System for calculating and analyzing cost of whole process before iron making

Abstract

According to the system for calculating and analyzing the whole process cost before iron making, standardized storage of raw materials and process data is achieved through the database module before iron making, and data support is provided for an evaluation system; the raw material evaluation module establishes a scientific cost performance quantitative model, and breaks through the limitation of traditional experience evaluation; the ratio optimization module realizes global cost optimization of sintering, pelletizing, coking and blast furnace ratio based on a constraint equation and a planning solution algorithm; the quality and energy analysis module ensures that the scheme conforms to the actual production through a dynamic feedback mechanism; and the cost calculation module integrates the cost of each process to form a whole-process total cost view. Full-chain closed-loop optimization of raw material selection-process proportioning-mass-energy balance-cost accounting is realized, and compared with artificial experience decision, fine management of material flow and energy flow of each process before iron making is more convenient, material and heat consumption is reduced, resource recycling is increased, and cost is reduced. And the timeliness and adaptability of the optimization scheme are continuously maintained through the dynamic updating module and the feedback adjusting module.

Description

Technical Field

[0001] The present invention relates to the technical field of steel smelting, and in particular to a full-process cost calculation and analysis system for ironmaking. Background Art

[0002] my country's steel production capacity is in excess, with the long process accounting for nearly 90%. It is also a typical high-energy consumption and high-emission industry. The implementation of the dual-carbon economy will further squeeze corporate profits, and the level of production costs will determine the survival and development of enterprises. The cost of the ironmaking unit accounts for 70-80% of the total cost of steel, the process energy consumption accounts for 70% of the energy consumption per ton of steel, and raw materials account for nearly 70%. The energy consumption and cost of the ironmaking unit determine the competitiveness of steel companies. The analysis in the existing technology is often relatively one-sided. For example, it only considers the pre-iron economic benefit calculation of the linkage between sintering and blast furnace, or does not calculate the coking process in detail and does not calculate the theoretical energy consumption of the process. The calculation is not comprehensive or not accurate enough. Summary of the Invention

[0003] In view of this, the present invention provides a full-process cost calculation and analysis system for ironmaking to solve the technical defects existing in the prior art.

[0004] Specifically, the present invention provides a full-process cost calculation and analysis system for ironmaking, including: The ironmaking database module is used to store market information and full composition data of iron ore fines, coking coal, PCI coal, and flux, as well as production medium energy consumption data for sintering, pelletizing, coking, and blast furnace processes; The raw material evaluation module establishes a cost-effectiveness evaluation system for iron ore fines, sintered ore, pelletized ore, lump ore, and coking coal based on the data in the pre-iron database module; The proportion optimization module generates the lowest cost proportion scheme for sintering ore proportion, pelletizing ore proportion, coking coal proportion and blast furnace charge structure based on the output of the raw material evaluation module; The mass-energy analysis module performs material balance and energy balance calculations on the ratio schemes for each process generated by the ratio optimization module, and feeds the calculation results back to the ratio optimization module; The cost calculation module integrates the ratio schemes of each process output by the ratio optimization module and calculates the total cost of the entire pre-ironization process.

[0005] In some embodiments, the evaluation of iron ore by the raw material evaluation module includes: Set weight coefficients based on the impact of iron oxide, harmful elements and alkali metal elements in the raw materials on the cost of molten iron; Considering titanium dioxide as acidic slag, the comprehensive iron grade excluding the influence of moisture and burnout is calculated using the preset five-element basicity calculation formula. The five-element basicity calculation formula includes:

[0006] Wherein, TFe represents the comprehensive iron grade of iron ore, TFe represents the iron grade of iron ore, R5 represents the five-element basicity of blast furnace slag, Ig represents the loss on burn of iron ore, Fe represents the iron content, FeO represents the iron oxide content, SiO2 represents the silicon dioxide content, Al2O3 represents the aluminum oxide content, Ti represents the titanium content, CaO represents the calcium oxide content, MgO represents the magnesium oxide content, S represents the sulfur content, P represents the phosphorus content, K represents the potassium content, Na represents the sodium content, Zn represents the zinc content, As represents the arsenic content, Cu represents the copper content, and Pb represents the lead content; The price per ton is calculated with reference to the benchmark ore and the price-performance ratio is ranked.

[0007] In some embodiments, the evaluation of coking coal by the raw material evaluation module includes: Calculate the conventional cost per ton of coke based on the consumption per ton of coking coal, unit price, coke processing fee and backwash fee; Calculate the corrected cost per ton of coke based on the corresponding relationship between coke quality and energy indicators and blast furnace economic indicators; Add the regular cost per ton of coke and the corrected cost per ton of coke to get the total cost and sort them.

[0008] In some embodiments, the sintering ore blending system in the blending optimization module includes: Based on the sintering ore blending system, constraint equations are established for the chemical composition, alkalinity, and drum strength of the sintered ore. The process setting takes into account raw material moisture, burn-off, and element distribution rate. Upper and lower limits are set for some specific raw materials. Planning and solving are performed with the lowest cost as the goal. During planning and solving, the number of constraint equations and the number of unknowns are equal, resulting in a minimum cost blending solution that meets the process intensity requirements. The relevant basic characteristic values ​​of the batch material, as well as the chemical composition and relevant basic characteristic values ​​of the sintered ore are calculated based on the proportioning results.

[0009] In some embodiments, the pellet blending system in the blending optimization module includes: Based on the pelletizing ore blending system, constraint equations are established for the chemical composition, alkalinity, green ball strength, and cooked ball strength of the pellet ore. The setting process takes into account the raw material moisture content, burn-off loss, and element distribution rate. Burn-off loss is the comprehensive burn-off loss of the raw material, including the amount of oxygen added to the raw material. Upper and lower limit constraints are then set for the preset specific raw materials. Planning and solving are performed with the lowest cost as the goal. During planning and solving, the number of constraint equations and the number of unknowns are equal, resulting in a minimum cost blending solution that meets the process intensity requirements. The relevant basic characteristic values ​​of the batch material, as well as the chemical composition and relevant basic characteristic values ​​of the pellets are calculated based on the proportioning results.

