A coal mill fineness control method and system

Abstract

The application discloses a coal pulverizer coal powder fineness control method, comprising: obtaining historical operation parameters of a coal pulverizer and corresponding coal powder fineness data; determining influence factors of coal powder fineness according to the historical operation parameters and the coal powder fineness data, wherein the influence factors include dynamic separator rotating speed, primary air volume and coal pulverizer output; establishing a coal powder fineness prediction model according to the influence factors; obtaining real-time operation parameters of the coal pulverizer, determining predicted coal powder fineness of the coal pulverizer according to the coal powder fineness prediction model; and adjusting the operation state of the coal pulverizer according to the predicted coal powder fineness, so as to realize control of the coal powder fineness of the coal pulverizer. The application realizes online detection of the coal powder fineness and real-time adjustment of the operation state of the coal pulverizer.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal mill pulverizing, and in particular to a coal mill coal fineness control method and system. BACKGROUND

[0002] In order to improve the combustion efficiency of a boiler, a coal-fired power plant grinds raw coal into coal powder and sprays the coal powder into a boiler for combustion. The finer the coal powder, the lower the mechanical incomplete combustion heat loss, the higher the combustion efficiency, and the higher the boiler efficiency. At the same time, the amount of NOx generated also decreases as the coal powder becomes finer. Therefore, in order to make the power plant boiler burn qualified coal powder and obtain high combustion efficiency, the coal mill is adjusted to the best value under the premise of ensuring the normal operation of the unit, so that the boiler can operate in a safe and economic state.

[0003] However, the detection of coal fineness in the pulverizing system is generally performed by discharging coal powder onto a coal powder pipeline and then sampling and measuring, which cannot realize online detection, and the timeliness of this method is not strong, and the control and guidance of the real-time operation state of the coal mill are limited. Therefore, there is an urgent need for a coal mill coal fineness control method to realize online detection of coal fineness and control thereof. SUMMARY

[0004] The purpose of the present application is to provide a coal mill coal fineness control method to solve the problem that the existing method cannot realize online detection, the timeliness is not strong, and the state of the coal mill cannot be adjusted and the coal fineness cannot be controlled.

[0005] The present application provides a coal mill coal fineness control method, comprising:

[0006] obtaining historical operation parameters of a coal mill and corresponding coal fineness data;

[0007] determining influencing factors of coal fineness according to the historical operation parameters and the coal fineness data, the influencing factors including dynamic separator speed, primary air volume, and coal mill output;

[0008] establishing a coal fineness prediction model according to the influencing factors;

[0009] obtaining real-time operation parameters of the coal mill, and determining predicted coal fineness of the coal mill according to the coal fineness prediction model;

[0010] adjusting the operation state of the coal mill according to the predicted coal fineness to control the coal fineness of the coal mill.

[0011] In some embodiments of the present application, determining influencing factors of coal fineness according to the historical operation parameters and the coal fineness data comprises:

[0012] Screening the coal powder fineness data with the same other historical operation parameters for the i th historical operation parameter;

[0013] Determining the change amount of the coal powder fineness data according to the i th historical operation parameter;

[0014] If the change amount is greater than the set change amount, determining that the i th historical operation parameter is the influencing factor of the coal powder fineness;

[0015] If the change amount is less than the set change amount, determining that the i th historical operation parameter is not the influencing factor of the coal powder fineness.

[0016] In some embodiments of the present application, the method further comprises:

[0017] Determining the independence of the influence of the influencing factor on the coal powder fineness according to the historical operation parameters and the coal powder fineness data;

[0018] Determining the corresponding coal powder fineness prediction model according to the independence of the influence of the influencing factor on the coal powder fineness.

[0019] In some embodiments of the present application, determining the corresponding coal powder fineness prediction model according to the independence of the influence of the influencing factor on the coal powder fineness comprises:

[0020] If the influence of the influencing factor on the coal powder fineness is independent and highly linear, establishing a linear coal powder fineness prediction model;

[0021] If the influence of the influencing factor on the coal powder fineness is mutual, establishing a curve coal powder fineness prediction model.

[0022] In some embodiments of the present application, establishing a coal powder fineness prediction model according to the influencing factor comprises:

[0023] Processing the historical operation parameters and the coal powder fineness data corresponding to the influencing factor to obtain a processed data set, the data set comprising a training data set and a test data set;

[0024] Training an initial coal powder fineness prediction model according to the training data set to obtain the parameters of the initial coal powder fineness prediction model;

[0025] Testing the parameters of the initial coal powder fineness prediction model according to the test data set to obtain a coal powder fineness prediction model.

