CN102766728B - Method and device for real-time prediction of sulfur content of molten steel in refining process of ladle refining furnace - Google Patents
Method and device for real-time prediction of sulfur content of molten steel in refining process of ladle refining furnace Download PDFInfo
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Abstract
The present invention provides a method and a device for real-time prediction of sulfur content of molten steel in a refining process of a ladle refining furnace. According to the invention, based on a mechanism model, operation states including electric heating, argon bottom blowing, slag and alloy addition in an LF process are tracked online; it is taken into consideration that changes of model input parameters caused by changes of a variety of operation conditions further affect changes of model control parameters; changes of factors having major influences on the control parameters in the refining process are fully consider; in calculation of every calculation cycle, the model control parameters are updated; and finally an on-line dynamic desulfurization model of the LF refining process is established, so as to ensure rationality and accuracy of the prediction of the sulfur content of the molten steel, and provide guidance for on-spot production.
Description
Technical field
The present invention relates to ferrous metallurgy refining techniques field, more particularly, relate to a kind of method and device thereof of ladle refining furnace refining process molten steel sulphur content forecast.
Background technology
In smelting iron and steel, for most of steel grades, sulphur is a kind of harmful element in steel.Element sulphur can cause hot-short, the ductility and the toughness that reduce steel of steel, is forging and is causing crackle during rolling, and sulphur is also unfavorable for the welding property of steel.Ladle refining furnace (, LF stove) be the important step in Iron and Steel Production, due to its good Deoxidation Atmosphere in Furnace that adopts white slag refining and creation, add that argon gas good in refining process stirs, for the desulfurization of molten steel provides good thermodynamics and kinetics condition.In the situation that desulfurization condition is good, the desulfurization degree of LF stove can reach 70%～80%.
Set up the desulfurization model of LF stove refining process, accurate forecast molten steel sulphur content, to producing qualified (surpassing) low-sulfur steel, improve steel performance and quality and improve steelmaking process automatization level, reduce labour intensity, become that product is preliminary etc. to have great importance.
Make a general survey of at present the research and development to ladle desulfurization model both at home and abroad, can find that the molten steel sulphur content forecasting model of setting up is at present mainly the mechanism model based on desulphurization mechanism reaction.By analyze the mechanism of desulphurization reaction in ladle furnace refining process set up can reaction desulfuration process mathematical model, the parameters that then calculates model finally carries out CALCULATING PREDICTION.Document " Modelling Pyrometallurgical Kinetics:Ladle Desulphurization " (< < South African Journal of Science > > 1999 for example, 377th～380 pages of the 9th phases of 95 volumes, Pistorius P C etc.) think that the mass transfer of sulphur in molten steel is restricted link, by the two-film theory of desulphurization reaction, set up desulfurization kinetics model, wherein mass transfer coefficient rule of thumb formula try to achieve, think the partition ratio L of sulphur
slmbe a definite value, according to take thermodynamic(al)equilibrium, as basic experimental formula, calculate and try to achieve.Document " in LF refining process sulfur partition ratio and desulfurization kinetics equation research " (< < Acta Metallurgica Sinica > > calendar year 2001,37 (10): 1014～1016, Wu's clang etc.) according to desulfurization kinetics establishing equation desulphurization mechanism model, in model, calculate L
sbe to take thermodynamic(al)equilibrium as basic experimental formula calculating gained, think that there are good linear relationship desulfurization rate and time.Above model is all to set up the mathematical model that can reflect sweetening process by analyzing the mechanism of desulphurization reaction in ladle furnace refining process, and the parameters that then calculates model finally calculates sulphur content.But relevant LF stove desulphurization mechanism model is not all considered in the middle of actual refining process due to operational condition, the variation of the model parameter that the change of the molten steel states such as slag composition and temperature causes, does not embody the model of variable element.
On the other hand, the "black box" model based on intelligent algorithms such as neural networks.This algorithm is only considered input and output, give no thought to process mechanism, in model, mainly take and affect the input that the principal element of molten steel desulfurizing and some original bulies are model, by a large amount of production data analyses are carried out to repetition learning and training, obtain an approximating function that can forecast molten steel sulphur content.Document " research of the static desulfurization model of ladle " (< < Chinese journal of scientific instrument > > 2006,27 (6) supplementary issues 1014～1016, Zhang Junwei) situation based on the desulfurization of 150t ladle furnace refining has been set up artificial intelligence model, if error allows in 0.001%, its forecast hit rate can reach 80%.This method too relies on data, only considers that input and output ignore process mechanism, lacks technique and instructs, and is difficult to carry out on-the-spot steady running at the scene and debugging is optimized in the situation of changeable, the influence factor numerous complicated of flexible operation.
