CN106906351A - A kind of board briquette forecasting model and optimum furnace method - Google Patents
A kind of board briquette forecasting model and optimum furnace method Download PDFInfo
- Publication number
- CN106906351A CN106906351A CN201710075219.1A CN201710075219A CN106906351A CN 106906351 A CN106906351 A CN 106906351A CN 201710075219 A CN201710075219 A CN 201710075219A CN 106906351 A CN106906351 A CN 106906351A
- Authority
- CN
- China
- Prior art keywords
- furnace
- temperature
- slab
- blank
- heating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
-
- 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
- C21D11/00—Process control or regulation for heat treatments
Landscapes
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Thermal Sciences (AREA)
- Crystallography & Structural Chemistry (AREA)
- Mechanical Engineering (AREA)
- Materials Engineering (AREA)
- Metallurgy (AREA)
- Organic Chemistry (AREA)
- Tunnel Furnaces (AREA)
Abstract
The invention discloses a kind of board briquette forecasting model and optimum furnace method, belong to metallurgical automation process control field.The optimum furnace method includes：The minimum object function controlled as optimum furnace of area is enclosed using Preform surface temperature and furnace superintendent, using the maximum difference and furnace temperature bound of the maximum section temperature difference of blank, maximum programming rate, blank tapping temperature and target tapping temperature as constraints, optimizing is carried out using heuristic power genetic algorithm, output furnace temperature Optimal Distribution curve so that the energy consumption of heating furnace reaches minimum.The board briquette forecasting model, water beam is taken into account to the influence that slab exchanges heat so that slab heating curve and the measured value that model is calculated are more nearly, and can play accurate temperature forecast, improves the heating quality of slab.
Description
Technical field
The present invention relates to metallurgical automation process control field, specifically, more particularly to a kind of board briquette forecasting model
And optimum furnace method.
Background technology
With China market expanding economy, iron and steel enterprise is in the national economic development in occupation of more and more important position
Put.Steel and iron industry is the rich and influential family of energy resource consumption, therefore launches to seem more and more important to the energy-saving and emission-reduction work of steel and iron industry.Plus
One of production line key equipment in Re Lushi steel and iron industries steel rolling mill, is main energy consumption equipment in steel and iron industry, improves heating
Furnace thermal efficiency, reduction furnace energy consumption can be greatly reduced the energy resource consumption of steel and iron industry.
The main target of Heating Furnace Control is the real-time control to furnace temperature, and it is mainly according to slab in heating furnace heated
Profiling temperatures in journey are controlled.The purpose that control is optimized to furnace temperature is with rational heating cycle heating plate
Base, makes it that the Temperature Distribution required by rolling is reached after coming out of the stove, so as to improve steel product quality.But, due in actual production
The real-time detection of board briquette cannot be realized in journey, furnace temperature of the accurate board briquette forecasting model for heating furnace is hence set up
Real-time control seems particularly important.With the innovation of technology, the theoretical research on slab heating temperature control emerges in an endless stream, stove
The theoretical research that temperature is automatically controlled also has reached at a relatively high degree, but the effect applied in actual production process is not very
It is preferable.Many stoves possess second control system, but because the result that two grades of Mathematical Modelings are calculated is inaccurate, cause very great Cheng
Degree is still to carry out Control for Kiln Temperature manually by operative employee.
At present, to the research method of slab temperature prediction model in heating furnace, mainly using computational fluid dynamics
CFD is carried out the method for analogue simulation to the temperature field in heating furnace and is entered by the basic mathematical physical equation to Slab Heat
The method of row numerical solution.CFD approach is mainly used in description heating temperature field in furnace, the steady-state process of velocity field, and numerical value side
Method can be used for predicting heating process of the slab in heating furnace.The existing temperature forecast mould to slab heating process in heating furnace
The research of type, generally have ignored the influence that water beam is distributed to board briquette so that the slab that temperature prediction model is obtained is in heating
Heating curve in stove has relatively large deviation with measured value.
The content of the invention
In view of the foregoing, it is an object to a kind of board briquette forecasting model and optimum furnace method are provided, it is right
Furnace temperature optimizes control in heating furnace, obtains optimal furnace curve, improves operation efficiency, reducing energy consumption, and in plate
In base temperature prediction model, it is contemplated that influence of the water beam to heating of plate blank, with the heating of plate blank process heating curve for solving to obtain
The problem larger with measured value deviation.
