CN116434856B - Converter oxygen supply prediction method based on sectional oxygen decarburization efficiency - Google Patents
Converter oxygen supply prediction method based on sectional oxygen decarburization efficiency Download PDFInfo
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- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 title claims abstract description 178
- 229910052760 oxygen Inorganic materials 0.000 title claims abstract description 178
- 239000001301 oxygen Substances 0.000 title claims abstract description 178
- 238000000034 method Methods 0.000 title claims abstract description 119
- 238000005261 decarburization Methods 0.000 title claims abstract description 73
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims abstract description 104
- 230000008569 process Effects 0.000 claims abstract description 78
- 238000007664 blowing Methods 0.000 claims abstract description 52
- 229910052742 iron Inorganic materials 0.000 claims abstract description 52
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 8
- 229910052799 carbon Inorganic materials 0.000 claims description 89
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 88
- 229910000831 Steel Inorganic materials 0.000 claims description 50
- 239000010959 steel Substances 0.000 claims description 50
- 238000007254 oxidation reaction Methods 0.000 claims description 20
- 229910052710 silicon Inorganic materials 0.000 claims description 20
- 239000007789 gas Substances 0.000 claims description 19
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 claims description 18
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 18
- 239000010703 silicon Substances 0.000 claims description 18
- 230000003647 oxidation Effects 0.000 claims description 17
- 230000008859 change Effects 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000002844 melting Methods 0.000 claims description 9
- 230000008018 melting Effects 0.000 claims description 9
- 230000036284 oxygen consumption Effects 0.000 claims description 7
- 230000005251 gamma ray Effects 0.000 claims description 6
- 230000001590 oxidative effect Effects 0.000 claims description 6
- 239000002699 waste material Substances 0.000 claims description 4
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 238000002485 combustion reaction Methods 0.000 claims description 3
- 238000009628 steelmaking Methods 0.000 abstract description 14
- 238000006243 chemical reaction Methods 0.000 description 22
- 239000002184 metal Substances 0.000 description 13
- 229910052751 metal Inorganic materials 0.000 description 13
- 238000010586 diagram Methods 0.000 description 10
- 238000012360 testing method Methods 0.000 description 9
- 238000004458 analytical method Methods 0.000 description 7
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- 230000015654 memory Effects 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000012417 linear regression Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 5
- 238000003723 Smelting Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
- 238000003062 neural network model Methods 0.000 description 4
- 238000003860 storage Methods 0.000 description 4
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- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- -1 carbon oxidation Chemical compound 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 229910052698 phosphorus Inorganic materials 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 239000002893 slag Substances 0.000 description 2
- 238000000638 solvent extraction Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000013179 statistical model Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 231100000807 Critical Body Residue (CBR) model Toxicity 0.000 description 1
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 1
- 229910004298 SiO 2 Inorganic materials 0.000 description 1
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
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- 239000012535 impurity Substances 0.000 description 1
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- 230000003287 optical effect Effects 0.000 description 1
- 239000011574 phosphorus Substances 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 229910052717 sulfur Inorganic materials 0.000 description 1
- 239000011593 sulfur Substances 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
- C21C5/00—Manufacture of carbon-steel, e.g. plain mild steel, medium carbon steel or cast steel or stainless steel
- C21C5/28—Manufacture of steel in the converter
- C21C5/30—Regulating or controlling the blowing
- C21C5/32—Blowing from above
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
- C21C7/00—Treating molten ferrous alloys, e.g. steel, not covered by groups C21C1/00 - C21C5/00
- C21C7/04—Removing impurities by adding a treating agent
- C21C7/068—Decarburising
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- C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
- C21C2300/00—Process aspects
- C21C2300/06—Modeling of the process, e.g. for control purposes; CII
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention discloses a converter oxygen supply prediction method based on sectional oxygen decarburization efficiency, which comprises the following steps: according to the furnace charging molten iron condition and the end point control target of the furnace to be solved, searching a past case with highest similarity with the furnace to be solved in a past case library by using a case reasoning algorithm as a reference case; the method comprises the steps of dividing a converting process of a reference case into a converting early stage, a converting middle stage and a converting later stage by utilizing different influencing factors of oxygen decarburization efficiency in different stages in the converting process of a converter; based on preset assumption conditions, predicting the oxygen supply of the heat to be solved according to the blowing stage division time of the reference case and the oxygen decarburization efficiency of different stages, and obtaining the oxygen supply prediction result of the heat to be solved. Compared with the traditional model, the technical scheme of the invention can effectively improve the prediction accuracy of the oxygen supply in the converter steelmaking process.
Description
Technical Field
The invention relates to the technical field of converter steelmaking, in particular to a converter oxygen supply prediction method based on sectional oxygen decarburization efficiency.
