CN114708924B - Model construction method and device for predicting soot blower soot blowing interval time in SCR system - Google Patents
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- 239000002956 ash Substances 0.000 description 23
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- 230000000903 blocking effect Effects 0.000 description 4
- 239000003054 catalyst Substances 0.000 description 3
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 2
- 238000010531 catalytic reduction reaction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- 229910021529 ammonia Inorganic materials 0.000 description 1
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Abstract
The invention provides a model construction method and a device for predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology, wherein the method comprises the following steps: acquiring structural parameters, operation data and historical soot blowing time intervals of an SCR system to construct an initial database; normalizing the initial database, and dividing the initial database into a training set and a testing set; selecting an excitation function of the BP neural network, determining an input layer node, an output layer node and an hidden layer node of the BP neural network, and constructing a soot blowing time interval model; training the soot blowing time interval model by adopting a training set; and optimizing the weight and the threshold value of the soot blowing time interval model through the test set until the weight and the threshold value converge, so as to obtain the trained soot blowing time interval model. According to the invention, the optimal soot blowing time is determined rapidly, conveniently and accurately according to the existing structural parameters and operation data of the SCR system, the debugging process in production is avoided, and the time and economic cost are saved greatly.
Description
Technical Field
The document relates to the technical field of machine learning, in particular to a model construction method and device for predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology.
Background
The Selective Catalytic Reduction (SCR) technology becomes the most dominant coal-fired boiler denitration method at home and abroad by the mature technology and higher denitration efficiency. Denitration catalysts are the most important functional materials in SCR systems, and the service life of the denitration catalysts has a great influence on denitration efficiency and economic cost of SCR technology. However, high concentration of fly ash particles in the flue gas can cause catalyst plugging and attrition when flowing through the SCR system, which can greatly reduce denitration device performance, lead to increased ammonia slip and air preheater plugging, and even lead to unplanned shutdown when severe. Therefore, the method effectively separates large-particle ash and ensures the safe and efficient operation of the SCR denitration system, which is a problem to be solved in each power plant.
The large-particle ash interception device is arranged at the proper position of the flue, is a main technology for separating and capturing large-particle ash at present, and has the advantages of low design requirement on a convection field, high capturing efficiency, convenience in transformation and installation and the like. At present, the large-particle ash interception device is successfully applied to a selective catalytic reduction denitration system of a domestic million units. In order to prevent the blocking of the blocking net, a self-ash-cleaning structure is arranged at the blocking device, such as an air cannon soot blower is arranged on the lee surface of the blocking net. On the premise of determining the type and the installation position of the soot blower, the soot blowing interval is the most important factor influencing the large-particle soot interception and removal efficiency. Although the SCR system of the coal-fired power plant is provided with a soot blower for soot blowing, most power plants have defects in an empirically formulated on-time quantitative soot blowing mode due to lack of real-time visual data of soot blower blockage, or the defects of soot blowing lead to failure in effectively reducing pressure drop loss and efficiency reduction, or excessive soot blowing times lead to increase of energy consumption and cost. Therefore, how to predict the blockage of the soot blower in real time and formulate a reasonable soot blowing scheme is of great significance to the stable operation of the SCR system. The existing large-particle ash interception technology of the SCR denitration system is still in a starting stage, the soot blowing interval time setting in a soot blowing control scheme is still obtained empirically, and the applicability is limited under the complex operation working condition of a power plant.
Disclosure of Invention
The invention aims to provide a model construction method and device for predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology, and aims to solve the problems in the prior art.
The invention provides a model construction method for predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology, which comprises the following steps:
acquiring structural parameters, operation data and historical soot blowing time intervals of an SCR system, and constructing an initial database based on the structural parameters, the operation data and the historical soot blowing time intervals of the SCR system;
Normalizing the initial database, and dividing the normalized initial database into a training set and a testing set according to a specific proportion;
selecting an excitation function of the BP neural network, determining an input layer node, an output layer node and an hidden layer node of the BP neural network, and constructing a soot blowing time interval model;
training the soot blowing time interval model by adopting a training set;
And optimizing the weight and the threshold value of the soot blowing time interval model through the test set until the weight and the threshold value converge, so as to obtain the trained soot blowing time interval model.