[0026] In some embodiments of the present application, the expression of the coal powder fineness prediction model is:

[0027] R = R0 + k1n + k2Q + k3B;

[0028] Wherein, R is the coal fineness, R0 is the coal fineness constant, n is the dynamic separator speed, k1 is the influence coefficient of the dynamic separator speed on the coal fineness, Q is the primary air flow, k2 is the influence coefficient of the primary air flow on the coal fineness, B is the mill output, and k3 is the influence coefficient of the mill output on the coal fineness.

[0029] In some embodiments of the present application, the operating state of the mill is adjusted according to the predicted coal fineness, including:

[0030] Determining the coal fineness difference between the predicted coal fineness and the set coal fineness;

[0031] Adjusting the operating state of the mill according to the coal fineness difference.

[0032] In some embodiments of the present application, the operating state of the mill is adjusted according to the coal fineness difference, including:

[0033] Determining the first ratio between the coal fineness difference and the influence coefficient of the dynamic separator speed on the coal fineness, the second ratio between the coal fineness difference and the influence coefficient of the primary air flow on the coal fineness, and the third ratio between the coal fineness difference and the influence coefficient of the mill output on the coal fineness;

[0034] Comparing the first ratio, the second ratio and the third ratio to determine the minimum value;

[0035] If the minimum value is the first ratio, the dynamic separator speed is adjusted;

[0036] If the minimum value is the second ratio, the primary air flow is adjusted;

[0037] If the minimum value is the third ratio, the mill output is adjusted.

[0038] The present application also discloses a coal fineness control system for a mill, including:

[0039] An acquisition module, configured to acquire historical operating parameters of the mill, corresponding coal fineness data and real-time operating parameters of the mill;

[0040] A model establishing module, configured to determine influencing factors of the coal fineness according to the historical operating parameters and the coal fineness data, the influencing factors including the dynamic separator speed, the primary air flow and the mill output, and to establish a coal fineness prediction model according to the influencing factors;

[0041] A prediction module, configured to determine the predicted coal fineness of the mill according to the coal fineness prediction model;

[0042] A control module is configured to adjust the operation state of the coal mill according to the predicted coal fineness, so as to control the coal fineness of the coal mill.

[0043] In some embodiments of the present application, a judgment module is further included, which is configured to judge the independence of the influence factors on the coal fineness according to the historical operation parameters and the coal fineness data, and determine the corresponding coal fineness prediction model according to the independence of the influence factors on the coal fineness.

[0044] The present application provides a coal mill coal fineness control method, which comprises the following steps: obtaining historical operation parameters of a coal mill and corresponding coal fineness data; determining influence factors of the coal fineness according to the historical operation parameters and the coal fineness data, wherein the influence factors include dynamic separator rotating speed, primary air volume and coal mill output; establishing a coal fineness prediction model according to the influence factors; obtaining real-time operation parameters of the coal mill, determining predicted coal fineness of the coal mill according to the coal fineness prediction model; and adjusting the operation state of the coal mill according to the predicted coal fineness, so as to control the coal fineness of the coal mill.

[0045] The present application trains and tests the model by the historical operation parameters and the corresponding coal fineness data, ensures the accuracy of the prediction of the coal fineness prediction model, predicts the coal fineness under the current operation state of the coal mill according to the coal fineness prediction model, determines the coal fineness difference between the predicted coal fineness and the set coal fineness, and adjusts the operation state of the coal mill according to the coal fineness difference. The present application realizes online detection and automatic adjustment of the coal fineness of the coal mill, makes the current coal fineness reach the set coal fineness, improves the control precision and accuracy, provides data support for the optimized operation of the coal mill, and realizes efficient operation of the unit.

[0046] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 Fig. 1 is a flowchart of a coal mill coal fineness control method according to the present application;

[0048] Figure 2 Fig. 2 is a functional block diagram of a coal mill coal fineness control system according to the present application.

