CN112508226A - Thermal power plant coal yard loss prediction method and system - Google Patents
Thermal power plant coal yard loss prediction method and system Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a system for predicting coal yard loss of a thermal power plant, wherein the method comprises the following steps: and collecting loss parameters of the coal pile in the coal yard. Carrying out data processing on the collected coal pile loss parameters of the coal yard; wherein the data processing comprises data cleaning and data normalization processing. And establishing a coal yard loss prediction model by using a physical modeling method based on the coal yard coal pile loss parameters after data processing, and predicting the real-time coal yard loss. Aiming at the phenomena that coal yard loss data are difficult to obtain quantitatively, heat value loss is difficult to predict and the like in the management process of a large coal-fired coal yard in China at present, the coal yard loss prediction analysis research of a thermal power plant is developed, a coal yard loss prediction analysis system and a method of the thermal power plant are established, the coal quality change rule, especially the raw coal heat value loss change rule, of coal in the storage process of an open coal yard and a round coal yard can be obtained, the reason of the heat value difference of coal entering a plant and coal entering a furnace can be analyzed, a basis is provided for improving the coal yard management, and considerable economic benefits are brought to the thermal power plant.
Description
Technical Field
The invention relates to the field of coal yard management, in particular to a method and a system for predicting loss of a coal yard of a thermal power plant.
Background
The coal burning cost occupies about 2/3 of the production and operation cost of a thermal power plant, so the loss of coal quality and coal quantity of the coal has important influence on the economic benefit of the power plant. During the storage of the fire coal, a series of changes such as coal loss, water increase, heat productivity reduction, fire coal spontaneous combustion and the like can occur due to the influence of external environmental factors and self characteristics. These changes are important factors for coal quality and coal consumption, and are directly related to the production and management benefits of power plants. Therefore, the method is a main subject of coal yard management work of a thermal power plant, wherein the loss analysis of coal quantity and coal quality is well done, and loss control measures are carefully executed to reduce the loss of the coal quantity and the coal quality.
The open-air coal yard and the round coal yard of the coal-fired power plant in China have huge coal storage amount. The heat value loss of the coal piles in the open coal yard and the circular coal yard is effectively controlled, the heat value difference of the coal as fired of the coal-fired power plant is greatly reduced, and the economical efficiency of the power plant is improved. Therefore, the optimal stacking time of different coal qualities under different environmental conditions is obtained by continuously monitoring the coal pile characteristic indexes, the climate parameters and the coal quality change indexes of the coal piles of the open-air coal yard and the circular coal yard and finding out the change rule of the coal quality indexes along with the coal pile characteristic indexes. And the change rule of the coal quality, especially the change rule of the heat value loss of raw coal, of the coal in the storage process of the open-air coal yard and the round coal yard is obtained, the reason of the heat value difference of the coal entering the factory and the coal entering the furnace can be analyzed, a basis is provided for improving the management of the coal yard, and considerable economic benefit is brought to the power plant.
Disclosure of Invention
The invention provides a road network matching method and system among different-source high-precision maps, which are used for solving the problems that in the prior art, a change detection recall rate is high based on a feature method of manual design, but the change detection recall rate is too sensitive to some simple changes such as color, gradient and the like, so that a large number of false detections are caused.
In a first aspect, an embodiment of the present invention provides a method for predicting a coal yard loss of a thermal power plant, including:
101, collecting coal pile loss parameters of a coal yard; the coal yard coal pile loss parameters comprise time parameters, environment parameters, coal pile physical parameters, coal quality characteristic parameters, coal pile temperature field parameters and spraying water quantity parameters;
102, processing data of the collected coal pile loss parameters of the coal yard; wherein the data processing comprises data cleaning and data normalization processing;
and 103, establishing a coal yard loss prediction model by using a physical modeling method based on the coal yard coal pile loss parameters after data processing, and predicting the real-time coal yard loss.
Further, the method for predicting the loss of the coal yard of the thermal power plant further comprises the following steps:
104, displaying the coal yard loss information obtained by prediction; the loss of the coal yard is the loss rate of the heat value and the loss rate of the weight of the coal pile caused by the factors of the separation of volatile components of stored coal, the ambient temperature, the sunlight, the ambient wind speed, the humidity and the like in the coal piling process of the coal yard.
Further, the time parameters collected in step 101 include coal pile stacking time and sampling time, and are obtained through real-time recording; wherein the coal pile stacking time is the time difference between the current time and the stacking starting time; the sampling time is the time difference between the current time and the start sampling time.