The invention provides a model construction device for predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology, which comprises the following components:
The data acquisition module is used for acquiring the structural parameters, the operation data and the historical soot blowing time interval of the SCR system, and constructing an initial database based on the structural parameters, the operation data and the historical soot blowing time interval of the SCR system;
The normalization module is used for carrying out normalization processing on the initial database and dividing the normalized initial database into a training set and a testing set according to a specific proportion;
the model construction module is used for constructing a soot blowing time interval model by selecting an excitation function of the BP neural network and determining an input layer node, an output layer node and an hidden layer node of the BP neural network;
the model training module is used for training the soot blowing time interval model through a training set;
and the model optimization module is used for optimizing the weight and the threshold value of the soot blowing time interval model through the test set until the weight and the threshold value converge, so as to obtain the trained soot blowing time interval model.
The invention provides an electronic device, comprising: a processor;
And a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the steps of a model building method for predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology as described above
The invention provides a storage medium for storing computer executable instructions which when executed implement the steps of the model construction method for predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology
By adopting the embodiment of the invention, the soot blowing time interval model is constructed by collecting the structural parameters, the operation data and the historical soot blowing time interval of the SCR system, and the weight and the threshold value of the soot blowing time interval model are optimized, so that the optimal soot blowing time interval model is obtained, the optimal soot blowing time is rapidly, conveniently and accurately determined, the debugging process in production is avoided, and the time and the economic cost are greatly saved.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
For a clearer description of one or more embodiments of the present description or of the solutions of the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description that follow are only some of the embodiments described in the description, from which, for a person skilled in the art, other drawings can be obtained without inventive faculty.
FIG. 1 is a flow chart of a model construction method for predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology in an embodiment of the invention;
Fig. 2 is a schematic structural diagram of a BP neural network according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a model building device for predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology according to an embodiment of the invention.
Detailed Description
In order to enable a person skilled in the art to better understand the technical solutions in one or more embodiments of the present specification, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one or more embodiments of the present disclosure without inventive faculty, are intended to be within the scope of the present disclosure.
Method embodiment
According to an embodiment of the present invention, a model construction method for predicting soot blower soot blowing interval time in an SCR system based on a BP neural network technology is provided, fig. 1 is a flowchart of a model construction method for predicting soot blower soot blowing interval time in an SCR system based on a BP neural network technology according to an embodiment of the present invention, and as shown in fig. 1, a model construction method for predicting soot blower soot blowing interval time in an SCR system based on a BP neural network technology according to an embodiment of the present invention specifically includes:
step S101, acquiring structural parameters, operation data and historical soot blowing time intervals of an SCR system, and constructing an initial database based on the structural parameters, the operation data and the historical soot blowing time intervals of the SCR system; the step S101 specifically includes: and (3) finishing a large number of simulation and tests completed before, collecting structural parameters and operation data of the SCR system, recording historical soot blowing time intervals, and constructing a corresponding database of the parameters and soot blowing time of the soot blowers.
Step S102, carrying out normalization processing on an initial database, and dividing the normalized initial database into a training set and a testing set according to a specific proportion; the step S102 specifically includes: the normalization formula is adopted as follows:
Where X i is the ith normalized input parameter, X i is the ith input parameter, X min,i is the minimum value of the ith input parameter, and X max,i is the maximum value of the ith input parameter. Typically using a 7:3 ratio to divide into training and test sets.
Step S103, selecting an excitation function of the BP neural network, determining an input layer node, an output layer node and an hidden layer node of the BP neural network, and constructing a soot blowing time interval model;
adopting sigmoiod functions as excitation functions, wherein the sigmoid functions are as follows:
Where s is the argument of the excitation function and is the input variable transmitted from layer to layer.
Fig. 2 is a schematic structural diagram of a BP neural network according to an embodiment of the present invention, according to fig. 2, it can be known that the input parameter x 1～x6 is the installation height of the interception net, the air flow, the diameter of the large particle ash, the flow of the large particle ash, the number of soot blowers, and the installation angle of the soot blowers, and the output parameter y 1 is the ash cleaning interval time.
