (such as alkalinity balance), making it difficult to simultaneously and quickly calculate and compare key economic and technical indicators such as production costs, fuel ratio, slag volume, and harmful element load under different batching schemes. This results in insufficient data support for production decisions, which is not conducive to cost control and efficiency optimization.

[0007] The aforementioned problems can easily lead to inaccurate ingredient calculations, which in turn can cause fluctuations in product quality, increased production costs, unsatisfactory blast furnace operation, and even production accidents, severely restricting the improvement of blast furnace production efficiency and intelligent development.

[0008] Therefore, there is an urgent need in this field for a technical solution that can automatically and intelligently complete blast furnace batching calculations and integrate production and cost data in real time, in order to overcome the shortcomings of existing technologies and achieve precise guidance and scientific decision-making for blast furnace production. Summary of the Invention

[0009] To address these issues, embodiments of the present invention provide a blast furnace batching optimization system, method, equipment, and computer storage medium, which solve the problems of low efficiency, insufficient calculation accuracy, and difficulty in real-time optimization of production costs caused by the reliance on manual, offline batching calculation methods in the prior art.

[0010] To address the aforementioned technical problems, embodiments of the present invention provide a blast furnace feeding optimization system, the system comprising: The data acquisition module is used to automatically collect real-time composition data of various raw materials and fuels for the blast furnace from the database; The data processing module, connected to the data acquisition module, is used to perform batch averaging on the acquired component data to obtain the original conditions for ingredient calculation and to acquire material prices and operating parameters. The calculation logic module is connected to the data processing module and is used to perform batching calculations based on the original conditions, material prices and operating parameters to obtain blast furnace production parameters and cost indicators. The front-end presentation module is connected to the calculation logic module and is used to provide a parameter input interface and display the calculation results.

[0011] Preferably, the raw materials include multiple types of sintered ore, pellets, lump ore, coke, pulverized coal, and flux; the composition data includes at least Fe, CaO MgO, S, P , And the content of ZnO.

[0012] Preferably, the batch averaging process performed by the data processing module is to take the average value of the latest N batches of component data.

[0013] Preferably, the blast furnace production parameters calculated by the calculation logic module include at least one of the following: slag basicity, slag quantity, coke ratio, coal ratio, fuel ratio, theoretical iron content, sulfur load, alkali metal load, zinc load, theoretical combustion temperature, blast kinetic energy, or blast furnace gas quantity.

[0014] Preferably, the cost indicators calculated by the calculation logic module include the cost per ton of iron ore, the cost per ton of iron fuel, and the cost per ton of iron raw materials.

[0015] Preferably, the front-end presentation module includes a raw material weight and price input panel, an operation parameter input panel, a pig iron composition setting panel, a raw material bulk density setting panel, an element distribution coefficient setting panel, and an ore blending calculation result panel.

[0016] This invention also provides a blast furnace feeding optimization method, which is applied to the blast furnace feeding optimization system described above, and includes the following steps: The data acquisition module automatically collects real-time composition data of various raw materials and fuels for the blast furnace from the database. The data processing module performs batch averaging on the collected component data to obtain the original conditions for ingredient calculation and to acquire material prices and operating parameters. The calculation logic module performs batching calculations based on the original conditions, material prices, and operating parameters to obtain blast furnace production parameters and cost indicators. The front-end presentation module provides a parameter input interface and displays the calculation results.

[0017] Preferably, the step of performing batching calculation includes: calculating the content of multiple elements in a batch of material based on the input raw material batch weight; calculating the quantity of each element entering pig iron and slag based on the element distribution coefficient; calculating the batch iron quantity and pig iron composition based on the quantity of elements entering pig iron; and calculating the slag composition and slag quantity based on the quantity of elements entering slag.

[0018] Preferably, the step of performing batching calculation further includes: calculating at least one of the following based on the batch iron quantity, fuel blending quantity, and operating parameters: coke ratio, coal ratio, fuel ratio, coke load, comprehensive load, theoretical output, theoretical combustion temperature, blast kinetic energy, and vortex zone depth.

[0019] Preferably, the step of performing batching calculation further includes: calculating the cost per ton of iron ore, the cost per ton of fuel, and the cost per ton of raw materials based on the batch iron quantity, the combined weight of various materials, and their prices.

[0020] This invention also provides an electronic device, which includes a processor, a memory, and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the blast furnace feed optimization method described above.

[0021] This invention also provides a computer storage medium storing a computer software product, the computer software product including several instructions to cause a computer device to execute the blast furnace batching optimization method described above.

[0022] As can be seen from the above technical solutions, this invention application has the following beneficial effects: (1) This invention, through a data acquisition module and a data processing module, realizes the automatic and real-time acquisition of raw material composition data from the database, and adopts strategies such as batch averaging, completely replacing the traditional cumbersome and error-prone manual entry and offline calculation mode. This not only greatly improves the calculation efficiency and can quickly respond to the changing needs of production materials, but also ensures the accuracy and timeliness of the calculation basis from the data source, providing a solid foundation for stable production.