[0010] In some embodiments, the coking coal blending system in the blending optimization module includes: Based on the coking coal blending system, constraint equations are established for the coke ash content, sulfur content, volatile matter, fixed carbon, G value, and Y value. The setting process takes into account the raw material moisture and volatile matter as well as the element distribution rate. Then, upper and lower limit constraints are set for each type of coal. Planning and solving are carried out with the lowest cost as the goal. During the planning and solving, the number of constraint equations is equal to the number of unknowns, and the lowest cost ratio solution that meets the process intensity requirements is obtained; The coke components and ash chemical composition are calculated based on the ratio results.

[0011] In some embodiments, the blast furnace charge structure optimization system in the proportion optimization module includes: Set the sulfur and silicon content in the molten iron in advance, and calculate the iron and slag rates of each raw material based on the element distribution rate; Based on the tapping rate of each raw material, a constraint equation for the tapping rate is set. Constraint equations are also set for the slag basicity, magnesium-aluminum ratio, aluminum-silicon ratio, and the SiO2 and Al2O3 contents in the slag. Upper and lower limits are set for the preset specific raw materials. Planning and solving are performed with the lowest cost as the goal, so that the number of constraint equations and the number of unknowns are equal, and the lowest cost mix required for production is obtained. The composition of molten iron and slag is calculated based on the ratio results.

[0012] In some embodiments, the mass-energy analysis module includes: Calculate theoretical material balance and energy balance based on the proportion results of each process; Analyze process energy flows and optimize fuel consumption; The calculation results are fed back to the corresponding ratio system for constraint adjustment.

[0013] In some embodiments, the cost calculation module includes: Calculate the independent costs for sintering, pelletizing, coking, and blast furnace processes separately; Integrate the costs of each process to form the total cost analysis of the entire ironmaking process; Optimize the overall matching plan based on the cost analysis results.

[0014] In some embodiments, the system further comprises: Dynamic update module to regularly obtain the latest raw material market information and process production data; Feedback adjustment module adjusts evaluation weights and constraints based on actual production results.

[0015] At least one embodiment of the present invention implements standardized storage of raw material and process data through the ironmaking database module, providing data support for the evaluation system; the raw material evaluation module establishes a scientific cost-performance quantitative model, breaking through the limitations of traditional empirical evaluation; the proportion optimization module achieves global cost optimization of sintering, pelletizing, coking and blast furnace proportioning based on constraint equations and planning solution algorithms; the mass-energy analysis module ensures that the plan conforms to production reality through a dynamic feedback mechanism; and the cost calculation module integrates the costs of each process to form a total cost view of the entire process. This achieves closed-loop optimization of the entire chain of raw material selection - process proportioning - mass-energy balance - cost accounting. Compared with manual experience-based decision-making, it is more convenient to manage the refined flow of materials and energy in each ironmaking process, reduce material and heat consumption, increase resource recycling and reduce costs, and continuously maintain the timeliness and adaptability of the optimization plan through the dynamic update module and feedback adjustment module. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a structural block diagram of a full-process cost calculation and analysis system for ironmaking provided by the present invention. DETAILED DESCRIPTION

[0017] The following description sets forth many specific details to facilitate a thorough understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the scope of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0018] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms of "a" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items. The modifications of "one" and "a plurality" mentioned in this disclosure are illustrative and not restrictive, and those skilled in the art should understand that unless the context clearly indicates otherwise, it should be understood as "one or more".

[0019] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0020] In traditional ironmaking production management, raw material ratio decisions have long relied on engineers' experience and judgment, resulting in three major technical bottlenecks: First, raw material evaluation is limited to a single dimension, focusing only on explicit indicators such as iron grade and coal price, while ignoring the implicit cost impact of harmful elements on subsequent processes; second, insufficient coordination between processes means that local optimization of sintering and coking processes can deteriorate blast furnace performance, creating a "barrel effect"; third, manual calculations struggle to handle the complex coupling of multiple objective constraints (such as alkalinity balance, harmful element control, and energy consumption minimization). Experience at a large steel plant shows that this fragmented decision-making model often leads to fluctuations in pre-iron costs of up to ±12%.

[0021] Existing patent CN113088600A discloses a method for evaluating the integrated benefits of iron ore powder for sintering. By setting a benchmark ore, setting the ratio of ore powder to be compared, and calculating according to sintering and blast furnace process parameters, the impact of ore powder on blast furnace costs needs to be evaluated quantitatively. Only the economic benefit calculation before iron production of sintering and blast furnace linkage is considered, and the calculation is not comprehensive. Patent CN116993064A discloses a method and system for optimizing integrated batching before iron production. By putting mixed batching, available raw material resources, and process processing costs into an integrated batching model for overall consideration, the integrated batching optimization of the entire process is achieved. The economic evaluation of raw materials before iron production is not performed, the coking process is not calculated in detail, and the theoretical energy consumption of the process is not calculated. The results are not accurate enough. There is still a need to establish a low-cost calculation and analysis system covering the entire process before iron production.

[0022] This invention builds a comprehensive database of pre-iron production factors, digitizing over 300 parameters, including raw material composition and process energy consumption. It also develops a five-element alkalinity dynamic calculation model, quantifying for the first time the impact of elements like TiO2 on overall iron grade. Finally, it establishes a three-dimensional "cost-quality-energy" optimization algorithm, achieving cross-process collaboration through a closed-loop mass-energy feedback loop. See the following for details.