Step S104, training the soot blowing time interval model by adopting a training set; the step S104 specifically includes:
Solving the output of each node of the hidden layer and the output layer, calculating the deviation between the target value and the actual output value, if the error is larger than the allowable error, reversely transmitting the error to the input layer through the hidden layer, updating the weight of the hidden layer according to the signal, and then forward transmitting again until the target value and the predicted value meet the allowable error, and finishing learning.
And step 105, optimizing the weight and the threshold value of the soot blowing time interval model through the test set until the weight and the threshold value converge, and obtaining the trained soot blowing time interval model. The step S105 specifically includes: and verifying the precision of the prediction model by using the data of the test set, and constructing the BP neural network prediction model successfully when the precision reaches the standard. And when the precision does not reach the standard, repeating the training process until the precision reaches the standard. The evaluation index is a correlation coefficient and a variance, and the correlation coefficient is:
The variance is:
wherein y i is the actual value of the j-th ash removal interval time, Is the j-th ash removal interval time predicted value,/>Is the average value of ash removal interval time, and N is the number of samples in the test set.
After the BP neural network prediction model is successfully constructed, in the actual power plant operation, firstly determining a large-particle ash interception scheme (such as the interception net position, the interception net opening shape and the interception net structure shape) and a soot blowing scheme (such as the soot blower type, the soot blower installation position and the soot blowing mode), and then inputting specific numerical values of six input parameters (such as the interception net installation height, the air flow, the large-particle ash diameter, the large-particle ash flow, the number of soot blowers and the soot blower installation angle) into the constructed BP neural network to obtain the soot cleaning interval time meeting the optimal soot cleaning efficiency condition.
Device embodiment 1
According to an embodiment of the present invention, a model building device for predicting soot blower soot blowing interval time in an SCR system based on a BP neural network technology is provided, and fig. 3 is a schematic diagram of a model building device for predicting soot blower soot blowing interval time in an SCR system based on a BP neural network technology according to an embodiment of the present invention, as shown in fig. 3, a model building device for predicting soot blower soot blowing interval time in an SCR system based on a BP neural network technology according to an embodiment of the present invention specifically includes:
The data acquisition module 30 is configured to acquire SCR system structural parameters, operation data and a historical soot blowing time interval, and construct an initial database based on the SCR system structural parameters, the operation data and the historical soot blowing time interval; the data acquisition module is specifically used for: and (3) finishing a large number of simulation and tests completed before, collecting structural parameters and operation data of the SCR system, recording historical soot blowing time intervals, and constructing a corresponding database of the parameters and soot blowing time of the soot blowers.
The normalization module 32 is configured to normalize the initial database, and divide the normalized initial database into a training set and a testing set according to a specific proportion; the normalization module is specifically used for: the normalization formula is adopted as follows:
Where X i is the ith normalized input parameter, X i is the ith input parameter, X min,i is the minimum value of the ith input parameter, and X max,i is the maximum value of the ith input parameter. Typically using a 7:3 ratio to divide into training and test sets.
The model building module 34 is configured to build a soot blowing time interval model by selecting an excitation function of the BP neural network and determining an input layer node, an output layer node and an hidden layer node of the BP neural network; the model building module 34 is specifically configured to: adopting sigmoiod functions as excitation functions, wherein the sigmoid functions are as follows:
Where s is the argument of the excitation function and is the input variable transmitted from layer to layer.
Fig. 2 is a schematic structural diagram of a BP neural network according to an embodiment of the present invention, according to fig. 2, it can be known that the input parameter x 1～x6 is the installation height of the interception net, the air flow, the diameter of the large particle ash, the flow of the large particle ash, the number of soot blowers, and the installation angle of the soot blowers, and the output parameter y 1 is the ash cleaning interval time.
A model training module 36 for training the soot blowing time interval model through a training set; the model training module is specifically used for: solving the output of each node of the hidden layer and the output layer, calculating the deviation between the target value and the actual output value, if the error is larger than the allowable error, reversely transmitting the error to the input layer through the hidden layer, updating the weight of the hidden layer according to the signal, and then forward transmitting again until the target value and the predicted value meet the allowable error, and finishing learning.
