Modeling method for synergism degree of material flow and energy flow in iron-making process of ferrous metallurgy

Abstract

The invention discloses a substance flow and energy flow collaboration degree modeling method for a ferrous metallurgy ironmaking process. On the basis of analyzing material flow and energy flow in the ironmaking process, through theoretical analysis, generation of substances such as molten iron, slag and blast furnace ash and consumption of energy such as coke, coal, blast furnace gas, oxygen, nitrogen, steam, compressed air and electricity are established; the mechanism model is used for energy loss of coal gas waste heat, hot blast stove body heat dissipation, blast furnace body heat dissipation, slag sensible heat and the like and energy recovery of electricity, blast furnace coal gas, steam and the like. A data driving method is adopted, and a PSO-LightGBM method is used for constructing a data model of blast furnace gas recovery flow. And finally, a substance flow and energy flow collaboration degree model of the ironmaking process is obtained by combining a mechanism and a data model, collaborative evaluation of the substance utilization rate and the energy utilization rate of the ironmaking process is realized, support is provided for iron and steel enterprises to realize high-level system energy conservation and emission reduction, and the method has important significance for green development of the enterprises.

Description

Technical field:

[0001] The present invention relates to the field of iron and steel metallurgy industry, and in particular to a method for modeling the synergy between material flow and energy flow in an ironmaking process. Background technology:

[0002] The iron and steel metallurgical industry is a key foundational industry in my country, characterized by high output, high energy consumption, and high carbon emissions. The ironmaking process is the most energy-intensive and carbon-emitting process in the entire iron and steel metallurgical industry. The smelting process is essentially a process of movement and transformation of material and energy flows. Material flow is the primary operating mechanism, while energy flow provides the driving force for the chemical and physical changes in material flow. The two are mutually coupled and jointly maintain production. Therefore, the degree of coordination between material and energy flows in the ironmaking process has a significant impact on its energy consumption and carbon emissions. This paper analyzes the operating patterns of material and energy flows in the ironmaking process, establishes a coordination model for material and energy flows in the ironmaking process using mechanism analysis and data analysis, and evaluates the degree of coordination between the two. This provides support for steel enterprises to achieve high-level systematic energy conservation and emission reduction, and is of great significance to the green development of enterprises. Summary of the invention:

[0003] The present invention provides a mechanism-data driven modeling method for the synergy between material flow and energy flow in the ironmaking process, providing a model basis for evaluating the synergy between material flow and energy flow in the ironmaking process.

[0004] The technical solution of the present invention is: a mechanism-data driven ironmaking process material flow and energy flow synergy modeling method, comprising the following steps:

[0005] Step 1: Analyze the smelting process and the operating rules of material and energy flows in the ironmaking process to reveal the material and energy production and consumption characteristics of the ironmaking process;

[0006] Step 2: Establish a mechanism model for the interaction between substances such as molten iron, blast furnace ash, and slag, and energies such as oxygen, nitrogen, and compressed air, as well as a data model for the blast furnace gas recovery flow rate;

[0007] Step 3: Based on the mechanism and data model established in step 2, a mechanism-data hybrid driven synergy model of material flow and energy flow in the ironmaking process is established to evaluate the degree of synergy between the two.

[0008] Preferably, in step 1, the operation rules of the smelting process and the material flow and energy flow of the ironmaking process are analyzed to reveal the material and energy production and consumption characteristics of the ironmaking process as follows:

[0009] (1) Analyze the smelting process of the ironmaking process, including charging, hot blast, gas injection, gas treatment, ironmaking and other process sections. Among them, the charging process uses a belt conveyor to send the proportioned iron-containing raw materials and fuel into the blast furnace; the hot blast uses a blower to send the oxygen heated by the hot blast furnace into the blast furnace to improve the combustion efficiency and increase the reducing gas; the injection process uses a blower to blow coal powder into the blast furnace to replace part of the coke; the gas treatment removes the dust from the blast furnace gas, and then uses the waste heat and waste pressure to generate electricity through the waste pressure turbine power generation device; during the ironmaking process, the fuel and air are burned in the blast furnace to generate CO gas, which reduces the iron oxides to molten iron, and simultaneously produces by-products such as gas and slag.

