CN118308584A - Annealing furnace energy-saving cooling method based on performance prediction model - Google Patents
Annealing furnace energy-saving cooling method based on performance prediction model Download PDFInfo
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- 238000001816 cooling Methods 0.000 title claims abstract description 20
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- 238000000034 method Methods 0.000 claims abstract description 54
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- 229910000831 Steel Inorganic materials 0.000 claims abstract description 41
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- 238000005097 cold rolling Methods 0.000 claims abstract description 29
- 238000005098 hot rolling Methods 0.000 claims abstract description 21
- 238000005457 optimization Methods 0.000 claims abstract description 17
- 238000009628 steelmaking Methods 0.000 claims abstract description 17
- 238000004364 calculation method Methods 0.000 claims abstract description 14
- 238000005265 energy consumption Methods 0.000 claims abstract description 11
- 230000007246 mechanism Effects 0.000 claims abstract description 5
- 238000005096 rolling process Methods 0.000 claims description 16
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- 238000004519 manufacturing process Methods 0.000 description 12
- 238000013461 design Methods 0.000 description 4
- 238000012797 qualification Methods 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 238000011009 performance qualification Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
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- 229910001220 stainless steel Inorganic materials 0.000 description 3
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Abstract
An energy-saving cooling method of an annealing furnace based on a performance prediction model belongs to the field of control. Data such as steelmaking component data, hot rolling process actual results, cold rolling process set values, annealing furnace temperature required values, performance contract required values, specification parameters and the like are retrieved; substituting the data into a performance forecasting model to forecast the mechanical properties of the cold-rolled strip steel; comparing the mechanical property forecast value and the performance contract target value of the strip steel, and judging whether the temperature optimization of the annealing furnace can be performed; if the optimal calculation of the annealing furnace temperature can be performed, calculating an optimal value of the soaking temperature of the annealing furnace based on a performance prediction model, and producing according to the new soaking temperature; otherwise, the annealing furnace is produced according to the original soaking temperature. The soaking temperature range allowed by a metallurgical mechanism is optimized and adjusted based on a performance prediction model, a performance prediction value and contract performance requirements, so that the soaking temperature of annealing is reduced, and the energy consumption of unit products of the annealing furnace is reduced. Can be widely applied to the field of temperature control of cold-rolled strip steel annealing furnaces.
Description
Technical Field
The invention belongs to the field of temperature control, and particularly relates to an energy-saving cooling method of an annealing furnace in the cold rolling annealing process of a ferrous metallurgy cold-rolled product.
Background
Annealing is a metal heat treatment process in which the metal is heated to a temperature and held for a sufficient period of time and then cooled (typically slowly, sometimes with controlled cooling) at a suitable rate. The annealing aims to reduce hardness and improve machinability; residual stress is reduced, the size is stabilized, and the deformation and crack tendency are reduced; fine grains, adjust the structure, eliminate the defect of the structure, etc. For common cold rolled products, the soaking temperature of the annealing furnace is critical to the effect of the final properties of the product.
In the running process of the annealing furnace, the heat flow is promoted to keep a reciprocating circulation phenomenon in the furnace by a resistance wire heating mode at two sides, so that the temperature uniformity in the cranial cavity of the annealing furnace is ensured. Therefore, the higher the annealing soaking temperature is set, the higher the energy consumption for realizing the temperature stabilization in the furnace is.
Typically, the target value of the annealing temperature is entered into the computer by the process personnel before the cold rolling schedule is issued. Therefore, by setting a reasonable annealing temperature target value, the final performance of the product can be ensured, the energy consumption can be reduced, the production cost can be reduced, and the carbon emission can be reduced.
Therefore, the research on the energy-saving control problem of the annealing furnace has great significance on the process control of the cold rolling annealing process, and the related research is also very much.