Background
Oxygen is an essential key factor in the converter steelmaking process, and the blown oxygen can react with carbon, silicon, phosphorus and sulfur in molten iron in a molten poolThe elements undergo oxidation reaction, so that the purpose of reducing carbon and removing impurities is achieved, and meanwhile, the oxidation reaction releases heat to provide the temperature required in the smelting process. Because the converter steelmaking smelting production environment is complex, the reaction change in the converter is quick, and the factors influencing the oxygen supply are more, the oxygen supply is difficult to control, the energy consumption is influenced, and the production efficiency is also influenced. Therefore, the accurate control of the oxygen supply of the converter steelmaking provides important guidance for saving cost, improving the utilization rate of oxygen and increasing the smelting stability. The current method for controlling the oxygen supply in converter steelmaking mainly comprises manual experience control, mechanism model control, statistical model control, intelligent model control and the like. Since the human experience depends on the judgment of the person, the human experience is influenced by the operation level of the field personnel, so that the stability and the precision are poor, and a large number of assumptions exist in the traditional mechanism model, for example: carbon is oxidized to 90% CO and 10% CO 2 The content of free oxygen in the furnace gas is 0.5% of the total furnace gas amount, the final slag Σw (FeO) =15% and the like, so that the error of a mechanism model is larger, the oxygen supply amount cannot be accurately controlled, and the control precision of the converter end point is also reduced.
Regarding the problem of predicting the oxygen supply in the converter steelmaking process, some students have conducted various methods of research and put forward some practical prediction methods or calculation models. Li Hua and the like design analysis and prediction model control of steelmaking and smelting oxygen consumption through deduction and establishment of a relation equation between the steelmaking oxygen consumption of the converter and related factors, so that analysis and control of oxygen supply process are realized, but the model relates to more data quantity and has slower calculation efficiency. Zhu Guangjun and the like calculate a regression equation between the oxygen supply amount and the scrap steel amount by adopting a statistical regression analysis method, optimize parameters in the regression equation, optimize an oxygen supply amount static control model and influence factors of oxygen supply, namely scrap steel, and also need to consider other influencing factors. Li Yang and the like, and then static oxygen supply and dynamic oxygen supply are predicted by using an oxygen decarburization efficiency prediction model, so that the oxygen calculation precision is improved; li Ailian and the like propose a converter oxygen supply prediction model for improving a deep belief network, and simulation results show that the model effectively improves oxygen supply prediction accuracy. The models are statistical models or data driving models which directly take oxygen supply as output items, do not consider physical and chemical reactions in the converter steelmaking process, only consider the relation between the input quantity and the output quantity of the system, lack intermediate processes, belong to black box modeling, and can also have room for improvement to improve prediction accuracy.
Disclosure of Invention
The invention provides a converter oxygen supply amount prediction method based on sectional oxygen decarburization efficiency, which aims to solve the technical problem that the existing converter oxygen supply amount prediction method is not accurate enough in oxygen supply amount prediction.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the invention provides a converter oxygen supply prediction method based on sectional oxygen decarburization efficiency, which comprises the following steps:
according to the furnace charging molten iron condition and the end point control target of the furnace to be solved, searching a past case with highest similarity with the furnace to be solved in a past case library by using a case reasoning algorithm as a reference case;
the method comprises the steps of dividing a converting process of a reference case into a converting early stage, a converting middle stage and a converting later stage by utilizing different influencing factors of oxygen decarburization efficiency in different stages in the converting process of a converter;
based on preset assumption conditions, predicting the oxygen supply of the heat to be solved according to the blowing stage division time of the reference case and the oxygen decarburization efficiency of different stages, and obtaining the oxygen supply prediction result of the heat to be solved.
Further, the inflection point judgment from the early stage of converting to the middle stage of converting is based on the silicon content in the converting process, and the inflection point judgment from the middle stage of converting to the later stage of converting is based on the carbon content in the converting process.
Further, the inflection points of each stage are determined in the following manner: when the silicon content is oxidized to 0.03%, ending the earlier stage of converting, and entering the middle stage of converting; and when the carbon content is oxidized to 0.4%, ending the middle stage of converting, and entering the later stage of converting.
Further, the step of dividing the converting process of the reference case includes:
fitting a carbon content change curve and a silicon content change curve of the blowing process of the reference case according to the carbon content of the molten iron in the furnace, the carbon content of the end point and the furnace gas data of the blowing process of the reference case;
and (3) dividing the reference case converting process into stages based on the carbon content change curve and the silicon content change curve.
Further, according to the blowing stage division time of the reference case and the oxygen decarburization efficiency of different stages, predicting the oxygen supply of the heat to be solved to obtain the oxygen supply prediction result of the heat to be solved, including:
determining the carbon content, molten iron/molten steel weight, oxygen consumption and CO in furnace gas of each stage according to the stage division result of the blowing process of the reference case 2 The ratio and finally the oxygen decarburization efficiency of each stage are determined;
calculating the carbon content of inflection points of different converting stages of the heat to be solved based on the converting process stage division time of the reference case and the relation between the converting stage time and the carbon content fitted by the reference case, wherein constant items in a fitting formula are replaced by the carbon content of molten iron in the heat to be solved during calculation;
calculating the weight of molten iron/molten steel in the blowing process of the heat to be solved;
based on the oxygen decarburization efficiency of each stage, the carbon content of molten iron in a furnace to be solved, the weight of molten iron/molten steel in a blowing process and the carbon content of inflection points in different blowing stages are combined, and the oxygen supply quantity of the furnace to be solved is predicted to obtain an oxygen supply quantity prediction result of the furnace to be solved.