[0023] (2) The computational logic module of this invention integrates a complete metallurgical thermodynamics and material balance model, which can calculate key process parameters such as furnace charge structure, slag basicity, slag quantity, coke ratio, and fuel ratio, as well as advanced parameters reflecting furnace conditions such as theoretical combustion temperature and blast kinetic energy, based on real-time data. This multi-parameter, systematic online calculation provides operators with comprehensive and accurate production forecasts, enabling them to optimize operating procedures in advance, effectively prevent furnace condition fluctuations, and achieve stable and smooth operation of the blast furnace and optimization of indicators.

[0024] (3) This invention innovatively integrates cost index calculation into the batching calculation process, and can simultaneously output economic indicators such as cost per ton of iron ore and cost per ton of iron fuel. This allows blast furnace operators to see the impact on production costs in real time when adjusting technical solutions, so as to quickly select the technically feasible and economically optimal batching solution while ensuring process requirements. This achieves deep integration and synergistic optimization of production technology management and cost control, and directly improves the economic benefits of enterprises. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Referring to the drawings will make the features and advantages of the present invention clearer. The drawings are illustrative and should not be construed as limiting the present invention in any way. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a block diagram of a blast furnace feed optimization system provided by the present invention; Figure 2 This is the raw material weight and price input section in this invention; Figure 3 This is the operation parameter input module in this invention; Figure 4 This is the iron composition setting module in this invention; Figure 5 This is the raw material bulk density setting plate in this invention; Figure 6 This is the element allocation coefficient setting module in this invention; Figure 7 This is the ore blending calculation results section in this invention; Figure 8 This is the ore blending calculation results section in this invention; Figure 9 This is a flowchart of a blast furnace feed optimization method provided by the present invention; Figure 10 This is the calculation process of the ingredient batching system in this invention.

[0026] Explanation of reference numerals in the accompanying drawings: 100, Data acquisition module; 200, Data processing module; 300, Calculation logic module; 400, Front-end presentation module. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] This embodiment aims to solve the technical problems of low efficiency, poor accuracy, data lag, and lack of real-time cost optimization in the existing manual offline calculation of blast furnace batching. By providing a blast furnace batching optimization system, method, electronic equipment, and computer storage medium, it realizes the automation, real-time and accuracy of batching calculation, while simultaneously calculating cost indicators, providing data support for blast furnace production decisions.

[0029] Example 1: To address the inefficiencies, insufficient accuracy, and difficulty in real-time optimization of production costs caused by manual, offline batching calculations in blast furnace batching, such as... Figure 1 As shown, this invention proposes a blast furnace feeding optimization system, which includes: The data acquisition module 100 is used to automatically acquire real-time composition data of various raw materials and fuels for the blast furnace from the database; The data processing module 200 is connected to the data acquisition module 100 and is used to perform batch averaging on the acquired component data to obtain the original conditions for ingredient calculation and to acquire material prices and operating parameters. The calculation logic module 300 is connected to the data processing module 200 and is used to perform batching calculations based on the original conditions, material prices and operating parameters to obtain blast furnace production parameters and cost indicators. The front-end presentation module 400 is connected to the calculation logic module 300 and is used to provide a parameter input interface and display the calculation results.

[0030] As can be seen from the above technical solution, this invention proposes a blast furnace batching optimization system. This system uses a data acquisition module 100 to automatically and in real-time acquire raw material composition data, avoiding errors and delays caused by manual data entry. A data processing module 200 performs batch averaging of the data and integrates price and operating parameters, ensuring the stability and reliability of the calculation basis. A calculation logic module 300 performs comprehensive batching calculations, simultaneously outputting key production parameters and cost indicators, achieving collaborative analysis and optimization of technical solutions and economics. Finally, a front-end presentation module 400 provides an intuitive interactive interface and clear result display, effectively supporting rapid decision-making by production personnel. This system forms a complete closed loop from automatic data acquisition, intelligent processing, integrated calculation to result visualization, significantly improving the accuracy, efficiency, and intelligence level of blast furnace batching calculations.

[0031] In this embodiment, the core function of the data acquisition module 100 is to automatically collect real-time composition data of blast furnace raw materials from the enterprise's real-time database (such as the MES system database and raw material testing database), replacing traditional manual entry and eliminating data entry errors and delays.