For example, the utility model patent with the grant bulletin day of 2022, 7 months and 5 days and the grant bulletin number of CN 216891080U discloses a full-automatic energy-saving annealing furnace for stainless steel seamless steel pipes, which comprises an annealing furnace body, wherein the annealing furnace body comprises a control device, a sealing device, a burner, a rolling device, a recovery device and a blower, the outer surface of one side of the annealing furnace body is provided with the control device, the sealing devices are distributed on the left side and the right side of the annealing furnace body, the burners distributed in a parallel mode are arranged at the bottom of the control device, the burners are symmetrically distributed on two sides of the annealing furnace body, the rolling device is arranged at the bottom of the burner, and the rolling device is connected with the annealing furnace body in a penetrating manner. This full-automatic energy-saving annealing furnace for stainless steel seamless steel tube can preheat for this internal annealing furnace through recovery unit's setting, and sealing device's setting has avoided the heat to run off, has avoided current annealing furnace when using, and the leakproofness is not strong, causes the heat to run off easily, leads to stainless steel seamless steel tube not can fully heated problem. The furnace body structure is optimized, so that heat loss is avoided.
For another example, the invention patent with the publication number of CN 113293280B and the publication number of 2022, 6 and 17 discloses a continuous high-temperature hood-type annealing furnace for oriented silicon steel and an annealing process thereof, wherein the continuous high-temperature hood-type annealing furnace comprises a gradient kiln, and the gradient kiln comprises an A1 section annealing furnace, a B section annealing furnace, a C section annealing furnace, a D1 section annealing furnace, an A2 section annealing furnace and a D2 section annealing furnace; the A1 section annealing furnace, the B section annealing furnace, the C section annealing furnace, the D1 section annealing furnace, the A2 section annealing furnace and the D2 section annealing furnace are sequentially connected; wherein, the heights of the A1 section annealing furnace, the B section annealing furnace, the C section annealing furnace and the D1 section annealing furnace are different, the heights of the A1 section annealing furnace and the A2 section annealing furnace are the same, and the heights of the D1 section annealing furnace and the D2 section annealing furnace are the same. According to the technical scheme, according to the production process, the gradient hierarchical layout is adopted, so that the gradient hierarchical layout forms a barrier on the furnace top, the circulation speed of air flow is slowed down to achieve the purpose of energy conservation, meanwhile, the uniformity of the temperature in the furnace kiln of each process section is improved, and the quality of products can be improved.
However, the above related patent mainly shows the structural optimization aspect of the annealing furnace with respect to the energy-saving control of the annealing furnace, and little research is done on the process control of the annealing furnace, especially the temperature control aspect of the annealing furnace.
Disclosure of Invention
The invention aims to provide an energy-saving cooling method of an annealing furnace based on a performance prediction model. Extracting actual data of pre-working procedures such as steelmaking, hot rolling and the like and setting data of a cold rolling working procedure, substituting the actual data and the setting data into a performance prediction model, and judging whether the soaking temperature of the annealing furnace can be reduced by combining the relation between the predicted performance and a performance target value; the temperature of the soaking section of the annealing furnace is optimally adjusted, so that the soaking temperature of the annealing furnace is reduced, and the energy consumption of unit products of the annealing furnace is reduced, thereby realizing energy conservation and emission reduction of the annealing furnace.
The technical scheme of the invention is as follows: the energy-saving cooling method of the annealing furnace based on the performance prediction model is characterized by comprising the following steps of:
A) Before issuing a cold rolling plan, calling data including steelmaking component data, hot rolling process actual results, cold rolling process set values, annealing furnace temperature required values, performance contract required values and specification parameters;
b) Substituting the data into a performance forecasting model to forecast the mechanical properties of the cold-rolled strip steel;
C) Comparing the mechanical property forecast value and the performance contract target value of the strip steel, and judging whether the temperature optimization of the annealing furnace can be performed;
D) If the optimal calculation of the annealing furnace temperature can be performed, calculating an optimal value of the soaking temperature of the annealing furnace based on a performance prediction model, and producing according to the new soaking temperature; otherwise, the annealing furnace is produced according to the original soaking temperature;
E) And in the soaking temperature range allowed by the metallurgical mechanism, the soaking temperature of the annealing furnace is optimally adjusted based on the performance prediction model, the performance prediction value and the contract performance requirement, so that the annealing soaking temperature is reduced, and the energy consumption of unit products of the annealing furnace is reduced.
