A green smelting method of converter blowing energy flow-material flow collaborative optimization

Abstract

This invention relates to a green smelting method for the coordinated optimization of energy and material flow in converter blowing, comprising: collecting material and energy data for the entire converter blowing process; constructing a material flow network based on the material balance law; constructing an energy flow network based on the energy conservation law; constructing a material flow-energy flow coupling network through multivariate regression analysis; constructing a multi-objective optimization function for energy and material flow based on the material flow-energy flow coupling network; solving the multi-objective optimization function to obtain optimized process parameters; and using the optimized process parameters to control the entire converter blowing process. This invention constructs a material-energy coupling network for the entire converter process based on systems engineering theory, implementing global coordinated optimization of materials, energy, by-products, and carbon emissions. This effectively reduces material and energy consumption per ton of steel, improves the level of secondary resource recycling, reduces carbon emissions in the production process, and achieves the integrated production goal of efficient, green, and high-quality converter steelmaking.

Description

Technical Field

[0001] This invention relates to the field of converter smelting technology, and more specifically to a green smelting method for synergistic optimization of energy flow and material flow in converter blowing. Background Technology

[0002] In traditional converter smelting, material control and energy control are disconnected. Process parameters such as slag formation, cooling, and oxygen supply are set only around the single objective of steel quality, without considering the synergistic optimization of material consumption, energy utilization, and by-product recovery. This results in high steel material consumption, low energy utilization, and generally high energy consumption per ton of steel. At the same time, the recovery efficiency of by-products such as coal gas and steam is low, the total iron in the slag is not fully recovered, and the waste heat of flue gas is not utilized in a cascade manner, resulting in serious resource waste and persistently high carbon emissions.

[0003] Existing technologies only offer individual material or energy optimization solutions: for example, a single material optimization method for the slag-forming process focuses only on slag-forming agent consumption and molten steel quality, without addressing energy flow control; another example is a single energy optimization method for flue gas waste heat recovery, which is disconnected from the control of core converter smelting process parameters and does not integrate with material flow for synergistic optimization. A few other green smelting technologies focus only on single emission reduction targets, failing to construct a comprehensive material-energy coupling optimization system, resulting in limited optimization effects and an inability to achieve green and efficient converter smelting at the system level. Summary of the Invention

[0004] To address the aforementioned problems, the purpose of this invention is to provide a green smelting method that optimizes the energy flow and material flow in converter blowing.

[0005] A green smelting method for co-optimizing energy flow and material flow in converter blowing includes:

[0006] Step 1: Collect material-energy data for the entire converter blowing process;

[0007] Step 2: Construct a material flow network based on material balance principles;

[0008] Step 3: Construct an energy flow network based on the law of energy conservation;

[0009] Step 4: Quantify the coupling coefficient between material flow and energy flow through multiple regression analysis and construct a material flow-energy flow coupling network;

[0010] Step 5: Based on the material flow-energy flow coupled network, construct a multi-objective optimization function for energy flow-material flow with the optimization objectives of achieving the target carbon temperature at the end of molten steel, minimizing material consumption per ton of steel, maximizing energy utilization, and minimizing carbon emissions per ton of steel.

[0011] Step 6: Solve the energy flow-material flow multi-objective optimization function to obtain the optimized process parameters;

[0012] Step 7: Use the optimized process parameters to control the entire converter blowing process.

[0013] Preferably, in step 1, the material-energy data includes: raw material and auxiliary material data, process operation data, by-product recovery data, carbon emission data, and energy loss data; wherein, the raw material and auxiliary material data includes: the amount and composition of molten iron, scrap steel, quicklime, lightly calcined dolomite, and iron balls added; the process operation data includes: oxygen supply intensity, lance position, bottom blowing flow rate, and timing and amount of coolant added; the by-product recovery data includes: gas production and calorific value, steam production and pressure, and slag production and composition; the energy loss data includes: furnace heat dissipation, flue gas waste heat, and cooling water loss; and the carbon emission data includes: CO2, CO, and CH4 gas emission concentrations and flow rates.