[0023] See also Figure 1 , Figure 1The present invention shows a structural block diagram of a pre-ironization full-process cost calculation and analysis system provided according to some embodiments of the present specification, which comprises: a pre-ironization database module for storing market information and full component data of iron ore powder, coking coal, pulverized coal injection, and flux, as well as production medium energy consumption data of sintering, pelletizing, coking, and blast furnace processes; a raw material evaluation module for establishing a cost-effectiveness evaluation system for iron ore powder, sintered ore, pelletized ore, lump ore, and coking coal based on the data in the pre-ironization database module; a proportion optimization module for generating the lowest cost proportioning scheme for sintering ore proportioning, pelletizing ore proportioning, coking coal proportioning, and blast furnace charge structure according to the output results of the raw material evaluation module; a mass-energy analysis module for performing material balance and energy balance calculations on the proportioning schemes for each process generated by the proportion optimization module, and feeding back the calculation results to the proportion optimization module; a cost calculation module for integrating the proportioning schemes for each process output by the proportion optimization module to calculate the total cost of the pre-ironization full process.

[0024] Iron ore fines, coking coal, PCI coal, flux, sintering, pelletizing, coking, and blast furnace processes are commonly used terms in this field and will not be elaborated on in detail. Full composition data can refer to the complete set of raw material chemical elements, including the content of harmful elements such as potassium, sodium, and zinc in iron ore. This is used in the raw material evaluation module to calculate weights and mitigate metallurgical risks. Production medium energy consumption data can refer to process water, electricity, and gas consumption. By linking raw material ratios to databases, unit energy consumption can be verified in the mass-energy analysis module to identify potential energy savings. A cost-effectiveness evaluation system can refer to a model that quantifies raw material value, comparing benchmark ore ton prices with total coking coal cost rankings to guide raw material procurement priorities. A minimum cost ratio solution can refer to an economically optimal production formula. Raw material usage ratios are solved through linear programming and verified in the cost calculation module to optimize total costs, thereby reducing the cost per ton of iron. Material balance can refer to a calculation of conservation of input and output materials, tracking the flow of iron, sulfur, and other substances based on elemental distribution ratios, and identifying points of metal recovery loss in the process. The total cost of the entire pre-iron process can refer to the comprehensive cost from raw materials to molten iron, aggregating the raw material costs, processing costs and energy costs of each process. The beneficial effects support overall efficiency decisions.

[0025] The present invention is further described below by a detailed embodiment: Implemented at a steel conglomerate with an annual output of 10 million tons, this ironmaking process optimization system achieves cost control throughout the entire process, from raw material procurement to blast furnace production, through the coordinated operation of seven core modules. During system deployment, the pre-iron database module was first constructed. This distributed storage architecture collects real-time data on 152 components of 12 raw materials, including iron ore fines and coking coal. Data from 368 energy consumption monitoring points for sintering machines, coke ovens, and other equipment is integrated via an industrial IoT gateway, with data updated once per minute. After the raw material evaluation module was activated, the five-element basicity calculation for Brazil's Carajás fine ore was performed, incorporating its 2.1% TiO2 content into the slag system balance. Using a dynamic weighting algorithm, the system's combined iron grade was found to be 1.8 percentage points lower than the apparent grade. This correction caused the ore to move down three places in the price-performance ranking.

[0026] When the proportion optimization module is running, the sintering ore proportioning subsystem establishes a linear programming model containing 18 constraint equations for 8 types of iron ore fines and 3 types of fluxes. In specific implementation, when the market price of Australian PB powder increases by 5%, the system re-solves the new proportion within 3 minutes: reducing the PB powder proportion from 22% to 15%, while increasing the proportion of South African Isco ore to 18%. Under the premise of ensuring the sintering ore basicity of 2.0±0.1, the cost per ton of ore is reduced by 4.7 yuan. In particular, the system innovatively converts the nonlinear relationship between sintering solid fuel consumption and FeO content into a constraint condition, so that the optimized proportion scheme simultaneously reduces fuel consumption by 1.8kg / t.

[0027] The application of the mass-energy analysis module in the coking process has shown significant value. When it was detected that the volatile content of a batch of coking coal fluctuated by more than 0.5%, the system recalculated the gas generation rate through the element distribution rate matrix and found that the theoretical calorific value deviation reached 83MJ / h. Feedback was then sent to the ratio optimization module to adjust the gas-coal ratio by 2%, ultimately stabilizing the coke oven thermal efficiency at 92.3%±0.5%. When the cost calculation module generates the pre-iron cost analysis report every month, it not only calculates the independent costs of each process, but also calculates the implicit cost transmission between processes through the input-output model. For example, a pelletizing process cost reduction of 5 yuan / t resulted in a 2kg / t increase in the blast furnace fuel ratio, which was promptly warned.

[0028] After 12 months of continuous operation, the system adjusted the raw material evaluation weight coefficient 37 times through the dynamic update module. The feedback adjustment module revised the equation relating SiO2 content in sintered ore to drum strength based on actual production data. Ultimately, the overall cost per ton of iron was reduced by 19.5 yuan, with the raw material cost ratio dropping from 83.2% to 80.7%. Collaborative process optimization contributed 61% of the cost reduction.