[0010] (2) Analyze the material flow and energy flow of the ironmaking process. Material flow is the process of converting iron-containing raw materials such as sintered ore into products such as molten iron. Energy flow is the process of energy consumption and recovery during the material flow conversion. The ironmaking process includes four material flows, such as Figure 2 As shown in Figure 1, they are consumable materials, waste by-products, main products, and recyclable by-products. Among them, consumable materials include sintered ore, pelletized ore, and lump ore, the main product is molten iron, the waste by-product is slag, and the recyclable by-product is blast furnace ash. In addition, the ironmaking process also includes 5 energy flows, such as Figure 3 As shown in the figure, they are non-fuel energy, fuel energy, lost energy, product sensible heat, and recoverable energy. Non-fuel energy includes oxygen, nitrogen, steam, compressed air, and electricity; fuel energy includes coke, coal, and blast furnace gas; lost energy includes waste heat from coal gas, heat dissipation from the hot blast stove, heat dissipation from the blast furnace, and sensible heat from slag; product sensible heat is the sensible heat of molten iron; and recoverable energy includes electricity, blast furnace gas, and steam.

[0011] Preferably, the specific process of constructing the mechanism and data model of the material flow and energy flow of the ironmaking process in step 2 is as follows:

[0012] (1) Mechanism model

[0013] 1) Coke consumption

[0014] The role of coke in the ironmaking process is to provide a large amount of heat and react with oxygen to generate reducing gas, which reduces iron ore to produce molten iron. The consumption of coke can be expressed as:

[0015] E coke =k coke ·m coke ·t coke

[0016] Among them, k coke Coke conversion coefficient to standard coal, kgce / t; m coke is the mass of coke put in per hour, t; t coke is the total time of coke input, h.

[0017] 2) Coal consumption

[0018] In the ironmaking process, coal is added to replace part of the coke to save costs. The coal consumption can be expressed as:

[0019] E coal =k coal ·m coal ·t coal

[0020] Among them, k coal Coal conversion coefficient to standard coal, kgce / t; m coal is the mass of coal added per hour, t; t coal is the total time of adding coal, h.

[0021] 3) Oxygen consumption

[0022] Oxygen is introduced into the ironmaking process to improve combustion efficiency and is introduced during the blast process. Oxygen consumption can be expressed as:

[0023] E oxygen =k oxygen Q oxygen ·t oxygen

[0024] Among them, k oxygen is the oxygen conversion coefficient to standard coal, kgce / m 3 ;Q oxygen is the oxygen flow rate per hour, m 3 ;t oxygen is the total time of oxygen introduction, h.

[0025] 4) Nitrogen consumption

[0026] In the ironmaking process, the role of nitrogen is to inertify the coal during coal injection, so the nitrogen flow rate and the coal injection amount have a fixed ratio. The nitrogen consumption can be expressed as:

[0027]

[0028] Among them, k nitrogen is the nitrogen conversion coefficient to standard coal, kgce / m 3 ;λ AN is the ratio of compressed air to nitrogen; SG is the ratio of coal injection volume to mixed gas, t / m 3 .

[0029] 5) Steam consumption

[0030] The role of steam in the ironmaking process is to drive away the blast furnace gas at the top of the blast furnace to prevent explosion. The steam consumption can be expressed as:

[0031] Esteam =k steam Q steam ·t steam

[0032] Among them, k steam is the steam conversion coefficient to standard coal, kgce / m 3 ;Q steam is the steam quality introduced per hour, m 3 ;t steam is the total time of steam introduction, h.

[0033] 6) Compressed air consumption

[0034] Compressed air is used as the driving gas for coal injection in the ironmaking process. The amount of compressed air introduced has a fixed ratio to the amount of coal injected. The consumption of compressed air can be expressed as:

[0035]

[0036] Among them, k air is the standard coal coefficient of compressed air, kgce / m 3 .