[0014] Preferably, step 2: constructing a material flow network based on material balance principles includes:

[0015] Step 2.1: Divide the entire converter blowing process into the following nodes: raw material input node, smelting reaction node, slag formation node, molten steel output node, slag by-product node, and flue dust recovery node;

[0016] Step 2.2: Establish material balance equations and component transformation calculation models for each node based on the law of conservation of mass;

[0017] Step 2.3: Using a directed network topology, connect all nodes to obtain the material flow network.

[0018] Preferably, step 3: constructing an energy flow network based on the law of energy conservation includes:

[0019] Step 3.1: According to the energy utilization process in the converter blowing process, the process is divided into chemical reaction heat release node, gas combustion heat release node, flue gas waste heat carrying node, furnace body heat dissipation loss node, steam and waste heat recovery and utilization node, and the energy inflow, conversion, dissipation and recovery boundary range of each node is determined.

[0020] Step 3.2: Based on the thermodynamic law of conservation of energy, establish a total energy balance model for the converter system and calculate the energy transfer efficiency of each energy node;

[0021] Step 3.3: Connect all energy nodes in a directed topology to obtain an energy flow network.

[0022] Preferably, step 4: quantifying the coupling coefficient between material flow and energy flow and constructing a material flow-energy flow coupling network through multiple regression analysis, includes:

[0023] Step 4.1: Select the following as the main control coupling input factors: slag-forming agent addition amount, oxygen supply intensity, oxygen lance position, bottom blowing flow rate, and coolant addition amount;

[0024] Step 4.2: Use the multiple linear regression algorithm to establish the correlation model, the waste heat segmented energy distribution model and the coupling sensitivity coefficient, and set the coupling constraint boundary.

[0025] Preferably, in step 5, the constraints of the energy flow-material flow multi-objective optimization function include: equipment operation constraints, process operation constraints, and steel grade quality constraints; wherein, the equipment operation constraints are: oxygen supply intensity ≤ 4.5 m³ / min·t, oxygen lance position ≥ 1.0 m; the process operation constraints are: slag basicity 2.0~3.0, FeO content 15%~25%; and the steel grade quality constraints are S≤0.005%, P≤0.008%.

[0026] The present invention also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that the computer program, when executed by the processor, implements the steps in the above-described green smelting method for co-optimization of energy flow and material flow in converter blowing.

[0027] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps in the above-described green smelting method for co-optimization of energy flow and material flow in converter blowing.

[0028] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0029] This invention relates to a green smelting method that optimizes the energy flow and material flow in converter blowing. Compared with the prior art, this invention constructs a material-energy coupling network for the entire converter process based on systems engineering theory, and performs global synergistic optimization of materials, energy, by-products, and carbon emissions. This can effectively reduce the material and energy consumption per ton of steel, improve the level of secondary resource recycling, reduce carbon emissions in the production process, and achieve the integrated production goal of efficient, green, and high-quality converter steelmaking.

[0030] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram of a green smelting method for co-optimizing energy flow and material flow in converter blowing, provided by the present invention. Detailed Implementation

[0033] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0034] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0035] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0036] Please see Figure 1 A green smelting method for co-optimizing energy flow and material flow in converter blowing, comprising:

[0037] Step 1: Collect material-energy data for the entire converter blowing process;

[0038] Step 1 includes:

[0039] 1. Data Acquisition System Setup: Deploy high-precision detection and measurement equipment. Equipment accuracy requirements are: material metering equipment ±0.5%, energy detection equipment ±1%, carbon emission monitoring equipment ±2ppm; data acquisition range includes:

[0040] 2. Raw material and auxiliary material data: amount and composition of molten iron, scrap steel, quicklime, lightly calcined dolomite, and iron balls;

[0041] 3. Process operation data: oxygen supply intensity, nozzle position, bottom blowing flow rate, timing and amount of coolant added;

[0042] 4. By-product recovery data: gas production and calorific value, steam production and pressure, slag production and composition;

[0043] 5. Energy loss data: furnace body heat dissipation, flue gas waste heat, and cooling water loss;

[0044] 6. Carbon emission data: Concentrations and flow rates of gases such as CO2, CO, and CH4;

[0045] 7. Database Construction: A full-process material-energy database was built based on MySQL, with a data acquisition frequency of 1Hz to achieve real-time data storage, querying, and statistics; the database contains production data from over 30,000 heats of Baogang No. 1 converter over the past 3 years, providing data support for model construction.