[0049] REFERENCE NUMERALS DETAILED DESCRIPTION

[0050] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0051] It should be noted that the following detailed description is illustrative only, and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0052] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof. The use herein of terms such as "first", "second" and other such terminology does not imply any order, quantity, or importance, but rather are used to distinguish one element from another, and the terms "front", "rear", "upper", "lower", "left", "right", "vertical", "horizontal", "side", "bottom", and the like are used for convenience in describing the orientations of the components or elements shown in the drawings, and are not intended to limit the present application to any particular orientation of the components or elements. The terms "fixedly connected", "connected", "linked", and the like, should be construed broadly, and can include a fixed connection, an integrally formed connection, or a detachable connection, and can be directly connected, or indirectly connected via an intermediate medium. Those skilled in the relevant arts can determine the specific meaning of the above terms in the present application according to the specific circumstances, and the above terms should not be construed as limiting the present application.

[0053] Embodiments

[0054] The present application provides a coal mill fineness control method, as shown in the accompanying drawings, comprising: Figure 1

[0055] Obtaining historical operating parameters of the coal mill and corresponding coal fineness data.

[0056] Determining influencing factors of the coal fineness according to the historical operating parameters and the coal fineness data, the influencing factors including dynamic separator speed, primary air volume, and coal mill output.

[0057] Establishing a coal fineness prediction model according to the influencing factors.

[0058] Obtaining real-time operating parameters of the coal mill, and determining predicted coal fineness of the coal mill according to the coal fineness prediction model.

[0059] Adjusting the operating state of the coal mill according to the predicted coal fineness to achieve control of the coal mill coal fineness. ​

[0060] In some embodiments of the present application, the influencing factors of the coal powder fineness are determined according to the historical operation parameters and the coal powder fineness data, including:

[0061] For the i-th historical operation parameter, the coal powder fineness data with the same other historical operation parameters are screened out;

[0062] The change amount of the coal powder fineness data is determined according to the i-th historical operation parameter.

[0063] If the change amount is greater than a set change amount, it is determined that the i-th historical operation parameter is an influencing factor of the coal powder fineness.

[0064] If the change amount is less than the set change amount, it is determined that the i-th historical operation parameter is not an influencing factor of the coal powder fineness.

[0065] In the present embodiment, the influencing factors of the coal powder fineness are determined by the method of controlling variables. When determining whether a factor is an influencing factor, other factors are kept unchanged, and it is determined whether the coal powder fineness will change. For example, when determining whether the dynamic separator rotating speed is an influencing factor, the coal powder fineness data with the same other factors and different dynamic separator rotating speeds are screened out from the historical operation parameters, and it is determined whether the coal powder fineness will change with the change of the dynamic separator rotating speed. If the coal powder fineness will change, it is determined that the dynamic separator rotating speed is an influencing factor.

[0066] In some embodiments of the present application, the method further includes:

[0067] The independence of the influence of the influencing factors on the coal powder fineness is determined according to the historical operation parameters and the coal powder fineness data.

[0068] The corresponding coal powder fineness prediction model is determined according to the independence of the influence of the influencing factors on the coal powder fineness.

[0069] In the present embodiment, it is determined which coal powder fineness prediction model to use by determining whether each influencing factor has an independent influence or a comprehensive influence on the coal powder fineness.

[0070] In some embodiments of the present application, the corresponding coal powder fineness prediction model is determined according to the independence of the influence of the influencing factors on the coal powder fineness, including:

[0071] If the influence of the influencing factors on the coal powder fineness is independent and highly linear, a linear coal powder fineness prediction model is established.

[0072] If the influence of the influencing factors on the coal powder fineness is interdependent, a curve coal powder fineness prediction model is established.

[0073] In some embodiments of the present application, a coal fineness prediction model is established according to the influencing factors, which comprises:

[0074] The historical operation parameters and the coal fineness data corresponding to the influencing factors are processed to obtain a processed data set, which comprises a training data set and a test data set.

[0075] The initial coal fineness prediction model is trained according to the training data set to obtain the parameters of the initial coal fineness prediction model.

[0076] The parameters of the initial coal fineness prediction model are tested according to the test data set to obtain the coal fineness prediction model.

[0077] In the present embodiment, the initial coal fineness prediction model with the determined parameters is tested by the test data set. If the test result is within the set result acceptance range, the parameters are retained to obtain the coal fineness prediction model. If the test result is not within the set result acceptance range, the parameters of the initial coal fineness prediction model are corrected and retested.

