CN108446799B - Residual pressure turbine device power generation power prediction method based on Elman neural network - Google Patents
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Abstract
The invention discloses a method for predicting the power generation power of a residual pressure turbine device based on an Elman neural network. The method comprises the following steps: carrying out interpolation processing on the unevenly sampled data to form a sequence with equal time intervals, analyzing the overall trend and correlation coefficient of each input variable, selecting six variables with high correlation degree with the generated energy as input variables, and carrying out interpolation and normalization preprocessing on the input variables by utilizing a python and Matlab platform to ensure that the sampling frequency is uniform; step two: selecting an Elman neural network as a basic model for prediction, adopting an error back propagation learning algorithm (BPTT) and combining a sliding window model for predicting the generated power of the residual pressure turbine device; step three: and after initializing the model, training the model by using the normalized training sample, and using the trained model for predicting the power generation power of the residual pressure turbine device. The invention ensures that the TRT generated power large prediction has higher precision and smaller mean square error, and can be used for on-line real-time prediction.
Description
Technical Field
The invention belongs to the field of industrial process monitoring, modeling and simulation, and particularly relates to a method for predicting the power generation capacity of a residual pressure turbine device by an Elman-BPTT neural network.
Technical Field
The steel industry currently commonly uses blast furnace residual pressure recovery devices to recover part of the energy. The capacity prediction based on Top Gas Pressure Recovery Turbine (TRT) of the blast furnace is an important basis for optimizing and controlling the power generation of the blast furnace. Accurate capacity prediction (or model prediction) is essential to improve Real-time optimization (RTO). Because the RTO adopts a static model, when interference occurs, the controlled system can be optimized only when reaching the steady state again, so that the optimization is delayed, and the inconsistency that the RTO nonlinear steady state optimization period is too long and the loop control period is too short can be solved by combining with the capacity prediction. The direct online optimization method adopts nonlinear model predictive control to optimize economic performance indexes online in a limited step length, and the method requires an accurate nonlinear process model.
The current research on similar industrial processes is mainly divided into two main ways.
The method comprises the steps of firstly, modeling by a traditional mechanism, establishing a dynamic mathematical model of each unit on the basis of analyzing the working mechanism of the TRT system, and deducing a generating capacity mathematical model of the TRT system by combining each unit model based on an empirical formula of a blast furnace gas generation process and a material balance relation. However, because the blast furnace ironmaking process is complex, it is difficult to establish an accurate model through a mechanism, and the true value is very likely to be greatly different from the predicted value by modeling.
Second, data driven modeling. Statistical methods and machine learning methods are mainly used. The statistical method mainly comprises the following steps: partial least squares, principal component analysis, autoregressive analysis, regression analysis. The machine learning method includes a neural network model, a support vector machine and the like. In the case of a large amount of data that can be analyzed or a neural network trained, the prediction is expected to achieve a better effect.
However, data-driven modeling has its own drawbacks, and conventional data modeling approaches suffer from problems due to certain characteristics of the large data in the process industry. Firstly, the existing data modeling method mostly focuses on modeling and analyzing regularly sampled data, and cannot model and analyze irregularly sampled data. This requires preprocessing of the irregularly sampled data, but this part of the process loses some of the data. Secondly, the current data modeling method mainly extracts static data statistical analysis of a space, does not consider time dynamics, cannot extract operation condition characteristic information from historical dynamic big data, and an industrial model needs dynamic analysis.
Third, existing data modeling methods require either non-contaminated data or modeling of pre-processed data. Otherwise, the abnormal point returns to bring great influence to the model parameters, even leading to model mismatch.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for predicting the power generation power of a residual pressure turbine device based on an Elman model.
A method for predicting the power generation power of a residual pressure turbine device based on an Elman neural network,
the method comprises the following steps: carrying out interpolation processing on unevenly sampled data to form a sequence with equal time intervals, analyzing the overall trend and correlation coefficient of each input variable, selecting six variables with high correlation degree with generated energy as input variables, wherein the input variables are gas flow, gas inlet pressure, gas inlet temperature, carbon monoxide content, carbon dioxide content and hydrogen content in gas, and carrying out interpolation and normalization preprocessing on the input variables by utilizing a python and Matlab platform to ensure that the sampling frequency is uniform;
step two: selecting an Elman neural network as a basic model for prediction, adopting an error back propagation learning algorithm (BPTT) and combining a sliding window model for predicting the generated power of the residual pressure turbine device;
step three: and after initializing the model, training the model by using the normalized training sample, and using the trained model for predicting the power generation power of the residual pressure turbine device.