[0032] (1) Collection objects and data scope The collected raw materials cover all materials required for the entire blast furnace smelting process, specifically including 6 major categories. The composition data for each category covers at least 10 key elements / indicators, as detailed below: Sintered ore: Fe content, Content, CaO content, MgO content, FeO content, content, content, Content, S content, P content, Content, ZnO content (12 items in total); Pellet: Fe content, Content, CaO content, MgO content, Content, S content, P content, R (alkalinity) Content, FeO content, ZnO content (11 items in total); Lump ore: Consistent with the composition of sintered ore (12 items), including composition data for specific categories such as Newman mixed lump ore, Australian PB lump ore, Lop River lump ore, and vanadium-titanium lump ore; Coke: Moisture, sulfur content, ash content, volatile matter, fixed carbon (5 basic indicators), and the ash content of coke. CaO, MgO PbO, ZnO, , , (9 ash components); Pulverized coal (injected fuel): moisture, sulfur content, ash content, volatile matter, fixed carbon (5 basic indicators), and 12 elemental components consistent with sintered ore; Flux: including dolomite, magnesite, silica, serpentine, etc., with the same composition as sintered ore (12 items).

[0033] (2) Data collection method The module establishes a real-time connection with the company's existing database through a database interface, and sets the data collection frequency to within 1 minute after each batch of raw materials arrives at the factory / is tested, ensuring data real-time performance. If the database is temporarily interrupted, the module has offline caching and online retransmission functions to avoid data loss.

[0034] In this embodiment, the data processing module 200 is connected to the data acquisition module 100. It performs noise reduction and standardization processing on the acquired raw component data to generate the original conditions for ingredient calculation. At the same time, it integrates the auxiliary parameters required for calculation to provide accurate input for the subsequent calculation logic module 300.

[0035] (1) Batch average treatment For the same type of raw materials (such as sintered ore), since there are slight fluctuations in the composition between different batches, the module uses the average of the composition data from the latest N batches for processing (N=3 in this embodiment, which can be adjusted according to the frequency of production batches of the enterprise). The specific logic is as follows: For example, if the Fe content of batches n-2, n-1, and n of sintered ore is 56.25%, 56.80%, and 56.36% respectively, then the average Fe content after treatment is (56.25 + 56.80 + 56.36) / 3 = 56.47%. If there are fewer than 3 batches of a certain type of raw material (such as a newly added category), the actual average value of the collected batches will be used; if there is only 1 batch, the data of that batch will be used directly.

[0036] (2) Acquisition of auxiliary parameters In addition to component data, the module also automatically / manually acquires the following parameters (some parameters can be preset to default values ​​and can be manually adjusted): Material prices: Real-time purchase prices (RMB / t) of various raw materials and fuels, such as B# sintered ore RMB 966 / t, Australian sintered ore RMB 1302 / t, and coke RMB 2277 / t, synchronized from the enterprise procurement management system; Operating parameters: feed line (m), hourly oxygen enrichment (m³ / h), hourly coal quantity (t / h), pulverized coal carrier gas flow rate (m³ / h), hourly material speed (batch / h), compression ratio (%), number of tuyeres, air supply area (m²), air volume (m³ / min), air temperature (°C), air pressure (kPa), blast humidity (g / m³), top pressure (kPa), top temperature (°C), humidification capacity (t / h), average tuyer diameter (m), total iron ore quantity (t); Raw material bulk density: coke 0.51t / m³, sinter 1.85t / m³, pellets 2.38t / m³, lump ore 2.2t / m³, manganese ore 1.8t / m³, limestone 1.55t / m³, dolomite 1.55t / m³, silica 1.5t / m³, serpentine 1.54t / m³, magnesite 1.54t / m³ (preset values, can be adjusted according to the actual bulk density of the raw materials); Element distribution coefficients: The distribution ratio of Fe, Mn, P, and S elements in pig iron and slag. For example, the distribution coefficient of Fe in pig iron is 99.7%, the distribution coefficient of Mn in pig iron is 60%, the distribution coefficient of P in pig iron is 100%, the distribution coefficient of S in pig iron is 5%, the distribution coefficient of Fe in slag is 0.3%, the distribution coefficient of Mn in slag is 40%, the distribution coefficient of P in slag is 0%, and the distribution coefficient of S in slag is 85% (based on the preset blast furnace smelting system, and supports fine-tuning by technical personnel).

[0037] In this embodiment, the calculation logic module 300 is the core of the system and is connected to the data processing module 200. Based on the original component data and auxiliary parameters, it calculates the blast furnace production parameters and cost indicators through a preset mathematical model. The calculation process is fully automated and requires no manual intervention.

[0038] The calculation logic follows the sequence of "elemental balance → pig iron / slag composition calculation → production parameter calculation → cost index calculation", and the specific steps are as follows: Step 1: Calculate the elemental content of a single material Based on the processed composition data and the combined weight of the materials, the actual content of each element in each material is calculated using the following formula (taking Fe as an example; the same applies to other elements): Fe content (t) of each material = Fe ratio of material (%) / 100 × weight of material combination (t); Example: If the weight of sinter #b is 46t and the Fe ratio is 56.25%, then the Fe content of sinter #b is 56.25 / 100×46=25.875t; Alkalinity of each material = CaO content of the material / material content.