[0046] Step 2: Construct a material flow network based on material balance principles;

[0047] In step 2, based on the material balance law, a converter blowing material flow network covering molten iron decarburization, silicon-manganese oxidation, slag-forming reaction, and steel composition adjustment is constructed to quantify the material conversion efficiency of each link; a total iron content control item in slag is introduced, setting the total iron content in slag to ≤10%, and the iron resource recovery efficiency is improved by optimizing the slag-forming system.

[0048] This invention constructs a material flow network according to the following specific steps and embeds corresponding algorithms and formulas:

[0049] (1) Divide the material flow node units: Divide the entire converter blowing process into six major material nodes: raw material input node, smelting reaction node, slag formation node, molten steel output node, slag by-product node, and flue dust recovery node, and define the material input, conversion, consumption, and output boundaries of each node.

[0050] (2) Establishing the material balance equation for a single node: Constructing a general model based on the law of conservation of mass:

[0051] Mᵢ n = M o ᵤ t + M ons + M loss

[0052] In the formula: Mᵢ n Input the total amount of material for each node; M o ᵤ t M represents the total output of materials at the node. ons M represents the consumption of smelting reaction materials; lossThis refers to the loss of materials such as splashes and dust.

[0053] (3) Quantitative modeling of key component conversion: A component conversion calculation model is established for the main reactions of silicon-manganese oxidation, decarbonization, and slag formation.

[0054] ΔMᵢ = ηᵢ·Mᵣ a w·ωᵢ

[0055] In the formula: ΔMᵢ is the amount of the i-th component consumed in the reaction; ηᵢ is the oxidation conversion efficiency of the component; Mᵣ a w represents the amount of molten iron and scrap steel input; ωᵢ represents the initial mass fraction of the raw material components.

[0056] (4) Full iron constraint control embedded in slag: Add constraint condition TFe sla g ≤ [TFe] l ᵢ m ᵢ t The total iron content in the slag is limited to ≤10%, and the optimal recovery of iron resources is achieved through the coordinated control of slag-forming agent ratio, gun position, and oxygen supply system.

[0057] (5) Topology Network Integration: A directed network topology structure is adopted to connect all material nodes in the process and form a matrix material flow topology network, which clearly represents the flow direction, flow rate, conversion rate and loss distribution.

[0058] The complete steps for constructing the topology network are as follows:

[0059] (1) Node unit division

[0060] The converter smelting process is broken down into six material nodes: raw material input node, smelting reaction node, slag formation node, molten steel output node, slag by-product node, and flue dust recovery node; the material input, reaction consumption, output, and loss boundaries of each node are clearly defined.

[0061] (2) Single-node material balance modeling

[0062] Using the general equilibrium equation M in =M out +M cons +M loss Establish material accounting relationships for each node.

[0063] (3) Refined modeling of component transformation

[0064] Through ΔM i = η i ·M raw ·ω i Calculate the oxidation consumption of elements such as silicon, manganese, and carbon, and quantify the conversion ratio of components within the node.

[0065] (4) Add production constraints

[0066] All-iron confinement TFe embedded in slag slag ≤[TFe] limit ≤10%, limiting the upper limit of material conversion at the slag-forming node.

[0067] (5) Directed topology serial networking

[0068] Directed arrows represent the direction of material flow, and the six major nodes are connected in series according to the smelting sequence to form a matrix material topology network.

[0069] Field examples

[0070] Taking the slag formation node as an example for network configuration:

[0071] 1. Upstream raw material input node outputs quicklime, dolomite, and iron ball material flow rate M in ;

[0072] Substituting into the equilibrium equation to calculate the M consumed in the slag-forming reaction cons Splash dust loss M loss ;

[0073] The conversion amounts of CaO, FeO, and SiO2 were calculated using a component model, with a constraint that TFe in the slag is ≤10%.

[0074] The directed edges connect downstream to the slag by-product node (slag production) and the smelting reaction node (participating in steel oxidation).

[0075] All six nodes are connected in series according to the formula "raw material input → smelting reaction → slag formation → molten steel output + slag by-products + dust recovery", resulting in a complete directed topology network for material flow.