Step one correlation coefficient is as follows:
gas flow rate in m3H, correlation coefficient 0.9294;
gas inlet pressure in kPa, correlation coefficient-0.0397;
the gas inlet temperature, unit ℃, correlation coefficient 0.0352;
volume content of CO, 0.1278%;
CO2volume content, 0.1935%;
the correlation coefficient is between 0 and 1, and the larger the correlation coefficient is, the stronger the correlation is;
the normalization method in the first step is as follows:
the normalized result is between 0 and 1.
The structure of the Elman neural network in the step two is as follows:
the ELman neural network is composed of four layers, namely an input layer, a hidden layer, a carrying layer and an output layer:
1) from input layer to hidden layer:
x(k)＝f(w1xc(k)+w2(u(k-1)))；
2) from hidden layer to output layer:
y(k)＝g(w3x(k))；
3) the receiving layer stores short-term information:
xc(k)＝x(k-1)；
4) learning index function: using the sum of squared error function, training ends when E <0.0001
5) The weight value updating method comprises the following steps:
w1for the connection weight of the bearer layer to the hidden layer, w2Is the connection weight of the input layer to the hidden layer, w3The three weights are required to be adjusted repeatedly for connecting the hidden layer with the output layer, error is the difference between prediction and reality in the training process, eta represents the adjustment degree of feedback each time and is set to be 0.001, the weights are updated through continuous feedback, and finally the function value of the learning index meets the requirement to achieve the aim of finishing the training of the neural network
w1(n)＝w1(n-1)-η·2·error·w3·xc，
w2(n＝w2(n-1)-η·2·error·w3·u，
w3(n)＝w3(n-1)-η·2·error·w3·xc。
The sliding window model principle of the second step is as follows:
the sliding window model is established on the assumption that the current output depends on the current input, the mapping rule between the input and the output is obtained through historical data, a certain amount of training set samples are preset according to the assumption, then the sample data is continuously updated, the earliest data point is discarded, and the ELMAN neural network continuously updates the structural parameters and gives the latest predicted value along with the sliding of the window.
The model in the third step is suitable for the characteristics of time variation, dynamics, nonlinearity and strong inertia of the blast furnace gas residual pressure recovery turbine power generation.
The invention ensures that the TRT generated power large prediction has higher precision and smaller mean square error, and can be used for on-line real-time prediction.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a schematic diagram of the structure of an Elman neural network;
fig. 2 is a schematic view of a sliding window.
Detailed Description
In the blast furnace ironmaking process, the chemical reaction is relatively slow, the current furnace condition has strong correlation with the historical furnace condition, the correlation is expressed as a dynamic time sequence, and a neural network is required to dynamically memorize historical information. Therefore, a Recurrent Neural Network (RNN) is used to predict TRT generated power. In the branch of the recurrent neural network, according to the characteristic of TRT operation, we select the Elman neural network sensitive to historical data, which can be regarded as a recurrent neural network with local memory units and local feedback connection, in the hidden layer, the output is self-connected to the input through the delay and storage of the receiving layer, so that the Elman neural network has sensitivity to the data of the historical state, and the internal feedback also enhances the capability of the Elman network to process dynamic information.
Currently, in industrial data mining, the Elman neural network has great potential in analyzing time series. The invention uses the neural network model for the prediction of TRT generated power, obtains better effect, and has higher application value for the industrial field needing to analyze dynamic time sequence.
The invention provides a method for predicting the power generation capacity of a residual pressure turbine device based on an Elman model, which comprises the following steps:
the method comprises the following steps: the method is characterized in that 6 variables with high correlation degree with the generated energy are determined as input variables (coal gas flow, coal gas inlet pressure, coal gas inlet temperature, carbon monoxide content in coal gas, carbon dioxide content and hydrogen content) by combining analysis of related data and considering the whole trend and correlation coefficient of each dependent variable, and preprocessing such as interpolation and normalization are carried out on the input variables by utilizing a python and Matlab platform, so that the sampling frequency is uniform.
Step two: the Elman neural network is selected as a basic model for prediction (shown in figure 1), and an error back propagation learning algorithm (BPTT) is adopted and combined with a sliding window model (shown in figure 2) to be used for predicting the generated power of the TRT device.
Step three: and after initializing the model, training the model by using the normalized training sample, and using the trained model for predicting the generated power of the TRT device.