Further, the environmental parameters collected in step 101 include air humidity and ambient temperature;
the coal pile physical parameters comprise the height of the coal pile, the diameter of the bottom of the coal pile, the total mass of the coal pile and the density of the coal pile; wherein, the height of the coal pile and the diameter of the bottom of the coal pile are obtained by scanning through a coal traying instrument; the coal bulk density is obtained by detection in a measuring container; the total mass of the coal pile is calculated by the volume of the coal pile and the density of the coal pile, and the volume of the coal pile is obtained by scanning of a coal traying instrument.
Further, the coal quality characteristic parameters comprise a coal pile heat value, received base moisture and received base volatile components, and are obtained by carrying out industrial analysis after sampling at a sampling time point.
Further, the coal pile temperature field parameters are temperature values of different positions and depths of the coal pile; the acquisition mode of the coal pile temperature field parameters is as follows: providing a coal pile real-time temperature field at a sampling time point, and obtaining the average temperature of each layer in the coal pile after acquiring temperature data in the coal pile by an on-site temperature measuring system and carrying out program processing;
the spraying water quantity parameter is the spraying water flow of the coal yard and is obtained by recording the accumulated spraying water quality from the beginning of stacking in real time.
Further, the physical modeling method in step 103 includes differential equation model, gray prediction model, differential equation prediction, markov prediction, interpolation and fitting, and neural network model method.
Further, in step 103, when the neural network model method is adopted, the step of establishing the coal yard field loss prediction model includes:
(1) reading historical coal yard loss data;
(2) carrying out data processing on the historical coal yard site loss data, including data cleaning and normalization processing;
(3) generating a sample database;
(4) determining a neural network learning model, including a convolutional neural network or a BP neural network model;
(5) determining parameters of an input layer and a hidden layer, namely an output layer, of the neural network learning model;
(6) determining the composition of a training sample, a test sample and a verification sample in a sample database;
(7) adjusting neural network learning model parameters;
(8) training a neural network learning model;
(9) checking whether the training error is larger than a set value or not, and if not, outputting a coal yard loss prediction model; and (5) if the error is larger than the set value, returning to the step (7) until the error is smaller than the set value, and outputting the coal yard loss prediction model.
In a second aspect, an embodiment of the present invention further provides a system for predicting a coal yard loss of a thermal power plant, including:
the data acquisition module is used for acquiring coal pile loss parameters of a coal yard; the coal yard coal pile loss parameters comprise time parameters, environment parameters, coal pile physical parameters, coal quality characteristic parameters, coal pile temperature field parameters and spraying water quantity parameters;
the data processing module is used for carrying out data processing on the collected coal yard coal pile loss parameters; wherein the data processing comprises data cleaning and data normalization processing;
and the field loss prediction module is used for establishing a coal yard loss prediction model by using a physical modeling method based on the coal yard coal pile loss parameters after data processing, and predicting the real-time coal yard loss.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a memory, a communication interface, and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the method for predicting the coal yard loss of the thermal power plant.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium storing computer instructions that cause a computer to execute the method for predicting the coal yard loss of a thermal power plant.
Compared with the prior art, the method and the system for predicting the coal yard loss of the thermal power plant have the following beneficial effects:
1) according to the scheme of the invention, the coal pile characteristic indexes in the open-air coal yard and the circular coal yard and the actual situation of the coal pile heat value loss are combined to obtain the rule of the influence of the coal pile characteristic indexes on the raw coal heat value loss, so that coal piling work of coal operators is guided, and the coal yard heat value loss is reduced.
2) According to the invention, a coal yard loss prediction model is established, and the heat value loss rules (heat value loss rate along with time) of different coal types in an open-air coal yard and a circular coal yard under different weather parameter indexes are obtained, so that the coal-fired management work of coal-fired operators is guided, and the economical efficiency of a power plant is improved.
3) The invention deeply explores the rule between the coal quality index change of coal stored in the coal yard and the heat value loss of the coal yard, effectively guides the coal yard management and coal blending operation of coal-fired operators, reduces the heat value loss of the coal yard, improves the heat value of coal as fired, reduces the heat value difference and finally improves the unit economy.