[0029] The beneficial effects of one of the embodiments of the present specification include at least: standardized storage of raw material and process data is achieved through the ironmaking database module, providing data support for the evaluation system; the raw material evaluation module establishes a scientific cost-performance quantitative model, breaking through the limitations of traditional experience evaluation; the proportion optimization module achieves the global cost optimization of sintering, pelletizing, coking and blast furnace proportioning based on constraint equations and planning solution algorithms; the mass-energy analysis module ensures that the plan conforms to production reality through a dynamic feedback mechanism; the cost calculation module integrates the costs of each process to form a total cost view of the entire process. It realizes the closed-loop optimization of the entire chain of raw material selection-process proportioning-mass-energy balance-cost accounting. Compared with manual experience decision-making, it is more convenient to manage the refined flow of materials and energy in each process before ironmaking, reduce material and heat consumption, increase resource recycling and reduce costs, and continuously maintain the timeliness and adaptability of the optimization plan through the dynamic update module and feedback adjustment module.

[0030] In some embodiments, the raw material evaluation module evaluates iron ore by: setting weight coefficients based on the impact of iron oxide, harmful elements, and alkali metal elements in the raw material on the cost of molten iron; considering titanium dioxide as acidic slag, and calculating the comprehensive iron grade excluding the effects of moisture and burnout using a preset five-element basicity calculation formula, wherein the five-element basicity calculation formula includes:

[0031] Among them, TFe comprehensive represents the comprehensive iron grade of iron ore, TFe represents the iron grade of iron ore, R5 represents the five-element basicity of blast furnace slag, Ig represents the loss on burn of iron ore, Fe is the iron content, FeO is the iron oxide content, SiO2 is the silicon dioxide content, Al2O3 is the aluminum oxide content, Ti is the titanium content, CaO is the calcium oxide content, MgO is the magnesium oxide content, S is the sulfur content, P is the phosphorus content, K is the potassium content, Na is the sodium content, Zn is the zinc content, As is the arsenic content, Cu is the copper content, and Pb is the lead content; the ton price is calculated with reference to the benchmark ore and the cost-effectiveness is ranked.

[0032] Harmful elements can refer to impurities such as sulfur, phosphorus, and arsenic. Their impact on blast furnace lining erosion and environmental treatment costs is quantified using negative weight coefficients, thereby avoiding technical risks in raw material selection. Alkali metals can refer to active metals such as potassium and sodium. By monitoring their content and limiting their concentration in the furnace, this can help maintain blast furnace operational stability. The five-element alkalinity calculation formula can refer to a preset ratio formula. Incorporating the influence of titanium when evaluating slag metallurgical properties can improve slag system control accuracy. Benchmark ore can refer to a price-performance reference standard. It serves as the denominator in price-per-ton calculations to compare current ore values, enabling economic comparisons across mining areas.

[0033] By establishing a comprehensive elemental analysis model for iron ore, the relationship between chemical composition, metallurgical behavior, and economic benefits is quantified. Titanium dioxide is innovatively incorporated into slag system adjustments, enabling precise control of slag properties through five-element basicity calculations. A weighted coefficient method objectively reflects the varying impacts of various elements on production costs, and combined with per-ton price comparisons, standardized raw material evaluation across mining areas is achieved. Ultimately, a decision-making system for iron ore selection has been developed that balances process feasibility, environmental compliance, and economic optimization, providing a multi-dimensional scientific basis for raw material procurement in steel companies.

[0034] In some embodiments, the evaluation of coking coal by the raw material evaluation module includes: calculating the conventional cost per ton of coke based on the consumption per ton of coking coal, unit price, coke processing fee and backwash fee; calculating the corrected cost per ton of coke based on the correspondence between the coke quality and energy indicators and the blast furnace economic indicators; adding the conventional cost per ton of coke and the corrected cost per ton of coke to obtain the total cost and sorting them.

[0035] Coke quality indicators can refer to coke quality parameters such as M40 and CSR. Coke performance under different coal blending schemes is measured through coke oven tests and can be used to predict blast furnace performance. Blast furnace economic indicators can refer to production parameters such as fuel ratio and utilization coefficient. Regression models linking coke quality and blast furnace operating indicators can be established to quantify coke's value contribution. The corrected cost per ton of coke can refer to the quality premium cost. This cost compensation is calculated based on the degree to which coke strength improves the blast furnace fuel ratio and is used for comprehensive cost accounting.

[0036] A dual costing mechanism not only considers direct coking coal procurement and processing expenses, but also incorporates adjustments for the economic impact of coke quality on downstream processes. An innovative quantitative relationship is established between coke strength indicators and blast furnace fuel consumption, reflecting the implicit value of high-quality coking coal through cost compensation. A dynamic price response mechanism ensures that evaluation results are updated in sync with market conditions. Ultimately, a coking coal selection standard that covers the entire coal-to-coke conversion process, balancing immediate costs and long-term benefits, is established, providing a technically sound and economically optimal decision-making basis for coal blending optimization.

[0037] In some embodiments, the sintering ore blending system in the proportioning optimization module includes: establishing constraint equations for the chemical composition, alkalinity and drum strength of the sintering ore according to the sintering ore blending system, considering the moisture content, burn-off loss and element distribution rate of the raw materials in the setting process, setting upper and lower limit constraints for some specific raw materials, and planning and solving with the lowest cost as the goal. During the planning and solving, the number of constraint equations is made equal to the number of unknowns to obtain the lowest cost proportioning scheme that meets the process intensity requirements; the relevant basic characteristic values ​​of the batch material, as well as the chemical composition and relevant basic characteristic values ​​of the sintered ore are calculated according to the proportioning results.

[0038] The sintering ore blending system refers to an iron ore fines mixing optimization platform. This platform generates sinter formulations that meet blast furnace requirements by establishing a multi-objective optimization model based on chemical composition, physical strength, and cost. Basic batch characteristics, such as particle size and moisture content, are weighted and predicted based on raw material properties to guide sintering operations. Basic sinter ore characteristics, such as the finished product drum index and RI, are predicted through a database mapping relationship between mix proportions and performance, enabling early assessment of ore blend quality.