[0078] In some embodiments of the present application, the expression of the coal fineness prediction model is:

[0079] R=R0+k1n+k2Q+k3B

[0080] wherein R is the coal fineness, R0 is the coal fineness constant, n is the dynamic separator speed, k1 is the influence coefficient of the dynamic separator speed on the coal fineness, Q is the primary air volume, k2 is the influence coefficient of the primary air volume on the coal fineness, B is the mill output, and k3 is the influence coefficient of the mill output on the coal fineness.

[0081] In the present embodiment, by analyzing the historical operation parameters and the coal fineness data, it can be determined that the influence of the influencing factors on the coal fineness is independent and highly linear. Therefore, a linear coal fineness prediction model is established, and it is tested and trained according to the training data set and the test data set to obtain R0, k1, k2 and k3.

[0082] In some embodiments of the present application, the operation state of the coal mill is adjusted according to the predicted coal fineness, which comprises:

[0083] The coal fineness difference between the predicted coal fineness and the set coal fineness is determined.

[0084] The operation state of the coal mill is adjusted according to the coal fineness difference.

[0085] In some embodiments of the present application, the operation state of the coal mill is adjusted according to the coal fineness difference, which comprises:

[0086] determining a first ratio between the coal fineness difference and a dynamic separator speed influence coefficient of the coal fineness, a second ratio between the coal fineness difference and a primary air flow influence coefficient of the coal fineness, and a third ratio between the coal fineness difference and a coal mill output influence coefficient of the coal fineness.

[0087] comparing the first ratio, the second ratio and the third ratio to determine a minimum value.

[0088] If the minimum value is the first ratio, the dynamic separator speed is adjusted.

[0089] If the minimum value is the second ratio, the primary air flow is adjusted.

[0090] If the minimum value is the third ratio, the coal mill output is adjusted.

[0091] In the embodiment, when the coal mill operating state is adjusted, all parameters cannot be adjusted according to the optimization principle, and it is necessary to select an adjustment of a certain influencing factor with the lowest adjustment amount. Therefore, the minimum value is determined by comparing the first ratio, the second ratio and the third ratio, and the coal mill is adjusted according to the minimum value.

[0092] The application further discloses a coal mill coal fineness control system using the coal mill coal fineness control method, as shown in the accompanying drawings, comprising: Figure 2

[0093] An acquisition module is configured to acquire historical operating parameters of a coal mill, corresponding coal fineness data and real-time operating parameters of the coal mill.

[0094] A model establishing module is configured to determine influencing factors of coal fineness according to the historical operating parameters and the coal fineness data, wherein the influencing factors include a dynamic separator speed, a primary air flow and a coal mill output; and establish a coal fineness prediction model according to the influencing factors.

[0095] A prediction module is configured to determine a predicted coal fineness of the coal mill according to the coal fineness prediction model.

[0096] A control module is configured to adjust an operating state of the coal mill according to the predicted coal fineness to control the coal fineness of the coal mill.

[0097] In some embodiments of the application, a judgment module is further configured to judge independence of the influencing factors on the coal fineness according to the historical operating parameters and the coal fineness data; and determine a corresponding coal fineness prediction model according to the independence of the influencing factors on the coal fineness.​

[0098] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can still be modified or equivalently replaced, and these modifications or equivalent replacements should not make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

[0099] The system provided by the above examples is only illustrated by the division of the above functional modules. In actual application, the above functions can be completed by different functional modules according to needs, that is, the modules or steps in the embodiments of the present application are further decomposed or combined, for example, the modules of the above examples can be combined into one module, or can be further split into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present application are only for distinguishing the respective modules and steps, and should not be considered as improper limitation of the present application.

[0100] Those skilled in the art should be able to realize that the modules and method steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. In order to clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been described in the above description. Whether the functions are executed by electronic hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