Further, predicting the oxygen supply of the heat to be solved, including:
the oxygen supply in the different converting stages was calculated by the following formula:
the oxygen supply amounts of the blowing stages are accumulated to obtain the total oxygen supply amount
Q Total (S) ＝Q 1 +Q 2 +Q 3
Wherein Q is 1 The oxygen supply amount in the earlier stage of converting is shown; q (Q) 2 The oxygen supply amount in the middle of converting is shown; q (Q) 3 The oxygen supply amount at the later stage of blowing is shown; w (C) 0 Representing the carbon content in molten iron in a furnace; m is m 0 Representing the weight of molten iron into the furnace; w (C) 1 Representing the carbon content of the middle inflection point before converting; m is m 1 The weight of the molten iron at the middle inflection point before converting is expressed; η (eta) 1 Indicating oxygen decarburization efficiency in the earlier stage of converting; w (C) 2 Representing the carbon content of inflection points in the middle and later stages of converting; m is m 2 The weight of the molten steel at the inflection point in the middle and later stages of blowing is expressed; η (eta) 2 The oxygen decarburization efficiency in the middle of converting is shown; w (C) 3 Indicating the carbon content of the converting end point; m is m 3 Indicating the weight of molten steel at the blowing end point; η (eta) 3 The oxygen decarburization efficiency in the later stage of blowing is shown; mu represents the oxygen amount required for oxidizing each 1kg of carbon.
Further, the molten iron/molten steel weight calculation formula in the blowing process is as follows:
m 3 ＝m 0 ·α+m s ·β
m 2 ＝m 0 +(m 3 -m 0 )·(γ 1 +γ 2 )
m 1 ＝m 0 +(m 3 -m 0 )·γ 1
wherein alpha represents the molten iron yield; beta represents the yield of scrap steel; gamma ray 1 Representing the melting percentage of the waste steel in the earlier stage of converting; gamma ray 2 The melting percentage of the scrap steel in the middle of converting is shown; lambda (lambda) 1 Represents the ratio of carbon oxidation to CO; lambda (lambda) 2 Indicating the formation of CO by carbon oxidation 2 Is a ratio of (2).
Further, the preset assumption condition includes:
in the actual converter converting process, the yields of molten iron and scrap steel are not 100%; assuming that the molten iron yield is 95%, and the scrap steel yield is 85%; the scrap steel is melted by 20% in the early stage of converting, and the melting residual 80% in the middle stage of converting;
in the actual converter converting process, carbon oxidation does not generate 100% CO, but generates a certain proportion of CO 2 The secondary combustion rate of the furnace gas outside the converter is 10%;
according to the reference case obtained by case reasoning, assume that the reference case and the furnace to be solved are oxidized to generate CO and CO at each converting stage 2 The ratio of (2) is the same, and the oxygen decarburization rate is the same in each converting stage.
In yet another aspect, the present invention also provides an electronic device including a processor and a memory; wherein the memory stores at least one instruction that is loaded and executed by the processor to implement the above-described method.
In yet another aspect, the present invention also provides a computer readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the above method.
The technical scheme provided by the invention has the beneficial effects that at least:
the invention provides a converter oxygen supply quantity prediction method based on sectional oxygen decarburization efficiency, which is characterized in that inflection points of decarburization three stages in a converter and oxygen decarburization efficiencies of different stages are determined through mechanism analysis, reference case data with highest similarity is searched in a previous case base by utilizing case reasoning, and oxygen supply quantity of a to-be-solved furnace number is predicted according to reference case stage dividing time and the oxygen decarburization efficiencies of different stages. Compared with the traditional model, the technical scheme of the invention can effectively improve the prediction accuracy of the oxygen supply in the converter steelmaking process.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an implementation flow of a method for predicting oxygen supply to a converter based on sectional oxygen decarburization efficiency according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a process for solving the oxygen supply based on an oxygen supply prediction model of the segmented oxygen decarburization efficiency according to the embodiment of the present invention;
fig. 3 is a stage division result of a converting process of a reference case provided in an embodiment of the present invention; wherein, (a) is a converting process stage division result of the E heat, (b) is a converting process stage division result of the F heat, (c) is a converting process stage division result of the G heat, and (d) is a converting process stage division result of the H heat;
FIG. 4 is a schematic diagram of a test set model prediction result provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of prediction results of each model of a test heat provided by an embodiment of the present invention; wherein, (a) is a BPNN model prediction result, (b) is a CBR model prediction result, (c) is an MLR model prediction result, and (d) is an SVR model prediction result;
FIG. 6 is a graph showing the comparison of the predicted hit rate results for each model error range of the test heat provided by the embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
First embodiment
The embodiment provides a converter oxygen supply prediction method based on sectional oxygen decarburization efficiency, which can be realized by electronic equipment, and the execution flow of the method is shown in fig. 1, and comprises the following steps:
s1, searching a previous case with highest similarity with the heat to be solved in a previous case library by using a case reasoning algorithm according to the heat entering molten iron condition and the end point control target of the heat to be solved, and taking the previous case as a reference case;
s2, dividing the converting process of the reference case into a front converting stage, a middle converting stage and a rear converting stage by utilizing different influencing factors of oxygen decarburization efficiency in different stages in the converting process of the converter;
s3, based on preset assumption conditions, predicting the oxygen supply of the heat to be solved according to the blowing stage division time of the reference case and the oxygen decarburization efficiency of different stages, and obtaining an oxygen supply prediction result.
Next, the implementation procedure of the method of this embodiment will be described in detail.
1 model principle
1.1 establishment of an oxygen supply prediction model
The main relevant reactions of oxygen in the converter steelmaking process are as follows:
[Si]+{O 2 }＝(SiO 2 ) (3)
[S]+{O 2 }＝(SO 2 ) (6)
because the reaction preconditions of different elements are different, a certain sequence exists for oxidizing different elements. The converter steelmaking process can be divided into 3 stages:
1) In the earlier stage of blowing, fe, si and Mn elements are oxidized in a large amount quickly after blowing is started, and meanwhile, C is oxidized in a small amount when Si and Mn are oxidized, so that oxygen blown in the earlier stage is only partially used for decarburization reaction.