[0039] By constructing a multi-dimensional constraint system, the chemical composition requirements, physical strength standards, and raw material usage restrictions for sintered ore are transformed into a computable mathematical model. Innovatively integrating burn-loss correction with element distribution rate prediction allows for more accurate sintering calculations that align closely with the actual production conversion process. A planning and solving algorithm is employed to simultaneously address complex constraints and economic objectives, ensuring sintered ore quality meets standards while minimizing raw material costs. Ultimately, a closed-loop predictive optimization system, from raw material characteristics to finished product performance, is formed, providing an intelligent ore blending solution for the sintering process that combines process rationality with economic efficiency.

[0040] In some embodiments, the pellet ore blending system in the proportioning optimization module includes: establishing constraint equations for the chemical composition, alkalinity, green ball strength and cooked ball strength of the pellet ore according to the pellet ore blending system, and considering the moisture content and burn loss of the raw materials and the element distribution rate in the setting process, wherein the burn loss is the comprehensive burn loss of the raw materials, including the oxygen addition amount of the raw materials, and then setting upper and lower limit constraints for the preset specific raw materials, and planning and solving with the lowest cost as the goal. During the planning and solving, the number of constraint equations is equal to the number of unknowns, and the lowest cost proportioning scheme that meets the process strength requirements is obtained; the relevant basic characteristic values ​​of the batch material, as well as the chemical composition and relevant basic characteristic values ​​of the pellet ore are calculated according to the proportioning results.

[0041] Green ball strength can refer to the wet ball compressive strength. Regression analysis is used to establish a predictive model linking raw material properties and green ball strength, which helps ensure the integrity of grate transport. Cooked ball strength can refer to the compressive strength after roasting. By combining the raw material melting characteristics and roasting schedule to simulate and calculate the finished product strength, it can predict blast furnace performance. Raw material comprehensive burn-out can refer to the total mass change including oxidation weight gain. By integrating FeO oxidation heat release and volatile matter losses into the mix ratio calculation, it accurately reflects the actual feed rate. Oxygen addition can refer to the oxidation increment from Fe to Fe₂O₃. The mass increase is converted to the stoichiometric ratio based on the change in the valence state of the iron element and used to correct the burn-out calculation. Basic batch properties can refer to parameters such as mix particle size and specific surface area. Predicted values ​​are calculated based on weighted raw material physical property data and can guide parameter setting in the pelletizing process. Basic pellet properties can refer to indicators such as finished product abrasion resistance and reduction expansion ratio. Predictions are made through a mix ratio-performance database mapping relationship, which helps to assess product quality in advance.

[0042] By constructing a full-process constraint system encompassing chemical composition, physical strength, and roasting behavior, the process requirements for pellet production are transformed into a computable mathematical model. The innovative integration of oxidation weight gain factors modifies the traditional loss on burn calculation model, ensuring that the mix design more closely reflects the quality variations observed during the actual roasting process. A multi-objective optimization algorithm simultaneously addresses strength standards, compositional constraints, and economic requirements, ensuring that the pellets meet metallurgical performance standards while optimizing raw material costs. Ultimately, a closed-loop prediction system, from raw material properties to finished product quality, is formed, providing a scientifically accurate, intelligent ore blending solution for pellet production.

[0043] In some embodiments, the coking coal blending system in the proportioning optimization module includes: establishing constraint equations for the coking coal blending system, coke ash, sulfur, volatile matter, fixed carbon, G value and Y value, taking into account the raw material moisture and volatile matter and element distribution rate in the setting process, and then setting upper and lower limit constraints for each type of coal, and planning and solving with the lowest cost as the goal. During the planning and solving, the number of constraint equations is equal to the number of unknowns, and the lowest cost proportioning scheme that meets the process intensity requirements is obtained; the coke components and ash chemical composition are calculated based on the proportioning results.

[0044] Coke ash refers to the residual inorganic content. Using inequality constraints to define the upper limit of ash content can control blast furnace slag volume and heat loss. Sulfur refers to the content of harmful elements. Setting a strict upper limit and diluting high-sulfur coal through coal blending can effectively reduce the desulfurization burden of the blast furnace. Volatile matter refers to the gasifiable components in coal. Classifying and adjusting the blending ratio based on the degree of coal metamorphism can optimize coke oven gas yield. Fixed carbon refers to the active components in coke formation. Coal petrographic composition analysis determines the carbon content contribution of each coal type and can be used to predict coke yield. G value refers to the maximum thickness of the colloidal layer. Using a Kielder flow test to measure the coking properties of coal can assess coking ability. Y value refers to the colloidal expansion. Using the Oya expansion test to quantify the plastic range of coal can predict the coke pore structure. Coke composition refers to the microstructural structure. Predicting parameters such as the anisotropy index based on the coal blending plan can be beneficial for evaluating the thermal properties of coke. The chemical composition of ash can refer to the composition of oxides such as Al2O3 and SiO2. The predicted value is calculated based on the weighted ash composition of the raw materials, and the slag basicity can be predicted.

[0045] By constructing a multi-parameter constraint system, coke quality indicators, coal type characteristic restrictions, and economic objectives are integrated into a computable optimization model. By innovatively combining coal petrological parameters (G value, Y value) with industrial analytical data, coal blending design ensures that both coke strength requirements and coke oven operational stability are met. A linear programming algorithm is employed to simultaneously address complex coal type compatibility relationships and minimize costs, achieving optimal raw material costs while ensuring coke metallurgical properties. Ultimately, a closed-loop prediction system from coal quality characteristics to coke quality is formed, providing a scientifically accurate, intelligent coal blending solution for the coking process, significantly improving coke quality stability and coal blending economics.