To achieve these goals, the present invention uses following technical scheme：
Optimum furnace method of the present invention, including：Input blank and heating furnace relevant parameter are minimum as stove using energy consumption
The object function of warm optimal control, and determine constraints, optimizing is carried out using heuristic power genetic algorithm, export optimal stove
Warm distribution curve.
Preferably, object function encloses area minimum by Preform surface temperature and heating furnace furnace superintendent,
In formula, J is object function；L is heating furnace furnace superintendent, m；TsL () is surface temperature of the blank at a length of l of heating-furnace
Degree, DEG C；
Wherein, constraints includes：The maximum programming rate of blank, the maximum section temperature difference of blank, blank tapping temperature
With the maximum difference and furnace temperature upper and lower limit of target tapping temperature；
Blank and heating furnace relevant parameter include：Blank material specification, charging temperature, tapping temperature, rhythm of production, heating
Stove furnace superintendent, Shui Liang positions, thermocouple location, heat time.
Preferably, heuristic power genetic algorithm is comprised the following steps：
(1) initial population is generated at random；
(2) judge whether to meet iteration stopping condition, if meeting, export furnace；If it is not satisfied, then carrying out step
(3)；
(3) stochastical sampling chooses two parents；
(4) it is heuristic to intersect two filial generations of generation；
(5) furnace curve is calculated；
(6) blank heat flow density and temperature field are calculated；
(7) evaluate, father and son's competition sequence generates new population；
(8) new individuality is generated based on fitness difference duplicate checking；
(9) judge whether to reach the iterations of setting, if not up to setting iterations, is back to step (2), if
Setting iterations is reached, is then directly exported.
A kind of board briquette forecasting model based on above-mentioned optimum furnace method of the present invention, including,
It is distributed with heating furnace in walking beam, and walking beam and is provided with water beam,
Computational fields are chosen, and be included in for water beam by computational fields, it is contemplated that the influence that water beam heats up to slab in stove；
Set up the two-dimension unsteady heat conduction differential equation of slab inside heat conduction；
Boundary condition is set：
Slab upper surface uses comprehensive heat flow density boundary condition,
Third boundary condition is used at slab lower surface and water beam shoe contact position,
Slab lower surface other positions use comprehensive heat flow density boundary condition,
In formula,It is heat flow density, W/m2；σ is Boltzmann constant, 5.67 × 10-8W/(m2·K4)；φCFIt is blanket heat
Absorptivity；H is the coefficient of heat transfer, W/ (m2·K)；TfIt is furnace temperature, K；TsIt is steel slab surface temperature, K；TwIt is the water temperature in water beam, K；
The computational fields left and right sides uses adiabatic boundary condition；
Solve equation.
Further, when carrying out mesh generation to computational fields, to net at slab lower surface and water beam shoe contact position
Lattice are encrypted, the proportional loose grid of other positions.
Further, the fictitious emissivity method of upper lower hearth is measured by black box experiment, and slab is contacted with water beam
When the coefficient of heat transfer.
Compared with prior art, the present invention has advantages below and beneficial effect：
First, the present invention encloses the minimum target letter controlled as optimum furnace of area using Preform surface temperature and furnace superintendent
Number, with the maximum difference of the maximum section temperature difference of blank, maximum programming rate, blank tapping temperature and target tapping temperature and
Furnace temperature bound carries out optimizing as constraints using heuristic power genetic algorithm, finally obtains optimal furnace bent
Line so that the energy consumption of heating furnace reaches minimum.
2nd, board briquette forecasting model of the present invention based on optimum furnace method, water beam is examined the influence that slab exchanges heat
Including considering so that slab heating curve and the measured value that model is calculated are more nearly, and can play accurate temperature forecast,
Improve the heating quality of slab.
Brief description of the drawings
Fig. 1 is optimum furnace method of the present invention；
Fig. 2 is the FB(flow block) of heuristic power genetic algorithm of the present invention；
Fig. 3 is board briquette forecasting model schematic diagram of the present invention；
Specific embodiment
In conjunction with accompanying drawing, the present invention is described further, more understands in order to the present invention and should be readily appreciated that.
Fig. 1 is optimum furnace method of the present invention.As shown in figure 1, optimum furnace method includes：
Input blank and heating furnace relevant parameter, using the minimum object function as optimum furnace control of energy consumption, and determine
Constraints, optimizing is carried out using heuristic power genetic algorithm, exports optimal furnace curve.