2) In the middle stage of the blowing, when the oxidation of Si and Mn is basically finished, the blowing enters the middle stage, and at this time, C begins to be oxidized in a large amount, the decarburization rate reaches the highest, and most of the oxygen blown in the stage is consumed in the decarburization reaction.
3) In the later stage of blowing, when the carbon content of molten steel in a molten pool is reduced to a certain critical value along with the progress of decarburization reaction, the decarburization rate starts to be reduced in the later stage of blowing, part of oxygen enters the molten steel and slag, and part of oxygen blown in the stage is consumed in the decarburization reaction.
From the above theoretical analysis, it is known that the ratio of oxygen blown in at different stages in the blowing process to participate in the decarburization reaction is different, so that a learner introduces a concept of oxygen decarburization efficiency, which is defined as a ratio of an amount of oxygen consumed for carbon oxidation in a molten pool to an actual oxygen supply, and the formula is as follows:
wherein: q (Q) mech Oxygen amount Q as carbon element oxide real Is the amount of oxygen actually blown into the bath.
However, the above model has the following problems:
1) The model assumes that the final molten steel amount of the converter is the total loading amount (molten iron amount and scrap steel amount), and the yield of molten iron and scrap steel is not 100% in the actual converter blowing process.
2) The model assumes that about 0.933m is consumed for oxidizing 1kg of carbon 3 Oxygen, i.e. carbon oxidation, generates 100% CO, and from the change curve of the composition of converter converting gas, it is known that carbon oxidation generates a certain proportion of CO during converter converting 2 。
Therefore, in this embodiment, for the above existing problem correction model, the oxidation proportion of carbon in different stages is analyzed by using the furnace gas data, and the required oxygen blowing amount is calculated in stages according to the characteristics of different stages in the decarburization process of the converter, and the oxygen blowing amount calculation formulas in different stages are as follows:
wherein: q (Q) 1 : oxygen supply amount in the earlier stage of converting; q (Q) 2 : oxygen supply in the middle of converting; q (Q) 3 : oxygen supply amount in the later stage of blowing; w (C) 0 : carbon content in molten iron in the furnace; m is m 0 : the weight of molten iron in the furnace; w (C) 1 : carbon content of middle inflection point before converting; m is m 1 : the weight of molten iron at the middle inflection point before converting; η (eta) 1 : oxygen decarburization efficiency in the earlier stage of converting; w (C) 2 : carbon content of inflection points in middle and later periods of converting; m is m 2 : the weight of molten steel at the inflection point in the middle and later stages of blowing; η (eta) 2 : oxygen decarburization efficiency in the middle of converting; w (C) 3 : converting the end carbon content; m is m 3 : the weight of molten steel at the end of blowing; η (eta) 3 : oxygen decarburization in later stage of blowingEfficiency is improved; mu: the amount of oxygen required for oxidation of each 1kg of carbon.
The weight change of molten iron/molten steel in the converter blowing process is shown in formulas (13) - (16):
m 3 ＝m 0 ·α+m s ·β (13)
m 2 ＝m 0 +(m 3 -m 0 )·(γ 1 +γ 2 ) (14)
m 1 ＝m 0 +(m 3 -m 0 )·γ 1 (15)
wherein: alpha: molten iron yield; beta: yield of scrap steel; gamma ray 1 : the melting percentage of the waste steel in the earlier stage of converting; gamma ray 2 : the melting percentage of the waste steel in the middle of converting; lambda (lambda) 1 The ratio of carbon oxidation to CO; lambda (lambda) 2 CO formation for carbon oxidation 2 Is a ratio of (2).
Adding up the oxygen supply in three stages to obtain the total oxygen supply, as shown in a formula (17):
Q total (S) ＝Q 1 +Q 2 +Q 3 (17)
1.2 oxygen decarburization efficiency staging rules
The stage division rule of the oxygen decarburization efficiency mainly utilizes different influencing factors of the oxygen decarburization efficiency at different stages in the converting process of the converter to divide the converting process. It is generally considered that the decarburization process of the converter can be divided into three stages. Wherein the inflection point from the early stage of converting to the middle stage of converting is judged as the silicon content, and the inflection point from the middle stage of converting to the later stage of converting is judged as the carbon content. In the embodiment, when the silicon content is set to be oxidized to 0.03%, converting is carried out in the middle stage; when the carbon content is oxidized to 0.4%, blowing enters the later stage. The desilication reaction in the converter mainly occurs in the gas-metal reaction zone and the slag-metal reaction zone, and the desilication reaction rate can be expressed as a desilication rate shown in a formula (18):
wherein:desilication reaction Rate, kg.s -1 ；A iz : area of gas-metal reaction zone, m 2 ；k gm : gas-metal mass transfer coefficient, m/s; a is that sm : area of slag-metal reaction zone, m 2 ；k sm : mass transfer coefficient of slag-metal interface, m/s; ρ m : density of metal 7000kg/m 3 ；(w Si％ ): concentration of Si in molten iron,%; (w) Si％ ) sm : interface concentration of silicon at slag-metal interface,%; (w) Si％ ) gm : interface concentration of silicon at gas-metal interface,%; interfacial concentration of silicon (w Si％ ) 0.25%.