[0046] In some embodiments, the blast furnace charge structure optimization system in the proportion optimization module includes: setting the content of sulfur and silicon elements in the molten iron in advance, and calculating the iron tapping rate and slag tapping rate of each raw material according to the element distribution rate; setting the iron tapping rate constraint equation according to the iron tapping rate of each raw material, and also setting constraint equations for the slag alkalinity, magnesium-aluminum ratio, aluminum-silicon ratio, and SiO2 and Al2O3 content in the slag, setting upper and lower limit constraints for preset specific raw materials, and planning and solving with the lowest cost as the goal to make the number of constraint equations equal to the number of unknowns, and obtain the lowest cost proportioning scheme required for production; calculating the molten iron composition and slag composition according to the proportioning results.

[0047] The iron yield rate can refer to the conversion rate of the raw material iron element. By calculating the iron recovery rate of each ore type through elemental balance, the molten iron yield can be accurately predicted. The slag yield rate can refer to the conversion rate of the raw material slag-forming components. The slag volume is calculated by combining the ore gangue content and flux ratio to assess slag fluidity. The slag basicity can refer to the (CaO+MgO) / SiO2 ratio. Setting a target range as a hard constraint can ensure the slag desulfurization capacity. The slag composition can refer to the composition of oxides such as CaO and MgO. Through weighted calculation of multiple raw material components, the metallurgical properties of the slag can be predicted.

[0048] By constructing a comprehensive optimization model encompassing molten iron quality, slag properties, and economic indicators, the process requirements for blast furnace smelting are transformed into a quantifiable mathematical constraint system. The innovative integration of element distribution rates and thermodynamic calculation models ensures that charge ratio design more closely aligns with the elemental trends observed during the actual smelting process. A multi-objective planning algorithm simultaneously addresses the requirements for molten iron composition control, slag property adjustment, and cost minimization, optimizing the charge structure while ensuring that pig iron quality meets standards. Ultimately, a closed-loop prediction system, encompassing raw material characteristics and smelting output, is formed, providing blast furnace operators with intelligent charge ratio solutions that combine process rationality with economic benefits, effectively improving blast furnace smelting efficiency and economic indicators.

[0049] In some embodiments, the mass-energy analysis module includes: calculating theoretical material balance and energy balance based on the proportioning results of each process; analyzing the energy flow of the process and optimizing fuel consumption; and feeding back the calculation results to the corresponding proportioning system for constraint adjustment.

[0050] As an example, in some optional embodiments, the steps of forming a sintering mass-energy balance analysis include: collecting sintering production process parameter data based on the sintering ore blending results, calculating the theoretical material balance and energy balance of the sintering process, clarifying the energy flow of the sintering process, reducing energy consumption, and feeding back the fuel consumption calculated based on the mass-energy balance to the sintering ore blending system, setting constraints on the fuel, and optimizing the ore blending results.

[0051] In some optional embodiments, the steps of forming a pellet mass-energy balance analysis include: collecting pellet belt roaster production process parameter data based on the pellet blending results, calculating the theoretical material balance and energy balance of the pelletizing process, clarifying the energy flow of the pelletizing process, reducing energy consumption, and feeding back the fuel consumption calculated based on the mass-energy balance to the pellet blending system, setting constraints on the fuel, and optimizing the blending results.

[0052] In some optional embodiments, the steps of forming a coking mass-energy balance analysis include: collecting coke oven production process parameter data based on the coking coal blending results, calculating the theoretical material balance and energy balance of the coking process, clarifying the energy flow of the coking process, reducing energy consumption, and feeding back the fuel consumption calculated based on the mass-energy balance to the coking coal blending system, setting constraints on the fuel, and optimizing the coal blending results.

[0053] In some optional embodiments, the steps of forming a blast furnace mass-energy balance analysis include: collecting blast furnace production process parameter data based on blast furnace batching results, calculating the theoretical material balance and energy balance of the blast furnace process, clarifying the energy flow of the blast furnace process, reducing energy consumption, and returning the fuel consumption calculated based on the mass-energy balance to the blast furnace ore matching system, setting constraints on the fuel, and optimizing the ore matching results.

[0054] A theoretical material balance can refer to a conservation of matter calculation model. This model calculates the flow of components based on chemical reaction equations and element yields, enabling accurate predictions of raw material consumption and product yields. A theoretical energy balance can refer to a thermodynamic calculation system that integrates energy terms such as reaction heat, sensible heat, and heat loss to effectively assess process energy requirements. A process energy flow diagram can refer to an energy consumption distribution diagram. Heat flow diagrams visualize the energy transfer paths of sensible and latent heat, identifying key energy saving points. Sintering process parameters can refer to variables such as mix moisture and ignition temperature. Field instrument data is collected to build a database for establishing energy efficiency benchmarks. Belt roaster process parameters can refer to indicators such as drying section temperature and roasting section oxygen content. Real-time process data collection through a DCS system enables optimization of roasting schedules. Coke oven process parameters include parameters such as coking time and standard temperature. Combined with heating gas consumption statistics, these parameters can be used to assess coke oven thermal efficiency. Blast furnace process parameters can refer to operational variables such as air temperature and oxygen enrichment rate. These parameters integrate output indicators such as molten iron temperature to construct thermal state models. Fuel consumption constraints can refer to upper limits on energy usage. Heat balance calculations can be used to infer the minimum fuel requirements for each process, effectively avoiding energy waste. Ore blending optimization can refer to adjusting the raw material mix. Adjusting the iron ore fines mix based on feedback from energy efficiency analysis can achieve material-energy synergy optimization.