[0039] Step 2: Calculate the total element content of a batch of material The formula for summing the total content of the same element in iron ore (sintered ore + pellets + lump ore), fuel (coke + pulverized coal), and flux is as follows: The content of a certain element in a batch of materials (t) = Σ (the content of that element in iron ore + the content of that element in fuel + the content of that element in flux); Example: Total Fe content in a batch of material = Fe content of sintered ore + Fe content of pellets + Fe content of lump ore + Fe content of coke + Fe content of pulverized coal + Fe content of flux ≈ 39.81t.

[0040] Step 3: Calculate the amount of elemental reduction and slag formation. Based on the element distribution coefficient, the elements entering the pig iron (reduction quantity) and the elements entering the slag (slag formation quantity) are distinguished. The core formula is as follows: Reduction quantity (Fe element) = Fe distribution coefficient of iron (%) / 100 × Fe content of a batch (t); Example: If the Fe distribution coefficient is 99.7%, then the amount of Fe reduced = 99.7 / 100 × 39.81 ≈ 39.69t; Slag formation quantity (Fe element) = (100 - Fe distribution coefficient of iron) / 100 × Fe content of a batch (t); Example: Fe content in slag formation = (100 - 99.7) / 100 × 39.81 ≈ 0.20t; The amount of Si element reduced = the Si content of pig iron (%) × 60 / 28 (the atomic weight conversion factor of Si); Slag production quantity (Si element) = Si content of a batch (t) - Reduction quantity (Si element) (t); The calculation logic for the reduction / slag formation of Mn, P, and S elements is consistent with that for Fe, while that for CaO, All slag-forming elements such as MgO enter the slag (slag quantity = the content of this element in a batch of material).

[0041] Step 4: Calculate the composition and alkalinity of the slag. Based on the amount of slag produced, the content of each component in the slag and its basicity (a key indicator for measuring slag fluidity) are calculated using the following formula: Slag composition Content (t) = Slag production quantity (Si element) (t); Slag composition CaO content (t) = slag quantity (CaO) (t); Slag composition MgO content (t) = slag quantity (MgO) (t); Slag composition MnO content (t) = slag quantity (MnO) (t); Slag composition FeO content (t) = slag quantity (FeO) (t); Slag composition (S / 2) = Slag production quantity (S element) / 2; Total slag composition (t) = Σ ( +CaO+ Content of (MgO+MnO+FeO+S / 2); Slag composition Ratio = Slag Composition Content / Total slag composition × 100; CaO content in slag = CaO content in slag / Total slag composition × 100; Slag composition Ratio = Slag Composition Content / Total slag composition × 100; MgO content in slag = MgO content in slag / Total slag composition × 100; MnO content in slag = MnO content in slag / Total slag composition × 100; FeO proportion in slag = FeO content in slag / Total slag composition × 100; Slag composition S / 2 ratio = Slag composition (S / 2) / Total slag composition × 100; Slag basicity R2 = Slag CaO content / Slag SiO2 content (calculated value in this example ≈ 1.195); Slag basicity R3 = (Slag CaO content + Slag MgO content) / Slag Content (calculated value in this example ≈ 1.495); Slag basicity R4 = (Slag CaO content + Slag MgO content) / (Slag content) Content + Slag Content (calculated value in this embodiment ≈ 1.035); Sulfur distribution coefficient Ls = S distribution coefficient of slag / S distribution coefficient of iron (in this example, Ls = 95% / 5% = 19).

[0042] Step 5: Calculate the pig iron composition and batch iron quantity Based on the reduction quantity, the proportions of each component in pig iron and the theoretical iron yield (batch iron quantity) for each batch are calculated using the following formula: The Fe content of pig iron (t) = the amount of Fe element reduced (t) ≈ 39.69t; The pig iron composition has the following Si ratios: 0.40% (preset value, which can be adjusted according to steelmaking requirements) and C ratios: 4.8% (preset value). The Mn / P / S content of pig iron = the amount of each element reduced (t); Iron quantity (t) = (Fe content + Mn content + P content + S content) / (100 - Si ratio - C ratio) × 100; Example: Fe=39.69t, Mn=0.01t, P=0.073t, S=0.009t, then the batch iron quantity = (39.69+0.01+0.073+0.009) / (100-0.4-4.8)×100≈41.96t; The proportion of each component in pig iron = the content of the corresponding element / the amount of iron in the batch × 100 (e.g., Fe proportion = 39.69 / 41.96 × 100 ≈ 94.59 (%)).