The interfacial area of the gas-metal reaction can be expressed as:
wherein: n is n n : the number of nozzles; r is (r) iz : radius of the gas-metal reaction zone; h is a iz : height of the gas-metal reaction zone; the calculation formula is as follows:
wherein: d, d th : diameter of throat of oxygen lance; p (P) a : ambient pressure; p (P) 0 : top blowing oxygen pressure; m is m t : total momentum flow; m is m n : momentum flow of each nozzle; θ: nozzle inclination; m is M h And M d : a dimensionless total momentum flow and a momentum flow for each nozzle.
The area of the slag-metal reaction zone can be expressed as:
from the above analysis, the change in silicon content during converting in the converter can be calculated using equation (18). When the silicon content is reduced to 0.03%, the earlier stage of converting is considered to be finished, and the middle stage of converting is entered.
In the middle and later stages of converting, as the decarburization reaction proceeds, the decarburization reaction is limited by carbon mass transfer, the decarburization rate is affected by the carbon content, and a critical carbon content exists. When the carbon content reaches the critical carbon content, it means that the converting is put into the later stage. Specifically, in the present embodiment, the critical carbon content was selected to be 0.4%, and when the carbon content was reduced to 0.4%, the mid-converting period was considered to be ended and the latter-converting period was entered.
1.3 model assumption and solution Process
The oxygen supply quantity prediction model based on the sectional oxygen decarburization efficiency is to calculate different carbon oxidation ratios of each stage by using furnace gas data to obtain the decarburization efficiency of each stage, calculate the oxygen supply quantity of each stage according to a formula (10) -a formula (12), and then add the oxygen supply quantity to obtain the total predicted oxygen supply quantity. The calculation process involves some hypothetical conditions:
(1) In the actual converter converting process, the yields of molten iron and scrap steel are not 100%. Therefore, the molten iron yield is assumed to be 95%, and the scrap steel yield is assumed to be 85%; the scrap steel is melted by 20% in the early stage of converting, and the melting residual 80% in the middle stage of converting;
(2) In general, about 0.933m is consumed for oxidizing 1kg of carbon 3 Oxygen, i.e. carbon oxidation, generates 100% CO, and from the change curve of the composition of converter converting gas, it is known that carbon oxidation generates a certain proportion of CO during converter converting 2 The secondary combustion rate of the furnace gas outside the converter is 10%;
(3) Since the furnace gas data of the furnace to be solved is unknown, carbon is oxidized to generate CO and CO 2 The ratio of (2) cannot be obtained; therefore, according to the reference case obtained by case-based reasoning, the reference case and the carbon oxidization of each stage of the furnace to be solved are assumed to generate CO and CO 2 The ratio of (2) is the same and the oxygen decarburization rate is the same in each stage.
Based on the above, the process of solving the oxygen supply according to the oxygen supply prediction model based on the segmented oxygen decarburization efficiency of the present embodiment is shown in fig. 2, and the specific steps are as follows:
(1) Case reasoning and searching reference case
And searching out similar heat as a reference case by using a case reasoning algorithm according to the heat charging molten iron condition and the end point control target to be solved.
(2) Fitting a carbon content change curve in a converting process of a reference case
According to the carbon content of molten iron in the furnace of the reference case, the carbon content of the end point and the furnace gas data of the converting process, a carbon content change curve of the converting process of the reference case is fitted, and the converting process is divided into stages.
(3) Reference case oxygen decarburization efficiency partitioning at different stages
According to the dividing result of the blowing process of the reference case, determining the carbon content, the molten iron/molten steel weight, the oxygen consumption and the CO in the furnace gas of each stage 2 The ratio and finally the oxygen decarburization efficiency of each stage are determined.
(4) To solve carbon content of medium-term inflection point before converting of heat
And (3) calculating the time required for oxidizing the silicon to 0.03% according to a desilication rate formula (18), substituting the relation between the blowing early time and the carbon content fitted by a reference case, and calculating the carbon content of the middle-stage inflection point before blowing of the heat to be solved, wherein a constant term in the fitted formula is replaced by the carbon content of the molten iron in the heat to be solved.
And (3) calculating the weight of molten iron/molten steel in the blowing process of the heat to be solved according to the formulas (13) - (15).
(5) To-be-solved heat oxygen supply calculation
After the steps (1) - (4) are completed, the oxygen supply amount and the total oxygen supply amount at different stages of the blowing of the heat to be solved are obtained through calculation according to the formulas (10) - (12).
2 data set statistics
The data used in the model are SPHC series steel grade data of J steel mill 842 furnace, wherein data of 692 furnace are randomly selected as training set, and the remaining 150 furnace times are used as test set. The input of the model includes: the mass fraction (C, si, mn, P) of each component of the molten iron, the molten iron temperature, the molten iron weight, the scrap steel amount, the terminal molten steel temperature and the terminal molten steel C content, and the output result is the total oxygen supply amount of the furnace number. The data distribution statistics are as follows:
table 1 model data distribution statistics
3 simulation experiment
3.1 similarity verification
Taking 4 heat to be solved in a test set as an example, verifying similarity between reference cases obtained by case reasoning, wherein data of the heat to be solved are shown in table 2:
TABLE 2 Process data for Heat to be solved
The references retrieved in the case library according to the case-based reasoning algorithm are for example shown in table 3:
table 3 process data for reference case
TABLE 4 Process parameter differences between Heat to be solved and reference case
The more similar the heat to be solved is to 1, the more similar the process parameters are, and as can be seen from the search results in table 3, the similarity between the 4 heat to be solved and the reference case is greater than 0.97, and the difference value of the process parameters is small, which indicates that the reference case can be effectively searched.