Specifically, the steelmaking component data in the step a at least comprises C, mn, P, nb, N, si, ti, B, al; the hot rolling process parameters at least comprise tapping temperature, rough rolling temperature, finish rolling temperature and coiling temperature; the technological parameters of the cold rolling at least comprise annealing speed, annealing soaking section temperature and flatness; the annealing furnace temperature requirement value comprises upper and lower limit thresholds of the soaking section temperature; the specification parameters comprise hot rolling rough rolling outlet thickness, hot rolling finish rolling outlet thickness and product finished product thickness; the performance contract requirement value at least comprises an upper limit and a lower limit of a performance index.
Specifically, in the step B, the predicted performance predicted by the performance prediction model at least includes yield strength and tensile strength.
Further, in the step C, when the predicted value of the mechanical property of the strip steel is compared with the target value of the property contract, the relationship between the predicted values of the indexes and the target value of the property is compared, and the down-regulation value of the temperature of the soaking section of the annealing furnace is calculated based on the relationship between the soaking temperature and the property of the annealing furnace.
Specifically, in the step D, the optimal value of the soaking temperature of the annealing furnace is calculated based on the performance prediction model, and if there is an optimal calculation of the soaking temperature of the annealing furnace based on a plurality of index performance prediction values, a value with a smaller optimizing range of the annealing temperature is selected.
Further, in the step D, when the optimized annealing furnace soaking temperature value is calculated, the new soaking temperature adjustment value of the annealing furnace must be within the allowable soaking temperature range of the process and equipment.
Specifically, in the step D, after the temperature optimization value of the soaking section of the annealing furnace is calculated, new predicted values of various performance indexes need to meet the contract performance requirements.
According to the energy-saving cooling method for the annealing furnace, provided by the invention, the energy consumption of unit products of the annealing furnace is reduced by optimizing and reducing the temperature of the soaking section of the annealing furnace meeting the technological requirements under the condition that the predicted performance meets the contractual requirements based on the performance prediction model.
Further, when judging whether the performance is qualified or not by using the predicted value of the mechanical performance, the judgment is performed by calculating the probability that the predicted value meets the upper and lower performance limit requirements, and the probability of + -2 sigma is selected to judge whether the predicted performance is qualified or not.
Compared with the prior art, the invention has the advantages that:
1. According to the technical scheme, before a cold rolling plan is issued, component data of steelmaking, hot rolling process data, cold rolling process setting data and the like are extracted and substituted into a performance prediction model to predict mechanical properties;
2. in a soaking temperature range allowed by a metallurgical mechanism, the annealing soaking temperature is reduced by optimizing and adjusting the temperature of a soaking section of the annealing furnace based on a performance prediction model, a performance prediction value and contract performance requirements, so that the energy consumption of unit products of the annealing furnace is reduced;
3. The technical scheme of the invention has wide covered production line and steel grade range, and can be widely applied to the field of temperature control of cold-rolled strip steel annealing furnaces.
Drawings
FIG. 1 is a schematic block diagram of a process flow of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and examples.