Summary of the invention
For prior art above shortcomings, one of object of the present invention is to provide a kind of method and apparatus that can real-time estimate sulphur content in molten steel in ladle refining furnace refining process.
To achieve these goals, an aspect of of the present present invention provides a kind of method of ladle refining furnace refining process molten steel sulphur content real-time estimate.Wherein, described method through type (a):
Calculate the sulphur content [%S] in molten steel, wherein, L
sthrough type (b) draws, formula (b) is:
Wherein, T
0for initial temperature; [%C], [%Si], [%Mn], [%P], [%S] are the content of C, Si, Mn, P, S in molten steel; ω [Al]
0for the molten aluminium amount of molten steel initial acid; t
1for the electrically heated time; t
2for the time of non-heating phase; T is overall treatment time; Wherein, J draws by solving formula (c), formula (d), formula (e), and formula (c) is:
Formula (d) is:
Formula (e) is: T=T
0+ (57.02469-0.03492T
0) t
1-0.93t
2, wherein, W is the molten steel amount in ladle refining furnace, H is the pool depth in the ladle refining furnace of corresponding molten steel amount W, the BOTTOM ARGON BLOWING flow that Q is ladle refining process.
Another aspect of the present invention provides a kind of device of ladle refining furnace refining process molten steel sulphur content real-time estimate.Described device comprises data management connected to one another and tracking module, mode input amount acquisition module, information interaction module and host computer module, wherein, described data management and tracking module come steel information and smelting requirements for record, and initial temperature-measuring results, the thickness of slag layer of sampling of record; The parameter information that described mode input amount acquisition module needs for obtaining host computer module; Described information interaction module is used for realizing the real-time, interactive of same L2 (Level2 process automation level) tracking system, thereby obtains production process state, real-time parameter, reinforced information and the relevant parameter information in mode input amount acquisition module is upgraded; Described host computer module is carried out real-time estimate according to the parameter information obtaining and renewal thereof to the sulphur content of calculating in molten steel.
In an exemplary embodiment of molten steel sulphur content real-time estimate device of the present invention, described device carrys out real-time estimate sulphur content according to method as above.
Compared with prior art, beneficial effect of the present invention comprises: provide a kind of and can carry out to the sulphur content of LF refining process the method and apparatus of online dynamic prediction, reasonableness and the accuracy that can guarantee final molten steel sulphur content prediction, level of automation is high, has reduced labour intensity.
Accompanying drawing explanation
By the description of carrying out below in conjunction with accompanying drawing, above and other object of the present invention and feature will become apparent, wherein:
Fig. 1 shows the schema of the device of ladle refining furnace refining process molten steel sulphur content real-time estimate of the present invention;
Fig. 2 is model system Organization Chart of the present invention;
Fig. 3 is the variation diagram of liquid steel temperature in the middle of LF refining process;
Fig. 4 is LF furnace bottom Argon curvilinear motion figure;
Fig. 5 is the comparison diagram of the initial acid-soluble aluminum content of molten steel and terminal acid-soluble aluminum content;
Fig. 6 is the variation of molten steel oxygen activity before and after refining.
Embodiment
Hereinafter, describe with reference to the accompanying drawings exemplary embodiment of the present invention in detail.
A kind of method that the object of this invention is to provide forecast LF stove refining process molten steel sulphur content of and the accurate combination of the LF of steel mill stove actual production.The method is the electrically heated in the on-line tracing LF of this steel mill stove production process on the basis of mechanism model, BOTTOM ARGON BLOWING, slag charge adds and the operational stage such as alloying, the model of considering the change of each operational condition and causing is controlled the change of parameter, while is along with the passing of refining process, in model, some inpuies can be carried out dynamic change according to certain rule, in the calculating of next cycle, model parameter is according to the information in a upper cycle with result is upgraded rather than whole process is all unalterable, in whole computation process, model control parameter and Output rusults are all dynamic changes.The present invention finally sets up the online dynamically desulfurization model of LF stove refining process, guarantees reasonableness and the accuracy of final sulphur content prediction, for situ production provides guidance.