4) The method for predicting the loss of the coal yard of the thermal power plant obtains the loss parameters of each coal yard, obtains the heat value loss rule of raw coal and obtains the optimal stacking time of each coal in different environments. Therefore, the coal yard management and coal blending combustion work can be guided, the heat value loss of the coal yard is reduced, the heat value difference of the coal as fired in the yard is reduced, and the fuel economy is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a coal yard loss prediction method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a coal yard coal pile temperature measuring point provided by the embodiment of the invention;
FIG. 3 is a schematic diagram of a coal yard loss neural network method prediction model provided by an embodiment of the invention;
FIG. 4 is a block diagram of a coal yard loss neural network method prediction model system according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a coal yard forecast provided by an embodiment of the present invention;
fig. 6 is a block diagram illustrating a structure of a coal yard loss prediction system of a thermal power plant according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Fig. 1 is a schematic flow chart of a method for predicting a coal yard loss of a thermal power plant according to an embodiment of the present invention, and first, an overall principle of the method according to the embodiment of the present invention is briefly described, where the method for predicting the coal yard loss of the thermal power plant includes:
Specifically, in this embodiment, a thermal power plant in a certain market is taken as an example, and the thermal power plant adopts a 4 × 1000MW ultra-supercritical unit and is equipped with an HG-2953/27.46-YM1 type voltage transformation operation direct current boiler and an N1000-26.25/600/600(TC4F) type turbo generator unit. The boiler adopts an n-shaped arrangement, a single hearth, a low NOx-MPM main burner and a MACT combustion technology, and reverse double tangential circle combustion. The boiler adopts the structure of once intermediate reheating, balanced ventilation, open-air arrangement, solid slag discharge, all-steel framework and full suspension, and uses Shenfu Dongsheng coal and Jinbei coal.
Preferably, the thermal power plant is provided with four coal yards which are arranged in a passing mode, the area of each coal yard is about 2 x (200 x 430 square meters), the width of a single coal pile is 42m, the pile height is 12m, and the maximum coal storage capacity is about 58.96 ten thousand tons.
Firstly, collecting loss parameters of a coal pile in a coal yard, wherein the loss parameters comprise time parameters, environmental parameters, physical parameters of the coal pile, coal quality characteristic parameters, temperature field parameters of the coal pile and spray water quantity parameters.
In this embodiment, a yuhuan power plant is selected to divide 0 o 'clock at 5/10/2020 into start time, and divide 0 o' clock at 5/20/2020 into sampling time, so as to obtain sampling time and initial time coal yard data, where the initial heat value and mass of each coal pile in the coal yard are as shown in table 1:
TABLE 1
In this embodiment, for example, 0 point of 20 days in 5 months and 20 days in 2020 of the thermal power plant is selected as the sampling time, and the temperature data of each sampling position and depth of the coal pile is obtained, as shown in table 2. The coal pile sampling measuring point positions are shown in figure 2.
TABLE 2
102, processing data of the collected coal pile loss parameters of the coal yard; wherein the data processing comprises data cleaning and data normalization processing.
In this embodiment, data processing is mainly performed on the coal pile temperature measurement point. When the temperature of a certain temperature measuring point of the coal pile is 5 ℃ higher or lower than the average temperature of the temperature measuring points at the same depth position, namely the system judges that the temperature is a bad point, the temperature of the measuring point is not calculated from the average temperature of each layer of distance at the distance.
In this embodiment, the coal pile temperature measurement points are normalized. Generally, the coal pile controls the burnable temperature TStandard of meritAt 60 ℃, taking a temperature measuring point T1 with a distance of 4m from the surface as an example, the normalized temperature characteristic value is:
Tnormalization＝T1/TStandard of merit＝49.9℃/60℃＝0.8317
In the subsequent prediction model, the temperature parameter is characterized by the normalized temperature characteristic value and is a dimensionless parameter. The standard parameters corresponding to the site loss parameters of each coal yard are as follows, and are shown in table 3:
and 103, establishing a coal yard loss prediction model by using a physical modeling method based on the coal yard coal pile loss parameters after data processing, and predicting the real-time coal yard loss.
The loss of the coal yard is the loss rate of the heat value and the loss rate of the weight of the coal pile caused by the factors of the separation of volatile components of stored coal, the ambient temperature, the sunlight, the ambient wind speed, the humidity and the like in the coal piling process of the coal yard. The calorific value loss rate is the ratio of the calorific value of the coal loss to the initial calorific value of the coal; the weight loss rate is the ratio of the weight lost by the coal pile in the coal yard to the initial coal pile weight.