[0076] Step 3: Construct an energy flow network based on the law of energy conservation;

[0077] In step 3, based on the law of energy conservation, an energy flow network for converter blowing is constructed, covering the exothermic reaction of chemical reactions, the exothermic combustion of coal gas, the waste heat of flue gas, the heat dissipation of the furnace body, and the steam recovery, and the energy transfer efficiency of each link is quantified; a waste heat utilization item for flue gas is introduced, and the waste heat of flue gas is divided into a high temperature section of 800~1200℃, a medium temperature section of 400~800℃, and a low temperature section of 150~400℃, which are respectively used for steam production, waste heat power generation, and preheating of combustion air;

[0078] This invention constructs an energy flow network through the following specific steps:

[0079] (1) Divide the energy flow network into node units

[0080] Based on the entire energy input, conversion, transmission, loss, and recovery chain of converter blowing, it is divided into five major energy nodes: chemical reaction exothermic node, gas combustion exothermic node, flue gas waste heat carrying node, furnace body heat dissipation loss node, and steam and waste heat recovery and utilization node. The boundary range of energy inflow, conversion, dissipation, and recovery of each node is clearly defined.

[0081] (2) Establish the fundamental equations for energy conservation of the entire system.

[0082] Based on the thermodynamic law of conservation of energy, a total energy balance model for the converter system is established:

[0083] Q total,in = Q reaction + Q gas = Q waste + Q loss + Q recover

[0084] In the formula: Q total,in Q is the total chemical energy input to the system. reaction The smelting oxidation reaction releases heat; Q gas Q is the heat released during the combustion of converter gas; waste To carry away residual heat in the flue gas; Q loss Q represents the radiative and convective heat dissipation of the furnace body. recover It is a source of recyclable energy, such as steam and waste heat power generation.

[0085] (3) Quantitative modeling of energy transfer efficiency in each stage

[0086] Establish a formula for calculating the energy transfer efficiency for each energy node:

[0087] η i =Q i,out / Q i,in

[0088] In the formula: Let Q be the energy transfer efficiency of the i-th energy node; i,in Input energy into the node; Q i,out The system outputs usable energy at each node; it quantifies the reaction heat release utilization rate, flue gas waste heat transfer rate, furnace heat loss rate, and steam recovery efficiency at each node.

[0089] (4) Segmented model for the cascade utilization of waste heat from embedded flue gas

[0090] The waste heat from the flue gas is precisely segmented according to temperature ranges, and a segmented energy distribution formula is established:

[0091] Q waste =Q h (800-1200℃+ Q m (400-800℃) + Ql (150-400℃)

[0092] High temperature section Q h Prioritized for use in waste heat boilers to produce high-pressure steam; medium-temperature section Q m Used for waste heat power generation in steam turbine units; Low-temperature section Q l Used for preheating combustion air and softened water to achieve temperature matching, cascade utilization, and energy level matching.

[0093] The following supplementary explanations of the above steps are based on various models:

[0094] (1) Divide energy nodes into 5 categories

[0095] The nodes for exothermic chemical reactions, exothermic gas combustion, waste heat load of flue gas, heat loss of furnace body, and waste heat recovery of steam are defined, and the boundaries of energy inflow / conversion / loss / recovery at each node are delineated.

[0096] (2) Global energy conservation equation

[0097] Using total balance Q total,in =Q reaction +Q gas =Q waste +Q loss +Q recover To unify the energy revenue and expenditure benchmarks across the entire system.

[0098] (3) Calculate energy transfer efficiency node by node

[0099] Apply the efficiency formula η to each energy node i =Q i,out / Q i,in The efficiency of heat release utilization, waste heat recovery efficiency, and heat loss ratio of the reaction are quantified.

[0100] (4) Embedded waste heat cascade segmentation model

[0101] Dismantling flue gas waste heat

[0102] Q waste =Q h (800-1200℃)+Q m (400-800℃)+Q L (150-400℃), matching the corresponding waste heat recovery equipment for different temperatures.

[0103] (5) All energy nodes are connected in a directed topology and networked according to the energy generation → transmission → loss → recycling flow.

