CN110283986A - A kind of three based on machine learning section continuous stepped heating furnace Optimization of Energy Saving method - Google Patents
A kind of three based on machine learning section continuous stepped heating furnace Optimization of Energy Saving method Download PDFInfo
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- CN110283986A CN110283986A CN201910526161.7A CN201910526161A CN110283986A CN 110283986 A CN110283986 A CN 110283986A CN 201910526161 A CN201910526161 A CN 201910526161A CN 110283986 A CN110283986 A CN 110283986A
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- 238000010438 heat treatment Methods 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000010801 machine learning Methods 0.000 title claims abstract description 18
- 238000005457 optimization Methods 0.000 title claims abstract description 16
- 238000004519 manufacturing process Methods 0.000 claims abstract description 121
- 229910000831 Steel Inorganic materials 0.000 claims abstract description 60
- 239000010959 steel Substances 0.000 claims abstract description 60
- 238000010079 rubber tapping Methods 0.000 claims abstract description 39
- 230000008569 process Effects 0.000 claims abstract description 7
- 238000005096 rolling process Methods 0.000 claims description 17
- 238000002791 soaking Methods 0.000 claims description 7
- 239000000446 fuel Substances 0.000 claims description 6
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- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000004062 sedimentation Methods 0.000 abstract description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 10
- 229910052742 iron Inorganic materials 0.000 description 5
- 238000005265 energy consumption Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000002485 combustion reaction Methods 0.000 description 2
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- 238000002474 experimental method Methods 0.000 description 2
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- 239000002994 raw material Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
- C21D8/00—Modifying the physical properties by deformation combined with, or followed by, heat treatment
- C21D8/02—Modifying the physical properties by deformation combined with, or followed by, heat treatment during manufacturing of plates or strips
- C21D8/0205—Modifying the physical properties by deformation combined with, or followed by, heat treatment during manufacturing of plates or strips of ferrous alloys
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
- C21D9/00—Heat treatment, e.g. annealing, hardening, quenching or tempering, adapted for particular articles; Furnaces therefor
- C21D9/70—Furnaces for ingots, i.e. soaking pits
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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Abstract
The invention discloses a kind of three based on machine learning section continuous stepped heating furnace Optimization of Energy Saving methods, time dimension is introduced into operating experience, it takes out the rule that thermal efficiency of heating furnace is decayed at any time and solves the problems, such as personnel experience deviation, which comprises S10: establishing operation instruction model；The operation instruction model includes production target and operation index；S20: study production process, the production process includes overall process of the slab from steel loading to tapping, and the record of production is changed into relation chain as production target-operation index-gas unit consumption amount；S30: the average thermal efficiency of the daily steel billet of coming out of the stove of study；S40: operation instruction；The production target currently produced is obtained, the identical history record of production of production target is matched in the operation instruction model, obtains corresponding operation index and gas unit consumption amount.It the present invention is based on artificial intelligence technology, solves to burn steel experience sedimentation problem with artificial intelligence approach, realizes on-line study, in real time application.
Description
Technical field
The present invention relates to production operation technical fields more particularly to a kind of three based on machine learning section continuous stepped to add
Hot stove Optimization of Energy Saving method.
Background technique
In Chinese large iron and steel enterprise, hot-continuous-rolling strip steel production capacity in occupation of leading position, but as hot-continuous-rolling strip steel factory dragon
The heating furnace of head region is then energy consumption rich and influential family, and 1580 production line of Baosteel, Anshan iron and steel plant 2150,1780 production lines, Wuhan Iron and Steel Plant 2250 are raw
Producing line has been all made of three sections of large-scale continuous walking beam heating furnaces, although they are in burner form and arrange upper different from,
But its energy consumption ratio still very big in occupation of product cost, therefore there is this energy saving requirement of drop in this kind of enterprise.
The heating furnace of these large iron and steel enterprises uses more advanced three-level computer management, i.e. level-one mostly at present
Basic automation systems, second level mathematical model automatic combustion control system, three-level be rolling scaduled and logistics system, but because by raw material
The limitation of the problems such as situation, rolling scaduled, automatic combustion control system do not give full play of its section that should be played
Energy consumption reduction effect, the still more serious experience for relying on skilled worker of the operation of heating furnace, new skilled worker need the experience accumulation ability of many years
It controls the accurate operation of complex condition effectively to guarantee heating quality and rolling quality, how to precipitate the operation warp of veteran worker
It tests, is the problem and managing risk that enterprise faces.