[0043] Step Six: Calculate Blast Furnace Production Parameters Based on batch iron quantity, blend weight, and operating parameters, key production parameters of the blast furnace are calculated, covering categories such as load, specific consumption, output, wind speed, and temperature. The core parameters and formulas are as follows: Load type: Coke load = Total iron ore blend (t) / Coke blend (t); Overall load = Total iron ore blend amount / (Pulverized coal blend amount × 0.8 + Coking coal blend amount × 0.8 + Coke blend amount × (1 - Coke moisture / 100)); Slag quantity (kg / t iron) = Total slag composition / Batch iron quantity × 1000; Coal ratio (kg / t iron) = Coal powder blending amount (t) / Batch iron amount (t) × 1000; Coke-to-butane ratio (kg / t iron) = Coke-to-butane blending amount × (1 - Coke-to-butane moisture content / 100) / batch iron amount × 1000; Dry coke ratio in the furnace (kg / t iron) = coke blending amount × (1 - coke moisture / 100) / batch iron amount × 1000; Overall coke ratio (kg / t iron) = dry coke ratio fed into the furnace + coal ratio × 0.8 + coke-to-butane ratio × 0.8; Fuel ratio (kg / t iron) = Dry coke ratio in the furnace + Coal ratio + Coke-butane ratio; Coke loss = coke-to-coke ratio + dry coke ratio fed into the furnace; Ore consumption (kg / t iron) = Total iron ore blending amount / Batch iron amount / 0.993 × 1000 (0.993 is the iron ore recovery rate coefficient); Flux consumption per unit (kg / t iron) = Σ (dolomite + magnesite + silica + serpentine mix amount) / batch iron quantity × 1000; Dolomite consumption per unit (kg / t iron) = Dolomite block quantity / batch iron quantity × 1000; Air consumption per ton of iron = (air volume × 60 + hourly oxygen enrichment) / hourly material speed / batch iron quantity; Air consumption per ton of coke = (air volume × 60 + hourly oxygen enrichment) / hourly material rate / (coke blending amount + coke-butane blending amount); Load control type: S load (kg / t iron) = S content of a batch of material / iron quantity of a batch × 1000; Titanium load (kg / t iron) = Ti element content of a batch / iron quantity of a batch × 1000; Alkali metal load (kg / t iron) = (K content of a batch of material + Na content) / iron quantity of a batch × 1000; Zn load (kg / t iron) = Zn element content of a batch / iron quantity of a batch × 1000; Blast furnace charging operation and volume matching: Batch volume = Σ(amount of each material / bulk density of each material) / compression ratio; Number of batches reaching the tuyere = (working volume - material line × 3.14 × furnace throat radius × furnace throat radius) / batch volume; Output and efficiency categories: Theoretical shift output (t) = hourly material rate (batch / h) × batch iron quantity (t / batch) × 8 (hour / shift); Theoretical daily output (t) = Theoretical shift output × 3 (shifts / day); Utilizing the coefficient (t / ( = Theoretical daily output / effective blast furnace volume (in this embodiment, the blast furnace volume is 2500m³, so the utilization coefficient = theoretical daily output / 2500); Wind temperature and wind speed: Theoretical combustion temperature (°C) = 1559 + 0.839 × air temperature + 4.972 × hourly oxygen enrichment / (60 × air volume + hourly oxygen enrichment) × 1000 - 6.033 × air humidity - 3.15 × hourly coal quantity × / (60 × air volume + hourly oxygen enrichment); Actual wind speed (m / s) = (air volume + hourly oxygen enrichment / 60 + humidification capacity × 1000 / 60 × 22.4 / 18) × (273 + air temperature) / 273 × 101 / (101 + air pressure) / 60 / air supply area × 803.6 / (803.6 - blower air humidity); Blower kinetic energy (kJ / s) = (1.251 × 0.79 × air volume + 1.429 × (0.21 × air volume + hourly oxygen enrichment / 60) + (air volume + hourly oxygen enrichment / 60) × blower humidity / 1000 / (1 - blower humidity / 803.6)) × actual wind speed² / 2 / 60 / 9.81 / number of air outlets; Gas and swirl zone type: Furnace gas volume (m³ / min) = 1.24 × air volume + hourly oxygen enrichment / 30 + 38 × hourly coal volume; The gas flow velocity in the throat (m / s) = (air volume × 1.24 + hourly oxygen enrichment / 30 + hourly coal quantity × 38) / (throat radius × throat radius × 3.14 × 60) × (273 + top temperature / 4096 × 600) / 273 × 101 / (101 + top pressure); The depth of the swirling zone (m) = 0.88 + 0.000092 × [(0.5 × ((4 × (hourly oxygen enrichment / 60 + 0.21 × air volume) / (hourly oxygen enrichment / 60 + air volume) + 28) / 22.4 + blower humidity / 1000) / 9.8 × (air volume + hourly oxygen enrichment / 60) / 60³ × (1 + 22.4 × blower humidity / 18000)² × (101.3 / (101.3 + air pressure))² × ((273 + air temperature) / 273)²) / (number of tuyeres³ × (0.785 × average tuyere diameter²)²)] - 0.00031 × hourly coal quantity / number of tuyeres; Height of the swirling zone (m) = 22.856 / (Actual wind speed² / (9.81 × 0.05))^0.404 × [0.88 + 0.000092 × ((0.5 × ((4 × (hourly oxygen enrichment / 60 + 0.21 × air volume) / (hourly oxygen enrichment / 60 + air volume) + 28) / 22.4 + blower humidity / 1000) / 9.8 × (air volume + hourly oxygen enrichment)] Oxygen enrichment / 60) / 60³×(1+22.4×blowing humidity / 18000)²×(101.3 / (101.3+wind pressure))²×((273+wind temperature) / 273)²) / (number of tuyeres³×(0.785×average tuyere diameter²)²))-0.00031×hourly coal consumption / number of tuyeres]^1.286 / 0.05^0.286.