In model development of the present invention, traditional desulfurization model governing equation is optimized, obtain the governing equation of molten steel sulphur content as formula (1), its main optimization method is: optimized the calculation formula of core parameter J and Ls in equation, thereby realized more accurate molten steel sulphur content calculating.
In formula (1), (S)
0for the initial sulphur content in slag, [%S]
0for the initial sulphur content of molten steel.In Traditional calculating methods, Ls obtains by formula (2):
In formula, T is temperature; Cs is sulfur capacity; Fs is sulphur activity quotient; A[O] be molten steel oxygen activity.
In formula: ε is stirring merit,
In traditional method, do not consider temperature T, Sulfur capacity Cs, oxygen activity a[O] etc. the variation in refining process, but calculate with a given numerical value, ignore the variation of Ls and J in refining process, in whole refining process with fixing parameter L s and J.
The present invention, by a large amount of data analyses, thinks that Ls and J are not unalterable in refining process, but constantly change along with the passing of process and the change of operational condition, practical situation are reacted in this variation meeting more really.Embodiment is: the temperature T in formula (2), oxygen activity α [O] are improved, and result is as follows:
T＝T
0+(57.02469-0.03492T
0)·t
1-0.93·t
2 (4)
Consider that refining process adds alloy may make the variation of C in molten steel, Si, Mn content larger, this can make lgf
svalue change, in order to show the variation of molten steel composition, make lgf
svalue change, lgf
swith formula (6), represent.
lgf
s＝0.11[％C]+0.063[％Si]-0.026[％Mn]+0.029[％P]+0.028[％S] (6)
The calculating formula that therefore can obtain Ls in the present invention is formula (7).
In above formula, T
0for initial temperature; [%C]～[%S] is the content of C, Si, Mn, P, S in molten steel; ω [Al]
0for the molten aluminium amount of molten steel initial acid; t
1for the electrically heated time; t
2for the time of non-heating phase; T is overall treatment time.
In the calculating of J, to stirring merit
calculating done some optimizations and supplemented.The first, temperature term T is wherein expressed as to formula (4); The second, provide the calculating formula of 135t ladle pool depth H under known molten steel amount W, as formula (8), formula (8) solves by Newton iteration method.Here, the ladle of other capacity pool depth H under known molten steel amount W also can draw by similar mode.(note: traditional method is not calculated the depth H in molten bath under concrete heat of molten steel amount for concrete heat, a but definite value of getting according to the treatment capacity of ladle, this is irrational, and in actual production, the molten steel amount of each heat is different, and this can make the pool depth of each heat also different)
Aggregative formula (3), formula (4), formula (8), and the expression formula of ε can react practical situation more accurately, the J obtaining is like this also more accurate.
Model in computation process, also consider occur in the refining process slag charge and alloy add fashionable, the impact on steel, slag ingredient.This impact is mainly divided into following two aspects: first, adding slag charge can have influence on top slag ingredient forms, this can have influence on the value of sulfur capacity Cs in formula (2) and formula (7), add alloy can have influence on molten steel composition, the content that this can have influence on element in formula (6), now should recalculate Ls and the value of Ls is upgraded.The second, in slag charge and alloy, more or less contain certain sulphur, after slag charge and alloy add, this part sulphur can be transferred in molten steel, thereby affects molten steel sulphur content.
In one exemplary embodiment of the present invention, the online dynamic forecasting model of LF stove molten steel sulphur content comprises following several large module: (1) data management and tracking module.This module records is come steel information and smelting requirements, as steel grade, melting number, molten steel weight, processing target etc.; The temperature-measuring results of the initial sampling of record simultaneously, thickness of slag layer, for other technology controlling and process module provides corresponding information.(2) mode input amount acquisition module.Before starting to calculate, model needs to guarantee that each input acquisition of information of model need is complete; Model can each input message of automatic acquisition under normal circumstances, when data can not normally obtain or the problematic situation of data under can select manual input, complete to confirm the input message of model.(3) information interaction module.When there is the change of Argon amount, thermometric and the situation such as reinforced, detect its corresponding information, online model system, by L2 database interface table, realization is with the real-time, interactive of L2 tracking system, thereby obtain production process state, real-time parameter, the information such as reinforced, and the model calculation is offered to L2 tracking system, for operator reference, and effective input message is upgraded and then Renewal model parameter proceed to calculate.(4) host computer module.By the interface defining, make integration module according to production status, utilize parameter that model is constantly updated to realize real-time calling model and calculate, and give main calling module by result feedback, for it, show and deposit database interface table etc. in.The schema of forecasting model as shown in Figure 1.Fig. 1 shows the schema of the device (in the present invention, also referred to as the online dynamic forecasting model of LF stove molten steel sulphur content, referred to as at line model or model) of ladle refining furnace refining process molten steel sulphur content real-time estimate of the present invention.
