CN102393884A - Hot continuous rolling electromagnetic induction heating temperature prediction method based on BP (back-propagation) neural network - Google Patents
Hot continuous rolling electromagnetic induction heating temperature prediction method based on BP (back-propagation) neural network Download PDFInfo
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Abstract
The invention discloses a hot continuous rolling electromagnetic induction heating temperature prediction method based on BP (back-propagation) neural network. The existing prediction method is dependent on manual work, and thus, has the defects of low efficiency and poor reliability. The method disclosed by the invention comprises the following steps: selecting prediction model variables: reasonably selecting input/output variables of a prediction model by utilizing mechanism analysis and prior information; normalizing data to be input; establishing a BP neural network, and training and testing the neural network; and finally, denormalizing the data obtained by the neural network, thereby obtaining the predicted heating temperature. In the invention, the steel billet temperature is predicted according to the operation history data of the electromagnetic induction heater, and has higher prediction precision than the traditional engineering computation method.
Description
Technical field
The invention belongs to technical field of automation, be specifically related to a kind of Forecasting Methodology of the hot continuous rolling electromagnetic induction heating steel billet temperature based on neural network.
Background technology
At steel industry, traditional steel-making, continuous casting, steel rolling process are production links independently separately.But modern production model progressively changes the hot continuous rolling integral process of " steel smelting-continuous casting-steel rolling " into, the hot continuous rolling automatic assembly line pattern of the height intensification that Here it is generally praises highly at present both at home and abroad.The firing equipment that between continuous casting and tandem rolling, adopts (heating, soaking, holding furnace) is the key equipment that is connected continuous casting and rolling production line.Say that from technological angle middle firing equipment need carry out concurrent heating so that the strand temperature evenly reaches the function of tandem rolling temperature requirement to it in accomplishing the process of carrying continuous casting billet, solve the uneven problem in board briquette field.Heating furnace is as the buffer zone between two kinds of different process speed parts of conticaster and tandem mill, and casting machine is connected, cushions with logistics between milling train, coordinates the production of the two.
Because the plurality of advantages of electromagnetic induction heating technology; For example firing rate is fast, power density is controlled, pollution-free, be easy to control, oxidization burning loss is few etc., than traditional gas-fired furnace, need not the naked light thermal source; Advantage with zero-emission is more suitable in hot continuous rolling production line, using.Therefore, high-power medium frequency induction heater progressively is applied to hot continuous rolling production line, referring to Fig. 1.
Because electromagnetic induction heating is the non-linear large time delay process of a complicacy, is difficult to set up accurate mechanism model, be difficult to obtain satisfied effect with conventional control method (regulating) like PID, generally adopt artificial experience to debug and control.
In view of neural network has strong non-linear mapping ability and good fault-tolerance, can well approach the time of day data of system.Therefore, on a large amount of induction heating service datas basis that has accumulated, adopt neural net method to set up hot continuous rolling electromagnetic induction heating temperature prediction model, steel billet temperature is predicted, can be the electromagnetic induction heating accurate temperature controlling foundation is provided.
Summary of the invention
The present invention is directed to the deficiency of prior art, a kind of Forecasting Methodology of the hot continuous rolling electromagnetic induction heating steel billet temperature based on neural network is provided.The concrete steps of this method are:
Step (1) is selected the forecast model variable.
Adopt nerual network technique to set up hot continuous rolling electromagnetic induction heating steel billet temperature forecast model; For guaranteeing neural net model establishing validity based on data; Avoid the blindness of black case modeling, at first utilize Analysis on Mechanism and prior imformation, the input/output variable of choose reasonable forecast model.
Selecting the steel billet temperature after the electromagnetic induction heating based on Analysis on Mechanism is the output variable of neural network model, and the principal element of selecting influence steel billet temperature is the neural network model input variable: the 1. preceding steel billet temperature of electromagnetic induction heating; 2. the voltage of induction heater; 3. the electric current of induction heater.The neural network model output variable is the steel billet temperature after the electromagnetic induction heating.
In inductive coil, be induction heated process for steel billet, be get into coil magnetic field and begin to break away from magnetic field from steel billet to steel billet till.At first, ignoring under heat conduction, the heat radiation situation, heating up in a certain cross section of steel billet mainly is voltage, the function of current that receives this process induction heater.Therefore, the voltage of induction heater, electric current are the sequence variable.Secondly, consider heat conduction, heat radiation situation, the steel billet temperature before and after the electromagnetic induction heating is also got the sequence variable.
Step (2) data normalization is handled.
Input data in the training sample comprise three; It is bigger that the order of magnitude differs; For guaranteeing each factor par; Accelerate speed of convergence; Data are carried out normalization handle, be converted into the value
of [0,1] interval range.
Wherein
is the maximal value in the input data,
for importing the minimum value in the data.For the input data,
is for importing the value after data normalization is handled
.
Step (3) is built the BP neural network framework.
The newff function that calls in the MatlabR2009a Neural Network Toolbox is set up the BP neural network, Net=newff (PR,
,
, BTF, BLF, PF); Net is the BP neural network framework, and PR is a span that is determined by greatest member and least member in the input matrix,
Be
iThe neuronic number of layer,
Be
iThe transport function of layer,
,
Be the total number of plies of neural network, BTF is the training function of BP neural network, and BLF is weights and bias, and PF is the network performance function.