In the embodiment of the invention, firstly, different physical modeling methods are utilized to generate a coal yard loss prediction model. Different physical modeling methods comprise a differential equation model, a grey prediction model, differential equation prediction, Markov prediction, interpolation and fitting, a neural network and the like, and the actually used model is selected according to the excellence of the fitting result of the data source. And then, predicting the real-time coal yard loss through a coal yard loss prediction model.
According to the invention, a coal yard loss prediction model is established, and the heat value loss rules (heat value loss rate along with time) of different coal types in an open-air coal yard and a circular coal yard under different weather parameter indexes are obtained, so that the coal-fired management work of coal-fired operators is guided, and the economical efficiency of a power plant is improved.
In one embodiment, the method for predicting the coal yard loss of the thermal power plant further comprises the following steps:
and 104, displaying the coal yard loss information obtained through prediction.
Specifically, the coal yard loss information displayed in the embodiment of the present invention includes information such as coal pile stacking time, coal storage days, heat value loss, residual heat value, and the like.
In one embodiment, the time parameters collected in step 101 include the coal pile stacking time and the sampling time, which are obtained by real-time recording; wherein the coal pile stacking time is the time difference between the current time and the stacking starting time; the sampling time is the time difference between the current time and the start sampling time.
In one embodiment, the environmental parameters collected in step 101 include air humidity and ambient temperature; the system can be obtained by manual real-time record uploading to the system or automatic grabbing by the system.
The coal pile physical parameters comprise the height of the coal pile, the diameter of the bottom of the coal pile, the total mass of the coal pile and the density of the coal pile; wherein, the height of the coal pile and the diameter of the bottom of the coal pile are obtained by scanning through a coal traying instrument; the coal bulk density is obtained by detection in a measuring container; the total mass of the coal pile is calculated by the volume of the coal pile and the density of the coal pile, and the volume of the coal pile is obtained by scanning of a coal traying instrument.
In one embodiment, the coal quality parameters collected in step 101 include a coal pile heating value, a received base moisture, and a received base volatile component, which are obtained by performing an industrial analysis after sampling at the sampling time point.
In one embodiment, the temperature field parameters of the coal pile collected in step 101 are temperature values of different positions and depths of the coal pile; the acquisition mode of the coal pile temperature field parameters is as follows: providing a coal pile real-time temperature field at a sampling time point, and obtaining the average temperature of each layer in the coal pile after acquiring temperature data in the coal pile by an on-site temperature measuring system and carrying out program processing;
the spraying water quantity parameter is the spraying water flow of the coal yard and is obtained by recording the accumulated spraying water quality from the beginning of stacking in real time.
In one embodiment, when the neural network model method is used, the step of building a coal yard loss prediction model in step 103 includes:
(1) reading historical coal yard loss data;
(2) carrying out data processing on the historical coal yard site loss data, including data cleaning and normalization processing;
(3) generating a sample database;
(4) determining a neural network learning model, including a convolutional neural network or a BP neural network model;
(5) determining parameters of an input layer and a hidden layer, namely an output layer, of the neural network learning model;
(6) determining the composition of a training sample, a test sample and a verification sample in a sample database;
(7) adjusting neural network learning model parameters;
(8) training a neural network learning model;
(9) checking whether the training error is larger than a set value or not, if not, outputting a coal yard loss prediction model to obtain a coal yard loss prediction jar packet; and (5) if the error is larger than the set value, returning to the step (7) until the error is smaller than the set value, outputting a coal yard loss prediction model, and finally outputting a coal yard loss prediction jar packet.
In this embodiment, a process of generating a coal yard loss prediction model by using a neural network model method is shown in fig. 3.
In one embodiment, the real-time field loss prediction process for the coal yard by using the coal yard loss prediction model comprises the following steps:
(1) reading coal yard loss data at a sampling time point;
(2) performing data processing and generating sample data;
(3) calling a jar packet for pre-measuring the coal yard loss, and predicting the yard loss;
(4) and outputting a field loss prediction result.
In this embodiment, a system process of predicting the real-time field loss of the coal yard by using the coal yard field loss prediction model is shown in fig. 4. The prediction result of 0 minute coal field of the thermal power plant at 0 time of 5 months, 20 days and 2020 is shown in fig. 5.
To sum up, compared with the prior art, the method and the system for predicting the coal yard loss of the thermal power plant provided by the embodiment of the invention have the following beneficial effects:
1) according to the scheme of the invention, the coal pile characteristic indexes in the open-air coal yard and the circular coal yard and the actual situation of the coal pile heat value loss are combined to obtain the rule of the influence of the coal pile characteristic indexes on the raw coal heat value loss, so that coal piling work of coal operators is guided, and the coal yard heat value loss is reduced.