[0104] Field example (flue gas waste heat carrying node)

[0105] 1. The total chemical energy input Q at the upstream chemical reaction exothermic node and the gas combustion exothermic node.total,in ;

[0106] 2. Substituting into the conservation equation, we can extract the waste heat Q carried by the flue gas. waste Furnace body heat dissipation Q loss Recoverable energy Q recover ;

[0107] 3. Calculate the transmission efficiency η=Q of this node. waste / (Q reaction +Q gas ), quantifying the proportion of energy carried away by flue gas;

[0108] 4. Q waste Disassembled into high temperature Q h , medium temperature Q m , low temperature Q L Three sections;

[0109] 5. Directed edge splitting: Q h Flow to waste heat boiler node, Q m Flow to turbine generator node, Q L The air flows to the combustion air preheating node, and the heat dissipation Q loss Point to the loss node to complete the energy flow topology construction.

[0110] Step 4: Quantify the coupling coefficient between material flow and energy flow through multiple regression analysis and construct a material flow-energy flow coupling network;

[0111] In step 4, the coupling coefficient between material flow and energy flow is quantified through multiple regression analysis, and a 10×10 coupling matrix is ​​constructed. The influence of core parameters such as the amount of slag-forming agent added and the oxygen supply intensity on material conversion and energy transfer is clarified. For example, increasing the amount of slag-forming agent added will increase the slag alkalinity and increase the heat dissipation of the furnace body.

[0112] This invention quantifies coupling relationships through the following steps, combining algorithms, formulas, and innovative logic:

[0113] (1) Screening the main control coupling input factors

[0114] Five types of process parameters, namely, slag-forming agent addition amount, oxygen supply intensity, oxygen lance position, bottom blowing flow rate, and coolant addition amount, are determined as input variables X.

[0115] (2) Establishing the basic coupling relationship of the multivariate linear association model

[0116] Y=A·X+B: X is the process parameter vector, Y is the material / energy index vector, and A is a 10×10 coupling coefficient matrix. The matrix coefficients are fitted with historical data from 3,000+ furnaces to establish a basic mapping between parameters and indices.

[0117] (3) Waste heat segmentation model binds material-energy linkage relationship

[0118] Q total =Q high +Q mid +Q low This directly links the input of oxygen supply and slag-forming materials to the output of waste heat in the three stages, thus opening up the coupling channel between material input and waste heat energy output.

[0119] (4) Coupling sensitivity coefficient quantifies the strength of coupling.

[0120] S ij = Y i / X j Calculate the degree of influence of the j-th process parameter on the i-th material / energy index, and distinguish between strong and weak coupling variables.

[0121] (5) Coupling constraint boundary limits the coupling interval

[0122] The fluctuation range of the coupling coefficient is defined based on the equipment, process, and steel grade indicators to avoid the optimized parameters from exceeding the feasible range on site, thus completing the closed loop of the coupling network.

[0123] Example (coupling of oxygen supply intensity and flue gas waste heat)

[0124] 1. Input factor X j =Oxygen supply intensity, output index Y i =High-temperature flue gas waste heat Q h ;

[0125] 2. Association Model Matrix A ij The fitting yields a positive coupling coefficient, representing a simultaneous increase in oxygen supply and waste heat.

[0126] 3. The waste heat segmentation model directly converts the exothermic reaction of oxygen supply oxidation into Q. h Incremental growth enables the binding of material oxygen supply behavior with waste heat energy;

[0127] 4. Calculate the sensitivity S ij =∂Q h / ∂Oxygen supply intensity = 0.76, which is a high absolute value and is therefore determined to be a strongly coupled master control parameter;

[0128] 5. Add constraint boundary: oxygen supply intensity ≤ 4.5 m³ / min·t, limit the maximum value of this coefficient in the coupling matrix to prevent the solution of parameters exceeding the upper limit of the equipment;

[0129] 6. Couple all 5 process parameters and 10 material energy indicators in pairs to form a complete material-energy coupling network.

[0130] Step 5: Based on the material flow-energy flow coupled network, construct a multi-objective optimization function for energy flow-material flow with the optimization objectives of achieving the target carbon temperature at the end of molten steel, minimizing material consumption per ton of steel, maximizing energy utilization, and minimizing carbon emissions per ton of steel.