[0037] 7) Blast furnace gas consumption

[0038] During the ironmaking process, blast furnace gas is introduced into the hot blast furnace for combustion, heating the blast air to improve combustion efficiency. The consumption of blast furnace gas can be expressed as:

[0039]

[0040] Among them, k BFG is the blast furnace gas to standard coal coefficient, kgce / m 3 ;Q blast is the blast flow rate, m 3 ; C blast 、C blast,in Respectively represent the specific heat capacity of the blast after heating and before heating, kJ / (m 3 ·K); T blast 、T blast,in Respectively represent the blast temperature before and after heating, K; η HBS is the thermal efficiency of the hot blast stove; q BFG is the calorific value of blast furnace gas, Kj / m 3 .

[0041] 8) Power consumption

[0042] There are three main power-consuming devices in the ironmaking process, including the belt conveyor, fresh air blower, and make-up air blower. The power consumption can be expressed as:

[0043] E elc =kelc ·(P belt conveyor ·t belt conveyor +P FAF ·t FAF +P MAF ·t MAF )

[0044] Among them, k elc is the electricity conversion coefficient of standard coal, kgce / kWh; P belt conveyor 、P FAF 、P MAF The power of belt conveyor, fresh air blower and make-up air blower, kW; t belt conveyor , t FAF , t MAF are the working hours of the belt conveyor, fresh air blower and make-up air blower, respectively, in hours.

[0045] 9) Product formation

[0046] The ironmaking process produces substances including molten iron m iron 、Slag m slag , blast furnace ash m dust In the ironmaking process, the amount of various elements input is calculated as the product of the mass of each raw material and the content of each element:

[0047] m in-i =∑m j ·ω i,j

[0048] Among them, m in-i Represents the mass of elements such as Fe, C, P, S, Si, and Mn input into the ironmaking process, t; m j Representative sintered ore m sin 、Pellets pel 、lump ore ore Equal raw material quality, t; ω i,j It indicates the proportion of elements such as Fe, C, P, S, Si, and Mn in raw materials such as sintered ore, pelletized ore, and natural ore. Therefore, the calculation method for blast furnace generated substances is:

[0049] m x =∑m in-i ·ω i,x

[0050] Among them, m x Represents the mass of molten iron, slag, and blast furnace ash produced in the ironmaking process, t; the conversion rate of elements such as Fe, C, P, S, Si, and Mn, ω i,x Calculated based on experience.

[0051] 10) Electricity recycling

[0052] In the ironmaking process, the waste pressure turbine power generation device can use the waste heat and waste pressure of the recovered blast furnace gas to generate electricity. The power generation capacity is calculated as follows:

[0053]

[0054] Among them, Q re-BFG is the flow rate of blast furnace gas recovered per hour, m 3 / h;t re-BFG is the time for recovering blast furnace gas, h; C re-BFG is the specific heat capacity of blast furnace gas at constant pressure, kJ / (kg·K); P in 、P out are the absolute gas pressures at the turbine inlet and outlet, Pa; m is the blast furnace gas volatility index; η T ,η G are the efficiencies of the turbine and generator, respectively.

[0055] 11) Steam recovery

[0056] During the ironmaking process, water is added to the high-temperature slag to generate steam. The amount of steam recovered can be expressed as:

[0057] E re-steam =k energy ·[C slag ·m slag ·(T slag,in -T slag,out )-E slag ]

[0058] Among them, k energy is the energy conversion coefficient of standard coal, kgce / kJ; C slag Indicates the specific heat capacity of slag, kJ / (kg·K); m slag is the mass of slag, t; T slag,in 、T slag,out is the slag temperature before and after the slag flushing water is added to the slag, K; E slag is the sensible heat loss of slag, kgce.