Claims

1. A method for controlling the fineness of pulverized coal in a coal mill, characterized in that, include: Obtain historical operating parameters of the coal mill and corresponding coal powder fineness data; The factors influencing the fineness of coal powder are determined based on the historical operating parameters and the coal powder fineness data. These factors include: dynamic separator speed, primary air volume, and coal mill output. A coal powder fineness prediction model is established based on the aforementioned influencing factors; the expression for the coal powder fineness prediction model is as follows: ;in, For coal powder fineness, Let be the coal powder fineness constant. For the dynamic separator speed, The coefficient representing the influence of the dynamic separator rotation speed on the fineness of pulverized coal. For primary air volume, The coefficient representing the influence of primary air volume on the fineness of pulverized coal. To provide power to the coal mill, The coefficient representing the influence of coal mill output on the fineness of pulverized coal; Obtain the real-time operating parameters of the coal mill, and determine the predicted coal powder fineness of the coal mill based on the coal powder fineness prediction model; The operating status of the coal mill is adjusted according to the predicted coal powder fineness to control the coal powder fineness; specifically, this includes: determining the difference between the predicted coal powder fineness and the set coal powder fineness; and adjusting the operating status of the coal mill according to the coal powder fineness difference. The adjustment of the operating status of the coal mill based on the coal powder fineness difference includes: determining a first ratio between the coal powder fineness difference and the influence coefficient of the dynamic separator speed on coal powder fineness, a second ratio between the coal powder fineness difference and the influence coefficient of the primary air volume on coal powder fineness, and a third ratio between the coal powder fineness difference and the influence coefficient of the coal mill output on coal powder fineness; comparing the first ratio, the second ratio, and the third ratio to determine the minimum value; if the minimum value is the first ratio, adjusting the dynamic separator speed; if the minimum value is the second ratio, adjusting the primary air volume; and if the minimum value is the third ratio, adjusting the coal mill output.

2. The method for controlling the fineness of pulverized coal in a coal mill according to claim 1, characterized in that, The factors influencing the fineness of pulverized coal are determined based on the historical operating parameters and the pulverized coal fineness data, including: For the i-th historical operating parameter, filter the coal powder fineness data when other historical operating parameters are the same; The change in the fineness data of the pulverized coal is determined based on the i-th historical operating parameter. If the change is greater than the set change, then the i-th historical operating parameter is determined to be an influencing factor on the fineness of the pulverized coal. If the change is less than the set change, then the i-th historical operating parameter is determined not to be a factor affecting the fineness of the pulverized coal.

3. The method for controlling the fineness of pulverized coal in a coal mill according to claim 1, characterized in that, The method further includes: The independence of the influence of the influencing factors on the fineness of the coal powder is determined based on the historical operating parameters and coal powder fineness data. The corresponding coal powder fineness prediction model is determined based on the independence of the influence of the aforementioned influencing factors on the coal powder fineness.

4. The method for controlling the fineness of pulverized coal in a coal mill according to claim 3, characterized in that, Based on the independence of the influence of the aforementioned influencing factors on the fineness of the pulverized coal, a corresponding pulverized coal fineness prediction model is determined, including: If the influence of the influencing factors on the fineness of the pulverized coal is independent and highly linear, then a linear pulverized coal fineness prediction model is established. If the influencing factors affect the fineness of the pulverized coal in a mutually influential manner, then a curve-based pulverized coal fineness prediction model is established.

5. The method for controlling the fineness of pulverized coal in a coal mill according to claim 1, characterized in that, A coal powder fineness prediction model is established based on the aforementioned influencing factors, including: The historical operating parameters and coal powder fineness data corresponding to the influencing factors are processed to obtain a processed dataset, which includes a training dataset and a test dataset. The initial coal powder fineness prediction model is trained based on the training dataset to obtain the parameters of the initial coal powder fineness prediction model; The parameters of the initial coal powder fineness prediction model are tested based on the test dataset to obtain the coal powder fineness prediction model.

6. A coal mill pulverized coal fineness control system, used to execute the coal mill pulverized coal fineness control method according to any one of claims 1-5, characterized in that, include: The acquisition module is used to acquire the historical operating parameters of the coal mill, the corresponding coal powder fineness data, and the real-time operating parameters of the coal mill. The model building module is used to determine the influencing factors of coal powder fineness based on the historical operating parameters and the coal powder fineness data. The influencing factors include: dynamic separator speed, primary air volume and coal mill output; and to build a coal powder fineness prediction model based on the influencing factors. The prediction module is used to determine the predicted coal powder fineness of the coal mill based on the coal powder fineness prediction model. The control module is used to adjust the operating status of the coal mill according to the predicted coal powder fineness, so as to control the coal powder fineness of the coal mill.

7. A coal mill pulverized coal fineness control system according to claim 6, characterized in that, It also includes a judgment module, which is used to judge the independence of the influence of the influencing factors on the fineness of the coal powder based on the historical operating parameters and coal powder fineness data; and to determine the corresponding coal powder fineness prediction model based on the independence of the influence of the influencing factors on the fineness of the coal powder.

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