[0131] In step 5, the energy flow-material flow multi-objective optimization function includes:

[0132] 1. Optimization objectives: ① Achieve the target carbon temperature at the end of molten steel (0.02%≤[C]≤0.04%, 1590~1610℃); ② Minimize the consumption of materials per ton of steel (steel feedstock, slag-forming agent, coolant); ③ Energy utilization rate ≥85%; ④ Carbon emissions per ton of steel ≤180kg;

[0133] 2. Constraints: Equipment operation constraints (oxygen supply intensity ≤ 4.5 m³ / min·t, oxygen lance position ≥ 1.0 m); process operation constraints (slag basicity 2.0~3.0, FeO content 15%~25%); steel grade quality constraints (S≤0.005%, P≤0.008%).

[0134] 3. Construction of multi-objective optimization function: The multi-objective optimization function is constructed using the weighted summation method.

[0135] (1) The input variables (oxygen supply intensity, slag-forming agent, gun position and other process parameters X) and output evaluation indicators (ton steel consumption, waste heat utilization rate, carbon emissions, steel carbon temperature Y) of the multi-objective function all come from the X and Y vectors of the coupled network.

[0136] (2) The coupling matrix A is the internal mapping relationship of the objective function.

[0137] The 10×10 coefficient matrix A of the coupled network is the calculation kernel of "process parameters → material / energy index" in the objective function; given a set of process parameters, the values ​​of various optimization indexes can be quickly calculated by Y=A·X+B.

[0138] (3) Constraints fully reuse coupled boundaries

[0139] The equipment constraints, process constraints, and steel grade quality constraints of the optimization function all come from the coupling constraint boundaries set by the coupled network.

[0140] (4) Sensitivity coefficient guides weight allocation

[0141] Sensitivity coefficient S of coupled network ij It is used to assist in setting the weighting coefficients of the multi-objective function (the F1 weight in this patent is 0.4, which is the highest because the steel quality index is the most sensitive).

[0142] II. Complete Steps for Constructing a Multi-Objective Function Based on Coupled Networks

[0143] (1) Extract four types of optimization indices from the coupled network

[0144] Based on the Y vector of the coupled network, four optimization objectives are selected:

[0145] F1: Final carbon temperature of molten steel meets the standard (quality index);

[0146] F2: Reciprocal of material consumption per ton of steel (material flow optimization);

[0147] F3: System energy utilization rate (energy flow optimization);

[0148] F4: Reciprocal carbon emissions per ton of steel (low-carbon optimization).

[0149] (2) Establish the index calculation logic based on the coupling matrix

[0150] For any set of process parameters X, the values ​​of four targets F1 / F2 / F3 / F4 can be calculated in real time through the coupled model Y=A·X+B, realizing the rapid conversion from parameter to target value.

[0151] (3) Weighted summation to construct a unified optimization objective function

[0152] F = 0.4F1 + 0.2F2 + 0.2F3 + 0.2F4

[0153] The weights are allocated based on the coupling sensitivity coefficient: the steel quality index has the highest sensitivity and a weight of 0.4; the materials, energy, and carbon emissions have similar sensitivity and the remaining weights are evenly distributed.

[0154] (4) Reuse all constraint boundaries of the coupled network as function constraints

[0155] Equipment constraints: oxygen supply intensity ≤ 4.5 m³ / min·t, gun position ≥ 1.0 m;

[0156] Process constraints: slag basicity 2.0~3.0, FeO: 15%~25%;

[0157] Steel grade constraints: S≤0.005%, P≤0.008%;

[0158] All constraints are parameter fluctuation ranges preset during coupled network modeling.

[0159] (5) Form a complete and solvable optimization model

[0160] With maximizing F as the optimization direction, the coupled network process parameters X are input, and the comprehensive optimal process parameters are output, thus completing the transformation from a coupled network to a multi-objective optimization function.

[0161] III. Examples

[0162] Given process parameters X = {quicklime 62 kg / t, oxygen supply 3.5 m³ / min·t, lance position 1.2 m}:

[0163] 1. Substituting into the coupled model Y=A·X+B, the following results were obtained: carbon temperature meets the standard, slag-forming agent consumption is 62kg per ton of steel, energy utilization rate is 88%, and carbon emissions are 172kg per ton.