3.2 different phase partitioning procedure
The reference case is oxidized to 0.03% according to the silicon content according to the carbon content of molten iron, the terminal carbon content and furnace gas data, and converting is carried out to the middle stage; when the carbon content is 0.4%, converting to enter the later stage; and (3) carrying out stage division, wherein the stage division result of the reference case converting process is shown in fig. 3.
According to the dividing result of the blowing process of the reference case, the carbon content, the molten iron/molten steel weight, the oxygen supply and the CO in the furnace gas of each stage can be determined 2 The ratio and finally the oxygen decarburization efficiency of each stage are determined.
Table 5 references carbon content at different stages of the case converting process
Table 6 reference to molten iron/molten steel weight at various stages of the case converting process
TABLE 7 reference to oxygen supply at various stages of the case converting process
Table 8 reference case converting process different stages of furnace gases CO and CO 2 Proportion of
Table 9 references oxygen decarburization efficiency at various stages of the case converting process
After determining the oxygen decarburization efficiency of the different stages of the reference case, it is considered that the reference case oxidizes carbon to produce CO and CO at each stage of the heat to be solved 2 The oxygen decarburization rate of each stage is the same, namely the oxygen supply of different stages of the heat to be solved can be calculated according to the formulas (10) - (12), and the oxygen supply of the whole converting process can be calculated according to the formula (17). The oxygen supply predictions applied to the 4 heats to be solved are shown in table 10:
table 10 oxygen consumption at different stages of converting to be solved
3.3 prediction results
The prediction of oxygen supply was performed as described above for all 150 heats of the test set, and the results are shown in fig. 4.
Prediction error analysis of the model is shown in table 11:
TABLE 11 error analysis between predicted oxygen supply and actual values for test set
As can be seen from the above table, the relative error of the model prediction in this embodiment is concentrated within [ -5%,5% ], the hit rate reaches 98%, wherein the hit rate reaches 79.3% within the error range of [ -3%,3% ], and the predicted hit rates of the model are 99.3% and 100% if the error ranges are [ -8%,8% ] and [ -10%,10% ], respectively.
3.4 method comparison
In order to verify the prediction accuracy of the model of the embodiment, the embodiment respectively establishes a neural network model, a case reasoning model, a multiple linear regression model and a support vector machine model to carry out a comparison experiment by using the same data. The neural network model network structure is 3 layers, the input layer nodes are 11, the hidden layer nodes are 10, the output layer nodes are 1, and the activation function is Sigmoid. Case-based reasoning model parameters: the similarity calculation method adopts Euclidean distance similarity, the weight of influencing factors is equal weight, and the number of reuse cases is 4. The multiple linear regression model adopts regression principle algorithm in machine learning. Support vector machine model parameters: the kernel function selects a poly kernel with an order of 1. The prediction results of the test heats using the respective models are shown in fig. 5. The predicted hit rates for the different models are compared as shown in fig. 6.
As can be seen from fig. 6, among the conventional data-driven models, the predictive hit rate of the multiple linear regression model is highest, and the predictive relative error is [ -3%,3% ] [ -5%,5% ] -8%,8% ] and [ -10%,10% ] hit rates are 46.7%, 65.3%, 93.3% and 98%, respectively, compared to the other models. The predictive hit rates of the models built in the embodiment are 32.6%, 32.7%, 6% and 2% higher than that of the multiple linear regression models in the error ranges of [ -3%,3% ] [ -5%,5% ] -8%,8% ] and [ -10%,10% ]. Compared with a neural network model, a case-based reasoning model, a multiple linear regression model and a support vector machine model, the technical scheme of the embodiment has higher hit rate in a small error range, thereby proving the prediction accuracy of the method of the embodiment.
In summary, the embodiment provides a converter oxygen supply prediction method based on sectional oxygen decarburization efficiency, which analyzes decarburization efficiency characteristics at different moments in the converter steelmaking process, divides the oxygen blowing decarburization process into three stages, and determines inflection point moments of each stage. And establishing a converter oxygen supply prediction model based on the sectional oxygen decarburization efficiency. And finding a reference case of the heat to be solved by using a case reasoning algorithm, dividing the reference case in stages, and applying inflection points to the heat to be solved at the moment, thereby calculating oxygen consumption of different stages of the heat to be solved. The actual production data of a certain steel plant is applied to the converter oxygen supply prediction model of the sectional oxygen decarburization efficiency of the embodiment to obtain that the relative prediction error of the model of the embodiment is within the range of < -3 >, 3 percent ], [ -5 percent ], [ -8 percent, 8 percent] and [ -10 percent), the hit rates of the 10 percent are 79.3 percent, 98 percent, 99.3 percent and 100 percent respectively, and the comparison test is carried out with a neural network model, a case reasoning model, a multiple linear regression model and a support vector machine model to obtain that the hit rates of the prediction error of the model within the range of [ -3 percent, 3 percent ], [ -5 percent ], [ -8 percent, 8 percent ] and [ -10 percent, and 10 percent are respectively improved by 32.6 percent, 32.7 percent, 6 percent and 2 percent, so that the prediction accuracy of the converter oxygen supply prediction model based on the sectional oxygen decarburization efficiency provided by the embodiment is higher.