[0044] Step 7: Calculate cost indicators Based on the material combination weight, price, and batch iron quantity, calculate the cost per ton of iron and the daily cost using the following formula: Cost per ton of iron ore (RMB / t iron) = Σ (amount of each type of iron ore × iron ore price × (1 - moisture content of iron ore / 100)) / quantity of iron ore in batch; Fuel cost per ton of iron (RMB / t iron) = Σ (coke blending amount × coke price × (1 - coke moisture / 100) + pulverized coal blending amount × pulverized coal price × (1 - pulverized coal moisture / 100) + coke-butane blending amount × coke-butane price × (1 - coke-butane moisture / 100)) / batch of iron; Cost of raw materials per ton of iron (RMB / t iron) = Cost of iron ore per ton + Cost of fuel per ton of iron; Daily ore cost (yuan) = Theoretical daily output (t) × Cost per ton of iron ore (yuan / t iron); Daily total raw material cost (yuan) = theoretical daily output × cost of iron raw materials per ton.

[0045] In this embodiment, the front-end presentation module 400 provides users (blast furnace technicians and operators) with a visual interface for parameter input and result display. It supports browser access, has a clear layout, and is easy to operate. Specifically, it includes six core modules (such as...). Figures 2 to 8 (as shown) (1) Raw material weight and price input section (e.g.) Figure 2 (As shown) Function: Input / adjust the blending ratio (%), blending amount (t), and price (yuan / t) of various raw materials and fuels. The default value for unused materials is 0. Interface elements: A list categorized by sinter, pellets, lump ore, coke, pulverized coal, and flux; each category of material has a corresponding ratio input box, a quantity display box (automatically calculated: quantity = total iron ore × ratio / 100), and a price input box; Example: Total iron ore volume is 67.90t, and the proportion of b# sinter is 78.03%. Therefore, the amount to be added is 67.90 × 78.03% ≈ 53.00t, and the price is 966 yuan / t.

[0046] (2) Operation parameter input panel (e.g.) Figure 3 (As shown) Function: Input / adjust blast furnace operating parameters; some parameters (such as air volume and air temperature) can be synchronized from the blast furnace main control system. Interface elements: Divided into 5 groups: material line parameters (material line, compression ratio), air supply parameters (air volume, air temperature, air pressure, blower humidity, hourly oxygen enrichment), pulverized coal injection parameters (hourly coal quantity, pulverized coal carrier gas flow rate), material velocity parameters (hourly material velocity), and duct parameters (number of ducts, air supply area, average duct diameter). Each parameter has a corresponding value input box and a unit selection box.

[0047] (3) Pig iron composition setting section (e.g.) Figure 4 (As shown) Function: Set the target Si and C content of pig iron (other components are calculated); Interface elements: Si ratio input box (default 0.4%), C ratio input box (default 4.8%).

[0048] (4) Raw material bulk density setting plate (e.g.) Figure 5 (As shown) Function: View / adjust preset values ​​for the bulk density of various raw materials; Interface elements: A list categorized by coke, sinter, pellets, lump ore, and flux; a bulk density display box (default value) for each type of raw material; and a modify button. After modification, the reason for the adjustment (such as changes in raw material particle size) must be entered.

[0049] (5) Element allocation coefficient setting section (e.g.) Figure 6 (As shown) Function: View / adjust the iron / slag distribution coefficients of Fe, Mn, P, and S elements; Interface elements: The table displays the element name, iron distribution coefficient (%), and slag distribution coefficient (%). Each coefficient has a corresponding value input box with adjustable range limits (e.g., iron Fe distribution coefficient 99%-99.8%).

[0050] (6) Ore blending calculation results section (e.g.) Figure 7 , Figure 8 (As shown) Features: Displays all calculation results in tabular form, supports exporting to Excel, printing, and comparison with historical solutions; Interface elements are divided into: basic results area (batch iron quantity, slag quantity, slag basicity), production parameters area (coke ratio, coal ratio, fuel ratio, theoretical daily output, utilization coefficient), load control area (S load, alkali metal load, Zn load), and cost indicators area (cost per ton of iron ore, fuel cost per ton of iron, total daily cost).

