CN108694288B - Method for rapidly acquiring set temperatures of walking beam type billet heating furnace under different yields - Google Patents
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Abstract
The invention discloses a method for quickly acquiring set temperatures of a walking beam type billet heating furnace under different yields, which comprises the following steps of: acquiring a plurality of groups of training data of the walking beam type billet heating furnace under different yields, wherein the training data comprises input data and output data; the input data comprises a stepping period, billet heating precision and the hot uniformity of discharged billets; the output data comprises the set temperature of each control area; b, training a BP neural network to obtain i BP neural network models; step C, determining a stepping period corresponding to the target yield, and simultaneously adjusting the hot uniformity degree value of the discharged steel billet to ensure that the heating precision value of the steel billet is in the range of-5 ℃; and D, taking the stepping period, the hot uniformity degree value of the discharged steel billet and the steel billet heating precision value in the step C as input data of the BP neural network model to obtain the set temperature value of each control area corresponding to the target yield. The invention has simple operation process, high heating precision, high energy utilization rate and low energy consumption.
Description
Technical Field
The invention belongs to the technical field of walking beam type steel billet heating furnaces, and particularly relates to a method for quickly acquiring set temperatures of a walking beam type steel billet heating furnace under different yields.
Background
In the prior art, a method for quickly obtaining the set temperatures of the walking beam type billet heating furnace under different production rates does not exist, so the set temperatures of the walking beam type billet heating furnace under different production rates are set according to experience, and therefore, the heating precision is low, the energy utilization rate is low, and the energy consumption is large. In order to meet the urgent needs of the steel industry on the background of energy conservation and consumption reduction, a method for quickly obtaining the set temperature of a walking beam type billet heating furnace under different yields is urgently needed, so that the heating precision and the energy utilization rate are improved, and the energy consumption is reduced.
Disclosure of Invention
The invention aims to provide a method for rapidly acquiring the set temperatures of a walking beam type billet heating furnace under different yields aiming at the defects of the prior art, and the method has the advantages of high heating precision, high energy utilization rate and low energy consumption.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a method for rapidly acquiring set temperatures of a walking beam type billet heating furnace under different yields is characterized by comprising the following steps:
step A, obtaining a plurality of groups of training data of the walking beam type billet heating furnace under different production rates, wherein the training data comprisesInput data and output data; the input data comprises a stepping period WR and billet heating precision delta TdisAnd the thermal uniformity degree delta T of the discharged steel billetmax(ii) a The output data includes set temperatures T of the control regions corresponding to the sets of input dataspi；
B, training the BP neural network by using the training data in the step A to obtain i BP neural network models, wherein i is the number of the control areas;
step C, determining a stepping period corresponding to the target yield, and simultaneously adjusting the input value of the neural network model obtained after training, wherein the adjustment of the input value of the neural network model obtained after training comprises the adjustment of the thermal uniformity degree value of the discharged steel billet, so that the heating precision value of the steel billet is in the range of-5 ℃; and D, taking the stepping period, the hot uniformity degree value of the discharged steel billet and the steel billet heating precision value in the step C as input data of the i BP neural network models in the step B to obtain the set temperature value of each control area corresponding to the target yield.
By the method, the heating process of the heating furnace is simulated by taking the heating process of the steel billet in the walking beam type steel billet heating furnace as a research object and adopting a Computational Fluid Dynamics (CFD) method. The method is equivalent to a 'virtual' experiment of the walking beam type billet heating furnace in a computer, and has the advantages of low cost, short period and wide environment. The CFD technology is introduced into the design of the heating furnace, so that design errors and cost required by experimental research can be reduced, distribution of air flow, temperature and the like in the furnace is predicted, indexes such as billet heating accuracy and the like are optimized and calculated through a neural network algorithm, and the aims of high heating accuracy, high energy utilization rate, high efficiency, low energy consumption and low emission are fulfilled.
Further, in the step A, the billet is heated with the billet heating precision delta TdisAnd the thermal uniformity degree delta T of the discharged steel billetmaxAnd solving a plurality of groups of training data of the walking beam type billet heating furnace under different yields by using a Hooke-Jeeves direct search algorithm.
In order to prepare enough training data for the BP neural network, the invention solves the optimal set temperature data of a series of heating furnaces with different yields by using a Hooke-Jeeves direct search algorithm. Based on the data, the BP neural network is established and trained, and the method has the advantages of fast calculation, low operation cost and high accuracy.
