CN112613237B - CFB unit NOx emission concentration prediction method based on LSTM - Google Patents
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Abstract
The invention relates to a CFB unit NOx emission concentration prediction method based on LSTM, which comprises the following steps: s1, determining main influence factors of the emission concentration of nitrogen oxides of the CFB unit through a grey correlation method; s2, collecting field data, and performing Gaussian smoothing on the air volume data; s3, in order to ensure the precision and the speed of data training, all input and output data of the LSTM are subjected to normalization processing, and the normalization processing interval is [ -1,1]; s4, establishing a CFB unit NOx emission concentration data model based on LSTM, and verifying through field data; and S5, changing the output delay order in the LSTM deep learning neural network to enable the model to have a prediction effect, so that the measurement delay caused by the reason that a NOx concentration measuring point is behind is overcome. The method adopts a machine learning mode to model the NOx emission concentration of the fluidized bed, and has high precision and simple process; the model has a prediction property by changing the delay order of the output values in the training set, and the NOx emission concentration can be predicted in 1 to 3 minutes.
Description
Technical Field
The invention belongs to the technical field of intelligent power generation, and particularly relates to a CFB unit NOx emission concentration prediction method based on LSTM, which predicts the NOx emission concentration of a circulating fluidized bed unit by machine learning.
Background
With the increasingly strict requirements of the national environmental protection agency on the pollutant emission of the thermal power plant, more and more pollutants emitted by the thermal power generating unit cannot reach the standard. In order to enable pollutant emission to reach an ultralow emission standard, most thermal power generating units are transformed into ultralow emission, for example, an SCR (selective catalytic reduction) denitration device, a wet desulphurization device and the like are additionally arranged at the tail part of flue gas. In recent years, circulating fluidized bed units have been rapidly developed due to the advantages of good coal adaptability, large load regulation range, low pollutant original concentration discharge and the like. In 2013, the first 600MW supercritical circulating fluidized bed boiler in the world was put into operation in the Sichuan white horse power plant, and in 2015, the first 350MW supercritical circulating fluidized bed boiler in the world was put into operation in the Shanxi national gold power plant. By the end of 2018, the total installed capacity of the circulating fluidized bed boiler put into China reaches 82.3GW, the 660MW efficient ultra-supercritical circulating fluidized bed boiler which is currently developed is quickly put into engineering construction, and the circulating fluidized bed boiler which is expected to be built is the circulating fluidized bed boiler with the lowest emission and energy consumption level and the highest capacity and efficiency in the world.
In order to save cost and have the advantage of natural low NOx concentration emission of the circulating fluidized bed unit, an SNCR device is usually additionally arranged at the top of a hearth for denitration of the circulating fluidized bed unit. However, the mode has a great defect that the NOx measuring points cannot resist high temperature, so that the NOx measuring points are generally installed at the inlet of the desulfurizing tower or the position of a chimney, the NOx concentration monitoring is relatively late, the measurement value of the SNCR control system is delayed for 3-5 minutes, and the SNCR automatic control investment is greatly influenced, so that the ammonia injection amount or the urea amount for denitration of the circulating fluidized bed unit is manually controlled, and the experience and physical strength of operators are seriously tested. In order to improve the SNCR automatic input rate of the circulating fluidized bed unit, the emission concentration of the circulating fluidized bed unit needs to be modeled, and the delay problem caused by measuring point reasons is solved. The data modeling mode only needs to substitute input and output into the neural network for machine learning, and part of parameters of the machine learning are adjusted in the process, so that the method is simple and high in precision, but the model built by the method is not advanced and limited in engineering application. The invention adopts the LSTM neural network with the functions of memory and forgetting to carry out modeling, and carries out training again after changing the delay order output in the training after obtaining the model parameters, so that the model has the function of prediction, the prediction precision is high, and the problem of delay caused by the fact that a field measuring point is close to the back can be solved.