[0164] 2. Calculate F1=1 (meets the standard), F2=1 / 62, F3=0.88, and F4=1 / 172 respectively;

[0165] 3. Substitute the values ​​into the weighted formula to calculate the overall objective F;

[0166] 4. The algorithm iterates repeatedly by adjusting X within the coupling constraint range to find a set of process parameters that maximizes F, i.e., the optimal solution.

[0167] Improved Particle Swarm Optimization (PSO) Algorithm: An improved PSO algorithm with adaptive inertia weights is used to solve the optimization function. The inertia weight ω is dynamically adjusted with the number of iterations, and the formula is: ω=0.9-0.4×t / T (t is the current iteration number, and T is the total number of iterations). The number of particles is set to 100, and the total number of iterations is 200. After the algorithm converges, the optimal process parameters are output as follows: quicklime 60~65kg / t, lightly calcined dolomite 35~40kg / t, iron balls 20~25kg / t, oxygen supply intensity 3.2~3.8m³ / min·t, and gas recovery flow rate 120~150m³ / min.

[0168] Step 6: Solve the energy flow-material flow multi-objective optimization function to obtain the optimized process parameters;

[0169] Step 7: Use the optimized process parameters to control the entire converter blowing process.

[0170] Step 7 includes:

[0171] 1. Process parameter execution: The optimal process parameters output by the optimization algorithm are sent to the converter control system to achieve precise quantitative addition of slagging agent and coolant, dynamic control of oxygen supply intensity and gun position, and linkage adjustment of by-product recovery equipment such as gas recovery valve opening and waste heat boiler load;

[0172] 2. Real-time feedback acquisition: Collect real-time data on material consumption, energy utilization, by-product recovery, and carbon emissions every 30 seconds, calculate the deviation between the actual value and the optimized target value, and generate feedback signals;

[0173] 3. Closed-loop optimization and correction: Construct a closed-loop control system for the entire process of "perception-analysis-decision-execution-feedback". Dynamically correct the weight coefficients of the material flow-energy flow coupling matrix and the multi-objective optimization function based on the feedback signal. If the deviation between the actual value and the target value is ≥10%, resolve the optimization function and output new process parameters to achieve dynamic collaborative optimization.

[0174] According to specific embodiments of the present invention, the following technical effects are disclosed:

[0175] 1. Significantly improved resource and energy utilization: Construct a coupled network of material flow and energy flow throughout the entire process to achieve coordinated control of materials and energy. The recovery rate of total iron in slag is increased by ≥5%, the utilization rate of waste heat in flue gas is increased by ≥8%, the consumption of steel materials per ton of steel is reduced by ≥1kg, and the energy consumption per ton of steel is reduced by ≥2kgce.

[0176] 2. Significant reduction in carbon emissions: Taking into account the optimization target of minimum carbon emissions per ton of steel, and combining efficient recovery of by-products and cascade utilization of waste heat, carbon emissions per ton of steel are reduced by ≥3%, helping steel companies achieve green and low-carbon development.

[0177] 3. Synergistic improvement of steel quality and production efficiency: While optimizing for green practices, the final carbon temperature of molten steel is ensured to meet the standards, the stability of molten steel quality is improved, and the optimized process parameters can shorten the smelting cycle, resulting in an increase in production efficiency of ≥2%.

[0178] 4. The optimized solution is highly practical: It adopts an improved particle swarm optimization algorithm, which has a fast convergence speed and high accuracy of the optimal solution. It also constructs a closed-loop control system to achieve dynamic optimization rather than static optimization, which is suitable for complex scenarios such as converter raw material fluctuations and changes in operating conditions. The optimized solution can directly guide on-site production.

[0179] 5. Easy to promote and implement: This method can be integrated with existing converter data acquisition and control systems without large-scale equipment modifications. The process parameters are clear and the operation steps are standardized, making it easy to promote and apply in existing converter production lines of steel enterprises.

