CN113671918A - Intelligent stable smelting control method for ladle refining furnace - Google Patents
Intelligent stable smelting control method for ladle refining furnace Download PDFInfo
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- 238000007670 refining Methods 0.000 title claims abstract description 171
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000003723 Smelting Methods 0.000 title claims abstract description 33
- 229910000831 Steel Inorganic materials 0.000 claims abstract description 60
- 239000010959 steel Substances 0.000 claims abstract description 60
- 238000004364 calculation method Methods 0.000 claims abstract description 26
- 230000008569 process Effects 0.000 claims abstract description 15
- 230000003044 adaptive effect Effects 0.000 claims abstract description 14
- 238000011156 evaluation Methods 0.000 claims abstract description 6
- 238000004519 manufacturing process Methods 0.000 claims description 13
- 230000009467 reduction Effects 0.000 claims description 12
- 239000007789 gas Substances 0.000 claims description 10
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 8
- 238000007664 blowing Methods 0.000 claims description 8
- 239000001301 oxygen Substances 0.000 claims description 8
- 229910052760 oxygen Inorganic materials 0.000 claims description 8
- 229910045601 alloy Inorganic materials 0.000 claims description 5
- 239000000956 alloy Substances 0.000 claims description 5
- 239000000203 mixture Substances 0.000 claims description 5
- 230000004048 modification Effects 0.000 claims description 4
- 238000012986 modification Methods 0.000 claims description 4
- 239000000463 material Substances 0.000 claims description 3
- 238000011161 development Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 abstract description 2
- 239000002893 slag Substances 0.000 abstract description 2
- 230000008859 change Effects 0.000 description 3
- 230000003749 cleanliness Effects 0.000 description 3
- XKRFYHLGVUSROY-UHFFFAOYSA-N Argon Chemical compound [Ar] XKRFYHLGVUSROY-UHFFFAOYSA-N 0.000 description 2
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- -1 but not limited to Inorganic materials 0.000 description 2
- 229910000838 Al alloy Inorganic materials 0.000 description 1
- 229910000599 Cr alloy Inorganic materials 0.000 description 1
- 229910000881 Cu alloy Inorganic materials 0.000 description 1
- 229910001209 Low-carbon steel Inorganic materials 0.000 description 1
- 229910000914 Mn alloy Inorganic materials 0.000 description 1
- 229910001182 Mo alloy Inorganic materials 0.000 description 1
- 229910001257 Nb alloy Inorganic materials 0.000 description 1
- 229910000990 Ni alloy Inorganic materials 0.000 description 1
- 229910000676 Si alloy Inorganic materials 0.000 description 1
- 229910000756 V alloy Inorganic materials 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000005275 alloying Methods 0.000 description 1
- 229910052786 argon Inorganic materials 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000011651 chromium Substances 0.000 description 1
- 239000000788 chromium alloy Substances 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000006477 desulfuration reaction Methods 0.000 description 1
- 230000023556 desulfurization Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 239000011572 manganese Substances 0.000 description 1
- PXHVJJICTQNCMI-UHFFFAOYSA-N nickel Substances [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 230000033764 rhythmic process Effects 0.000 description 1
- 239000002436 steel type Substances 0.000 description 1
- 238000009628 steelmaking Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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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
- C21C7/0075—Treating in a ladle furnace, e.g. up-/reheating of molten steel within the ladle
-
- 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
- C21C2300/00—Process aspects
- C21C2300/06—Modeling of the process, e.g. for control purposes; CII
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32368—Quality control
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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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P10/00—Technologies related to metal processing
- Y02P10/25—Process efficiency
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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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention relates to a method for intelligently and stably controlling smelting of a vacuum refining furnace, which comprises the following steps of firstly, determining a refining target parameter; secondly, taking the refining target parameter as an adaptive factor; then, the self-adaptive factor is used as a condition, the optimal refining outbound parameter meeting the condition is found out from the vacuum refining historical control database, and in order to obtain the optimal refining outbound parameter, the refining inbound parameter is continuously determined from the vacuum refining historical control database according to the specified range of the self-adaptive factor; carrying out evaluation calculation according to the refining inbound parameters and the refining outbound parameters, and determining the actual refining control operation; predicting the refining end point slag component according to the actual refining control operation; the invention realizes the intelligent and stable smelting control of the vacuum refining furnace according to the set refining target parameters and the evaluation and calculation refining actual control operation, and provides a key technical support for the intelligent development of the steel process technology.