[0051] Example 2: Figure 9 As shown, the present invention provides a blast furnace feeding optimization method, which is applied to the blast furnace feeding optimization system of the above embodiment one, and includes the following steps: S1: Automatically collects real-time composition data of various raw materials and fuels for the blast furnace from the database through the data acquisition module; S2: The data processing module performs batch averaging on the collected component data to obtain the original conditions for ingredient calculation and to acquire material prices and operating parameters. S3: Through the calculation logic module, based on the original conditions, material prices and operating parameters, the batching calculation is performed to obtain the blast furnace production parameters and cost indicators; S4: Provides a parameter input interface and displays calculation results through the front-end presentation module.

[0052] In step S1, the system's data acquisition module 100 automatically collects real-time composition data of sintered ore, pellets, lump ore, coke, pulverized coal, and flux from the enterprise database (MES, laboratory database). The acquisition frequency is within 1 minute after each batch is completed, and the acquisition scope covers Fe, etc. of various materials. Key elements such as CaO (e.g., 12 components of sinter and 14 components of coke).

[0053] If the test data for a certain batch of raw materials is delayed (e.g., the composition of lump ore is not uploaded), the module will send a data missing reminder to the person in charge and temporarily use the processed data from the previous batch (marked as temporary data). The data will be automatically recalculated after it is uploaded.

[0054] In step S2, the data processing module 200 performs batch averaging on the raw component data collected in step S1 (taking the average of the latest 3 batches) to generate the original conditions for ingredient calculation; at the same time, it automatically obtains material prices (synchronized from the procurement system), operating parameters (synchronized from the main control system), raw material bulk density (preset value), and element distribution coefficient (preset value), and supports manual adjustment of auxiliary parameters (such as modifying coke prices).

[0055] In step S2, the calculation logic module 300 calculates the original conditions and auxiliary parameters from step S2, such as... Figure 10 As shown, perform the calculations using the following sub-steps: S31: Calculate the total elemental content of a batch of material Based on the weight of each material multiplied by the proportion of the processed components, the content of each element in a single material is calculated, and then the total element content of a batch of materials is obtained by summing them up (e.g., total Fe content = Fe content of sintered ore + Fe content of pellets + ... + Fe content of flux ≈ 39.81t).

[0056] Step S32: Calculate the amount of element reduction and slag formation. Based on the element distribution coefficient, the amount of reduction (entering pig iron) and the amount of slag (entering slag) are distinguished. For example, the amount of Fe element reduction = 99.7% / 100×39.81≈39.69t, and the amount of slag = 0.3% / 100×39.81≈0.20t.

[0057] Step S33: Calculate the batch iron quantity and pig iron composition The batch iron quantity is approximately 41.96t according to the formula: (Fe + Mn + P + S reduction quantity) / (100 - Si ratio - C ratio) × 100. Then, the proportion of each component in the pig iron is calculated (e.g., Fe ratio = 39.61 / 41.96 × 100 ≈ 94.58 (%)).

[0058] Step S34: Calculate slag composition and slag quantity Based on the amount of slag produced, calculate the content of each component in the slag (e.g., (Content ≈ 4.12t, CaO content ≈ 4.93t), Total slag = Sum of all components ≈ 12.36t, Slag quantity = 12.36t / 41.96t × 1000 ≈ 294.6kg / t iron.

[0059] Step S35: Calculate blast furnace production parameters Calculate parameters such as coke ratio, coal ratio, and fuel ratio. For example, the dry coke ratio at the furnace = coke blend amount × (1 - moisture) / batch iron amount × 1000 ≈ 339.234 kg / t iron; the comprehensive coke ratio = 339.234 + coal ratio × 0.8 + coke-to-iron ratio × 0.8 ≈ 475.48 kg / t iron; the theoretical daily output = hourly feed rate × batch iron amount × 24 ≈ 6717.28 t; the utilization coefficient = 6717.28 / 2500 ≈ 2.687 t / ( ).

[0060] Step S36: Calculate cost indicators The cost per ton of iron ore is calculated as follows: = Σ (iron ore blending quantity × price × (1 - moisture)) / batch iron quantity ≈ 1665.7 yuan / ton of iron; the fuel cost per ton of iron is calculated as follows: = Σ (fuel blending quantity × price × (1 - moisture)) / batch iron quantity ≈ 1034.8 yuan / ton of iron; the raw material cost per ton of iron is calculated as follows: = cost per ton of iron ore + cost per ton of iron fuel; the total daily raw material cost is calculated as follows: = theoretical daily output × raw material cost per ton of iron.

[0061] In step S4, the front end displays the calculation results and supports parameter adjustment and scheme optimization.