The C/S system structure that in the present invention, the system of model adopts.Model and L2 carry out information interaction by database.Online model system is calculated by calling model core algorithm dynamic base, and deposits calculation result in database and obtain for L2.L2 system acquisition model needs L1 information, and deposits in interface table, for model system.Model system framework is as Fig. 2.The full name of L1 system, L2 system and steel-making L3 system is respectively Level1 basic automatization level system, Level2 process automation level system, Level3 production control level system.
Below in conjunction with accompanying drawing, the present invention will be further described in detail.
Fig. 2 is the structure iron of model system.Calling of model is mainly divided into following large step:
Step 1:L3 system is responsible for assigning the production program, and receives the significant process trace information that L2 uploads.
Step 2:L2 process tracking, plans by receiving L3, on the basis of L1 basic automation systems, realizes the on-line tracing to this heat production process, and for model on-line operation provides support, meanwhile, important tracking results is uploaded to L3 system in real time;
Step 3: model information to L1 system, is responsible for realize processing to heat refining process by L1 system by L2 systems communicate, and provide support for tracking system.
Wherein in step 2, can relate to calling of model, the invoked procedure of model is divided into again following several process:
Step 2-1:L2 system receives the production program that L3 system is assigned, and waits for that ladle enters processing position, and to molten steel to be processed is carried out to heat statement.
Step 2-2: ladle furnace enters LF process the statement of station heat after, by data management and tracking module, record steel information, as information such as steel grade, melting number, molten steel weight, thickness of slag layer.
Step 2-3: electrically heated starts model after processing and starting, and first carries out obtaining of each input message of model.Normal circumstances drag is by each input message of L2 database interface table automatic acquisition, when data can not normally obtain or the problematic situation of data under can select manual input, complete to confirm the input message of model.
Step 2-4: enter calculation window and calculate after each input message is correct complete, export in real time molten steel sulphur content, on interface, show, input out of the ordinary in model, for example temperature, molten steel acid-soluble aluminum content can constantly carry out dynamic change according to certain rule along with the passing of refining time, in next cycle model data, deposit database in, for L2 system call.
Step 2-5: in the middle of LF stove treating processes, by information interaction module, operational condition is carried out to state-detection, when there is the change of Argon amount, thermometric and the situation such as reinforced, detect its corresponding status information, by L2 database interface table, obtain the lastest imformations such as new Argon amount, temperature, then return to step 2-4, by model, control the accounting equation Renewal model parameter of parameter, then proceed to calculate.
Step 2-6: after LF processing finishes, model calls end, save data also carries out initialize.
Embodiment
LF stove molten steel sulphur content dynamic forecasting model should be as follows:
1. data survey analysis
In conjunction with the production of domestic certain LF of steel mill stove, on affecting the input of model parameter value, in the middle of refining process, change and analyze, for finding its dynamic rule in refining process.
The variation of 1.1 temperature
Certain steel mill controls and has following standard for the temperature of refining process: 1) for guaranteeing refining morningization slag in early stage, suitably improve the molten steel temperature of arriving at a station, preferably can make molten steel enter LF furnace temperature and be controlled at more than 1560 ℃.2) molten steel carries out rapidly heating operation slag for the first time after arriving at a station, and heat-up time is not at 3～8min etc.3) refining process adopts frequent short period of time heating temperature raising, to keep the stability of molten steel slag temperature, avoids temperature to fluctuate widely, and liquid steel temperature has corresponding requirement according to concrete steel grade.4) before departures, liquid steel temperature is brought up to and stipulated to arrive at a station the more than 10 ℃ of temperature, carry out soft blow argon, wait for into next process.
In refining process, a part of heat is due to heating frequently, and temperature is continuous ascendant trend, and some heat can stay for some time in processing position or carry out other operations after heating, can cause the reduction of temperature, and the actual change of temperature as shown in Figure 3.By temperature data analysis, can find in the molten steel heat temperature raising stage, the rising speed of temperature is relevant with molten steel initial temperature, and the relation between average temperature rise rate and molten steel initial temperature is coincidence formula (1) roughly.Can obtain by inquiry the average rate of temperature fall of molten steel is under normal circumstances 0.93 ℃/min.When there is thermometric operation, temperature is revised automatically.