Description
Technical Field
The invention relates to the field of refining furnace control, in particular to an intelligent and stable smelting control method for a ladle refining furnace.
Background
In the whole steel-making section, a ladle refining furnace (LadleFurnace) has the functions of temperature rise, desulfurization, deoxidation and alloying, and is suitable for most steel types except ultra-low carbon steel, such as medium-thick plates, constructional deformed steel bars and the like, because the equipment is relatively simple and the smelting cost is low;
although the existing LF refining model can guide smelting operation to a certain extent, the guide of specific process parameters in the whole refining process, the control of the whole refining and smelting time and the corresponding refining end point hit rate need to be further improved.
Disclosure of Invention
In order to overcome the defects in the background art, the invention provides an intelligent and stable smelting control method for a ladle refining furnace, which is used for guiding specific process parameters in the whole refining process, controlling the whole refining and smelting time and further improving the corresponding refining end point hit rate.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for intelligently and stably controlling smelting of a ladle refining furnace comprises the following steps:
s1, determining a refining target parameter;
s2, taking the refining target parameter as a self-adaptive factor;
s3, with the self-adaptive factor as a condition, finding out the optimal refining outbound parameter meeting the condition from a ladle refining history control database;
s4, in order to obtain the optimal refining outbound parameter, continuously determining a refining inbound parameter from a ladle refining historical control database according to a specified range of a self-adaptive factor;
s5, performing evaluation calculation according to the refining station-entering parameters and the refining station-exiting parameters to determine the actual refining control operation;
and S6, performing intelligent stable smelting control on the molten steel according to the control target value of each parameter and the actual control operation.
Further, the refining target parameters in the step S1 include a refining molten steel target component, a target molten steel temperature, a molten steel target total oxygen t.o content, a target smelting duration, and a target production cost.
Further, the S3 specifically includes: and (3) taking the adaptive factor as a condition, finding out N furnace data meeting the adaptive factor condition in a ladle refining history control database by adopting an iteration + adaptive calculation mode, stopping iterative calculation when N is more than or equal to 20, and outputting the corresponding optimal refining outbound parameter meeting the adaptive factor condition according to the found N furnace data.
Further, if the N furnace data can not be matched according to the conditions, increasing the upper limit and the lower limit of the value range of the refining target parameter by 10% under the condition of ensuring the consistency of the target components of the molten steel, and performing first iteration; if the N furnace data are not met, increasing the upper limit and the lower limit of the value range of the refining target parameter by 12% for second iteration; if the N furnace data are not met, increasing the upper limit and the lower limit of the value range of the refining target parameter by 15% for third iteration; and if the N furnace data are not met, increasing the upper limit and the lower limit of the value range of the refining target parameter by 20% and carrying out fourth iteration.
Further, in the steps S3 and S4, the refining inbound/outbound parameters are evaluated to obtain a subtraction score corresponding to each parameter, and the subtraction score is a product value of the absolute value obtained by subtracting the refining inbound/outbound parameter and the refining target parameter and a corresponding weight and coefficient;
the formula is as follows:
in the above formula, SeiA score reduction representing a refinery entry/exit parameter;
Qeirepresenting refining in/out parameters, obtainable from a ladle refining furnace historical control database;
Q* eirepresenting a predetermined refining target parameter;
gamma represents the weight of the refining in/out parameter;
η represents the coefficient of the refining in/out parameter;
i is 1 to n, and n is the number of refining in/out parameters.
Further, the calculation formula of the control target value of each parameter is as follows:
in the formula STotIndicating control target values of the respective parameters, STotThe set of the refinery entry/exit parameters with the highest score is the best refinery entry/exit parameter instance that best matches the target parameters determined in advance.
Further, the refining outbound parameters comprise molten steel components, molten steel temperature, T.O content, smelting duration and production cost during refining outbound.
Further, the refining station entering parameters comprise molten steel components, molten steel temperature and T.O content when the refining station enters.
Further, the step S5 refining actual control operation specifically includes: alloy and auxiliary material adding time and adding amount in the refining process, a vacuum furnace pressure drop curve, ladle bottom blowing gas parameters, lifting gas flow, an inclusion modification route in steel, refining wire feeding types and speed, and the whole refining time length accumulated by waiting time, vacuum time and soft blowing time.
A molten steel produced by the method of any one of claims 1 to 9.