1. And step one, carrying out interpolation processing on the unevenly sampled data to form a sequence with equal time intervals, and selecting 6 variables related to the generating power of the TRT device through analysis of related data and the overall trend and the correlation coefficient of each dependent variable. The correlation coefficients are shown in the following table:
| name of variable | Unit of | Correlation coefficient | |
| X1 | Gas flow | m3/h | 0.9294 |
| X2 | Gas inlet pressure | kPa | -0.0397 |
| X3 | Gas inlet temperature | ℃ | 0.0352 |
| X4 | CO content | ％ | 0.1278 |
| X5 | CO2Content (wt.) | ％ | 0.1935 |
| X6 | H2Content (wt.) | ％ | 0.1645 |
The normalization method in the first step is as follows:
the normalized result is between 0 and 1.
2. The structure of the Elman neural network in the step two is as follows:
the ELman neural network is composed of four layers, namely an input layer, a hidden layer, a carrying layer and an output layer.
(1) From input layer to hidden layer:
x(k)＝f(w1xc(k)+w2(u(k-1)))
(2) from hidden layer to output layer:
y(k)＝g(w3x(k))
(3) the receiving layer stores short-term information:
xc(k)＝x(k-1)
(4) learning index function: using the sum of squared error function, training ends when E <0.0001
(5) The weight value updating method comprises the following steps:
w1for the connection weight of the bearer layer to the hidden layer, w2Is the connection weight of the input layer to the hidden layer, w3The connection weight from the hidden layer to the output layer. In the method, three weights are required to be adjusted repeatedly, error is the difference between prediction and reality in the training process, eta represents the adjustment degree of feedback each time and is set to be 0.001, the weights are updated through continuous feedback, and finally the function value of the learning index meets the requirement to achieve the aim of finishing the training of the neural network
w1(n)＝w1(n-1)-η·2·error·w3·xc
w2(n)＝w2(n-1)-η·2·error·w3·u
w3(n)＝w3(n-1)-η·2·rrror·w3·xc。
3. The sliding window model principle of the second step is as follows:
the sliding window model is based on the assumption that the current output depends on the current input, and the mapping rule between the input and the output can be obtained through historical data. Based on this assumption, we preset a certain amount of training set samples, then continuously update the sample data and discard the oldest data point. As the window slides, the ELMAN neural network will continuously update its structural parameters and give up-to-date predicted values.
The model is suitable for the characteristics of time variation, dynamics, nonlinearity and strong inertia of the blast furnace gas residual pressure recovery turbine power generation.
Examples
The steel industry currently commonly uses blast furnace residual pressure recovery devices to recover part of the energy. The capacity prediction based on Top Gas Pressure Recovery Turbine (TRT) of the blast furnace is an important basis for optimizing and controlling the power generation of the blast furnace. Accurate capacity prediction (or model prediction) is essential to improve Real-time optimization (RTO).
We verified the accuracy of the proposed model by studying data from 2016 for 4-6 months on willow steel. In the following, we will describe the implementation steps in detail with reference to specific procedures:
the method comprises the following steps: the method is characterized in that 6 variables with high correlation degree with the generated energy are determined as input variables (coal gas flow, coal gas inlet pressure, coal gas inlet temperature, carbon monoxide content in coal gas, carbon dioxide content and hydrogen content) by combining analysis of related data and considering the whole trend and correlation coefficient of each dependent variable, and preprocessing such as interpolation and normalization are carried out on the input variables by utilizing a python and Matlab platform, so that the sampling frequency is uniform.
Step two: the method comprises the steps of selecting an Elman neural network as a basic model for prediction, adopting an error back propagation learning algorithm (BPTT) and combining a sliding window model for predicting the generating power of the TRT device.
Step three: and after initializing the model, training the model by using the normalized training sample, and using the trained model for predicting the generated power of the TRT device.
1. And step one, carrying out interpolation processing on the unevenly sampled data to form a sequence with equal time intervals, and selecting 6 variables related to the generating power of the TRT device through analysis of related data and the overall trend and the correlation coefficient of each dependent variable. The normalization method in the first step is as follows:
the normalized result is between 0 and 1.
2. The structure of the Elman neural network in the step two is as follows:
the ELman neural network is composed of four layers, namely an input layer, a hidden layer, a carrying layer and an output layer.
(1) From input layer to hidden layer:
x(k)＝f(w1xc(k)+w2(u(k-1)))
(2) from hidden layer to output layer:
y(k0＝g(w3x(k))
(3) the receiving layer stores short-term information:
xc(k)＝x(k-1)
(4) learning index function: using the sum of squared error function, training ends when E <0.0001
(5) The weight value updating method comprises the following steps:
w1for the connection weight of the bearer layer to the hidden layer, w2Is the connection weight of the input layer to the hidden layer, w3The connection weight from the hidden layer to the output layer.