The variation that can be obtained refining process molten steel by above analysis can be expressed as formula (2):
T
t＝T
0+(57.02469-0.03492T
0)·t
1-0.93·t
2 (10)
In formula, T
0for molten steel initial temperature; T
tfor t moment liquid steel temperature; t
1for the electrically heated time; t
2for the time of non-heating phase.
Formula (10) is applied in the calculation formula of model parameter Ls and J.
The variation of 1.2 BOTTOM ARGON BLOWING tolerance
Different according to the refining object that will reach in the middle of refining process, need constantly to adjust BOTTOM ARGON BLOWING flow.Before refining, need slag blanket to blow open and facilitate thermometric, will add thermalization slag early stage, too large, moderate for arc stability Argon amount is difficult for; Before thermometric sampling, to less Argon amount, make liquid steel temperature and composition mix; After electrically heated, to adopt strong mixing to promote molten steel circulation and slag fully to mix, carry out desulfurization; When electrically heated afterwards and alloying, use inferior strong stirring to promote alloy melting, improve the recovery rate of alloying element; A little less than the refining of heating after finishing will adopt latter stage, blow and make inclusion floating, uniform temperature composition, improves molten steel pourability simultaneously.BOTTOM ARGON BLOWING fluctuations in discharge under normal circumstances as shown in Figure 4.This model is considered the variation of different steps Argon amount, gathers different steps Argon amount data and realizes the dynamic tracking of Argon amount, and calculate for model.
The variation of the 1.3 molten steel molten aluminium of acid and oxygen activity
For aluminium killed steel, the oxygen activity of molten steel is mainly subject to the control of sour molten aluminium in steel, and the variation of acid-soluble aluminum content can reflect the variation of molten steel oxygen activity, and this can have influence on the desulfurization of numerical value and the molten steel of sulfur partition ratio.By analyzing a large amount of field data in the LF of this steel mill production process, the acid-soluble aluminum content that can find refining terminal and the LF initial acid-soluble aluminum content that enters the station has been compared obvious reduction, as shown in Figure 5.The reduction of acid-soluble aluminum content means that the oxygen activity of molten steel can raise to some extent in the refining later stage.Fig. 6 be molten steel before chain-wales argon to LF departures process molten steel oxygen activity changing trend diagram.In this process, the variation of corresponding molten steel acid-soluble aluminum content also shows in Fig. 6, and as can be seen from the figure the reduction of sour molten aluminium is really influential to molten steel oxygen activity, has confirmed that the reduction of sour aluminium means that molten steel oxygen activity can raise to some extent in the refining later stage.
Definition
for the velocity of variation of the molten aluminium of LF refining process acid, the mean value that can obtain the velocity of variation of sour molten aluminium by data analysis is
can roughly estimate thus sour molten aluminium being changed to refining time in refining process:
Δω[Al]
s＝ω[Al]
0-0.00043t (11)
Molten steel oxygen activity over time formula is:
Formula (12) is applied in the calculation formula of model parameter Ls.
The impacts on slag composition such as 1.4 slag charges, alloy add
In the middle of LF stove refining process, can to dropping into the slag charges such as lime, fluorite, high alkalinity refining slag, whipping agent in ladle, to bits adjustment, improve the slag making situation of LF and the submerged arc situation of electrode according to slag making situation, electrically heated situation.The requirement to composition according to the molten steel composition under different situations and steel grade simultaneously, often will add corresponding alloy to carry out the adjustment of molten steel composition in the middle of LF refining process.These operations can cause steel, slag ingredient to change, and this can have influence on model and control parameter value and calculation result.This model can detect the reinforced data in refining process, and when occurring to feed in raw material, model detects its state and reinforced data automatically, by calculating, upgrades slag compositional data and model parameter value.
When adding slag charge, will consider the impact on slag ingredient of the slag charge that adds, its calculation formula is as follows:
In formula:
ω (M)
0for the content of material M in the slag of top, %;
ω (M)
ifor the content of material M in added slag charge, %;
W
add, ifor the add-on of slag charge i, kg;
W
sfor top slag weight, kg.
If add alloy, need to consider add the impact of alloy on molten steel composition, its calculating formula is as follows:
In formula:
ω [m]
0for the initial content of m element in molten steel, %;
ω [m]
ifor the content of m element in added alloy, %;
F
mrecovery rate for element m;
G
ifor the weight of added alloy i, kg;
W
mfor Metal Weight in ladle, kg.