Compared with the prior art, the invention has the beneficial effects that:
according to the method for the intelligent stable smelting control of the ladle refining furnace, the intelligent stable smelting control of the vacuum refining furnace is realized according to the set refining target parameters and the evaluation calculation refining actual control operation, the specific process parameters in the whole refining process can be guided and the whole refining and smelting time can be controlled, the corresponding refining end point hit rate is further improved, and a key technical support is provided for the intelligent development of the steel process technology.
Drawings
FIG. 1 is a flow chart of the method for intelligently and stably controlling smelting of a ladle refining furnace according to the invention.
Detailed Description
The following detailed description of the present invention will be made with reference to the accompanying drawings.
As shown in figure 1, the method for intelligently and stably controlling smelting of the ladle refining furnace comprises the following steps:
s1, determining a refining target parameter;
s2, taking the refining target parameter as a self-adaptive factor;
s3, with the self-adaptive factor as a condition, finding out the optimal refining outbound parameter meeting the condition from a ladle refining history control database;
s4, in order to obtain the optimal refining outbound parameter, continuously determining a refining inbound parameter from a ladle refining historical control database according to a specified range of a self-adaptive factor;
s5, performing evaluation calculation according to the refining station-entering parameters and the refining station-exiting parameters to determine the actual refining control operation;
and S6, performing intelligent stable smelting control on the molten steel according to the control target value and the actual control operation.
The specific operation steps are as follows:
A01. determining the target components of the refined molten steel, the target molten steel temperature, the target total oxygen T.O content of the molten steel, the target smelting duration and the target production cost according to the process requirements;
A02. taking the target component of the refined molten steel, the target molten steel temperature, the target total oxygen T.O content of the molten steel, the target smelting duration and the target production cost as self-adaptive factors, and taking the self-adaptive factors as a basis for judging the optimal refining outbound parameters in sequence;
A03. and (3) taking the adaptive factor as a condition, finding out 20 furnace data meeting the adaptive factor condition in a ladle refining furnace historical control database by adopting an iteration + adaptive calculation mode, namely stopping iterative calculation, outputting the optimal refining outbound parameter corresponding to the condition according to the found data, and if the data meeting the adaptive factor condition is not found, outputting the optimal refining outbound parameter corresponding to the adaptive factor condition according to the data obtained after 4 times of iterative calculation.
If 20 furnaces of data cannot be matched according to the conditions, increasing the upper limit and the lower limit of the value range of the refining target parameter by 10% for first iteration under the condition of ensuring the consistency of the target components of the molten steel; if the data of 20 furnaces are not met, increasing the upper limit and the lower limit of the value range of the refining target parameter by 12 percent for second iteration; if the data of 20 furnaces are not met, increasing the upper limit and the lower limit of the value range of the refining target parameter by 15% for third iteration; and if the data of 20 furnaces are not met, increasing the upper limit and the lower limit of the value range of the refining target parameter by 20% and carrying out fourth iteration.
A04. After 20 furnace data in the optimal refining outbound parameter range are determined, the refining outbound parameters on the selected optimal refining path are subjected to scoring calculation through iterative calculation, the score reduction calculation is firstly carried out, the refining outbound parameters are evaluated to obtain the score reduction corresponding to each parameter, and the score reduction is carried out by subtracting the refining inbound parameters from the refining target parameters and taking absolute values, and then multiplying the absolute values by corresponding weights and coefficients. The formula is as follows:
in the above formula, SeiA score reduction representing a refined outbound parameter;
Qeirepresenting a refining outbound parameter, obtainable from a ladle refining furnace historical control database;
Q* eirepresenting a predetermined refining target parameter;
gamma represents the weight of the refining outbound parameter;
η represents the coefficient of the refining outbound parameter;
and i is 1, 2, 3, 4 or 5.
Wherein the weights are for prioritizing the calculation of the different refining outbound parameters, and the coefficients are for reducing the calculated difference due to the magnitude coefficients of the different refining outbound parameters.
Taking the calculation of the refined outbound score reduction as an example, the rest target parameters are scored according to the above process. When i is 1, Se1The method comprises the following steps of (1) expressing the division reduction of a target component parameter of molten steel discharged from refining; when i is 2, Se2The division of the target temperature parameter of the molten steel discharged from the refining station is represented; when i takes 3, Se3The division of the target total oxygen content parameter of the molten steel which is refined and discharged is represented; when i is 4, Se4When the molten steel is refined and discharged for target smeltingDivision by subtraction of the long parameter; when i is 5, Se5And (4) division reduction of the target production cost parameter of the molten steel discharged from the refining station.