Disclosure of Invention
The invention provides a CFB unit NOx emission concentration prediction method based on LSTM (least squares) for solving the technical problems in the prior art, so that the delay brought by a NOx concentration measuring point is overcome.
The invention comprises the following technical scheme: a CFB unit NOx emission concentration prediction method based on LSTM comprises the following steps:
s1, determining main influence factors of nitrogen oxide emission concentration of a CFB unit through a grey correlation method;
s2, collecting field data, wherein the air volume data are subjected to Gaussian smoothing treatment because the air volume data are easy to mutate and cause interference to an algorithm; the main purpose of the Gaussian smoothing processing data is to eliminate the interference of abnormal data and improve the accuracy of the model.
And S3, in order to ensure the precision and the speed of data training, all input and output data of the LSTM are subjected to normalization processing, and the normalization processing interval is [ -1,1].
S4, establishing a data model of the NOx emission concentration of the CFB unit based on the LSTM, and verifying through field data;
and S5, changing the output delay order in the LSTM deep learning neural network to enable the model to have a prediction effect, so that the measurement delay caused by the reason that the NOx concentration measuring point is behind is overcome.
Further, the grey correlation method in step S1 mainly includes: 1) Determining an analysis series, which generally comprises a reference series and a comparison series, wherein the reference series is the emission concentration of NOx, and the comparison series comprises 10 equipment parameters such as primary air volume, secondary air volume, bed temperature and the like; 2) The invention relates to a non-dimensionalized variable, which mainly aims to solve the problem that dimensions of data in a system are different and difficult to compare. 3) Calculating a correlation coefficient, wherein the larger the correlation coefficient is under a certain working condition, the larger the influence of factors in the comparison series on the reference series of the current working condition is; 4) Calculating the average value of the correlation coefficients under all working conditions to calculate the correlation degree, and preventing abnormal data from appearing; 5) And selecting proper parameters as input parameters of the model after the relevance ranking.
Further, the input parameters of the finally established model comprise coal feeding quantity, bed temperature, primary air quantity, secondary air quantity, ammonia injection quantity and air preheater inlet oxygen quantity.
Further, the window size of the gaussian smoothing process in step S2 is 20.
Further, the model to which the normalized data is applied in step S3 is converged by a gradient descent method, so that the normalized data can improve the convergence speed of the model and improve the accuracy of the model. The formula of the normalization process is:
in MATLAB the code is: c = mapminmax (a, -1, 1);
further, the data model input based on LSTM in step S4 is the coal supply amount, bed temperature, primary air amount, secondary air amount, ammonia injection amount, and oxygen amount after gaussian smoothing and normalization processing.
Further, the training samples of the data model are 1000 groups, the input layer structure of the training set is 100 × 10 (batch _ size is 100, step \/size is 10), the hidden layer is 2 LSTM layers composed of 10 neurons and 2 Dense layers composed of 40 neurons, and the output layer is NOx emission concentration.
Further, the learning rate of the data model is 0.015, the number of cells is 20, adam is adopted by an optimizer, and the learning step length is 80.
Further, in step S5, the change output delay order is 1min, 3min, and 5min, respectively, the model can be searched to predict the limit duration of the NOx emission concentration, the accuracy will begin to decrease as the predicted time is longer, and the deviation exceeds the range allowed by the engineering, which indicates that the model can no longer accurately predict the NOx emission concentration.
Furthermore, the data model in the step S5 can lead 1min and 3min to carry out more accurate prediction on the NOx concentration emission.
The invention has the advantages and positive effects that:
1. compared with the defects of complex modeling and limited precision of the traditional mechanism, the method adopts a machine learning mode to model the NOx emission concentration of the fluidized bed, has higher precision and is simple in process.
2. In the past, the fitting degree between the output value of the model and the actual value can be increased only by improving an algorithm in data modeling, the model is not predictive, so the practicability is limited, and the method leads the model to have the prediction property by changing the delay order of the output value in a training set and can predict the NOx emission concentration 1-3 minutes ahead.