2) According to the invention, a coal yard loss prediction model is established, and the heat value loss rules (heat value loss rate along with time) of different coal types in an open-air coal yard and a circular coal yard under different weather parameter indexes are obtained, so that the coal-fired management work of coal-fired operators is guided, and the economical efficiency of a power plant is improved.
3) The invention deeply explores the rule between the coal quality index change of coal stored in the coal yard and the heat value loss of the coal yard, effectively guides the coal yard management and coal blending operation of coal-fired operators, reduces the heat value loss of the coal yard, improves the heat value of coal as fired, reduces the heat value difference and finally improves the unit economy.
4) The method for predicting the loss of the coal yard of the thermal power plant obtains the loss parameters of each coal yard, obtains the heat value loss rule of raw coal and obtains the optimal stacking time of each coal in different environments. Therefore, the coal yard management and coal blending combustion work can be guided, the heat value loss of the coal yard is reduced, the heat value difference of the coal as fired in the yard is reduced, and the fuel economy is improved.
In an embodiment, fig. 6 is a block diagram of a structure of a coal yard loss prediction system of a thermal power plant according to an embodiment of the present invention, and referring to fig. 6, the system includes a data acquisition module 601, a data processing module 602, and a yard loss prediction module 603, where:
the data acquisition module 601 is used for acquiring loss parameters of the coal pile in the coal yard; the coal yard coal pile loss parameters comprise time parameters, environment parameters, coal pile physical parameters, coal quality characteristic parameters, coal pile temperature field parameters and spraying water quantity parameters. The data processing module 602 is configured to perform data processing on the acquired coal yard coal pile loss parameters; wherein the data processing comprises data cleaning and data normalization processing. The field loss prediction module 603 is configured to establish a coal yard loss prediction model by using a physical modeling method based on the coal yard coal pile loss parameters after data processing, and predict the real-time coal yard loss.
Specifically, how to predict the coal yard loss of the thermal power plant through the data acquisition module 601, the data processing module 602, and the yard loss prediction module 603 may refer to the above method embodiment, and the embodiment of the present invention is not described herein again.
Further, the system further comprises:
and the display output module is used for displaying the coal yard loss information, including the information of coal pile stacking time, coal storage days, heat value loss, residual heat value and the like.
In one embodiment, based on the same concept, an embodiment of the present invention provides an electronic device, which may include: a processor (processor)701, a communication Interface (Communications Interface)702, a memory (memory)703 and a communication bus 704, wherein the processor 701, the communication Interface 702 and the memory 703 complete communication with each other through the communication bus 704. The processor 701 may call logic instructions in the memory 703 to execute the method for predicting the coal yard loss of the thermal power plant provided by the foregoing embodiments, for example, the method includes: collecting loss parameters of a coal pile in a coal yard; the coal yard coal pile loss parameters comprise time parameters, environment parameters, coal pile physical parameters, coal quality characteristic parameters, coal pile temperature field parameters and spraying water quantity parameters. Carrying out data processing on the collected coal pile loss parameters of the coal yard; wherein the data processing comprises data cleaning and data normalization processing. And establishing a coal yard loss prediction model by using a physical modeling method based on the coal yard coal pile loss parameters after data processing, and predicting the real-time coal yard loss.
In an embodiment, based on the same concept, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to perform the method for predicting coal yard loss of a thermal power plant provided by the foregoing embodiments, for example, the method includes: collecting loss parameters of a coal pile in a coal yard; the coal yard coal pile loss parameters comprise time parameters, environment parameters, coal pile physical parameters, coal quality characteristic parameters, coal pile temperature field parameters and spraying water quantity parameters. Carrying out data processing on the collected coal pile loss parameters of the coal yard; wherein the data processing comprises data cleaning and data normalization processing. And establishing a coal yard loss prediction model by using a physical modeling method based on the coal yard coal pile loss parameters after data processing, and predicting the real-time coal yard loss.
The embodiments of the present invention can be arbitrarily combined to achieve different technical effects.