In the method, three weights are required to be adjusted repeatedly, error is the difference between prediction and reality in the training process, eta represents the adjustment degree of feedback each time and is set to be 0.001, the weights are updated through continuous feedback, and finally the function value of the learning index meets the requirement to achieve the aim of finishing the training of the neural network
w1(n)＝w1(n-1)-η·2·error·w3·xc
w2(n)＝w2(n-1)-η·2·error·w3·u
w3(n)＝w3(n-1)-η·2·error·w3·xc。
3. The sliding window model principle of the second step is as follows:
the sliding window model is based on the assumption that the current output depends on the current input, and the mapping rule between the input and the output can be obtained through historical data. Based on this assumption, we preset a certain amount of training set samples, then continuously update the sample data and discard the oldest data point. As the window slides, the ELMAN neural network will continuously update its structural parameters and give up-to-date predicted values.
The data of 500 time points are predicted, and under the prediction, the result of the Elman algorithm is stable and basically has no fluctuation. We verify the accuracy of model prediction with two indexes, prediction hit ratio and mean square error mse:
in the actual production process, the prediction error is less than 0.1, and the requirement can be met. The hit rate of the proposed model reaches 93.2%, and the mean square error is 0.0018. Has high precision and can completely meet the requirement of actual production.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit and scope of the claims.
Claims (3)
1. A method for predicting the power generation power of a residual pressure turbine device based on an Elman neural network is characterized in that,
the method comprises the following steps: carrying out interpolation processing on unevenly sampled data to form a sequence with equal time intervals, analyzing the overall trend and correlation coefficient of each input variable, selecting six variables with high correlation degree with generated energy as input variables, wherein the input variables are gas flow, gas inlet pressure, gas inlet temperature, carbon monoxide content, carbon dioxide content and hydrogen content in gas, and carrying out interpolation and normalization preprocessing on the input variables by utilizing a python and Matlab platform to ensure that the sampling frequency is uniform;
step two: selecting an Elman neural network as a basic model for prediction, adopting an error back propagation learning algorithm (BPTT) and combining a sliding window model for predicting the generated power of the residual pressure turbine device;
step three: after initializing the model, training the model by using a normalized training sample, and using the trained model for predicting the power generation power of the residual pressure turbine device;
step one correlation coefficient is as follows:
gas flow rate in m3H, correlation coefficient 0.9294;
gas inlet pressure in kPa, correlation coefficient-0.0397;
the gas inlet temperature, unit ℃, correlation coefficient 0.0352;
volume content of CO, 0.1278%;
CO2volume content, 0.1935%;
the correlation coefficient is between 0 and 1, and the larger the correlation coefficient is, the stronger the correlation is;
the normalization method in the first step is as follows:
the normalized result is between 0 and 1;
the structure of the Elman neural network in the step two is as follows:
the ELman neural network is composed of four layers, namely an input layer, a hidden layer, a carrying layer and an output layer:
1) from input layer to hidden layer:
x(k)＝f(w1xc(k)+w2(u(k-1)))；
2) from hidden layer to output layer:
y(k)＝g(w3x(k))；
3) the receiving layer stores short-term information:
xc(k)＝x(k-1)；
4) learning index function: using the sum of squared error function, the training is ended when E <0.0001
5) The weight value updating method comprises the following steps:
w1for the connection weight of the bearer layer to the hidden layer, w2Is the connection weight of the input layer to the hidden layer, w3The three weights are adjusted repeatedly, error is the difference between prediction and reality in the training process, eta represents the adjustment degree of feedback each time and is set to be 0.001, the weights are updated through continuous feedback, and finally the function value of the learning index meets the requirement to achieve the aim of finishing the training of the neural network
w1(n)＝w1(n-1)-η·2·error·w3·xc，
w2(n)＝w2(n-1)-η·2·error·w3·u，
w3(n)＝w3(n-1)-η·2·error·w3·xc。
2. The method of claim 1, wherein the sliding window model of step two is based on the following principle:
the sliding window model is built on an assumption that the current output depends on the current input, the mapping rule between the input and the output is obtained through historical data, according to the assumption, a certain amount of training set samples are preset, then the sample data are continuously updated, the earliest data points are discarded, and the Elman neural network continuously updates the structural parameters and gives the latest predicted value along with the sliding of the window.
3. The method of claim 1, wherein the model of step three is adapted to time-varying, dynamic, nonlinear, strong inertial characteristics of blast furnace gas top pressure recovery turbine power generation.
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