Wherein, blank and heating furnace relevant parameter include：Blank material specification, charging temperature, tapping temperature, production section
Play, heating furnace furnace superintendent, Shui Liang positions, thermocouple location, the heat time；Object function is Preform surface temperature and heating furnace furnace superintendent
Enclosed area is minimum；Constraints includes：The maximum programming rate of blank, the maximum section temperature difference of blank, blank tapping temperature
With the maximum difference and furnace temperature upper and lower limit of target tapping temperature；
Specifically it is expressed as：
In formula, J is object function；L is heating furnace furnace superintendent, unit m；TsL () is table of the blank at a length of l of heating-furnace
Face temperature, unit DEG C；
Constraints is expressed as：
a)
b)Ts(t)-Tc(t)≤ΔTs(max)
c)|Ts(tn)-Ta|≤ΔT
d)Tfmin(ti)≤Tf(ti)≤Tfmax(ti)
In formula, TsIt is Preform surface temperature, unit DEG C；TcIt is blank central temperature, unit DEG C；TfIt is furnace temperature, unit DEG C；t
The heat time for being blank in stove, unit s；It is the maximum programming rate of blank, unit DEG C/s；ΔTs(max)It is base
The maximum section temperature difference of material, unit DEG C；TaIt is blank target tapping temperature, unit DEG C；TfminAnd TfmaxThe respectively lower limit of furnace temperature
And ceiling temperature, unit DEG C；Δ T is the maximum difference of blank tapping temperature and target tapping temperature, unit DEG C；
Wherein, blank surface temperature at this moment is calculated by the Preform surface temperature of previous moment with the furnace temperature at this moment
Obtain, be specifically expressed as：
Ts(t+ Δs t)=F (Ts(t),Tf(t+Δt))
Fig. 2 is the FB(flow block) of heuristic power genetic algorithm of the present invention.As shown in Fig. 2 heuristic power genetic algorithm
Comprise the following steps：
(1) initial population is generated at random；
(2) judge whether to meet iteration stopping condition, if meeting, export furnace；If it is not satisfied, then carrying out step
(3)；
(3) stochastical sampling chooses two parents；
(4) it is heuristic to intersect two filial generations of generation；
(5) furnace curve is calculated；
(6) blank heat flow density and temperature field are calculated；
(7) evaluate, father and son's competition sequence generates new population；
(8) new individuality is generated based on fitness difference duplicate checking；
(9) judge whether to reach the iterations of setting, if not up to setting iterations, is back to step (2), if
Setting iterations is reached, is then directly exported.
Embodiment one
With reference to domestic certain rolling line 2# bar plate heating stove case history of steel mill 2250, optimum furnace method of the present invention is done
Further illustrate.Wherein, 2250 rolling line 2# bar plate heating stoves are five sections of walking beam reheating furnaces, are divided into heat-recovery section, preheating section, add
One section of heat, heating two sections and soaking zone.
The parameters such as material, specification, charging temperature, the rhythm of production of slab produced according to the heating furnace are used as optimum furnace
Input item, optimum furnace curve is used as output item.The area that furnace superintendent and steel slab surface temperature are surrounded is minimum as target
Function, by the maximum section temperature difference, maximum programming rate, tapping temperature and target tapping temperature maximum difference and furnace temperature bound
As constraints, oven temperature profile is optimized using heuristic power operator.
As a example by heating two kinds of slabs of different-thickness, slab thickness is respectively 180mm and 230mm, sets identical plate
Base charging temperature is 20 DEG C, and slab tapping temperature is 1250 DEG C, obtains each heating furnace section Temperature Distribution as follows：
Heat exchanging process of the slab in heating furnace is the factor such as furnace gases flowing towa taud and combustion heat release, radiant heat exchange
Coupling, because furnace flame temperature and furnace gas temperature are very high, therefore radiant heat exchange is occupied an leading position.For being heated in stove
Slab, except being influenceed by furnace temperature, water beam can also produce influence to its temperature-rise period on it.Therefore, in plate of the present invention
In base temperature prediction model, also water beam is taken into account with the heat exchange at slab contact position.Fig. 3 is slab temperature of the present invention
Degree forecasting model schematic diagram.It is distributed with heating furnace in walking beam, and walking beam and is provided with water beam 1, the computational fields of selection will
Water beam 1 is included in, it is contemplated that the influence that water beam heats up to slab in stove.