The above-described system embodiments are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for predicting the loss of a coal yard of a thermal power plant is characterized by comprising the following steps:
101, collecting coal pile loss parameters of a coal yard; the coal yard coal pile loss parameters comprise time parameters, environment parameters, coal pile physical parameters, coal quality characteristic parameters, coal pile temperature field parameters and spraying water quantity parameters;
102, processing data of the collected coal pile loss parameters of the coal yard; wherein the data processing comprises data cleaning and data normalization processing;
and 103, establishing a coal yard loss prediction model by using a physical modeling method based on the coal yard coal pile loss parameters after data processing, and predicting the real-time coal yard loss.
2. The method for predicting the site loss of the coal yard of the thermal power plant according to claim 1, further comprising:
104, displaying the coal yard loss information obtained by prediction; the loss of the coal yard is the loss rate of the heat value and the loss rate of the weight of the coal pile caused by the factors of the separation of volatile components of stored coal, the ambient temperature, the sunlight, the ambient wind speed, the humidity and the like in the coal piling process of the coal yard.
3. The method for predicting the coal yard loss of the thermal power plant according to claim 2, wherein the time parameters collected in the step 101 include stacking time and sampling time of the coal pile, and are obtained by real-time recording; wherein the coal pile stacking time is the time difference between the current time and the stacking starting time; the sampling time is the time difference between the current time and the start sampling time.
4. The method for predicting the coal yard loss of the thermal power plant according to claim 3, wherein the environmental parameters collected in step 101 include air humidity and ambient temperature;
the coal pile physical parameters comprise the height of the coal pile, the diameter of the bottom of the coal pile, the total mass of the coal pile and the density of the coal pile; wherein, the height of the coal pile and the diameter of the bottom of the coal pile are obtained by scanning through a coal traying instrument; the coal bulk density is obtained by detection in a measuring container; the total mass of the coal pile is calculated by the volume of the coal pile and the density of the coal pile, and the volume of the coal pile is obtained by scanning of a coal traying instrument.
5. The method for predicting the coal yard loss of the thermal power plant according to claim 1, wherein the coal quality characteristic parameters include a coal pile heating value, a received base moisture and a received base volatile component, and are obtained by performing industrial analysis after sampling at a sampling time point.
6. The method for predicting the coal yard loss of the thermal power plant according to claim 1, wherein the parameters of the coal yard temperature field are temperature values of different positions and depths of the coal yard; the acquisition mode of the coal pile temperature field parameters is as follows: providing a coal pile real-time temperature field at a sampling time point, and obtaining the average temperature of each layer in the coal pile after acquiring temperature data in the coal pile by an on-site temperature measuring system and carrying out program processing;
the spraying water quantity parameter is the spraying water flow of the coal yard and is obtained by recording the accumulated spraying water quality from the beginning of stacking in real time.
7. The method of claim 1, wherein the physical modeling method in step 103 comprises differential equation models, gray prediction models, differential equation prediction, markov prediction, interpolation and fitting, and neural network model methods.
8. The method for predicting the coal yard loss of the thermal power plant according to claim 1 or 7, wherein in the step 103, when the neural network model method is adopted, the step of establishing the coal yard loss prediction model comprises the following steps:
(1) reading historical coal yard loss data;
(2) carrying out data processing on the historical coal yard site loss data, including data cleaning and normalization processing;
(3) generating a sample database;
(4) determining a neural network learning model, including a convolutional neural network or a BP neural network model;
(5) determining parameters of an input layer and a hidden layer, namely an output layer, of the neural network learning model;
(6) determining the composition of a training sample, a test sample and a verification sample in a sample database;
(7) adjusting neural network learning model parameters;
(8) training a neural network learning model;
(9) checking whether the training error is larger than a set value or not, and if not, outputting a coal yard loss prediction model; and (5) if the error is larger than the set value, returning to the step (7) until the error is smaller than the set value, and outputting the coal yard loss prediction model.
9. A coal yard loss prediction system of a thermal power plant is characterized by comprising:
the data acquisition module is used for acquiring coal pile loss parameters of a coal yard; the coal yard coal pile loss parameters comprise time parameters, environment parameters, coal pile physical parameters, coal quality characteristic parameters, coal pile temperature field parameters and spraying water quantity parameters;
the data processing module is used for carrying out data processing on the collected coal yard coal pile loss parameters; wherein the data processing comprises data cleaning and data normalization processing;
and the field loss prediction module is used for establishing a coal yard loss prediction model by using a physical modeling method based on the coal yard coal pile loss parameters after data processing, and predicting the real-time coal yard loss.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method for predicting coal yard loss of a thermal power plant as claimed in any one of claims 1 to 8.
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