A05. Calculating the score of the refined outbound parameter according to the score reduction of the refined outbound parameter obtained by the calculation of the step A04, wherein the calculation formula is as follows:
wherein S isTotThe score of the refining outbound parameter is shown, where n is 5.
As can be seen from the above calculation, the score calculation uses the full score of 100, and the score of the refined outbound parameter is finally obtained by subtracting the subtracted score of the refined outbound parameter from the full score of 100. The refining outbound parameter with the highest score is the best refining outbound parameter example which is most matched with the target parameter determined in advance, and the next refining inbound parameter is searched by using the example.
A06. In the 20 furnace data, finding out the inbound parameter group capable of obtaining the optimal refining outbound parameter in A05, still adopting the same calculation mode as the steps A04-A05 to perform the subtraction calculation on the molten steel component, the molten steel temperature and the T.O content in the refining inbound, and controlling the molten steel component, the molten steel temperature and the T.O content of the refining inbound according to the corresponding parameters to finally obtain the score of each refining inbound parameter, wherein n in the formula is 3. And the refining inbound parameter with the highest score is the optimal refining inbound parameter.
A07. After the refining outbound parameters and the refining inbound parameters are obtained, according to the matched 20-furnace data, the alloy and auxiliary material adding time and adding amount, a vacuum furnace pressure drop curve, a ladle bottom blowing gas parameter, a lifting gas flow, an inclusion modification route in steel, the type and speed of a refining feeding line, the whole refining duration accumulated by the waiting time + the vacuum time + the soft blowing time are evaluated and calculated to obtain the actual control operation specific numerical values of all the parameters;
A08. predicting the refining end point slag component according to the concrete numerical value of the actual refining control operation;
A09. and finally, performing intelligent stable smelting control on the molten steel according to the control target value and the actual control variable to finally obtain the molten steel with excellent molten steel components, proper molten steel temperature, qualified molten steel total oxygen content [ T.O ], reasonable smelting duration in a refining stage and lowest production cost of a refining process.
Preferably, the refining outbound parameters in the embodiment of the invention comprise molten steel components, molten steel temperature, T.O content, smelting duration and production cost during refining outbound; the refining station-entering parameters comprise molten steel components, molten steel temperature and T.O content when refining station-entering is carried out.
Preferably, the actual refining control operation of each parameter in the embodiment of the present invention includes the whole refining duration of the alloy (including, but not limited to, manganese alloy, silicon alloy, aluminum alloy, chromium alloy, niobium alloy, copper alloy, vanadium alloy, molybdenum alloy, nickel alloy, etc.) addition timing and addition amount of the refining process, vacuum furnace pressure drop curve, ladle bottom blowing gas parameters (including, but not limited to, argon/nitrogen, gas flow rate, gas time), lift gas flow rate, inclusion modification route in steel (including, but not limited to, composition change of inclusions, shape change of inclusions and change of number of inclusions), waiting time + vacuum time + soft blowing time accumulation.
Preferably, step a05 in the embodiment of the present invention specifically includes the following:
a051, Table 1 is a rule database protocol table composed according to molten steel components, temperature, production rhythm, cleanliness and production cost.
TABLE 1 rule database calculation protocol Table
A052. As can be seen from Table 1, the data therein are roughly classified into 4 priorities, the highest priority being the composition of molten steel, among which [ C ] is]、[P]、[S]Is the element component which is controlled most strictly; [ Si ]]、[Mn]、[Ti]、[Als]、[Ni]、[Cr]、[V]The alloy components are equal as the second priority because the process is adjustable; the temperature of the molten steel is also taken into consideration as a second priority; the third priority is the smelting duration and the total oxygen [ T.O ] of the molten steel.]And molten steel is total oxygen [ T.O.]Is an important index of the cleanliness of the molten steel and is used as a reference of the cleanliness of the molten steel; the production costs are also taken into account in the rules database, where priority is placed on the end.
A053. The rule database utilizes a rule database protocol table, iterative computation is carried out on the computation items in the computation protocol table by utilizing component weight, component scoring proportion and computation time specific gravity difference, the component weight and the component scoring proportion are adjusted by a self-learning method, and the optimal scoring computation rule closest to field production data is gradually realized by continuous self-learning and self-correction.