As shown in fig. 1, the invention provides an annealing furnace energy-saving cooling method based on a performance prediction model, which comprises the following steps:
1) Before issuing a cold rolling plan, component data of steelmaking, hot rolling process actual results, cold rolling process set values, annealing furnace temperature required values, performance contract required values, specification parameters and the like are called;
2) Substituting the data into a performance forecasting model to forecast the mechanical properties of the cold-rolled strip steel;
3) Calculating the deviation delta_yi between each performance predicted value P_Yi and the performance target value Yi_AIM, and then calculating the adjustment value delta_ssi=delta_Yi/k_ SSi of the annealing soaking temperature, if delta_ssi >0 exists, the optimization calculation of the annealing soaking temperature can be carried out;
4) If a plurality of delta_SSi >0 exist, respectively carrying out annealing temperature optimization calculation according to different performance target values Yi_AIM, and selecting soaking temperature with smaller optimization range;
5) Judging whether the selected optimized soaking temperature is smaller than the minimum soaking temperature value, and if so, taking the minimum soaking temperature value as a new soaking temperature;
5) Substituting the new soaking temperature into a performance prediction model, and producing the steel coil with qualified performance according to a new process;
6) And (3) producing the steel coil with the mechanical property forecast value of the strip steel being greater than the performance contract target value or the steel coil with unqualified performance after adjustment according to the original process.
Examples:
example 1
The case relates to a whole-flow production process of cold-rolled products in a certain steel mill, and steel coils produced in a certain production line are selected as case description.
The performance prediction model selected by the invention is as follows:
YP＝f1(C,Si,Mn,P,Al,B,N,Nb,Ti,FRN,RT,FT,CT,SS,Speed,TPM,Thick)；
TS＝f2(C,Si,Mn,P,Al,B,N,Nb,Ti,FRN,RT,FT,CT,SS,Speed,TPM,Thick)；
...
wherein YP and TS are yield strength and tensile strength respectively.
Wherein C, si, mn, P, al, B, N, nb and Ti respectively represent carbon element content, silicon element content, manganese element content, phosphorus element content, aluminum element content, boron element content, nitrogen element content, niobium element content and titanium element content.
FRN, RT, FT, CT, SS, speed, TPM, thick, which respectively show the furnace temperature, rough rolling temperature, finish rolling temperature, coiling temperature, annealing speed, flatness and finished product thickness.
In particular, the mechanical property prediction model has a variable of the annealing speed, but the speed is not set to a target value in the actual production process. In consideration of strong correlation between speed and thickness, the invention additionally constructs a relational expression of speed and thickness, calculates target speed by using target thickness, and then substitutes into a performance prediction model for calculation.
Speed＝f4(Thick)；
The actual steel-making chemical component content of a certain selected cold-rolled strip steel coil 1 is shown in the following table;
The actual data of the hot rolling process of the steel coil 1 are as follows;
| Tapping temperature | Rough rolling temperature | Finishing temperature | Coiling temperature |
| 1204 | 975 | 898 | 627 |
The design value and the process range of the cold rolling process of the steel coil 1 are as follows;
| TPM_AIM | SS_AIM | SS_MIN | SS_MAX |
| 1 | 780 | 750 | 830 |
the contract performance requirement range of the steel coil 1 is as follows;
| YP_MIN | YP_MAX | YP_AIM | TS_MIN | TS_MAX | TS_AIM |
| 145 | 190 | 175 | 290 | 330 | 310 |
The target thickness of the finished product is 1.5mm.
Actual data of a pre-process such as steelmaking and hot rolling and cold rolling process setting data are extracted and substituted into a performance prediction model, and the prediction result of the model is as follows:
| yield strength (specified type) _forecast value | Tensile Strength_forecast value |
| 170.36 | 306.51 |
Calculating the predicted deviation of yield strength and tensile strength:
Delta_YP＝171.36-175＝-4.64；
Dleta_TS＝307.52-310＝-3.49；
the adjustment amounts of the soaking temperatures are calculated based on the deviations of the predicted performances, respectively:
Delta_SS_yp＝delta_YP/kss_yp＝-4.64/-0.244＝19.02；
Delta_SS_ts＝delta_YP/kss_yp＝-3.49/-0.116＝30.09；
It should be noted that, due to the requirement of the on-site soaking temperature control system, the soaking temperature is set to be one grade at every 10 ℃, so that the calculated soaking temperature adjustment amount needs to be rounded according to 10 ℃, namely:
Delta_SS_yp＝20；
Delta_SS_ts＝30；
An optimization calculation of the annealing temperature may be performed, and a soaking temperature after optimization based on the yield strength calculation is selected, ss_n=ss_yp=780-20=760, and ss_n is within the annealing temperature process requirement range.