Formula (13) and formula (14) are applied in the calculation formula of model parameter Ls.
2 model prediction effects
Production scene data variation analytical results is loaded in the middle of model, and mechanism model organically combines, can be more reasonable, show accurately the Changing Pattern of molten steel sulphur.The prediction error of the LF stove molten steel sulphur content forecasting model final sulfur content that the present invention sets up allows ± 5 * 10
-6the interior forecast hit rate of take is 60.5%; Prediction error allows ± 8 * 10
-6the interior forecast hit rate of take is 79%; Prediction error allows ± 10 * 10
-6the interior forecast hit rate of take is 88.9%.Produce at the scene in normal, stable situation, the dynamic desulfurization model of mechanism of setting up herein in certain limit of error can react on-the-spot LF practical sulphur removal situation.But on-the-spot actual production handiness is large, and the factor that has influence on molten steel desulfurizing is also intricate, and make the tolerance range of model be further improved also needs to do further investigation, debugging and optimization in conjunction with on-the-spot actual production.
Although described the present invention with exemplary embodiment by reference to the accompanying drawings above, those of ordinary skills should be clear, in the situation that do not depart from the spirit and scope of claim, can carry out various modifications to above-described embodiment.
Claims (2)
1. a method for ladle refining furnace refining process molten steel sulphur content real-time estimate, described method through type (a):
Calculate the sulphur content [%S] in molten steel, it is characterized in that, wherein,
L
sthrough type (b) draws, formula (b) is:
Wherein, T
0for initial temperature; [%C], [%Si], [%Mn], [%P], [%S] are the content of C, Si, Mn, P, S in molten steel; ω [Al]
0for the molten aluminium amount of molten steel initial acid; t
1for the electrically heated time; t
2for the time of non-heating phase; T is overall treatment time;
Wherein, J draws by solving formula (c), formula (d), formula (e),
Formula (c) is:
Formula (d) is:
Formula (e) is: T=T
0+ (57.02469-0.03492T
0) t
1-0.93t
2,
Wherein, W is the molten steel amount in ladle refining furnace, and H is the pool depth in the ladle refining furnace of corresponding molten steel amount W, and Q is the BOTTOM ARGON BLOWING flow in ladle refining process.
2. a device for ladle refining furnace refining process molten steel sulphur content real-time estimate, is characterized in that, described device comprises data management and tracking module, mode input amount acquisition module, information interaction module and the host computer module being linked in sequence, wherein,
Described data management and tracking module come steel information and smelting requirements for record, and initial temperature-measuring results, the thickness of slag layer of sampling of record;
The parameter information that described mode input amount acquisition module needs for obtaining host computer module;
Described information interaction module is for realizing the real-time, interactive of same Level2 process automation level tracking system, thereby obtains production process state, real-time parameter, reinforced information and the relevant parameter information in mode input amount acquisition module is upgraded;
Described host computer module is carried out real-time estimate according to the parameter information obtaining and renewal thereof to the sulphur content in molten steel,
And described device carrys out real-time estimate sulphur content according to the method for claim 1.
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| CN103382514B (en) * | 2013-07-19 | 2015-11-04 | 东北大学 | The system and method for molten steel composition in a kind of on-line prediction RH refining process |
| CN103397140B (en) * | 2013-07-19 | 2015-08-19 | 东北大学 | The system and method for required refining quantity of slag during a kind of on-line prediction LF refining desulfuration |
| CN110438295A (en) * | 2019-08-22 | 2019-11-12 | 厦门邑通软件科技有限公司 | A kind of method that wisdom promotes ladle refining furnace efficiency |
| CN112522474B (en) * | 2020-12-01 | 2022-03-29 | 攀钢集团西昌钢钒有限公司 | Method for controlling LF refining end point temperature |
| CN112522466A (en) * | 2020-12-01 | 2021-03-19 | 攀钢集团西昌钢钒有限公司 | Method for determining optimal desulfurization parameters |
| CN113192568B (en) * | 2021-03-15 | 2023-07-25 | 山东钢铁股份有限公司 | Method and system for forecasting desulfurization end point of refining furnace |
| CN116469481B (en) * | 2023-06-19 | 2023-08-29 | 苏州方兴信息技术有限公司 | LF refined molten steel composition forecasting method based on XGBoost algorithm |
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