Meanwhile heating furnace is repaired after furnace with the increase for using the time, soaking zone iron scale gradually increases, water beam pipe in furnace
The trend that the factors such as the increase that binder breakage falls off gradually decreased thermal efficiency of heating furnace once, generally quarterly with regard to needing to do
Black box experiment detection thermal efficiency of heating furnace state, but because black box experimental cost is higher, it is low that black box experiment wish is done by enterprise, leads to
It is often done by skilled worker's experience and substantially judges adjusting parameter, this, which also gives, is precisely controlled heating furnace burning steel quality, burns steel speed and energy conservation drop
Consumption brings very big obstacle.It is therefore desirable to propose a kind of solution precipitating high professional qualification personnel operating experience efficiently, inexpensive
Technical method, solve the problems, such as optimization production, reduce energy consumption.
Summary of the invention
The present invention is in order to solve the above technical problems, propose that a kind of three based on machine learning section continuous stepped heating furnace is excellent
Change power-economizing method, time dimension is introduced into operating experience, takes out the rule person of solving that thermal efficiency of heating furnace is decayed at any time
The problem of work experience deviation.
The described method includes:
S10: operation instruction model is established；The operation instruction model includes production target and operation index；
The production target includes: steel grade, specification, rolling specs, the calorific value of gas, slab charging temperature, slab of slab
Tapping temperature target value, blooming mill finally throw steel temperature (RDT) together；
The operation index include: total time inside furnace, preheating section time inside furnace, bringing-up section time inside furnace, bringing-up section temperature,
Soaking section temperature, air-fuel ratio, furnace pressure, bringing-up section gas flow, soaking zone gas flow, the air-fuel changed according to calorific value
Than；
S20: study production process, the production process includes overall process of the slab from steel loading to tapping, by the production
Index is matched to the identical history record of production of production target in the operation instruction model, and records the production process
Operation index and gas unit consumption amount；If matching is less than setting up new production target, and record the operation of the production process
The record of production is changed into relation chain as production target-operation index-gas unit consumption amount, phase by index and gas unit consumption amount
Same production target may correspond to multiple groups operation index and gas unit consumption amount；
In learning process, date of last survey, tapping date, date difference away from date of last survey etc. can also be learnt.
S30: the average thermal efficiency of the daily steel billet of coming out of the stove of study, the thermal efficiency are defined as " 1 degree of steel billet temperature of the every promotion of steel per ton
The gas volume of consumption and the product of calorific value of gas "；
S40: operation instruction；The production target currently produced is obtained, production is matched in the operation instruction model and refers to
The identical history record of production is marked, corresponding operation index and gas unit consumption amount are obtained；If the history record of production away from
From the time currently produced in preset time interval, then current production uses the operation index of the history record of production；If
The time that the described history record of production distance currently produces outside preset time interval, then according to the history record of production on the day of
The average thermal efficiency and the be averaged difference of the thermal efficiency of the previous day of current date the operation index is adjusted, recommend it is new plus
Hot arc gas flow；
New bringing-up section gas flow=original bringing-up section gas flow+gas flow adjustment amount；
Gas flow adjustment amount=poor thermal efficiency/today calorific value of gas * steel billet weight * (slab tapping temperature target value-plate
Base charging temperature)/bringing-up section time inside furnace.
Further, the method also includes:
S50: optimizing the operation instruction model, adjusts the slab tapping temperature target value in operation instruction model；
S51: the last of the blooming mill that acquisition currently produces throws steel temperature and slab tapping temperature target value together；
S52: calculate and throw steel temperature difference: the last of the blooming mill currently produced is thrown in steel temperature and operation instruction model together
The last of blooming mill for the history record of production for being matched to and using throws difference between steel temperature together；
Calculate slab tapping temperature target value difference: in the slab tapping temperature target value and operation instruction model currently produced
It is matched to and difference between the slab tapping temperature target value of the history record of production that uses；
S53: according to the slab in the throwing steel temperature difference and slab tapping temperature target value difference adjustment operation instruction model
Tapping temperature target value, unit adjustment amount=AVG (slab tapping temperature target value difference/throwing steel temperature difference).