Substituting the new annealing temperature into a performance prediction model, and calculating to obtain new predicted performance as follows:
| yield strength (specified type) _forecast value | Tensile Strength_forecast value |
| 175.24 | 309.84 |
| Yield strength (specified type) _standard deviation | Tensile Strength-standard deviation |
| 5.65 | 4.82 |
Since there may be a certain deviation between the model predicted value and the actual value of the mechanical property, when the predicted value of the mechanical property is used to determine whether the performance is acceptable, whether the predicted performance is acceptable is not determined directly according to the upper and lower limits, but is determined by calculating the probability that the predicted value meets the upper and lower limit requirements of the performance, and the probability of + -2σ (i.e. 0.9545) is selected to determine whether the predicted performance is acceptable.
The yield formula is as follows:
CDF_Y＝CDF('NORMAL',(Y_MAX-P_Y)/Y_STD)-CDF('NORMAL',(Y_MIN-P_Y)/Y_STD)；
wherein y=yp, TS, EL;
Y_max, y_min, and p_ Y, Y _std represent the performance maximum value, the performance minimum value, the performance prediction value, and the performance prediction standard deviation, respectively.
Substituting the performance requirements and the performance predicted values, and calculating to obtain the qualification rate of different performance indexes:
CDF_YP＝0.9955043674；CDF_TS＝0.9999663286；
The CDF_YP and the CDF_TS are both larger than 0.9545, namely, the two performance indexes are qualified after the annealing temperature is optimally regulated, so that the annealing process is successfully optimized, and the production of the annealing process is carried out according to the new process.
Example 2
The actual steel-making chemical component content of a certain selected cold-rolled strip steel coil 1 is shown in the following table;
The actual data of the hot rolling process of the steel coil 1 are as follows;
the design value and the process range of the cold rolling process of the steel coil 1 are as follows;
| TPM_AIM | SS_AIM | SS_MIN | SS_MAX |
| 1 | 780 | 750 | 830 |
the contract performance requirement range of the steel coil 1 is as follows;
the target thickness of the finished product is 0.95mm.
Actual data of a pre-process such as steelmaking and hot rolling and cold rolling process setting data are extracted and substituted into a performance prediction model, and the prediction result of the model is as follows:
| yield strength (specified type) _forecast value | Tensile Strength_forecast value |
| 165.69 | 303.37 |
Calculating the predicted deviation of yield strength and tensile strength:
Delta_YP＝165.69-175＝-9.31；
Dleta_TS＝303.37-310＝-6.63；
the adjustment amounts of the soaking temperatures are calculated based on the deviations of the predicted performances, respectively:
Delta_SS_yp＝delta_YP/kss_yp＝-9.31/-0.244＝38.16；
Delta_SS_ts＝delta_YP/kss_yp＝-6.63/-0.116＝57.16；
It should be noted that, due to the requirement of the on-site soaking temperature control system, the soaking temperature is set to be one grade at every 10 ℃, so that the calculated soaking temperature adjustment amount needs to be rounded according to 10 ℃, namely:
Delta_SS_yp＝40；
Delta_SS_ts＝60；
An optimization calculation of the annealing temperature may be performed and a soaking temperature after optimization based on the yield strength calculation is selected, ss_n=ss_yp=780-40=740, but ss_n < ss_min, ss_min=750 is selected as the post-optimization annealing temperature.