As a preferable mode, in the step B, the BP neural network is a three-layer BP neural network; the input layer of the BP neural network has 3 neurons, and the 3 neurons of the input layer respectively represent a stepping period WR and a billet heating precision delta TdisAnd the thermal uniformity degree delta T of the discharged steel billetmax(ii) a The hidden layer of the BP neural network has 8 neurons; the output layer of the BP neural network is provided with 1 neuron, and the neuron of the output layer correspondingly represents the set temperature T of a control areaspi(ii) a The i BP neural network models obtained by training are as follows:
compared with the prior art, the invention has the advantages of simple operation process, high heating precision, high energy utilization rate and low energy consumption.
Drawings
FIG. 1 is a schematic view of a walking beam type billet heating furnace.
FIG. 2 shows 10 training data sets for a walking beam billet furnace at different production rates.
FIG. 3 is a model diagram of a BP neural network.
FIG. 4 is a graph comparing the predicted value and the actual value of the BP neural network. Wherein, in FIG. 4, (1) is Tsp1The predicted value and the actual value of (2) are compared, and T is shown in FIG. 4sp2The predicted value and the actual value of (2) are compared, and T is shown in (3) of FIG. 4sp3The predicted value and the actual value of (2) are compared, and T is shown in (4) of FIG. 4sp4Comparing the predicted value with the actual value; the abscissa is the step period (in units of s) and the ordinate is the set temperature (in units of ℃); the curve with the triangular points represents the actual values and the curve with the square points represents the predicted values.
FIG. 5 is a graph of Δ T set for different ranges of step periodsmaxThe value is obtained.
FIG. 6 is a comparison graph of the tapping temperature of steel billets in different step periods.
Detailed Description
The method for rapidly acquiring the set temperatures of the walking beam type billet heating furnace shown in figure 1 under different yields by utilizing the method comprises the following steps:
a, acquiring a plurality of groups of accurate training data of the walking beam type billet heating furnace under different yields, wherein the training data comprises input data and output data; the input data comprises a stepping period WR and billet heating precision delta TdisAnd the thermal uniformity degree delta T of the discharged steel billetmax(ii) a The output data includes set temperatures T of the control regions corresponding to the sets of input dataspi. In the present embodiment, the number of control regions is 4, and thus TspiIncluding the set temperature T of the control area 1sp1 Control region 2 set temperature Tsp2And the set temperature T of the control area 3sp3The set temperature T of the control area 4sp4. The division of the different control areas is shown in fig. 1.
And B, training the BP neural network by using the training data in the step A to obtain 4 BP neural network models.
Step C, determining a stepping period corresponding to the target yield, and simultaneously adjusting the input value of the neural network model obtained after training, wherein the adjustment of the input value of the neural network model obtained after training comprises the key adjustment of the thermal uniformity degree value of the discharged steel billet, so that the heating precision value of the steel billet is in the range of-5 ℃; and D, taking the stepping period, the hot uniformity degree value of the discharged steel billet and the steel billet heating precision value in the step C as input data of the 4 BP neural network models in the step B to obtain the set temperature value of each control area corresponding to the target yield.
In the step A, the billet is heated with the precision delta TdisAnd the thermal uniformity degree delta T of the discharged steel billetmaxAnd solving a plurality of groups of training data of the walking beam type billet heating furnace under different yields by using a Hooke-Jeeves direct search algorithm.
Specifically, the yield varies from 65ton/hr to 130ton/hr, starting from the minimum yield, and every 1.75ton/hr increase in yield is calculated by the Hooke-Jeeves direct search algorithmThe set temperature at the corresponding yield is obtained. Since the productivity of the heating furnace increases in inverse proportion to the step period of the heating furnace, the main program of the heating furnace changes the productivity of the heating furnace by changing the step period of the heating furnace. The variation range of the corresponding heating furnace stepping period is 180s to 360s, and the set temperature under the corresponding stepping period is calculated every 5 seconds of the stepping period. Therefore, there are 37 sets of set temperature data at different step periods, including Δ T at the timedisAnd Δ Tmax. Of these, 10 sets of training data are shown in fig. 2.