[0062] The front-end presentation module 400 displays the calculation results of step S3 in a "table + chart" format, allowing users to view the results, adjust parameters, export solutions, and compare solutions. This embodiment provides a blast furnace batching optimization method, which is applied to the aforementioned blast furnace batching optimization system. Therefore, the specific implementation method of the blast furnace batching optimization method can be found in the embodiment section of the blast furnace batching optimization system above. For example, steps S1, S2, S3, and S4 are respectively applied to the data acquisition module 100, data processing module 200, calculation logic module 300, and front-end presentation module 400 in the aforementioned blast furnace batching optimization system. Therefore, the specific implementation method can be referred to the description of the corresponding embodiments. To avoid redundancy, it will not be repeated here.

[0063] Example 3: This embodiment of the invention provides an electronic device, which includes a processor, a memory, and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to realize the above-mentioned blast furnace batching optimization method.

[0064] Example 4: This embodiment of the invention provides a computer storage medium storing a computer software product, which includes several instructions to cause a computer device to execute the above-described blast furnace batching optimization method.

[0065] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0066] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0067] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0068] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A blast furnace burden optimization system characterized by, include: The data acquisition module is used to automatically collect real-time composition data of various raw materials and fuels for the blast furnace from the database; The data processing module, connected to the data acquisition module, is used to perform batch averaging on the acquired component data to obtain the original conditions for ingredient calculation and to acquire material prices and operating parameters. The calculation logic module is connected to the data processing module and is used to perform batching calculations based on the original conditions, material prices and operating parameters to obtain blast furnace production parameters and cost indicators. The front-end presentation module is connected to the calculation logic module and is used to provide a parameter input interface and display the calculation results.

2. The blast furnace feeding optimization system according to claim 1, characterized in that, The raw fuel includes multiple kinds of sintered ore, pellet, lump ore, coke, coal powder and flux; the component data at least includes contents of Fe, SiO2, CaO, , MgO, S, P, , and ZnO.

3. The blast furnace burden optimization system of claim 1, wherein, The batch averaging process performed by the data processing module is to take the average value of the latest N batches of component data.

4. The blast furnace burden optimization system of claim 1, wherein, The blast furnace production parameters calculated by the calculation logic module include at least one of the following: slag basicity, slag quantity, coke ratio, coal ratio, fuel ratio, theoretical iron content, sulfur load, alkali metal load, zinc load, theoretical combustion temperature, blast kinetic energy, or blast furnace gas quantity.

5. The blast furnace feeding optimization system according to claim 1, characterized in that, The cost indicators calculated by the calculation logic module include the cost per ton of iron ore, the cost per ton of iron fuel, and the cost per ton of iron raw materials.

6. The blast furnace feeding optimization system according to claim 1, characterized in that, The front-end presentation module includes a raw material weight and price input panel, an operation parameter input panel, a pig iron composition setting panel, a raw material bulk density setting panel, an element distribution coefficient setting panel, and an ore blending calculation result panel.

7. A method for optimizing blast furnace feed, characterized in that, The method, applied to the blast furnace feeding optimization system according to any one of claims 1 to 6, includes the following steps: The data acquisition module automatically collects real-time composition data of various raw materials and fuels for the blast furnace from the database. The data processing module performs batch averaging on the collected component data to obtain the original conditions for ingredient calculation and to acquire material prices and operating parameters. The calculation logic module performs batching calculations based on the original conditions, material prices, and operating parameters to obtain blast furnace production parameters and cost indicators. The front-end presentation module provides a parameter input interface and displays the calculation results.

8. The blast furnace feed optimization method according to claim 7, characterized in that, The steps for performing batching calculations include: calculating the content of multiple elements in a batch of material based on the input raw material weight; calculating the quantity of each element entering pig iron and slag based on the element distribution coefficient; calculating the batch iron quantity and pig iron composition based on the quantity of elements entering pig iron; and calculating the slag composition and slag quantity based on the quantity of elements entering slag.

9. The blast furnace feed optimization method according to claim 8, characterized in that, The step of performing batching calculation further includes: calculating at least one of the following based on the batch iron quantity, fuel blending quantity, and operating parameters: coke ratio, coal ratio, fuel ratio, coke load, comprehensive load, theoretical output, theoretical combustion temperature, blast kinetic energy, and vortex zone depth.

10. The blast furnace feed optimization method according to claim 7, characterized in that, The step of performing batching calculations also includes: calculating the cost per ton of iron ore, the cost per ton of fuel, and the cost per ton of raw materials based on the batch iron quantity, the combined weight of various materials, and their prices.

11. An electronic device, characterized in that, The electronic device includes a processor, a memory, and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the blast furnace batching optimization method according to any one of claims 7 to 10.

12. A computer storage medium, characterized in that, The computer storage medium stores a computer software product, which includes several instructions to cause a computer device to execute the blast furnace batching optimization method according to any one of claims 7 to 10.

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