[0180] 6. After the application of this embodiment, the consumption of steel materials per ton of steel produced by a 100-ton converter is reduced by 1.2 kg, and the consumption of slag-forming agent is reduced by 8 kg; the energy utilization rate is increased to over 88%, the gas recovery utilization rate is increased by 10%, and the flue gas waste heat utilization rate is increased by 8%; the comprehensive energy consumption per ton of steel is reduced by 18 kgce, the CO2 emission per ton of steel is reduced by 20 kg, and the total iron content in the slag is reduced to below 8.5%. Under the premise of ensuring the quality of molten steel, the synergistic optimization of materials and energy and green and low-carbon smelting are achieved.

[0181] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A green smelting method for co-optimizing energy flow and material flow in converter blowing, characterized in that, include: Step 1: Collect material-energy data for the entire converter blowing process; Step 2: Construct a material flow network based on material balance principles; Step 3: Construct an energy flow network based on the law of energy conservation; Step 4: Quantify the coupling coefficient between material flow and energy flow through multiple regression analysis and construct a material flow-energy flow coupling network; Step 5: Based on the material flow-energy flow coupled network, construct a multi-objective optimization function for energy flow-material flow with the optimization objectives of achieving the target carbon temperature at the end of molten steel, minimizing material consumption per ton of steel, maximizing energy utilization, and minimizing carbon emissions per ton of steel. Step 6: Solve the energy flow-material flow multi-objective optimization function to obtain the optimized process parameters; Step 7: Use the optimized process parameters to control the entire converter blowing process.

2. The green smelting method for co-optimization of energy flow and material flow in converter blowing according to claim 1, characterized in that, In step 1, the material-energy data includes: raw material and auxiliary material data, process operation data, by-product recovery data, carbon emission data, and energy loss data. Specifically, the raw material and auxiliary material data includes: the amount and composition of molten iron, scrap steel, quicklime, lightly calcined dolomite, and iron balls added; the process operation data includes: oxygen supply intensity, lance position, bottom blowing flow rate, and timing and amount of coolant added; the by-product recovery data includes: gas production and calorific value, steam production and pressure, and slag production and composition; the energy loss data includes: furnace heat dissipation, flue gas waste heat, and cooling water loss; and the carbon emission data includes: CO2, CO, and CH4 gas emission concentrations and flow rates.

3. The green smelting method for co-optimization of energy flow and material flow in converter blowing according to claim 2, characterized in that, Step 2: Constructing a material flow network based on material balance principles, including: Step 2.1: Divide the entire converter blowing process into the following nodes: raw material input node, smelting reaction node, slag formation node, molten steel output node, slag by-product node, and flue dust recovery node; Step 2.2: Establish material balance equations and component transformation calculation models for each node based on the law of conservation of mass; Step 2.3: Using a directed network topology, connect all nodes to obtain the material flow network.

4. The green smelting method for co-optimization of energy flow and material flow in converter blowing according to claim 3, characterized in that, Step 3: Constructing an energy flow network based on the law of energy conservation, including: Step 3.1: According to the energy utilization process in the converter blowing process, the process is divided into chemical reaction heat release node, gas combustion heat release node, flue gas waste heat carrying node, furnace body heat dissipation loss node, steam and waste heat recovery and utilization node, and the energy inflow, conversion, dissipation and recovery boundary range of each node is determined. Step 3.2: Based on the thermodynamic law of conservation of energy, establish a total energy balance model for the converter system and calculate the energy transfer efficiency of each energy node; Step 3.3: Connect all energy nodes in a directed topology to obtain an energy flow network.

5. A green smelting method for co-optimization of energy flow and material flow in converter blowing according to claim 4, characterized in that, In step 5, the constraints of the energy flow-material flow multi-objective optimization function include: equipment operation constraints, process operation constraints, and steel grade quality constraints; among which, the equipment operation constraints are: oxygen supply intensity ≤ 4.5 m³ / min·t, oxygen lance position ≥ 1.0 m; the process operation constraints are: slag basicity 2.0~3.0, FeO content 15%~25%; and the steel grade quality constraints are S≤0.005%, P≤0.008%.

6. An electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that, When the computer program is executed by the processor, it implements the steps in the green smelting method for co-optimization of energy flow and material flow in converter blowing as described in any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the green smelting method for co-optimization of energy flow and material flow in converter blowing as described in any one of claims 1-5.

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