The model optimization module 38 is configured to optimize the weight and the threshold of the soot blowing time interval model through the test set until the weight and the threshold converge, and obtain a trained soot blowing time interval model, where the model optimization module is specifically configured to: and verifying the precision of the prediction model by using the data of the test set, and constructing the BP neural network prediction model successfully when the precision reaches the standard. And when the precision does not reach the standard, repeating the training process until the precision reaches the standard. The evaluation index is a correlation coefficient and a variance, and the correlation coefficient is:
The variance is:
wherein y i is the actual value of the j-th ash removal interval time, Is the j-th ash removal interval time predicted value,/>Is the average value of ash removal interval time, and N is the number of samples in the test set.
After the BP neural network prediction model is successfully constructed, in the actual power plant operation, firstly determining a large-particle ash interception scheme (such as the interception net position, the interception net opening shape and the interception net structure shape) and a soot blowing scheme (such as the soot blower type, the soot blower installation position and the soot blowing mode), and then inputting specific numerical values of six input parameters (such as the interception net installation height, the air flow, the large-particle ash diameter, the large-particle ash flow, the number of soot blowers and the soot blower installation angle) into the constructed BP neural network to obtain the soot cleaning interval time meeting the optimal soot cleaning efficiency condition.
Device example two
An embodiment of the present invention provides an electronic device, including: a processor;
And a memory arranged to store computer executable instructions that when executed cause the processor to perform steps as described in method embodiments.
Device example III
Embodiments of the present invention provide a computer-readable storage medium having stored thereon a program for realizing information transmission, which when executed by a processor realizes the steps as described in the method embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (8)
1. A model construction method for predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology is characterized by comprising the following steps:
Acquiring SCR system structural parameters, operation data and historical soot blowing time intervals, and constructing an initial database based on the SCR system structural parameters, the operation data and the historical soot blowing time intervals;
Carrying out normalization processing on the initial database, and dividing the normalized initial database into a training set and a testing set according to a specific proportion;
selecting an excitation function of the BP neural network, determining an input layer node, an output layer node and an hidden layer node of the BP neural network, and constructing a soot blowing time interval model;
training the soot blowing time interval model by adopting a training set;
Optimizing the weight and the threshold value of the soot blowing time interval model through a test set until the weight and the threshold value converge, so as to obtain a trained soot blowing time interval model;
The method comprises the steps of selecting an excitation function of a BP neural network, determining an input layer node, an output layer node and an hidden layer node of the BP neural network to construct a soot blowing time interval model, and specifically comprises the following steps:
adopting sigmoiod functions as excitation functions, wherein the sigmoid functions are obtained through a formula 1;
Wherein s is the argument of the excitation function;
setting the node parameters of an input layer as x 1～x6, wherein x 1～x6 is the installation height of an interception net, the air flow, the diameter of large-particle ash, the flow of large-particle ash, the number of soot blowers and the installation angle of the soot blowers respectively;
setting an output layer node parameter y 1 as ash removal interval time;
Solving the hidden layer node number l according to a formula 2;
Wherein m represents the number of nodes of the input layer, n represents the number of nodes of the output layer, a is a constant, and the value range is [1,10].
2. The method of claim 1, wherein training the soot blowing time interval model with a training set specifically comprises:
And calculating the deviation between the target value and the actual output value according to the output of the hidden layer node and the output layer node, if the deviation is larger than a preset error, reversely transmitting the error to the input layer through the hidden layer, updating the weight of the hidden layer according to the signal, and then forward transmitting the signal again until the target value and the predicted value meet the preset error.
3. The method of claim 1, wherein optimizing the soot blowing time interval model for weights and thresholds by a test set specifically comprises:
optimizing weights and thresholds of the soot blowing time interval model through correlation coefficients and mean square deviations, wherein the correlation coefficients are obtained through a formula 3, and the mean square deviations are obtained through a formula 4;
wherein y i is the actual value of the j-th ash removal interval time, Is the j-th ash removal interval time predicted value,/>Is the average value of ash removal interval time, and N is the number of samples in the test set.