[0055] By constructing a comprehensive mass-energy balance analysis system covering the entire sintering, pelletizing, coking, and blast furnace processes, energy management in metallurgical processes has been elevated to a quantitative calculation level. An innovative closed-loop control model of "calculation-feedback-optimization" ensures that the mix ratios for each process not only meet material balance requirements but also take energy efficiency into account. By comparing and analyzing real-time production data with theoretically calculated values, energy waste is precisely identified, providing data support for process optimization. Ultimately, a comprehensive energy efficiency assessment and optimization system encompassing the entire metallurgical process has been established, significantly reducing process energy consumption while ensuring product quality, achieving green and low-carbon production goals, and providing intelligent solutions for energy conservation and emission reduction in steel companies.

[0056] In some embodiments, the cost calculation module includes: calculating the independent costs of sintering, pelletizing, coking and blast furnace processes respectively; integrating the costs of each process to form a total cost analysis of the entire pre-iron process; and optimizing the overall ratio scheme based on the cost analysis results.

[0057] Independent costs can refer to the operating expenses of a single process. By integrating cost elements such as raw materials, energy, and labor, these costs can accurately reflect the economic efficiency of each process. Sintering process costs can refer to the production costs of iron ore agglomeration. Calculating individual costs such as raw materials, fuel, and power based on the ore blending plan can be used to effectively evaluate sintering economics. Pelletizing process costs can refer to the production costs of concentrate pelletizing. Unit costs can be calculated by combining bentonite consumption and roasting energy consumption to optimize the cost-effectiveness of pelletizing. Coking process costs can refer to the conversion costs of coking coal. Resource consumption costs such as washed clean coal and coal gas are calculated based on the coal blending plan to guide coal blending optimization. Blast furnace process costs can refer to the comprehensive costs of the ironmaking process. These costs include inputs such as charge, coke, and blast air, and can reflect the economic efficiency of blast furnace operations. Total pre-ironization process costs can refer to the total costs from raw materials to molten iron output. By superimposing process costs and calculating the pricing of intermediate products, key cost control points can be identified.

[0058] By establishing a cost accounting system covering the entire process of sintering, pelletizing, coking, and blast furnace production, the economic management of metallurgical production has been elevated to a quantitative analysis level. The innovative closed-loop control model of "independent process accounting - full process integration - feedback optimization" ensures that the proportioning scheme for each process not only meets process quality requirements but also maximizes economic benefits. By accurately calculating the impact of each raw material and energy factor on total cost, an economic basis is provided for production decisions. While ensuring product quality, production costs in the ironmaking system are significantly reduced, thereby enhancing the company's market competitiveness. Ultimately, a cost control system that spans the entire metallurgical process has been established, providing an intelligent solution for refined management in steel companies.

[0059] In some embodiments, the system further includes: a dynamic update module for regularly acquiring the latest raw material market information and process production data; and a feedback adjustment module for adjusting evaluation weights and constraints based on actual production results.

[0060] Raw material market information can refer to prices for iron ore fines, coking coal, and other commodities. Real-time quotes from commodity exchanges are collected to quickly respond to market fluctuations. Process production data can refer to key performance indicators (KPIs) such as output and energy consumption. Real-time production reports are automatically extracted from the PLC control system, effectively monitoring process stability. The feedback adjustment module can refer to a parameter self-optimization system that dynamically adjusts model parameters based on deviation analysis between actual production performance and predicted values. Actual production results can refer to output indicators such as molten iron quality and energy consumption. Key quality parameters are collected through online testing equipment and used to verify the mix ratio. Evaluation weights can refer to priority coefficients for indicators such as cost and quality. The analytic hierarchy process (AHP) is used to dynamically adjust weight assignments to accommodate different production objectives. Constraint adjustment can refer to the flexible setting of process limits. Constraints can be relaxed or tightened based on changes in equipment status, improving solution feasibility.

[0061] By establishing a two-way data channel between the raw material market and actual production performance, dynamic optimization of metallurgical production decisions is achieved. Market fluctuations are innovatively incorporated into the mix optimization system, ensuring that production plans are both compliant with process requirements and economically viable. A feedback adjustment mechanism continuously adjusts model parameters, enabling the system to self-evolve and gradually improve the accuracy and reliability of mix plans. Ultimately, this forms an intelligent closed-loop control system that integrates market response, production execution, and performance feedback. This ensures stable and efficient operation of steel enterprises in complex and volatile market environments and provides real-time decision support for intelligent production management.

[0062] The following is a presentation of relevant data and experimental results of some implementation cases: 1. Results of raw material and fuel evaluation.

[0063] Table 1

[0064] Table 2

[0065] 2. Ingredients results Table 3 Sintering ingredient results

[0066] Table 4 Pellet batching results

[0067] Table 5 Coking coal blending results

[0068] Table 6 Blast furnace charge results

[0069] 3. Mass-energy balance calculation results Table 7 Sintering material balance

[0070] Table 8 Sintering heat balance

[0071] Table 9 Pellet material balance

[0072] Table 10 Pellet heat balance

[0073] Table 11 Coke oven material balance

[0074] Table 12 Coke oven heat balance

[0075] Table 13 Blast furnace material balance

[0076] Table 14 Blast furnace heat balance

[0077] 4. Cost calculation results Table 15 Sintering cost

[0078] Table 16 Pellet cost

[0079] Table 17 Coke oven costs

[0080] Table 18 Blast furnace costs

[0081] Table 19 Pre-iron costs

[0082] The preferred embodiments disclosed above are intended only to help illustrate this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of the present invention. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A full-process cost calculation and analysis system for ironmaking, characterized in that: include: The ironmaking database module is used to store market information and full composition data of iron ore fines, coking coal, PCI coal, and flux, as well as production medium energy consumption data for sintering, pelletizing, coking, and blast furnace processes; A raw material evaluation module, which establishes a cost-effectiveness evaluation system for iron ore fines, sintered ore, pelletized ore, lump ore and coking coal based on the data in the iron pre-database module; A proportion optimization module generates the lowest cost proportion scheme for sintering ore proportion, pelletizing ore proportion, coking coal proportion and blast furnace charge structure based on the output of the raw material evaluation module; A mass-energy analysis module performs material balance and energy balance calculations on the ratio schemes for each process generated by the ratio optimization module, and feeds back the calculation results to the ratio optimization module; The cost calculation module integrates the proportioning schemes of each process output by the proportioning optimization module and calculates the total cost of the entire pre-ironization process.