Step (4) training BP neural network.Concrete grammar is:
A, initialization BP neural network, the value assignment of utilizing random function to produce is given weights and bias, calls the init function then and comes initialization BP neural network.
B, network training number of times and training objective error are set.
C, training data is set is input matrix P, and it is matrix T that desired value is set, and the train function that calls in the MatlabR2009a Neural Network Toolbox carries out the data training until convergence to BP neural network Net, and Net=train (Net, P, T).
Step (5) test b P neural network.
BP neural network to training is tested; The historical data composition is used for electromagnetic induction heating temperature prediction network test matrix P_test; Directly call the sim function in the MatlabR2009a Neural Network Toolbox, and D=sim (Net, P_test); Test matrix is carried out emulation, and wherein D is an objective function.
The anti-normalization of step (6) data is handled.
Steel billet temperature after the electromagnetic induction heating of test gained is carried out anti-normalization according to formula
to be handled; Wherein
is the steel billet temperature after anti-normalization is handled; The steel billet temperature that
obtains for emulation testing;
is the steel billet maximum temperature, and
is the steel billet minimum temperature.
The present invention will utilize the simulation calculation ability of computing machine; Build an error back propagation (BP) neural network; From the induction heater operation history data, obtain knowledge automatically; Be attached to new knowledge in its mapping function step by step, thereby realize approaching of nonlinear function, can accurately predict the steel billet temperature after the inductor heating.
The beneficial effect of the inventive method:
1. the BP neural network has the ability of approaching the Nonlinear Mapping function, therefore utilizes the electromagnetic induction heater operation history data to predict steel billet temperature, and the precision of prediction that obtains than traditional engineering calculating method is high.
2. avoided finding the solution the finite element model of tradition based on the hot continuous rolling electromagnetic induction steel billet temperature prediction of BP neural network based on induction heating, heat conduction, the theoretical electromagnetic field of being set up of heat radiation and temperature field coupling; Also avoid simultaneously of the harsh requirement of this finite element model of computer solving to basic datas such as boundary conditions; Only need utilize a large amount of electromagnetic induction heating history datas, practicality is stronger.
3. increase the length of BP neural metwork training data, the dimension of input matrix, improve the computing velocity of computing machine, can improve precision of prediction.
Description of drawings
Fig. 1 is a hot continuous rolling induction heating synoptic diagram.
Embodiment
Steel billet induction heating with certain steel mill's hot continuous rolling production line is an example, carries out the hot continuous rolling electromagnetic induction heating steel billet temperature forecast model modeling embodiment based on the BP neural network.
Step (1) forecast model Variables Selection
Analysis on Mechanism.Model is output as the steel billet temperature after the heating.According to law of conservation of energy, the energy that the energy+steel billet before the energy after the steel billet heating=steel billet heating absorbs, wherein the energy of steel billet absorption is from induction heater.Can know voltage, the electric current that respectively records induction heater by the PLC control system of electromagnetic induction heater, if steel billet is known through the time of induction heater, the product of the voltage of induction heater, electric current, time is exactly the energy that steel billet absorbs so in theory.Can confirm be input as 3 of neural network model based on above-mentioned Analysis on Mechanism: the temperature before the steel billet heating, the voltage and current of inductor.
Before this modeling; Earlier each sampled data is carried out pre-service; Integrate for each sample (a steel billet induction heating process) data, wherein, temperature and heated temperatures before the steel billet heating are integrated into 100 values; According to the length of inductor and steel billet, voltage and current is integrated into 152 values.According to the position correlation analysis; The electric current and the magnitude of voltage of the inductor of the corresponding n+25 to n+46 of the steel billet temperature that n is ordered; Get preceding temperature and the corresponding electric current and voltage value thereof of steel billet heating that n is ordered, as input matrix, the steel billet heated temperatures that n is ordered is as output matrix at present; So, each sample just can constitute 100 pairs of input and output matrixes.
Make that the A matrix is the temperature before the steel billet heating, the B matrix is the voltage of induction heater, and the C matrix is the electric current of induction heater, and D is the steel billet heated temperatures.Input matrix: P=
then, concrete arrangement mode is:
Corresponding output matrix: D=
Step (2) data normalization is handled
Utilizing normalization formula
respectively the input and output matrix to be carried out normalization handles.
For input matrix P, specific practice is respectively to A, and B, three matrixes of C carry out normalization to be handled.With the A matrix is example:
A=
The A matrix is carried out normalization according to the normalization formula handle, obtain
.Again successively to B; The C matrix is similarly handled, and the input matrix after normalization is handled is:
=
.
For output matrix D=
;
is the steel billet heated temperatures;
is the minimum value of steel billet heated temperatures;
is the maximal value of steel billet heated temperatures,
be the temperature after the normalization processing steel billet heating afterwards.Input matrix after the normalization is
.