Shown in Fig. 3, the upper and lower surface of slab is heated in stove, and at the position of water beam 1 in walking beam, slab is subject to two
The transmission of aspect heat.A part is come from when being contacted with water beam 1, the heat exchange between slab and water beam 1.Another part is slab
When departing from water beam 1, the heat convection between slab and air.
The board briquette forecasting model, employs the Mathematical Modeling of two-dimension unsteady state, makes simplification to model, it is assumed that bar
Part is：
A) furnace temperature is distributed in piecewise linearity, is the function along furnace superintendent directional spreding；
B) furnace gas and slab heat convection and radiation heat transfer are comprehensive heat flow density boundary condition；
C) in heat exchanging process, the influence of slab iron scale is ignored；
D) slab uniform motion in stove in heating process.
The two-dimension unsteady heat conduction differential equation of slab inside heat conduction is set up, is specifically expressed as：
In formula, ρ represents the density of slab, unit K g/m3；Cp represents the specific heat of slab, unit J/ (kg DEG C)；T is represented
Board briquette, unit DEG C；T represents time, unit s；λ represents slab thermal conductivity factor, unit W/ (m DEG C).
It is as follows that boundary condition is set：
A) assume that upper and lower burner hearth fictitious emissivity method is identical in same stove section；
B) slab upper surface uses comprehensive heat flow density boundary condition,
C) third boundary condition is used at slab lower surface and water beam shoe contact position,
Slab lower surface other positions use comprehensive heat flow density boundary condition,
In formula,It is heat flow density, unit W/m2；σ is Boltzmann constant, 5.67 × 10-8W/(m2·K4)；φCFFor total
Include thermal absorptivity；H is the coefficient of heat transfer, unit W/ (m2·K)；TfIt is furnace temperature, unit K；TsIt is steel slab surface temperature, unit K；Tw
It is the water temperature in water beam, unit K；
D) the computational fields left and right sides uses adiabatic boundary condition.
When carrying out mesh generation to computational fields, grid is added at slab lower surface and water beam shoe contact position
It is close, the proportional loose grid of other positions.
The two-dimentional Heat Conduction Differential Equations set up to mesh point, using Iterative alternate differential reduced equation, become to three
Angular moment battle array is simultaneously solved with chasing method.
Heat exchange when being contacted with water beam 1 by the fictitious emissivity method of the upper lower hearth of black box experiment measurement, and slab
Coefficient h.
Embodiment two
Board briquette forecasting model of the present invention is done with reference to domestic certain rolling line 2# heating furnaces case history of steel mill 2250
Further illustrate.2250 rolling line 2# heating furnaces be five-part form walking beam reheating furnace, be divided into heat-recovery section, preheating section, heating one section,
Heating two sections and soaking zone.
Heating furnace effective length is 59m, and in preheating section and bringing-up section distribution 6 fixed beams and 4 walking beams, soaking zone is solid
Determine beam invariable number, but there is dislocation 42cm in Shui Liang positions.When computational fields are chosen, fully take into account water beam and slab is added
The influence of heat, have selected the position before and after soaking zone all comprising water beam as computational fields.
In computational fields mesh generation, having using closeer mesh generation at the position of water beam 1, other positions are used
Proportional density is divided.Two-dimentional Heat Conduction Differential Equations are set up to each control volume using volume control technique, using alternately implicit
The method of difference simplifies the differential equation, it is met positive triangular matrix, is solved using TDMA methods.
When boundary condition is processed, slab upper surface is using comprehensive heat flow density boundary condition, slab lower surface and Shui Liang
Third boundary condition is used at shoe contact position, slab lower surface other positions use comprehensive heat flow density boundary condition,
The left and right sides uses adiabatic boundary condition.Due to being walking beam furnace, it is all that walking beam accounts for whole stepping with slab time of contact
The 1/4 of phase, so the heat flow density in walking beam position is made up of two parts, a part is plate when slab is contacted with walking beam
Heat exchange between base and water beam, heat exchange when another part is slab disengaging walking beam and between furnace gas.
Black box 2 is installed in milling train side, the blanket heat of upper and lower burner hearth is obtained using black box experiment and by data processing
Coefficient of heat transfer when absorptivity and slab are contacted with water beam.In order to obtain accurate parameter value, during experimental designs, root
According to the position distribution and the position distribution of thermocouple of water beam, 21 points are taken on slab as testing site, respectively A1-
A6, B1-B6, C1-C7.Wherein, A, B, C represent water beam and thermocouple location distribution respectively, 1. represent different respectively to 6.