The method selects a typical three-layer BP neural network to predict the set temperature of the heating furnace. In the step B, the BP neural network is a three-layer BP neural network; as shown in fig. 3, the input layer of the BP neural network has 3 neurons, and the 3 neurons of the input layer represent the step period WR and the billet heating accuracy (deviation between the billet tapping temperature and the set temperature) Δ TdisAnd the degree of thermal uniformity (maximum temperature difference on the discharged steel billet) of the discharged steel billetmax(ii) a The hidden layer of the BP neural network has 8 neurons; the output layer of the BP neural network is provided with 1 neuron, and the neuron of the output layer correspondingly represents the set temperature T of a control areaspi. Therefore, the invention constructs 4 BP neural network models with the same structure to respectively predict Tsp1、Tsp2、Tsp3、Tsp4。
The 4 BP neural network models obtained by training are:
fig. 4 is a comparison graph of the predicted values and the actual values of the trained neural networks, and it can be seen from fig. 4 that the predicted values of the four trained neural networks well match the actual values.
In the step C, the value range of WR is 180-360 s, and delta T is used for ensuring the best tapping temperature of the tapped billetdisIs 0. In the formula,. DELTA.TmaxThe value of (D) is determined by the calculated Delta T of the heating furnace programdisBy manually setting Δ T in different WR rangesmaxValue of Δ T calculated by the programdisThe range is. + -. 5 ℃. Δ T in different WR rangesmaxThe values are shown in figure 5.
In order to verify the reasonability of the invention, the steel billet tapping temperature under different stepping periods is simulated and compared based on the actual working data of the heating furnace, and the simulation result is analyzed. As the furnace step cycle changes, the set temperature at the new step cycle can be calculated in a matter of hundreds of milliseconds. As can be seen from fig. 6, comparing the furnace installation temperature data obtained by the method of the present invention with the actual industrial data, the method of the present invention can greatly improve the heating accuracy of the furnace.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (3)
1. A method for rapidly acquiring set temperatures of a walking beam type billet heating furnace under different yields is characterized by comprising the following steps:
a, acquiring a plurality of groups of training data of the walking beam type billet heating furnace under different yields, wherein the training data comprises input data and output data; the input data comprises a stepping period WR and billet heating precision Delta TdisDelta T of degree of thermal uniformity of discharged steel billetsmax(ii) a The output data includes set temperatures T of the control regions corresponding to the sets of input dataspi(ii) a The heating precision delta T of the steel billetdisThe deviation of the tapping temperature of the steel billet and the set temperature is the delta T of the thermal uniformity degree of the tapped steel billetmaxThe maximum temperature difference on the discharged steel billet is obtained;
b, training the BP neural network by using the training data in the step A to obtain i BP neural network models, wherein i is the number of the control areas;
step C, determining a stepping period corresponding to the target yield, and simultaneously adjusting the input value of the neural network model obtained after training, wherein the adjustment of the input value of the neural network model obtained after training comprises the adjustment of the thermal uniformity degree value of the discharged steel billet, so that the heating precision value of the steel billet is in the range of-5 ℃; and D, taking the stepping period, the hot uniformity degree value of the discharged steel billet and the steel billet heating precision value in the step C as input data of the i BP neural network models in the step B to obtain the set temperature value of each control area corresponding to the target yield.
2. The method for rapidly obtaining the set temperatures at different productivities of the walking beam type billet heating furnace according to claim 1, wherein the billet heating accuracy Δ T is used in the step AdisDelta T of degree of thermal uniformity of discharged steel billetsmaxAnd solving a plurality of groups of training data of the walking beam type billet heating furnace under different yields by using a Hooke-Jeeves direct search algorithm.
3. The method for rapidly acquiring the set temperatures of the walking beam type billet heating furnace with different production rates as claimed in claim 1, wherein in the step B, the BP neural network is a three-layer BP neural network; the input layer of the BP neural network has 3 neurons, and the 3 neurons of the input layer respectively represent a stepping period WR and a billet heating precision Delta TdisDelta T of degree of thermal uniformity of discharged steel billetsmax(ii) a The hidden layer of the BP neural network has 8 neurons; the output layer of the BP neural network is provided with 1 neuron, and the neuron of the output layer correspondingly represents the set temperature T of a control areaspi(ii) a The i BP neural network models obtained by training are as follows:
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