Substituting the new annealing temperature into a performance prediction model, and calculating to obtain new predicted performance as follows:
| yield strength (specified type) _forecast value | Tensile Strength_forecast value |
| 173.01 | 306.85 |
| Yield strength (specified type) _standard deviation | Tensile Strength-standard deviation |
| 5.65 | 4.82 |
Substituting the performance requirements and the performance predicted values, and calculating to obtain the qualification rate of different performance indexes:
CDF_YP＝0.9986807898；CDF_TS＝0.9997629415；
The CDF_YP and the CDF_TS are both larger than 0.9545, namely, the two performance indexes are qualified after the annealing temperature is optimally regulated, so that the annealing process is successfully optimized, and the production of the annealing process is carried out according to the new process.
Example 3
The actual steel-making chemical component content of a certain selected cold-rolled strip steel coil 1 is shown in the following table;
| C_ACT | SI_ACT | MN_ACT | P_ACT | AL_ACT | B_ACT | N_ACT | NB_ACT | TI_ACT |
| 0.001 | 0.009 | 0.108 | 0.0124 | 0.0493 | 0.0004 | 0.001 | 0.0004 | 0.0518 |
The actual data of the hot rolling process of the steel coil 1 are as follows;
| Tapping temperature | Rough rolling temperature | Finishing temperature | Coiling temperature |
| 1211 | 971 | 903 | 698 |
The design value and the process range of the cold rolling process of the steel coil 1 are as follows;
| TPM_AIM | SS_AIM | SS_MIN | SS_MAX |
| 0.7 | 810 | 760 | 830 |
the contract performance requirement range of the steel coil 1 is as follows;
| YP_MIN | YP_MAX | YP_AIM | TS_MIN | TS_MAX | TS_AIM |
| 145 | 175 | 155 | 290 | 330 | 310 |
the final product thickness target value was 0.72mm.
Actual data of a pre-process such as steelmaking and hot rolling and cold rolling process setting data are extracted and substituted into a performance prediction model, and the prediction result of the model is as follows:
| yield strength (specified type) _forecast value | Tensile Strength_forecast value |
| 146.31 | 318.52 |
Calculating the predicted deviation of yield strength and tensile strength:
Delta_YP＝146.31-155＝-8.69；
Dleta_TS＝318.52-310＝8.52；
the adjustment amounts of the soaking temperatures are calculated based on the deviations of the predicted performances, respectively:
Delta_SS_yp＝delta_YP/kss_yp＝-8.69/-0.244＝35.61；
Delta_SS_ts＝delta_YP/kss_yp＝8.52/-0.116＝-73.45；
It should be noted that, due to the requirement of the on-site soaking temperature control system, the soaking temperature is set to be one grade at every 10 ℃, so that the calculated soaking temperature adjustment amount needs to be rounded according to 10 ℃, namely:
Delta_SS_yp＝40；
Delta_SS_ts＝-70；
an optimization calculation of the annealing temperature may be performed, and a soaking temperature after optimization based on the yield strength calculation is selected, ss_n=ss_yp=810-40=770, and ss_n is within the annealing temperature process requirement range.
Substituting the new annealing temperature into a performance prediction model, and calculating to obtain new predicted performance as follows:
| yield strength (specified type) _forecast value | Tensile Strength_forecast value |
| 156.07 | 323.16 |
| Yield strength (specified type) _standard deviation | Tensile Strength-standard deviation |
| 5.65 | 4.82 |
Substituting the performance requirements and the performance predicted values, and calculating to obtain the qualification rate of different performance indexes:
CDF_YP＝0.973944513；CDF_TS＝0.9220631932；
because CDF_TS is smaller than 0.9545, namely the predicted performance of the tensile strength after the optimal adjustment of the annealing temperature is unqualified, the annealing process is failed to be optimized, and the production of the annealing process is carried out according to the original process.