[0059] 12) Gas waste heat loss

[0060] After the blast furnace gas comes out of the blast furnace, there will be heat loss before entering the residual pressure recovery power generation device. re-gas-loss , which can be expressed as:

[0061] E re-BFG-loss =Q re-BFG ·t re-BFG ·C re-BFG ·(T re-BFG -Tre-BFG,in )

[0062] Among them, T re-BFG 、T re-BFG,in They are the temperatures of the recovered blast furnace gas when it is discharged from the furnace top and when it enters the residual pressure recovery power generation device, K.

[0063] 13) Slag sensible heat loss

[0064] After the waste heat is recovered from the slag, the slag-water mixture still has a certain temperature. This part of the waste heat loss is the sensible heat of the slag, which can be expressed as:

[0065]

[0066] Among them, C water is the specific heat capacity of water, kJ / (kg·K); m slag is the slag mass, t; λ slag water rate is the ratio of slag to water; T water,out 、T water,in It is the temperature of the slag flushing water before and after adding it to the slag, K.

[0067] 14) Heat dissipation loss of hot blast furnace

[0068] During the ironmaking process, the surface of the hot blast furnace loses heat, which can be expressed as:

[0069]

[0070] Among them, A i is the heat dissipation area of the furnace body, m 2 ;q i is the average surface heat flux of the furnace body, kJ·m -2 ·s -1 ;t HBS is the hot blast stove working time, h.

[0071] 15) Heat dissipation loss of blast furnace body

[0072] During the operation of the blast furnace, heat exchange occurs with cooling water and air, from which the heat loss outside the blast furnace can be obtained as shown below:

[0073] E BF-loss =C water Q c-water ·t c-water ·ρ water ΔT c-water +h env ·S BF ·(T BF -T env )·tiron 10 -3

[0074] Among them, Q c-water is the cooling water flow rate, m 3 / h;t c-water is the cooling time, h; ΔT c-water is the temperature difference of cooling water, ℃; h env is the air heat exchange coefficient, W / (m 2 ℃); S BF is the contact area between blast furnace and air, m 2 ;T BF is the blast furnace surface temperature, ℃.

[0075] (2) Data Model

[0076] 1) Collect data on sinter quality, pellet quality, lump quality, coke quality, coal injection rate, blast flow, oxygen flow, and blast furnace gas recovery flow during the ironmaking process.

[0077] 2) Preprocess the collected data, clean up outliers, supplement missing values, and normalize the data.

[0078] 3) Using parameters such as sinter quality, pellet quality, lump quality, coke quality, coal injection rate, blast flow rate, and oxygen flow rate as characteristic variables and blast furnace gas recovery flow rate as the target variable, a data model was established using the PSO-LightGBM method. The specific model is as follows:

[0079] Q re-BFG =f PSO-LightGBM (m sin ,m pel ,m ore ,m coke ,m coal ,Q blast ,Q oxygen )

[0080] 4) Based on the established blast furnace gas recovery flow model, the blast furnace gas recovery E re-BFG The model is as follows:

[0081] E re-BFG =k BFG Q re-BFG ·t re-BFG

[0082] Among them, k BFG is the blast furnace gas to standard coal coefficient, kgce / m 3 ;Q re-BFG is the blast furnace gas flow rate recovered per hour, m 3 ;t re-BFGis the total time for recovering blast furnace gas, h.

[0083] Preferably, step 3 combines the mechanism and data model of material flow and energy flow to establish a mechanism-data hybrid driven material flow and energy flow synergy model for the ironmaking process. The specific process is as follows:

[0084] (1) The geometric mean method is used to establish a synergy model of material flow and energy flow in the ironmaking process. The modeling method is as follows:

[0085]

[0086] Among them, OD1 and OD2 are the order of material flow and energy flow respectively.

[0087] (2) The order degree models of material flow and energy flow in the ironmaking process are constructed by weighted summation method. The modeling method is as follows:

[0088] OD1=ω 1,1 EC1(V 1,1 )+ω 1,2 EC1(V 1,2 )+ω 1,3 EC1(V 1,3 )

[0089] OD2=ω 2,1 EC2(V 2,1 )+ω 2,2 EC2(V 2,2 )+ω 2,3 EC2(V 2,3 )

[0090] Among them, ω m,n is the weight of the order parameter, calculated using the CRITIC method; EC m (V m,n ) is the power coefficient of the order parameter.