2. The system according to claim 1, wherein: The raw material evaluation module evaluates iron ore by: Set weight coefficients based on the impact of iron oxide, harmful elements and alkali metal elements in the raw materials on the cost of molten iron; Considering titanium dioxide as acidic slag, the comprehensive iron grade excluding the influence of moisture and burnout is calculated using the preset five-element basicity calculation formula, wherein the five-element basicity calculation formula includes: Wherein, TFe represents the comprehensive iron grade of iron ore, TFe represents the iron grade of iron ore, R5 represents the five-element basicity of blast furnace slag, Ig represents the loss on burn of iron ore, Fe represents the iron content, FeO represents the iron oxide content, SiO2 represents the silicon dioxide content, Al2O3 represents the aluminum oxide content, Ti represents the titanium content, CaO represents the calcium oxide content, MgO represents the magnesium oxide content, S represents the sulfur content, P represents the phosphorus content, K represents the potassium content, Na represents the sodium content, Zn represents the zinc content, As represents the arsenic content, Cu represents the copper content, and Pb represents the lead content; The price per ton is calculated with reference to the benchmark ore and the price-performance ratio is ranked.

3. The system according to claim 2, characterized in that The evaluation of coking coal by the raw material evaluation module includes: Calculate the conventional cost per ton of coke based on the consumption per ton of coking coal, unit price, coke processing fee and backwash fee; Calculate the corrected cost per ton of coke based on the corresponding relationship between coke quality and energy indicators and blast furnace economic indicators; The conventional cost per ton of coke and the corrected cost per ton of coke are added together to obtain the total cost and the total cost is sorted.

4. The system according to claim 1, wherein: The sintering ore blending system in the blending optimization module includes: Based on the sintering ore blending system, constraint equations are established for the chemical composition, alkalinity, and drum strength of the sintered ore. The process setting takes into account raw material moisture, burn-off, and element distribution rate. Upper and lower limits are set for some specific raw materials. Planning and solving are performed with the lowest cost as the goal. During planning and solving, the number of constraint equations and the number of unknowns are equal, resulting in a minimum cost blending solution that meets the process intensity requirements. The relevant basic characteristic values ​​of the batch material, as well as the chemical composition and relevant basic characteristic values ​​of the sintered ore are calculated based on the proportioning results.

5. The system according to claim 1, wherein: The pelletizing ore blending system in the blending optimization module includes: Based on the pelletizing ore blending system, constraint equations are established for the chemical composition, alkalinity, green ball strength, and cooked ball strength of the pellet ore. The setting process takes into account the raw material moisture content, burn-off loss, and element distribution rate. Burn-off loss is the comprehensive burn-off loss of the raw material, including the amount of oxygen added to the raw material. Upper and lower limit constraints are then set for the preset specific raw materials. Planning and solving are performed with the lowest cost as the goal. During planning and solving, the number of constraint equations and the number of unknowns are equal, resulting in a minimum cost blending solution that meets the process intensity requirements. The relevant basic characteristic values ​​of the batch material, as well as the chemical composition and relevant basic characteristic values ​​of the pellets are calculated based on the proportioning results.

6. The system according to claim 1, wherein: The coking coal blending system in the ratio optimization module includes: Based on the coking coal blending system, constraint equations are established for the coke ash content, sulfur content, volatile matter, fixed carbon, G value, and Y value. The setting process takes into account the raw material moisture and volatile matter as well as the element distribution rate. Then, upper and lower limit constraints are set for each type of coal. Planning and solving are carried out with the lowest cost as the goal. During the planning and solving, the number of constraint equations is equal to the number of unknowns, and the lowest cost ratio solution that meets the process intensity requirements is obtained; The coke components and ash chemical composition are calculated based on the ratio results.

7. The system according to claim 1, wherein: The blast furnace charge structure optimization system in the ratio optimization module includes: Set the sulfur and silicon content in the molten iron in advance, and calculate the iron and slag rates of each raw material based on the element distribution rate; Constraint equations for the tapping rate are set based on the tapping rate of each raw material. Constraint equations are also set for the slag basicity, magnesium-aluminum ratio, aluminum-silicon ratio, and the SiO2 and Al2O3 contents in the slag. Upper and lower limits are set for the preset specific raw materials. Planning and solving are performed with the lowest cost as the goal, so that the number of constraint equations and the number of unknowns are equal, and the lowest cost mix required for production is obtained. The composition of molten iron and slag is calculated based on the ratio results.

8. The system according to claim 1, wherein: The mass-energy analysis module includes: Calculate theoretical material balance and energy balance based on the proportion results of each process; Analyze process energy flows and optimize fuel consumption; The calculation results are fed back to the corresponding ratio system for constraint adjustment.

9. The system according to claim 1, wherein: The cost calculation module includes: Calculate the independent costs for sintering, pelletizing, coking, and blast furnace processes separately; Integrate the costs of each process to form the total cost analysis of the entire ironmaking process; Optimize the overall matching plan based on the cost analysis results.

10. The system according to any one of claims 1 to 9, characterized in that: The system further comprises: Dynamic update module to regularly obtain the latest raw material market information and process production data; Feedback adjustment module adjusts evaluation weights and constraints based on actual production results.

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