Example 4
The actual steel-making chemical component content of a certain selected cold-rolled strip steel coil 1 is shown in the following table;
| C_ACT | SI_ACT | MN_ACT | P_ACT | AL_ACT | B_ACT | N_ACT | NB_ACT | TI_ACT |
| 0.0018 | 0.003 | 0.139 | 0.0143 | 0.0647 | 0.0003 | 0.0025 | 0.0001 | 0.048 |
The actual data of the hot rolling process of the steel coil 1 are as follows;
| Tapping temperature | Rough rolling temperature | Finishing temperature | Coiling temperature |
| 1202 | 1057 | 922 | 682 |
The design value and the process range of the cold rolling process of the steel coil 1 are as follows;
| TPM_AIM | SS_AIM | SS_MIN | SS_MAX |
| 0.7 | 810 | 830 | 760 |
the contract performance requirement range of the steel coil 1 is as follows;
| YP_MIN | YP_MAX | YP_AIM | TS_MIN | TS_MAX | TS_AIM |
| 145 | 175 | 155 | 280 | 320 | 300 |
the final product thickness target value was 0.7mm.
Actual data of a pre-process such as steelmaking and hot rolling and cold rolling process setting data are extracted and substituted into a performance prediction model, and the prediction result of the model is as follows:
| yield strength (specified type) _forecast value | Tensile Strength_forecast value |
| 157.19 | 305.71 |
Calculating the predicted deviation of yield strength and tensile strength:
Delta_YP＝157.19-155＝2.19；
Dleta_TS＝305.71-300＝5.71；
the adjustment amounts of the soaking temperatures are calculated based on the deviations of the predicted performances, respectively:
Delta_SS_yp＝delta_YP/kss_yp＝2.19/-0.244＝-8.98；
Delta_SS_ts＝delta_YP/kss_yp＝5.71/-0.116＝-49.22；
the adjustment amount of the soaking temperature is calculated based on the yield strength and the tensile strength, respectively:
Delta_SS_yp＝-10；
Delta_SS_ts＝-50；
Because delta_SS_yp and delta_SS_ts are smaller than 0, optimal adjustment of annealing soaking temperature cannot be performed, annealing soaking temperature cannot be optimized, and annealing procedure production is performed according to the original technology.
Effect of the invention
According to the technical scheme, based on the performance prediction model, under the condition that the predicted performance meets the contract requirement, the temperature of the soaking section of the annealing furnace meeting the process requirement is optimally reduced, and the energy consumption of unit products of the annealing furnace is reduced. Through statistics, after a period of application, the cold rolling annealing temperature in the production process of the steel grade put into use is obviously reduced, and meanwhile, the performance qualification rate is not obviously changed.
The following table shows the main performance qualification rate and the annealing temperature average value of a typical cold-rolled product in the previous quarter of the application of the annealing furnace energy-saving cooling method based on the performance prediction model.
The following table shows the main performance yields and the annealing temperature averages of typical cold rolled products in the latter quarter of the application of the method described in this patent.
Comparing the conditions of the main performance qualification rate and the annealing temperature average value of typical cold-rolled products before and after the application of the method disclosed by the patent, the qualification rate of each main performance is not obviously changed after the application of the method, the average temperature average value of the cold-rolling annealing soaking section of each steel grade is averagely reduced by 15 ℃, and the energy-saving cooling of the cold-rolling annealing furnace is realized by reducing the cold-rolling annealing temperature.
According to the technical scheme, before a cold rolling plan is issued, data such as steelmaking component data, hot rolling process data, cold rolling process setting data and the like are extracted and substituted into a performance prediction model to predict mechanical properties; and in the soaking temperature range allowed by the metallurgical mechanism, the soaking temperature of the annealing furnace is optimally adjusted based on the performance prediction model, the performance prediction value and the contract performance requirement, so that the annealing soaking temperature is reduced, and the energy consumption of unit products of the annealing furnace is reduced. The control method covers a wide range of production lines and steel grades, and can be widely applied to the field of temperature control of cold-rolled strip steel annealing furnaces.