[0091] (3) The efficiency coefficient model of the ironmaking process is constructed by the normalization method. The modeling method is as follows:

[0092]

[0093] in, are the maximum and minimum values of the order parameter respectively.

[0094] (4) Finally, the order parameter model of the ironmaking process is constructed, including the material flow order parameter and the energy flow order parameter. The material flow order parameter selected for the ironmaking process includes the unit product consumption material V 1,1 , scrap rate V 1,2 , Recyclable by-product recycling rate V 1,3 , the energy flow order parameters include the energy consumption V2,1 , Recyclable energy generation rate V 2,2 and unit product loss energy V 2,3 The order parameters of material flow and energy flow are calculated as follows:

[0095] Description of the drawings:

[0096] Figure 1 Mechanism-Data-Driven Modeling Framework Diagram

[0097] Figure 2 Material flow diagram of ironmaking process

[0098] Figure 3 Energy flow diagram of ironmaking process

[0099] Figure 4 Data modeling framework diagram

[0100] Figure 5 Modeling effect diagram of PSO-LightGBM

[0101] Figure 6 LightGBM modeling effect diagram

[0102] Figure 7 SVR modeling effect diagram

[0103] Figure 8 ANN modeling effect diagram Specific implementation method:

[0104] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. The examples described are only part of the examples of the present invention, not all of the examples. Without departing from the above technical ideas of the present invention, various substitutions and modifications based on common technical knowledge and customary means in the field should be included in the scope of protection of the present invention.

[0105] This example collected 1,000 sets of ironmaking data from a steel plant in Rizhao City. Specifically, the data included hourly sinter quality, pellet quality, lump quality, molten iron quality, blast furnace ash quality, slag quality, oxygen flow rate, blast flow rate, coke quality, coal injection rate, steam consumption, power consumption, and blast furnace gas recovery flow rate. Some of this data is shown in Table 1.

[0106] Table 1 Material flow and energy flow data of ironmaking process

[0107]

[0108]

[0109] The collected data is preprocessed, and then a prediction model for blast furnace gas recovery flow is established using data-driven methods and frameworks. Figure 4 As shown. Remove outliers and missing values in the data set and then perform normalization. In order to further verify the effectiveness and superiority of the data-driven modeling method and data model adopted, PSO-LightGBM, LightGBM, SVR, and ANN are used to establish blast furnace gas recovery flow prediction models respectively. The model training results are shown in Figure 5 、 Figure 6 、 Figure 7 and Figure 8 In order to intuitively show the performance of different prediction models, the evaluation index results of each model are shown in Table 2. Obviously, the prediction model established by the PSO-LightGBM method adopted in the present invention has better indicators than several other prediction models.

[0110] Table 2 Evaluation index results of different models

[0111]

[0112] Combining the mechanism model with the blast furnace gas recovery flow model established by the PSO-LightGBM method, the order parameters of material flow and energy flow are obtained, followed by the efficiency coefficient of each order parameter, and then the order degree of material flow and the order degree of energy flow are obtained, and finally the synergy model of material flow and energy flow in the ironmaking process is obtained. The calculation results of each order parameter are shown in Table 3, and the calculation results of the efficiency coefficient, order degree, and synergy degree are shown in Table 4. The synergy value obtained by the synergy modeling method of material flow and energy flow in the ironmaking process of steel metallurgy proposed in the present invention is 0.4590, which is relatively low and has a lot of room for optimization and improvement.