3. The data model of the invention can predict the NOx emission concentration 1-3 minutes ahead, and can provide scientific reference for field operation and SNCR control system design.
4. Although the machine algorithm is widely applied to modeling of the thermal power generating unit, actual application on site is quite rare, and the invention provides a new idea for application of the machine algorithm to the thermal power generating unit.
Drawings
FIG. 1 is the LSTM cell renewal process;
FIG. 2 is a modeling process for CFB unit NOx emission concentration using an LSTM neural network.
FIG. 3 is a training set versus test set effect of the LSTM neural network;
FIG. 4 is a graph of the effect of an LSTM neural network on the different predicted times of NOx emission concentration;
Detailed Description
To further clarify the disclosure of the present invention, its features and advantages, reference is made to the following examples taken in conjunction with the accompanying drawings.
Example (b): referring to fig. 1-4, a method for predicting LSTM-based NOx emission concentration of a CFB unit includes the steps of: s1, determining main influence factors of the emission concentration of nitric oxides of a CFB unit through a grey correlation method; s2, collecting field data, and performing Gaussian smoothing on the air volume data because the air volume data is easy to mutate and can cause interference on an algorithm; the main purpose of the Gaussian smoothing processing data is to eliminate the interference of abnormal data and improve the accuracy of the model; the window size of the gaussian smoothing processing in the step S2 is 20; s3, in order to ensure the precision and the speed of data training, all input and output data of the LSTM are subjected to normalization processing, and the normalization processing interval is [ -1,1]; s4, establishing a CFB unit NOx emission concentration data model based on LSTM, and verifying through field data; and S5, changing the output delay order in the LSTM deep learning neural network to enable the model to have a prediction effect, so that the measurement delay caused by the reason that the NOx concentration measuring point is behind is overcome.
The grey correlation method in the step S1 mainly comprises the following steps: 1) Determining an analysis sequence, which generally comprises a reference sequence and a comparison sequence, wherein the reference sequence is the emission concentration of NOx, and the comparison sequence comprises 10 equipment parameters such as primary air volume, secondary air volume, bed temperature and the like; 2) The invention relates to a non-dimensionalized variable, which mainly aims to solve the problem that dimensions of data in a system are different and difficult to compare. 3) Calculating a correlation coefficient, wherein the larger the correlation coefficient is under a certain working condition, the larger the influence of factors in the comparison series on the reference series of the current working condition is; 4) Calculating the average value of the correlation coefficients under all working conditions to calculate the correlation degree, and preventing abnormal data from appearing; 5) And selecting proper parameters as input parameters of the model after the relevance ranking. And the input parameters for finally establishing the model comprise coal feeding quantity, bed temperature, primary air quantity, secondary air quantity, ammonia injection quantity and air preheater inlet oxygen quantity.
The model to which the normalized data is applied in the step S3 adopts a gradient descent method for convergence, so that the normalized data can improve the convergence speed of the model and improve the precision of the model. The formula of the normalization process is: in MATLAB
The middle code is: c = mapminmax (a, -1, 1).
The data model input quantity based on the LSTM in the step S4 is coal feeding quantity, bed temperature, primary air quantity, secondary air quantity, ammonia injection quantity and oxygen quantity after Gaussian smoothing and normalization processing. The training sample of the data model is 1000 groups, the input layer structure of the training set is 100 × 10 (batch _ size is 100, step \usize is 10), the hidden layer is 2 LSTM layers composed of 10 neurons and 2 Dense layers composed of 40 neurons, and the output layer is NOx emission concentration. The data model learning rate is 0.015, the cell number is 20, adam is adopted by an optimizer, and the learning step length is 80.