Claims (9)
1. An annealing furnace energy-saving cooling method based on a performance prediction model is characterized by comprising the following steps of:
A) Before issuing a cold rolling plan, calling data including steelmaking component data, hot rolling process actual results, cold rolling process set values, annealing furnace temperature required values, performance contract required values and specification parameters;
b) Substituting the data into a performance forecasting model to forecast the mechanical properties of the cold-rolled strip steel;
C) Comparing the mechanical property forecast value and the performance contract target value of the strip steel, and judging whether the temperature optimization of the annealing furnace can be performed;
D) If the optimal calculation of the annealing furnace temperature can be performed, calculating an optimal value of the soaking temperature of the annealing furnace based on a performance prediction model, and producing according to the new soaking temperature; otherwise, the annealing furnace is produced according to the original soaking temperature;
E) And in the soaking temperature range allowed by the metallurgical mechanism, the soaking temperature of the annealing furnace is optimally adjusted based on the performance prediction model, the performance prediction value and the contract performance requirement, so that the annealing soaking temperature is reduced, and the energy consumption of unit products of the annealing furnace is reduced.
2. The energy-saving cooling method for the annealing furnace based on the performance prediction model according to claim 1, wherein the steelmaking component data in the step A at least comprises C, mn, P, nb, N, si, ti, B, al;
the hot rolling process parameters at least comprise tapping temperature, rough rolling temperature, finish rolling temperature and coiling temperature;
the technological parameters of the cold rolling at least comprise annealing speed, annealing soaking section temperature and flatness;
the annealing furnace temperature requirement value comprises upper and lower limit thresholds of the soaking section temperature;
The specification parameters comprise hot rolling rough rolling outlet thickness, hot rolling finish rolling outlet thickness and product finished product thickness;
The performance contract requirement value at least comprises an upper limit and a lower limit of a performance index.
3. The energy-saving cooling method for the annealing furnace based on the performance prediction model according to claim 1, wherein in the step B, the predicted performance predicted by the performance prediction model at least comprises yield strength and tensile strength.
4. The energy-saving cooling method for the annealing furnace based on the performance prediction model according to claim 1, wherein in the step C, when the mechanical performance prediction value and the performance contract target value of the strip steel are compared, the relation between the predicted values of a plurality of indexes and the performance target value is compared, and the down regulation value of the temperature of the soaking section of the annealing furnace is calculated based on the relation between the soaking temperature and the performance of the annealing furnace.
5. The energy-saving cooling method of the annealing furnace based on the performance prediction model according to claim 1, wherein in the step D, the optimal value of the soaking temperature of the annealing furnace is calculated based on the performance prediction model, and if the optimal calculation of the soaking temperature of the annealing furnace is performed based on a plurality of index performance prediction values, a value with smaller optimizing amplitude of the annealing temperature is selected.
6. The energy-saving cooling method for the annealing furnace based on the performance prediction model according to claim 1, wherein in the step D, when the temperature optimization value of the soaking section of the annealing furnace is calculated, the obtained new soaking temperature adjustment value of the annealing furnace is required to be within the soaking temperature range allowed by the process and equipment.
7. The energy-saving cooling method for the annealing furnace based on the performance prediction model according to claim 1, wherein in the step D, after the temperature optimization value of the soaking section of the annealing furnace is calculated, new predicted values of all performance indexes are required to meet contractual performance requirements.
8. The energy-saving cooling method for the annealing furnace based on the performance prediction model is characterized in that the energy consumption of a unit product of the annealing furnace is reduced by optimizing and reducing the temperature of the soaking section of the annealing furnace meeting the process requirements under the condition that the predicted performance meets the contract requirements based on the performance prediction model.
9. The annealing furnace energy-saving cooling method based on the performance prediction model according to claim 1, wherein when the predicted value of the mechanical performance is used for judging whether the performance is qualified or not, the predicted value is calculated to meet the probability of the upper limit and the lower limit of the performance, and the probability of +/-2 sigma is selected to judge whether the predicted performance is qualified or not.
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