Second embodiment
The embodiment provides an electronic device, which comprises a processor and a memory; wherein the memory stores at least one instruction that is loaded and executed by the processor to implement the method of the first embodiment.
The electronic device may vary considerably in configuration or performance and may include one or more processors (central processing units, CPU) and one or more memories having at least one instruction stored therein that is loaded by the processors and performs the methods described above.
Third embodiment
The present embodiment provides a computer-readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the method of the first embodiment described above. The computer readable storage medium may be, among other things, ROM, random access memory, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. The instructions stored therein may be loaded by a processor in the terminal and perform the methods described above,
furthermore, it should be noted that the present invention can be provided as a method, an apparatus, or a computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable storage media having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
It is finally pointed out that the above description of the preferred embodiments of the invention, it being understood that although preferred embodiments of the invention have been described, it will be obvious to those skilled in the art that, once the basic inventive concepts of the invention are known, several modifications and adaptations can be made without departing from the principles of the invention, and these modifications and adaptations are intended to be within the scope of the invention. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Claims (5)
1. The converter oxygen supply amount prediction method based on the sectional oxygen decarburization efficiency is characterized by comprising the following steps of:
according to the furnace charging molten iron condition and the end point control target of the furnace to be solved, searching a past case with highest similarity with the furnace to be solved in a past case library by using a case reasoning algorithm as a reference case;
the method comprises the steps of dividing a converting process of a reference case into a converting early stage, a converting middle stage and a converting later stage by utilizing different influencing factors of oxygen decarburization efficiency in different stages in the converting process of a converter;
based on preset assumption conditions, predicting the oxygen supply of the heat to be solved according to the blowing stage division time of the reference case and the oxygen decarburization efficiency of different stages, and obtaining an oxygen supply prediction result of the heat to be solved;
the step of dividing the converting process of the reference case comprises the following steps:
fitting a carbon content change curve and a silicon content change curve of the blowing process of the reference case according to the carbon content of the molten iron in the furnace, the carbon content of the end point and the furnace gas data of the blowing process of the reference case;
based on the carbon content change curve and the silicon content change curve, carrying out stage division on the blowing process of the reference case;
predicting the oxygen supply of the heat to be solved according to the blowing stage dividing time of the reference case and the oxygen decarburization efficiency of different stages to obtain an oxygen supply prediction result of the heat to be solved, wherein the method comprises the following steps:
determining the carbon content, molten iron/molten steel weight, oxygen consumption and CO in furnace gas of each stage according to the stage division result of the blowing process of the reference case 2 The ratio and finally the oxygen decarburization efficiency of each stage are determined;
calculating the carbon content of inflection points of different converting stages of the heat to be solved based on the converting process stage division time of the reference case and the relation between the converting stage time and the carbon content fitted by the reference case, wherein constant items in a fitting formula are replaced by the carbon content of molten iron in the heat to be solved during calculation; the fitting formula refers to a mathematical expression of the relation between the time and the carbon content of each converting stage fitted based on a reference case;
calculating the weight of molten iron/molten steel in the blowing process of the heat to be solved;
based on the oxygen decarburization efficiency of each stage, the carbon content of molten iron in a furnace to be solved, the weight of molten iron/molten steel in a converting process and the carbon content of inflection points in different converting stages are combined, and the oxygen supply of the furnace to be solved is predicted to obtain an oxygen supply prediction result of the furnace to be solved;
the preset assumption conditions include:
in the actual converter converting process, the yields of molten iron and scrap steel are not 100%; assuming that the molten iron yield is 95%, and the scrap steel yield is 85%; the scrap steel is melted by 20% in the early stage of converting, and the melting residual 80% in the middle stage of converting;
in the actual converter converting process, carbon oxidation does not generate 100% CO, but generates a certain proportion of CO 2 The secondary combustion rate of the furnace gas outside the converter is 10%;
according to the reference case obtained by case reasoning, assume that the reference case and the furnace to be solved are oxidized to generate CO and CO at each converting stage 2 The ratio of (2) is the same, and the oxygen decarburization rate is the same in each converting stage.
2. The method for predicting oxygen supply to a converter based on a sectional oxygen decarburization efficiency according to claim 1, wherein the inflection point judgment from the early stage of converting to the middle stage of converting is based on the silicon content of the converting process, and the inflection point judgment from the middle stage of converting to the later stage of converting is based on the carbon content of the converting process.
3. The method for predicting oxygen supply to a converter based on the efficiency of decarbonizing sectional oxygen according to claim 2, wherein the inflection points of each stage are determined in the following manner: when the silicon content is oxidized to 0.03%, ending the earlier stage of converting, and entering the middle stage of converting; and when the carbon content is oxidized to 0.4%, ending the middle stage of converting, and entering the later stage of converting.