In the step S5, the change output delay order is 1min, 3min, and 5min, respectively, the model can be searched to predict the limit duration of the NOx emission concentration, the accuracy will start to decrease as the predicted time is longer, and the deviation exceeds the range allowed by the engineering, which indicates that the model can no longer accurately predict the NOx emission concentration. In the step S5, the data model can lead 1min and 3min to accurately predict the NOx concentration emission.
The working principle is as follows:
1. LSTM neural network
The conventional RNN (recurrent neural network) has improved the situation that nodes between layers are not connected, and the RNN selecting the convergence mode of the gradient descent method still does not get rid of the defect of gradient explosion, so on the basis of the original RNN, the LSTM changes the updating mode of the memory cell by introducing the concepts of input gate, forgetting gate and output gate, thereby solving the problem. FIG. 1 shows the cell renewal process of LSTM, which mainly comprises three steps:
1) The forgetting gate first decides which information to discard. The method mainly outputs a numerical value between 0 and 1 through a Sigmoid function according to the output of the previous moment and the input of the current moment. A value of 1 indicates complete retention and a value of 0 indicates complete discard. The forgetting door works as follows:
f t ＝simg(W f [h t-1 ,x t ]+b f ) (1)
in the formula: w f A weight matrix of the forgetting gate; h is t-1 For the output of the hidden layer at time t-1, x t Input for time t; b f Is the hidden layer offset vector.
2) The second step is to determine which information should be remembered. This step consists of two parts, one part of which calculates a candidate vector c 'by tanh' t The other part is that the input gate determines which values to update through Sigmoid function, and the operation is as follows (2) and (3):
c′ t ＝tanh(W c ·[h t-1 ,x t ]+b c ) (2)
i t ＝simg(W i ·[h t-1 ,x t ]+b i ) (3)
wherein: c' t Candidate value for cell state update, wc, wi are weight matrix of cell update and memory gate, b c 、b i The offset vector of the gate is updated and memorized for the cell.
3) Renewal of cell status, c t-1 The unnecessary information is discarded, and the remaining information and ct-1 constitute a new cell state c t The operation mode is as follows:
c t ＝f t *c t-1 +i t *c′ t (4)
4) The output gate outputs, and the working mode is as follows:
h t ＝o t *tanh(c t ) (5)
2. circulating fluidized bed NO x LSTM modeling process of emission concentration
FIG. 2 shows the utilization of the LSTM neural network for the circulating fluidized bed unit NO x Modeling process of emission concentration. Firstly, carrying out data processing on the selected 6 groups of input quantities and NOx concentration emission, substituting the processed data into a training set, and defining initial parameters of an LSTM model. The LSTM initial parameters were: batch _ size of 10, step size of 100, cell size of 20, LSTM layer number of 1, cell number of 10, and Learning Rate (LR) of 0.015. And after the training is finished, evaluating the current model parameters by utilizing the evaluation indexes RMSE and MAPE of the training result, adopting the current parameters when the indexes are reached, and changing the model parameters to train again until the evaluation indexes meet the engineering requirements. At this time, the current model parameters are used to test the test set, and the output and the true value of the test set are compared to observe whether the test set meets the engineering standard, and the final result is shown in fig. 3.
3. Circulating fluidized bed NO x LSTM prediction process of emission concentration
Many applications of machine learning to engineering are usually stopped at step 2, and more parameters are optimized and algorithm is improved to improve the fitting degree of the output of the test set to the true value, which is very limited to help the actual engineering application, and the model is not well utilized.