Further, the method also includes:
S60: the production target and operation index that the operation instruction model learning currently produces.
Further, the production target is divided into level-one production target and second level production target；
The level-one production target includes: the steel grade of slab, specification, rolling specs, calorific value of gas；
The second level production target includes: that slab charging temperature, slab tapping temperature target value, blooming mill are finally thrown together
Steel temperature.
Further, when the S40 includes: matching, level-one production target and second level production target matched side entirely are first pressed
Formula matching, if matching less than, reduce matching condition, matched entirely by level-one production target, second level production target matching or phase
Neighbour's matching.
Further, the S20 includes:
S21: the corresponding operation index of same production target is ranked up from low to high according to gas unit consumption.
Further, when the S20 includes: study production process, rolling quality or heating quality life not up to standard are rejected
Produce record.
To meet the conditions such as rolling quality, heating quality, the following configurable item of addition is as additional optimizations condition:
1, each section of fire box temperature is in specified bound.
2, last steel temperature (RDT) and the target temperature deviation of throwing together of blooming mill is no more than 30 degree.
3, the lower limit of calorific value of gas does not learn lower than lower limit, does not provide Optimizing Suggestions.
By the above-mentioned description of this invention it is found that compared to the prior art, one kind proposed by the present invention is based on machine learning
Three sections of continuous stepped heating furnace Optimization of Energy Saving methods have the advantages that
1, it is based on artificial intelligence technology, is come out of the stove with slab specification, rolling specs, calorific value of gas, slab charging temperature, slab
It is classification dimension that temperature target target value, blooming mill finally throw steel temperature (RDT) together, does taxology to technical operation experience
It practises, solves to burn steel experience sedimentation problem with artificial intelligence approach, realize on-line study, in real time application；
2, time dimension is added in machine learning, using averagely the thermal efficiency, the operation for calculating different time pass through daily
The compensation in gas consumption amount is tested, solves the problems, such as that thermal efficiency of heating furnace gradually changes the adjustment of bring manufacturing parameter at any time；
3, using last steel temperature (RDT) and the target temperature deviation of throwing together of blooming mill as regularization condition adjustment tapping temperature
Degree, reaching real time calibration model accuracy rate reduces error.
Specific embodiment
In order to be clearer and more clear technical problems, technical solutions and advantages to be solved, tie below
Embodiment is closed, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only to explain
The present invention is not intended to limit the present invention.
Embodiment one:
Time dimension is introduced and is operated by a kind of three based on machine learning section continuous stepped heating furnace Optimization of Energy Saving method
Experience takes out the rule that thermal efficiency of heating furnace is decayed at any time and solves the problems, such as personnel experience deviation.
The described method includes:
S10: operation instruction model is established；The operation instruction model includes production target and operation index；
The production target includes: steel grade, specification, rolling specs, the calorific value of gas, slab charging temperature, slab of slab
Tapping temperature target value, blooming mill finally throw steel temperature (RDT) together；The production target is divided into level-one production target and two
Grade production target；Wherein, the level-one production target includes: the steel grade of slab, specification, rolling specs, calorific value of gas；Described two
Grade production target includes: that slab charging temperature, slab tapping temperature target value, blooming mill finally throw steel temperature together.
The operation index include: total time inside furnace, preheating section time inside furnace, bringing-up section time inside furnace, bringing-up section temperature,
Soaking section temperature, air-fuel ratio, furnace pressure, bringing-up section gas flow, soaking zone gas flow, the air-fuel changed according to calorific value
Than；
S20: study production process, the production process includes overall process of the slab from steel loading to tapping, before study, is rejected
Rolling quality or the heating quality record of production not up to standard.
The identical history record of production of production target is matched in the operation instruction model by the production target, and
Record the operation index and gas unit consumption amount of the production process；If matching is less than setting up new production target, and record
The operation index and gas unit consumption amount of the production process, i.e., change into production target-operation index-gas unit consumption for the record of production
Such relation chain is measured, identical production target may correspond to multiple groups operation index and gas unit consumption amount；
In learning process, date of last survey, tapping date, date difference away from date of last survey etc. can also be learnt.
S21: the corresponding operation index of same production target is ranked up from low to high according to gas unit consumption.
S30: the average thermal efficiency of the daily steel billet of coming out of the stove of study, the thermal efficiency are defined as " 1 degree of steel billet temperature of the every promotion of steel per ton
The gas volume of consumption and the product of calorific value of gas ", which is added to time dimension in machine learning, utilizes daily evenly heat
Efficiency calculates compensation of the operating experience of different time in gas consumption amount, solves thermal efficiency of heating furnace and gradually becomes at any time
Change bring manufacturing parameter and adjusts problem；
S40: operation instruction；The production target currently produced is obtained, production is matched in the operation instruction model and refers to
The identical history record of production is marked, corresponding operation index and gas unit consumption amount are obtained；
In the matching process, first by level-one production target and second level production target, matched mode is matched entirely, if matching
Less than, then matching condition is reduced, is matched entirely by level-one production target, the matching of second level production target or neighbor；
After being matched to, the operation index of acquisition and corresponding gas unit consumption amount may have multiple groups, can choose gas unit consumption most
One group low of operation index is as operation instruction, before production, it is also necessary to take into account that with the increase for using the time, furnace thermal efficiency
In gradually decreasing trend, if the time that the history record of production distance currently produces in preset time interval, than
In ten days, then current production uses the operation index of the history record of production；If the history record of production distance is current
The time of production outside preset time interval, such as ten beyond the highest heavens, then according on the day of the history record of production the average thermal efficiency with work as
The be averaged difference of the thermal efficiency of the previous day on preceding date is adjusted the operation index, recommends new bringing-up section gas flow, counts
It is as follows to calculate formula:
New bringing-up section gas flow=original bringing-up section gas flow+gas flow adjustment amount；
Gas flow adjustment amount=poor thermal efficiency/today calorific value of gas * steel billet weight * (slab tapping temperature target value-plate
Base charging temperature)/bringing-up section time inside furnace.
Embodiment two:
On the basis of example 1, the present embodiment joined the prioritization scheme of operation instruction model.
The method also includes:
S50: optimizing the operation instruction model, adjusts the slab tapping temperature target value in operation instruction model；
S51: the last of the blooming mill that acquisition currently produces throws steel temperature and slab tapping temperature target value together；
S52: calculate and throw steel temperature difference: the last of the blooming mill currently produced is thrown in steel temperature and operation instruction model together
The last of blooming mill for the history record of production for being matched to and using throws difference between steel temperature together；
Calculate slab tapping temperature target value difference: in the slab tapping temperature target value and operation instruction model currently produced
It is matched to and difference between the slab tapping temperature target value of the history record of production that uses；
S53: according to the slab in the throwing steel temperature difference and slab tapping temperature target value difference adjustment operation instruction model
Tapping temperature target value, unit adjustment amount=AVG (slab tapping temperature target value difference/throwing steel temperature difference).
By the above-mentioned description of this invention it is found that compared to the prior art, one kind proposed by the present invention is based on machine learning
Three sections of continuous stepped heating furnace Optimization of Energy Saving methods have the advantages that
1, it is based on artificial intelligence technology, is come out of the stove with slab specification, rolling specs, calorific value of gas, slab charging temperature, slab
It is classification dimension that temperature target target value, blooming mill finally throw steel temperature (RDT) together, does taxology to technical operation experience
It practises, solves to burn steel experience sedimentation problem with artificial intelligence approach, realize on-line study, in real time application；
2, time dimension is added in machine learning, using averagely the thermal efficiency, the operation for calculating different time pass through daily
The compensation in gas consumption amount is tested, solves the problems, such as that thermal efficiency of heating furnace gradually changes the adjustment of bring manufacturing parameter at any time；
3, using last steel temperature (RDT) and the target temperature deviation of throwing together of blooming mill as regularization condition adjustment tapping temperature
Degree, reaching real time calibration model accuracy rate reduces error.
The present invention is exemplarily described above, it is clear that present invention specific implementation is not subject to the restrictions described above,
As long as using the improvement for the various unsubstantialities that the inventive concept and technical scheme of the present invention carry out, or not improved this is sent out
Bright conception and technical scheme directly apply to other occasions, within the scope of the present invention.
Claims (7)
1. a kind of three based on machine learning section continuous stepped heating furnace Optimization of Energy Saving method characterized by comprising
S10: operation instruction model is established；The operation instruction model includes production target and operation index；
The production target includes: that steel grade, specification, rolling specs, calorific value of gas, slab charging temperature, the slab of slab are come out of the stove
Temperature target, blooming mill finally throw steel temperature together；
The operation index includes: total time inside furnace, preheating section time inside furnace, bringing-up section time inside furnace, bringing-up section temperature, soaking
Duan Wendu, air-fuel ratio, furnace pressure, bringing-up section gas flow, soaking zone gas flow, the air-fuel ratio changed according to calorific value；
S20: study production process, the production process includes overall process of the slab from steel loading to tapping, by the production target
It is matched to the identical history record of production of production target in the operation instruction model, and records the operation of the production process
Index and gas unit consumption amount, if matching is less than setting up new production target, and record the operation index of the production process
And gas unit consumption amount；
S30: the average thermal efficiency of the daily steel billet of coming out of the stove of study；
S40: operation instruction；The production target currently produced is obtained, is matched to production target phase in the operation instruction model
The same history record of production, obtains corresponding operation index and gas unit consumption amount；If the history record of production distance is worked as
The time of preceding production, then current production used the operation index of the history record of production in preset time interval；If described
Time for currently producing of history record of production distance be more than preset time interval, then according to flat on the day of the history record of production
The be averaged difference of the thermal efficiency of the previous day of the equal thermal efficiency and current date is adjusted the operation index, recommends new bringing-up section
Gas flow；
New bringing-up section gas flow=original bringing-up section gas flow+gas flow adjustment amount；
Gas flow adjustment amount=average poor thermal efficiency/today calorific value of gas * steel billet weight * (slab tapping temperature target value-plate
Base charging temperature)/bringing-up section time inside furnace.
2. a kind of three based on machine learning section continuous stepped heating furnace Optimization of Energy Saving method according to claim 1,
It is characterised by comprising:
S50: optimizing the operation instruction model, adjusts the slab tapping temperature target value in operation instruction model；
S51: the last of the blooming mill that acquisition currently produces throws steel temperature and slab tapping temperature target value together；
S52: calculate and throw steel temperature difference: last throw in steel temperature and operation instruction model together of the blooming mill currently produced matches
To and the last of the blooming mill of the history record of production that uses throw difference between steel temperature together；
It calculates slab tapping temperature target value difference: the slab tapping temperature target value currently produced and being matched in operation instruction model
To and the slab tapping temperature target value of the history record of production that uses between difference；
S53: it is come out of the stove according to the slab in the throwing steel temperature difference and slab tapping temperature target value difference adjustment operation instruction model
Temperature target, unit adjustment amount=AVG (slab tapping temperature target value difference/throwing steel temperature difference).
3. a kind of three based on machine learning section continuous stepped heating furnace Optimization of Energy Saving method according to claim 1,
It is characterised by comprising:
S60: the production target and operation index that the operation instruction model learning currently produces.
4. a kind of three based on machine learning section continuous stepped heating furnace Optimization of Energy Saving method according to claim 1,
It is characterized in that, the production target is divided into level-one production target and second level production target；
The level-one production target includes: the steel grade of slab, specification, rolling specs, calorific value of gas；
The second level production target includes: that slab charging temperature, slab tapping temperature target value, blooming mill finally throw steel temperature together
Degree.
5. a kind of three based on machine learning section continuous stepped heating furnace Optimization of Energy Saving method according to claim 4,
It is characterized in that, first by level-one production target and second level production target, matched mode is matched full when the S40 includes: matching,
If matching less than, matched entirely by level-one production target, second level production target matching or neighbor.
6. a kind of three based on machine learning section continuous stepped heating furnace Optimization of Energy Saving method according to claim 1,
It is characterized in that, the S20 includes:
S21: the corresponding operation index of same production target is ranked up from low to high according to gas unit consumption.
7. a kind of three based on machine learning section continuous stepped heating furnace Optimization of Energy Saving method according to claim 1,
It is characterized in that, rejecting rolling quality or the heating quality record of production not up to standard when the S20 includes: study production process.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112781183A (en) * | 2019-11-07 | 2021-05-11 | 厦门邑通软件科技有限公司 | Intelligent consumption reduction method and system for air conditioning system and air conditioner |
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