Measuring point depth.
The preferred embodiments of the present invention are the foregoing is only, is not intended to limit the invention, for those skilled in the art
For member, the present invention can have various modifications and variations.All any modifications within the spirit and principles in the present invention, made,
Equivalent, improvement etc., should be included within the scope of the present invention.
Claims (8)
1. a kind of optimum furnace method, it is characterised in that including：Input blank and heating furnace relevant parameter, with the minimum work of energy consumption
It is the object function of optimum furnace control, and determines constraints, optimizing is carried out using heuristic power genetic algorithm, exports optimal
Furnace curve.
2. optimum furnace method according to claim 1, it is characterised in that the object function be Preform surface temperature with
Heating furnace furnace superintendent encloses area minimum,
In formula, J is object function；L is heating furnace furnace superintendent, m；TsL () is surface temperature of the blank at a length of l of heating-furnace,
℃。
3. optimum furnace method according to claim 1, it is characterised in that the constraints includes：The maximum of blank
Programming rate, the maximum section temperature difference, blank tapping temperature and target tapping temperature of blank maximum difference and furnace temperature it is upper and lower
Limit.
4. optimum furnace method according to claim 1, it is characterised in that the heuristic power genetic algorithm includes following
Step：
(1) initial population is generated at random；
(2) judge whether to meet iteration stopping condition, if meeting, export furnace；If it is not satisfied, then carrying out step (3)；
(3) stochastical sampling chooses two parents；
(4) it is heuristic to intersect two filial generations of generation；
(5) furnace curve is calculated；
(6) blank heat flow density and temperature field are calculated；
(7) evaluate, father and son's competition sequence generates new population；
(8) new individuality is generated based on fitness difference duplicate checking；
(9) judge whether to reach the iterations of setting, if not up to setting iterations, is back to the step (2), if
Setting iterations is reached, is then directly exported.
5. optimum furnace method according to claim 1, it is characterised in that the blank and heating furnace relevant parameter bag
Include：Blank material specification, charging temperature, tapping temperature, rhythm of production, heating furnace furnace superintendent, Shui Liang positions, thermocouple location, plus
The hot time.
6. a kind of board briquette forecasting model based on optimum furnace method described in claim 1, is distributed with work in heating furnace
Dynamic beam, is provided with water beam in the walking beam, it is characterised in that
Computational fields are chosen, be included in for the water beam by the computational fields；
Set up the two-dimension unsteady heat conduction differential equation of slab inside heat conduction；
Boundary condition is set：
Slab upper surface uses comprehensive heat flow density boundary condition,
Third boundary condition is used at slab lower surface and water beam shoe contact position,Slab lower surface
Other positions use comprehensive heat flow density boundary condition,
In formula,It is heat flow density, W/m2；σ is Boltzmann constant, 5.67 × 10-8W/(m2·K4)；φCFTo sum up heat absorption
Rate；H is the coefficient of heat transfer, W/ (m2·K)；TfIt is furnace temperature, K；TsIt is steel slab surface temperature, K；TwIt is the water temperature in water beam, K；
The computational fields left and right sides uses adiabatic boundary condition；
Solve equation.
7. board briquette forecasting model according to claim 6, it is characterised in that mesh generation is carried out to the computational fields
When, grid is encrypted at slab lower surface with water beam shoe contact position, the proportional loose grid of other positions.