Step (3) makes up the BP neural network
Build the BP neural network framework, call the newff function in the MatlabR2009a function library,
Net=newff（threshold,[5,1],’tansig’,’purelin’,trainlm）
Wherein Threshold is the minimum value and the maximal value of 45 input and output vectors of defined matrix of a 45*2; [5,1] expression ground floor has 5 neurons, and the second layer has 1 neuron; Tansig is the input layer transport function; Purelin is the output layer transport function; Trainlm is the training function based on the l-m algorithm.
Step (4) training BP neural network
A. initialization network
Net.initFcn is with the initialization function that decides whole network.Parameter net.layer{i}.initFcn is with the initialization function that decides each layer.The initwb function according to the initiation parameter of each layer oneself (initializes weights is made as rands usually for net.inputWeights{i, j}.initFcn) initializes weights matrix and biasing, and concrete grammar is following:
net.layers{1}.initFcn?=?'initwb';
net.inputWeights{1,1}.initFcn?=?'rands';
net.layerWeights{2,1}.initFcn?=?'rands';
net.biases{1,1}.initFcn?=?'rands';
net.biases{2,1}.initFcn?=?'rands';
net?=?init(net);
Net.IW{1,1} are the weight matrix of input layer to hidden layer
Net.LW{2,1} are the weight matrix between hidden layer and output layer; Net.b{1,1} are the threshold values vector of hidden layer, and net.b{2,1} are the threshold values of output contact;
B., the step number that network training number of times, training objective error is set and is used for showing
net.trainParam.epochs=2000;
net.trainParam.goal=0.002;
net.trainParam.show=50;
It was 2000 steps that the network training number of times is set, and the training objective error is 0.0002, showed that the training step number was 50 steps.
C. utilize input matrix
and objective matrix to be made as
; Through calling the train function; Net=train (net;
,
) carry out steel billet heated temperatures prediction network training until convergence.
Step (5) network test
The historical data that will be used for testing is formed the matrix p_test that is used for the steel billet temperature network test according to the input matrix form of step (1); Carry out normalization according to step (2) again and handle, the test matrix after the normalization is
.Call the sim () function in the Matlab tool box, the network that trains is carried out emulation.The calling program code is: D=sim (net,
); The D matrix is sewage pumping station forebay water level forecast value.
The anti-normalization of step (6) is handled
The steel billet heated temperatures of test gained is carried out anti-normalization according to formula
to be handled; Wherein
is that the final steel billet temperature in back is handled in anti-normalization; The steel billet temperature that
obtains for emulation testing;
is the maximal value of steel billet temperature, and
is the minimum value of steel billet temperature.Steel billet temperature is
after the anti-normalization, promptly tests resulting steel billet temperature for
.
Claims (1)
1. based on the hot continuous rolling electromagnetic induction heating temperature predicting method of BP neural network, it is characterized in that this method may further comprise the steps:
Step 1. is selected the forecast model variable, specifically is to utilize Analysis on Mechanism and prior imformation, selects the input/output variable of forecast model;
Described input variable comprises steel billet temperature, the voltage of induction heater and the electric current of induction heater before the electromagnetic induction heating;
Described output variable is the steel billet temperature after the electromagnetic induction heating;
Step 2. data normalization is handled, specifically:
Input data in the training sample comprise three; It is bigger that the order of magnitude differs; For guaranteeing each factor par; Accelerate speed of convergence, data are carried out normalization handle, be converted into [0; 1] value of interval range
Wherein
is the maximal value in the input data;
is the minimum value in the input data; For the input data,
is for importing the value after data normalization is handled
;
Step 3. is built the BP neural network framework, specifically:
The newff function that calls in the MatlabR2009a Neural Network Toolbox is set up the BP neural network, Net=newff (PR,
,
, BTF, BLF, PF); Net is the BP neural network framework, and PR is a span that is determined by greatest member and least member in the input matrix,
Be
iThe neuronic number of layer,
Be
iThe transport function of layer,
,
Be the total number of plies of neural network, BTF is the training function of BP neural network, and BLF is weights and bias, and PF is the network performance function;
Step 4. training BP neural network, concrete grammar is:
A, initialization BP neural network, the value assignment of utilizing random function to produce is given weights and bias, calls the init function then and comes initialization BP neural network;
B, network training number of times and training objective error are set;
C, training data is set is input matrix P, and it is matrix T that desired value is set, and the train function that calls in the MatlabR2009a Neural Network Toolbox carries out the data training until convergence to BP neural network Net, and Net=train (Net, P, T);
Step 5. test b P neural network, specifically:
BP neural network to training is tested; The historical data composition is used for electromagnetic induction heating temperature prediction network test matrix P_test; Directly call the sim function in the MatlabR2009a Neural Network Toolbox, and D=sim (Net, P_test); Test matrix is carried out emulation, and wherein D is an objective function;
The anti-normalization of step 6. data is handled, specifically:
Steel billet temperature after the electromagnetic induction heating of test gained is carried out anti-normalization according to formula
to be handled; Wherein
is the steel billet temperature after anti-normalization is handled; The steel billet temperature that
obtains for emulation testing;
is the steel billet maximum temperature, and
is the steel billet minimum temperature.
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