Through research on the field process and the combustion theory of the circulating fluidized bed unit, the circulating fluidized bed unit is found to be a large-inertia and large-delay object. In LSTM model input, primary air quantity, secondary air quantity, coal feeding quantity, ammonia injection quantity and bed temperature are all reflected hearth input, NOx generation and reduction are completed in a hearth, a large amount of complex chemical reaction processes are carried out in the process, the chemical reactions require about 2min, and NO x There is still a considerable length of flue gas duct from the SNCR outlet to the point location, and NO x The concentration measuring point also needs to sample the flue gas to a concentration analyzer, and the concentration analyzer can be used for NO x The measurement of the concentration causes a delay, and thus the currently measured NO x The concentration is actually determined by the input amount before 3-5 minutes, and the method is used for predicting NO of the circulating fluidized bed unit by the LSTM model x The concentration of the emissions providing the possibility of varying the output of the training setThe delay order is used for eliminating the delay caused by the reason of measuring points, and the invention respectively leads the output in the training set by 1min, 3min and 5min to search the predicted limit time. The results are shown in FIG. 4, and show that the LSTM model can lead NO by 1min and 3min x The concentration discharge is accurately predicted, and the prediction accuracy is sharply reduced in the 5 th min, so that the NO of the circulating fluidized bed unit can be obtained by the LSTM model x The predicted limit time for emission concentration is 3min.
While the preferred embodiments of the present invention have been illustrated and described, it will be appreciated by those skilled in the art that the foregoing embodiments are illustrative and not limiting, and that many changes may be made in the form and details of the embodiments of the invention without departing from the spirit and scope of the invention as defined in the appended claims. All falling within the scope of protection of the present invention.
Claims (5)
1. A CFB unit NOx emission concentration prediction method based on LSTM is characterized by comprising the following steps: s1, determining an influence factor of the emission concentration of nitrogen oxides of the CFB unit through a grey correlation method; s2, collecting field data, and performing Gaussian smoothing on the air volume data; s3, all input and output data of the LSTM are subjected to normalization processing, and the normalization processing interval is [ -1,1]; s4, establishing a CFB unit NOx emission concentration data model based on LSTM, and verifying through field data; the input quantity of the data model based on the LSTM in the step S4 is coal feeding quantity, bed temperature, primary air quantity, secondary air quantity, ammonia injection quantity and oxygen quantity after Gaussian smoothing and normalization processing; the training sample of the data model is 1000 groups, the structure of the input layer of the training set is 100 multiplied by 10, the hidden layer is 2 LSTM layers consisting of 10 neurons and 2 Dense layers consisting of 40 neurons, and the output layer is NOx emission concentration; the learning rate of the data model is 0.015, the number of cells is 20, adam is adopted by an optimizer, and the learning step length is 80; s5, changing an output delay order in the LSTM deep learning neural network, so that the data model of the NOx emission concentration has a prediction effect; in the step S5, the changing output delay order is 1min, 3min, and 5min respectively; the data model in the step S5 can lead 1min and 3min to accurately predict the NOx concentration emission.
2. The method for predicting LSTM-based CFB unit NOx emission concentration according to claim 1, wherein: the grey correlation method in the step S1 comprises the following steps: 1) Determining an analysis sequence comprising a reference sequence and a comparison sequence; wherein the reference series is the emission concentration of NOx, and the comparison series is the equipment parameter; 2) Carrying out equalization treatment on the dimensionless variables; 3) Calculating a correlation coefficient, wherein the larger the correlation coefficient under a certain working condition is, the larger the influence of factors in the comparison number series on the reference number series of the current working condition is; 4) Calculating the average value of the correlation coefficients under all working conditions to calculate the correlation degree, and preventing abnormal data from appearing; 5) And selecting proper parameters as input parameters of the model after the relevance ranking.
3. The LSTM-based CFB unit NOx emission concentration prediction method of claim 2, wherein: the input parameters comprise coal feeding quantity, bed temperature, primary air quantity, secondary air quantity, ammonia injection quantity and air preheater inlet oxygen quantity.
4. The method for predicting LSTM-based CFB unit NOx emission concentration according to claim 1, wherein: the window size of the gaussian smoothing process in step S2 is 20.
5. The method for predicting LSTM-based CFB unit NOx emission concentration according to claim 1, wherein: the normalization processing in the step S3 has the formula
: in MATLAB the code is: c = mapminmax (a, -1, 1).
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