4. The model construction device for predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology is characterized by comprising the following components:
The data acquisition module is used for acquiring the structural parameters, the operation data and the historical soot blowing time interval of the SCR system, and constructing an initial database based on the structural parameters, the operation data and the historical soot blowing time interval of the SCR system;
The normalization module is used for carrying out normalization processing on the initial database and dividing the normalized initial database into a training set and a testing set according to a specific proportion;
the model construction module is used for constructing a soot blowing time interval model by selecting an excitation function of the BP neural network and determining an input layer node, an output layer node and an hidden layer node of the BP neural network;
the model training module is used for training the soot blowing time interval model through a training set;
The model optimization module is used for optimizing the weight and the threshold value of the soot blowing time interval model through a test set until the weight and the threshold value converge, so as to obtain a trained soot blowing time interval model;
The model construction module is specifically used for:
adopting sigmoiod functions as excitation functions, wherein the sigmoid functions are obtained through a formula 1;
Wherein s is the argument of the excitation function;
setting the node parameters of an input layer as x 1～x6, wherein x 1～x6 is the installation height of an interception net, the air flow, the diameter of large-particle ash, the flow of large-particle ash, the number of soot blowers and the installation angle of the soot blowers respectively;
setting an output layer node parameter y 1 as ash removal interval time;
Solving the hidden layer node number l according to a formula 2;
Wherein m represents the number of nodes of the input layer, n represents the number of nodes of the output layer, a is a constant, and the value range is [1,10].
5. The apparatus of claim 4, wherein the model training module is specifically configured to:
And calculating the deviation between the target value and the actual output value according to the output of the hidden layer node and the output layer node, if the deviation is larger than a preset error, reversely transmitting the error to the input layer through the hidden layer, updating the weight of the hidden layer according to the signal, and then forward transmitting the signal again until the target value and the predicted value meet the preset error.
6. The apparatus of claim 4, wherein the model optimization module is specifically configured to:
optimizing weights and thresholds of the soot blowing time interval model through correlation coefficients and mean square deviations, wherein the correlation coefficients are obtained through a formula 3, and the mean square deviations are obtained through a formula 4;
wherein y i is the actual value of the j-th ash removal interval time, Is the j-th ash removal interval time predicted value,/>Is the average value of ash removal interval time, and N is the number of samples in the test set.
7. An electronic device, comprising: a processor;
And a memory arranged to store computer executable instructions that when executed cause the processor to perform the steps of the model building method of predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology as claimed in any one of claims 1 to 3.
8. A storage medium storing computer executable instructions which when executed implement the steps of the model building method of predicting soot blower soot blowing interval time in an SCR system based on BP neural network technology as claimed in any one of claims 1 to 3.
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| CN109654517A (en) * | 2018-12-05 | 2019-04-19 | 中北大学 | A kind of boiler soot-blowing optimization method based on the prediction of heating surface health status |
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| CN105069185A (en) * | 2015-07-14 | 2015-11-18 | 东南大学 | Method for establishing air pre-heater clean factor calculation model by using smoke pressure difference, and application |
| US10565522B2 (en) * | 2016-07-25 | 2020-02-18 | General Electric Company | System modeling, control and optimization |
| CN106599586B (en) * | 2016-12-19 | 2019-05-28 | 北京国能中电节能环保技术股份有限公司 | SCR neural network based intelligently sprays ammonia optimization method and device |
| KR20180076918A (en) * | 2016-12-28 | 2018-07-06 | 대우조선해양 주식회사 | Soot blowing apparatus and method of exhaust system for vessel |
| CN109603545B (en) * | 2018-12-28 | 2020-01-07 | 浙江大学 | SCR denitration method and device combining soot blowing |
| SG10201903974UA (en) * | 2019-04-06 | 2020-11-27 | Avanseus Holdings Pte Ltd | Method and system for accelerating convergence of recurrent neural network for machine failure prediction |
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| CN109654517A (en) * | 2018-12-05 | 2019-04-19 | 中北大学 | A kind of boiler soot-blowing optimization method based on the prediction of heating surface health status |
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