The above embodiments are implemented on the premise of the technical solution of the present invention, and detailed embodiments and specific operation procedures are given, but the scope of the present invention is not limited to the above embodiments. The methods used in the above examples are conventional methods unless otherwise specified.
Claims (10)
1. The method for intelligently and stably controlling smelting of the ladle refining furnace is characterized by comprising the following steps of:
s1, determining a refining target parameter;
s2, taking the refining target parameter as a self-adaptive factor;
s3, with the self-adaptive factor as a condition, finding out the optimal refining outbound parameter meeting the condition from a ladle refining history control database;
s4, in order to obtain the optimal refining outbound parameter, continuously determining a refining inbound parameter from a ladle refining historical control database according to a specified range of a self-adaptive factor;
s5, performing evaluation calculation according to the refining station-entering parameters and the refining station-exiting parameters to determine the actual refining control operation;
and S6, performing intelligent stable smelting control on the molten steel according to the control target value of each parameter and the actual control operation.
2. The method of claim 1, wherein the refining target parameters of step S1 include a refined molten steel target composition, a target molten steel temperature, a molten steel target total oxygen t.o content, a target smelting time, and a target production cost.
3. The method for intelligent stable smelting control of the ladle refining furnace according to claim 1, wherein the S3 specifically comprises: and (3) taking the adaptive factor as a condition, finding out N furnace data meeting the adaptive factor condition in a ladle refining history control database by adopting an iteration + adaptive calculation mode, stopping iterative calculation when N is more than or equal to 20, and outputting the corresponding optimal refining outbound parameter meeting the adaptive factor condition according to the found N furnace data.
4. The method for the intelligent and stable smelting control of the ladle refining furnace according to claim 3, wherein if N furnace data cannot be matched according to the conditions, the upper and lower limits of the value range of the refining target parameters are increased by 10% for the first iteration under the condition of ensuring the consistency of the target components of the molten steel; if the N furnace data are not met, increasing the upper limit and the lower limit of the value range of the refining target parameter by 12% for second iteration; if the N furnace data are not met, increasing the upper limit and the lower limit of the value range of the refining target parameter by 15% for third iteration; and if the N furnace data are not met, increasing the upper limit and the lower limit of the value range of the refining target parameter by 20% and carrying out fourth iteration.
5. The method of claim 1, wherein the refining inbound/outbound parameters are evaluated in steps S3 and S4 to obtain a score reduction corresponding to each parameter, the score reduction being the product of the absolute value of the difference between the refining inbound/outbound parameter and the refining target parameter and the corresponding weight and coefficient;
the formula is as follows:
in the above formula, SeiA score reduction representing a refinery entry/exit parameter;
Qeirepresenting refining in/out parameters, obtainable from a ladle refining furnace historical control database;
Q* eirepresenting a predetermined refining target parameter;
gamma represents the weight of the refining in/out parameter;
η represents the coefficient of the refining in/out parameter;
i is 1 to n, and n is the number of refining in/out parameters.
6. The method of claim 5, wherein the control target values for the parameters are calculated by the formula:
in the formula STotIndicating control target values of the respective parameters, STotThe set of the refinery entry/exit parameters with the highest score is the best refinery entry/exit parameter instance that best matches the target parameters determined in advance.
7. The method of claim 1, wherein the refining outbound parameters include molten steel composition, molten steel temperature, T.O content, duration of smelting, and production cost at the time of refining outbound.
8. The method of claim 1, wherein the refining inbound parameters include molten steel composition, molten steel temperature, and T.O content at the time of the refining inbound.
9. The method for intelligent and stable smelting control of the ladle refining furnace according to claim 1, wherein the actual refining control operation of step S5 is specifically: alloy and auxiliary material adding time and adding amount in the refining process, a vacuum furnace pressure drop curve, ladle bottom blowing gas parameters, lifting gas flow, an inclusion modification route in steel, refining wire feeding types and speed, and the whole refining time length accumulated by waiting time, vacuum time and soft blowing time.
10. A molten steel, characterized in that it is produced by the method according to any one of claims 1 to 9.
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| CN202110960278.3A CN113671918A (en) | 2021-08-20 | 2021-08-20 | Intelligent stable smelting control method for ladle refining furnace |
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| CN202110960278.3A CN113671918A (en) | 2021-08-20 | 2021-08-20 | Intelligent stable smelting control method for ladle refining furnace |
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