[0113] Table 3 Calculation results of variables in the synergy model

[0114]

[0115] Table 4 Calculation results of efficacy coefficient, order degree and synergy degree

[0116]

Claims

1. A method for modeling the synergy between material flow and energy flow in the ironmaking process of steel metallurgy, characterized in that: The following steps are involved: Step 1: Analyze the smelting process and the operating rules of material and energy flows in the ironmaking process to reveal the material and energy production and consumption characteristics of the ironmaking process; Step 2: Establish a mechanism model for the interaction between molten iron, blast furnace ash, slag, and other substances and energy sources such as oxygen, nitrogen, and compressed air, as well as a data model for the blast furnace gas recovery flow rate; Step 3: Based on the mechanism and data model established in step 2, a mechanism-data hybrid-driven synergy model of material flow and energy flow in the ironmaking process is established to evaluate the degree of synergy between the two.

2. A method for modeling the synergy between material flow and energy flow in a steel metallurgical ironmaking process according to claim 1, characterized in that In step 2, the material and energy consumption model for the ironmaking process is established, following the following process: (1) Establish coke consumption E coke 、Coal consumption E coal , oxygen consumption E oxygen , nitrogen consumption E nitrogen , steam consumption E steam , compressed air consumption E air , blast furnace gas consumption E BFG , electricity consumption E elc 、Product formation (molten iron m iron 、Slag m slag , blast furnace ash m dust ), Electricity Recycling E re-elc 、Steam recovery E re-steam , Gas waste heat loss E re-BFG-loss , slag sensible heat loss E slag , Heat dissipation loss of hot blast furnace body E HBS-loss and blast furnace heat loss E BF-loss The mechanism model is used to calculate the order parameters of material flow and energy flow in the ironmaking process; (2) Establishing a data model for the blast furnace gas recovery flow rate in the ironmaking process includes the following steps: 1) Collect data on sinter quality, pellet quality, lump quality, coke quality, coal injection rate, blast flow, oxygen flow, and blast furnace gas recovery flow during the ironmaking process; 2) Preprocess the collected data, clean up outliers, supplement missing values, and normalize the data; 3) Using parameters such as sinter quality, pellet quality, lump quality, coke quality, coal injection rate, blast flow rate, and oxygen flow rate as characteristic variables and blast furnace gas recovery flow rate as the target variable, a data model was established using the PSO-LightGBM method; 4) Based on the established blast furnace gas recovery flow model, the blast furnace gas recovery E re-BFG The model is as follows; E re-BFG =k BFG ·Q re-BFG ·t re-BFG 3. A method for modeling the synergy between material flow and energy flow in a steel metallurgical ironmaking process according to claim 1, characterized in that In step 3, a mechanism-data hybrid driven material flow and energy flow synergy model for the ironmaking process is established, following the following process: (1) The geometric mean method is used to establish a synergy model of material flow and energy flow in the ironmaking process. The modeling method is as follows: Among them, OD1 and OD2 are the ordering degrees of material flow and energy flow respectively; (2) The order degree models of material flow and energy flow in the ironmaking process are constructed by weighted summation method. The modeling method is as follows: OD1=ω 1,1 ·EC1(V 1,1 )+ω 1,2 ·EC1(V 1,2 )+ω 1,3 ·EC1(V 1,3 ) OD2=ω 2,1 EC2(V 2,1 )+ω 2,2 EC2(V 2,2 )+ω 2,3 EC2(V 2,3 ) Among them, ω m,n is the weight of the order parameter, calculated using the CRITIC method; EC m (V m,n ) is the power coefficient of the order parameter; (3) The efficiency coefficient model of the ironmaking process is constructed by the normalization method. The modeling method is as follows: in, are the maximum and minimum values of the order parameter respectively; (4) Finally, the order parameter model of the ironmaking process is constructed, including the material flow order parameter and the energy flow order parameter; the material flow order parameter selected for the ironmaking process includes the unit product consumption material V 1,1 , scrap rate V 1,2 , Recyclable by-product recycling rate V 1,3 , the energy flow order parameters include the energy consumption V 2,1 , Recyclable energy generation rate V 2,2 and unit product loss energy V 2,3 ; The order parameters of material flow and energy flow are calculated as follows.

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