8. board briquette forecasting model according to claim 6, it is characterised in that upper and lower stove is measured by black box experiment
The fictitious emissivity method of thorax, and the coefficient of heat transfer of slab when being contacted with water beam.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710075219.1A CN106906351B (en) | 2017-02-10 | 2017-02-10 | A kind of board briquette forecasting model and optimum furnace method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710075219.1A CN106906351B (en) | 2017-02-10 | 2017-02-10 | A kind of board briquette forecasting model and optimum furnace method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106906351A true CN106906351A (en) | 2017-06-30 |
| CN106906351B CN106906351B (en) | 2019-04-12 |
Family
ID=59208451
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710075219.1A Active CN106906351B (en) | 2017-02-10 | 2017-02-10 | A kind of board briquette forecasting model and optimum furnace method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106906351B (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108681794A (en) * | 2018-05-21 | 2018-10-19 | 山东钢铁集团日照有限公司 | A method of obtaining the optimal heating curve of mild steel |
| CN109926675A (en) * | 2019-03-29 | 2019-06-25 | 安徽双桦热交换***有限公司 | A kind of NB continous way soldering core heating means of Soldering Technology of Automobile Radiators |
| CN110231840A (en) * | 2019-06-05 | 2019-09-13 | 重庆赛迪热工环保工程技术有限公司 | The control method of the black print temperature difference of steel billet water beam in a kind of walking beam heating furnace furnace |
| CN110438318A (en) * | 2019-07-22 | 2019-11-12 | 中南大学 | A kind of large-scale vertical glowing furnace low energy consumption steepest method for controlling temperature rise and system |
| CN112632856A (en) * | 2020-12-21 | 2021-04-09 | 江苏警官学院 | Conveyor belt speed and temperature control method of reflow furnace |
| CN113191080A (en) * | 2021-04-26 | 2021-07-30 | 辽宁省交通高等专科学校 | Heating furnace billet temperature field prediction model optimization method based on HMPSO algorithm |
| CN115011786A (en) * | 2022-04-19 | 2022-09-06 | 北京科技大学 | Furnace temperature optimization method and device for dynamically sensing working condition of heating furnace |
| CN115065710A (en) * | 2022-04-29 | 2022-09-16 | 燕山大学 | Heating furnace wisdom control by temperature change PC end and remote cloud system of observing and controling of removal end |
| CN115096106A (en) * | 2022-07-01 | 2022-09-23 | 燕山大学 | Multi-target progressive optimization and early warning method for heating furnace |
| CN115109918A (en) * | 2022-05-13 | 2022-09-27 | 燕山大学 | Furnace temperature regulation and control method based on double-coupling target heating curve of heating furnace |
| CN115121631A (en) * | 2022-05-13 | 2022-09-30 | 燕山大学 | Temperature control method based on heating furnace blank temperature and furnace temperature collaborative pre-regulation partition decoupling |
| CN115828624A (en) * | 2022-12-21 | 2023-03-21 | 北京科技大学 | Accurate prediction method for plate blank heating temperature based on black box experiment optimization |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103697712A (en) * | 2012-09-27 | 2014-04-02 | 上海梅山钢铁股份有限公司 | Method for dynamically controlling furnace temperature of heating furnace based on time sensitivity |
| CN104498702A (en) * | 2014-09-03 | 2015-04-08 | 周玉杰 | Stepping heating furnace and use method thereof |
-
2017
- 2017-02-10 CN CN201710075219.1A patent/CN106906351B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103697712A (en) * | 2012-09-27 | 2014-04-02 | 上海梅山钢铁股份有限公司 | Method for dynamically controlling furnace temperature of heating furnace based on time sensitivity |
| CN104498702A (en) * | 2014-09-03 | 2015-04-08 | 周玉杰 | Stepping heating furnace and use method thereof |
Non-Patent Citations (1)
| Title |
|---|
| 宁树实等: "钢铁生产中的加热炉优化调度算法研究", 《控制与决策》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108681794A (en) * | 2018-05-21 | 2018-10-19 | 山东钢铁集团日照有限公司 | A method of obtaining the optimal heating curve of mild steel |
| CN109926675A (en) * | 2019-03-29 | 2019-06-25 | 安徽双桦热交换***有限公司 | A kind of NB continous way soldering core heating means of Soldering Technology of Automobile Radiators |
| CN110231840A (en) * | 2019-06-05 | 2019-09-13 | 重庆赛迪热工环保工程技术有限公司 | The control method of the black print temperature difference of steel billet water beam in a kind of walking beam heating furnace furnace |
| CN110438318A (en) * | 2019-07-22 | 2019-11-12 | 中南大学 | A kind of large-scale vertical glowing furnace low energy consumption steepest method for controlling temperature rise and system |
| CN112632856A (en) * | 2020-12-21 | 2021-04-09 | 江苏警官学院 | Conveyor belt speed and temperature control method of reflow furnace |