4. The method for predicting oxygen supply to a converter based on the efficiency of decarbonizing sectional oxygen according to claim 1, wherein predicting the oxygen supply to the heat to be solved comprises:
the oxygen supply in the different converting stages was calculated by the following formula:
the oxygen supply amounts of the blowing stages are accumulated to obtain the total oxygen supply amount Q Total (S) ：
Q Total (S) ＝Q 1 +Q 2 +Q 3
Wherein Q is 1 The oxygen supply amount in the earlier stage of converting is shown; q (Q) 2 The oxygen supply amount in the middle of converting is shown; q (Q) 3 The oxygen supply amount at the later stage of blowing is shown; w (C) 0 Representing the carbon content in molten iron in a furnace; m is m 0 Representing the weight of molten iron into the furnace; w (C) 1 Representing the carbon content of the middle inflection point before converting; m is m 1 The weight of the molten iron at the middle inflection point before converting is expressed; η (eta) 1 Indicating oxygen decarburization efficiency in the earlier stage of converting; w (C) 2 Representing the carbon content of inflection points in the middle and later stages of converting; m is m 2 The weight of the molten steel at the inflection point in the middle and later stages of blowing is expressed; η (eta) 2 The oxygen decarburization efficiency in the middle of converting is shown; w (C) 3 Indicating the carbon content of the converting end point; m is m 3 Indicating the weight of molten steel at the blowing end point; η (eta) 3 The oxygen decarburization efficiency in the later stage of blowing is shown; mu represents the oxygen amount required for oxidizing each 1kg of carbon.
5. The method for predicting oxygen supply of a converter based on the efficiency of decarbonizing sectional oxygen according to claim 4, wherein the weight calculation formula of molten iron/molten steel in the blowing process is as follows:
m 3 ＝m 0 ·α+m s ·β
m 2 ＝m 0 +(m 3 -m 0 )·(γ 1 +γ 2 )
m 1 ＝m 0 +(m 3 -m 0 )·γ 1
wherein alpha represents the molten iron yield; beta represents the yield of scrap steel; gamma ray 1 Representing the melting percentage of the waste steel in the earlier stage of converting; gamma ray 2 The melting percentage of the scrap steel in the middle of converting is shown; lambda (lambda) 1 Represents the ratio of carbon oxidation to CO; lambda (lambda) 2 Indicating the formation of CO by carbon oxidation 2 Is a ratio of (2).
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20060072404A (en) * | 2004-12-23 | 2006-06-28 | 주식회사 포스코 | Forecasting method of oxygen blowing volume for converter control |
| CN108676955A (en) * | 2018-05-02 | 2018-10-19 | 中南大学 | A kind of BOF Steelmaking Endpoint carbon content and temprature control method |
| CN111020118A (en) * | 2019-12-25 | 2020-04-17 | 武汉科技大学 | RH endpoint temperature prediction method based on particle swarm optimization case reasoning |
| CN111500815A (en) * | 2020-05-28 | 2020-08-07 | 北京科技大学 | Bottom blowing O2-CO2Dynamic control method for steelmaking process of CaO converter |
| CN113239482A (en) * | 2021-04-23 | 2021-08-10 | 北京科技大学 | Dynamic prediction method and device for converter post-blowing carbon content |
| CN113255102A (en) * | 2021-04-23 | 2021-08-13 | 北京科技大学 | Method and device for predicting carbon content and temperature of molten steel at converter end point |
| CN115232907A (en) * | 2022-09-23 | 2022-10-25 | 北京科技大学 | Method and system for predicting oxygen blowing amount in converter steelmaking |
| CN115341069A (en) * | 2021-05-14 | 2022-11-15 | 宝山钢铁股份有限公司 | Molten steel carbon content prediction control method of converter blowing end point based on online dynamic detection model |
| CN115522012A (en) * | 2022-08-25 | 2022-12-27 | 邯郸钢铁集团有限责任公司 | Method for controlling TSC (thyristor switched capacitor) stage oxygen blowing amount of large converter |
-
2023
- 2023-03-17 CN CN202310272827.7A patent/CN116434856B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20060072404A (en) * | 2004-12-23 | 2006-06-28 | 주식회사 포스코 | Forecasting method of oxygen blowing volume for converter control |
| CN108676955A (en) * | 2018-05-02 | 2018-10-19 | 中南大学 | A kind of BOF Steelmaking Endpoint carbon content and temprature control method |
| CN111020118A (en) * | 2019-12-25 | 2020-04-17 | 武汉科技大学 | RH endpoint temperature prediction method based on particle swarm optimization case reasoning |
| CN111500815A (en) * | 2020-05-28 | 2020-08-07 | 北京科技大学 | Bottom blowing O2-CO2Dynamic control method for steelmaking process of CaO converter |
| CN113239482A (en) * | 2021-04-23 | 2021-08-10 | 北京科技大学 | Dynamic prediction method and device for converter post-blowing carbon content |
| CN113255102A (en) * | 2021-04-23 | 2021-08-13 | 北京科技大学 | Method and device for predicting carbon content and temperature of molten steel at converter end point |
| CN115341069A (en) * | 2021-05-14 | 2022-11-15 | 宝山钢铁股份有限公司 | Molten steel carbon content prediction control method of converter blowing end point based on online dynamic detection model |
| CN115522012A (en) * | 2022-08-25 | 2022-12-27 | 邯郸钢铁集团有限责任公司 | Method for controlling TSC (thyristor switched capacitor) stage oxygen blowing amount of large converter |
| CN115232907A (en) * | 2022-09-23 | 2022-10-25 | 北京科技大学 | Method and system for predicting oxygen blowing amount in converter steelmaking |
Non-Patent Citations (1)
| Title |
|---|
| 基于互信息案例推理的氧气脱碳效率预测模型;李洋等;《信息与控制》;20120430;第261-266，272页 * |
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