| CN112632856B (en) * | 2020-12-21 | 2023-09-19 | 江苏警官学院 | Method for controlling speed and temperature of conveyor belt of reflow oven |
| CN113191080A (en) * | 2021-04-26 | 2021-07-30 | 辽宁省交通高等专科学校 | Heating furnace billet temperature field prediction model optimization method based on HMPSO algorithm |
| CN113191080B (en) * | 2021-04-26 | 2024-02-09 | 辽宁省交通高等专科学校 | Heating furnace billet temperature field prediction model optimization method based on HMPSO algorithm |
| CN115011786A (en) * | 2022-04-19 | 2022-09-06 | 北京科技大学 | Furnace temperature optimization method and device for dynamically sensing working condition of heating furnace |
| CN115065710A (en) * | 2022-04-29 | 2022-09-16 | 燕山大学 | Heating furnace wisdom control by temperature change PC end and remote cloud system of observing and controling of removal end |
| CN115065710B (en) * | 2022-04-29 | 2023-07-25 | 燕山大学 | Intelligent temperature control PC end and mobile end remote cloud measurement and control system of heating furnace |
| CN115109918B (en) * | 2022-05-13 | 2023-06-13 | 燕山大学 | Furnace temperature regulation and control method based on double-coupling target heating curve of heating furnace |
| CN115121631A (en) * | 2022-05-13 | 2022-09-30 | 燕山大学 | Temperature control method based on heating furnace blank temperature and furnace temperature collaborative pre-regulation partition decoupling |
| CN115109918A (en) * | 2022-05-13 | 2022-09-27 | 燕山大学 | Furnace temperature regulation and control method based on double-coupling target heating curve of heating furnace |
| CN115096106A (en) * | 2022-07-01 | 2022-09-23 | 燕山大学 | Multi-target progressive optimization and early warning method for heating furnace |
| CN115828624A (en) * | 2022-12-21 | 2023-03-21 | 北京科技大学 | Accurate prediction method for plate blank heating temperature based on black box experiment optimization |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106906351B (en) | 2019-04-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106906351B (en) | A kind of board briquette forecasting model and optimum furnace method | |
| Jang et al. | Optimization of a slab heating pattern for minimum energy consumption in a walking-beam type reheating furnace | |
| CN106906350B (en) | A kind of H profile steel base heating process temperature distribution calculation method | |
| Tang et al. | CFD modeling and validation of a dynamic slab heating process in an industrial walking beam reheating furnace | |
| Abbassi et al. | Numerical simulation and experimental analysis of an industrial glass melting furnace | |
| Tan et al. | Zone modelling of the thermal performances of a large-scale bloom reheating furnace | |
| CN104073623B (en) | A kind of roller bottom type annealing furnace temperature-controlled process | |
| Tucker et al. | Identifying and quantifying energy savings on fired plant using low cost modelling techniques | |
| CN105586476A (en) | High-strength steel quenching technology optimizing method based on numerical simulation technique | |
| CN110348174A (en) | A kind of steel billet temperature calculation method of heating furnace | |
| Hsieh et al. | A numerical study of skid marks on the slabs in a walking-beam type slab reheating furnace | |
| CN109182731A (en) | A kind of high-carbon-chromium bearing steel continuous casting billet method for heating and controlling based on temperature-time control | |
| Refaey et al. | Influence of fuel distribution and heat transfer on energy consumption in tunnel kilns | |
| Ji et al. | Modeling and determination of total heat exchange factor of regenerative reheating furnace based on instrumented slab trials | |
| Shen et al. | Soft sensor modeling of blast furnace wall temperature based on temporal–spatial dimensional finite-element extrapolation | |
| Lee et al. | Optimum residence time for steel productivity and energy saving in a hot rolled reheating furnace | |
| CN106874591B (en) | A kind of calculation method of square billet heating process temperature distribution | |
| CN102994730B (en) | Method for tracking temperature of steel billet in heating furnace | |
| Alves et al. | Improving the sustainability of porcelain tile manufacture by flowsheet simulation | |
| Refaey | Mathematical model to analyze the heat transfer in tunnel kilns for burning of ceramics | |
| CN106282533A (en) | A kind of temperature-controlled process to be rolled of heating furnace | |
| CN102184271B (en) | Method for designing vulcanization furnace based on heat flow coupling analysis technique | |
| Oumarou et al. | A dynamic process model for predicting the performance of horizontal anode baking furnaces | |
| Hu et al. | Development of transient mathematical models for a large-scale reheating furnace using hybrid zone-CFD methods | |
| CN105838869B (en) | A kind of steel plate quenching stove heat technique on-line tuning method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |