CN114841272A - X-ray coal quality multi-element-based coal blending classification modeling method - Google Patents
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- 239000011777 magnesium Substances 0.000 description 4
- 239000011593 sulfur Substances 0.000 description 4
- 229910052717 sulfur Inorganic materials 0.000 description 4
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 3
- 229910052749 magnesium Inorganic materials 0.000 description 3
- 238000000926 separation method Methods 0.000 description 3
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 2
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 2
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 2
- 229910052782 aluminium Inorganic materials 0.000 description 2
- 239000002956 ash Substances 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 239000011575 calcium Substances 0.000 description 2
- 229910052791 calcium Inorganic materials 0.000 description 2
- 229910052742 iron Inorganic materials 0.000 description 2
- 238000011897 real-time detection Methods 0.000 description 2
- 229910052710 silicon Inorganic materials 0.000 description 2
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- 238000001228 spectrum Methods 0.000 description 2
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- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
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Abstract
The invention discloses a coal blending classification modeling method based on X-ray coal quality multi-elements, which relates to the technical field of intelligent control of coal preparation plants.
Description
Technical Field
The invention relates to the technical field of intelligent control of coal preparation plants, in particular to a coal blending classification modeling method based on X-ray coal quality multiple elements.
Background
Coal sorting is a core link of high-quality development of the coal industry, and the intelligent technology is adopted to improve the process control and intelligent control of coal sorting, so that the method is the key content of the construction of the intelligent coal preparation plant. Coal blending is a typical scene frequently encountered by coal preparation plants, and the background generated by coal blending mainly comprises the following two situations, namely pre-selection coal blending and post-selection coal blending respectively, and certainly, the targets of the pre-selection coal blending and the post-selection coal blending are still obviously different; before-dressing coal blending is mainly considered to meet two problems of sorting and homogenization, and the core is to create conditions for sorting; the selected coal blending meets the product quality requirements, such as the requirements of ash content, sulfur content, calorific value and the like.
The coal separation is performed before a raw coal preparation stage, the situation that the raw coal sources are different is usually met, the problem of a central coal preparation plant and a cluster mine coal preparation plant is particularly obvious, particularly, the coal types from different sources are different in selectivity, the sulfur content, the caking index and the like, and in the coal separation link, if the problem is not well treated in the raw coal preparation stage, the whole separation process is deteriorated, qualified products cannot be obtained at all, or the maximum benefit of the coal preparation plant is sacrificed.
The basic problems of coal blending in the raw coal preparation stage include raw coal separate storage. Under the ideal condition, raw coal separate storage can be completed through underground mining, but the condition cannot be met, and how to carry out real-time coal quality detection and coal source judgment on incoming coal transported to a coal preparation plant is always a technical problem of neck.
Therefore, how to classify the coal sources is a problem to be solved urgently at present.
Disclosure of Invention
The invention aims to provide a coal blending classification modeling method based on X-ray coal quality multi-elements, which adopts an analyzer to collect multi-element components of coal sources, carries out LSSVM algorithm processing on the multi-element component data of a plurality of coal sources to obtain a coal source classification model, and analyzes the multi-element components of the coal sources to be classified based on the coal source classification model, thereby realizing classification of the coal sources, storing the coal sources according to classification results, and realizing real-time detection of the coal quality and judgment of the coal sources.
In a first aspect, the above object of the present invention is achieved by the following technical solutions:
a coal blending classification modeling method based on X-ray coal quality multi-elements obtains at least one coal quality multi-element component of a coal source, the coal quality multi-element components of all the coal sources form multi-element group diversity, algorithm processing is carried out on multi-element group diversity data, and a coal source classification model is established.
The invention is further configured to: and obtaining coal element components of each coal source by adopting an X-ray coal multi-element analyzer, and carrying out LSSVM algorithm processing on the element component data to establish a coal source classification model.
The invention is further configured to: setting coal quality multi-element components to comprise Q elements, setting the type of a coal source to be N, taking the coal quality multi-element components as Q-dimensional input vectors, establishing a nonlinear function, and mapping an input space to a feature space; based on a structured risk minimization principle, a Lagrange function is constructed, an optimization problem is solved, conditions of an optimal solution of the problem are set, a kernel function is obtained through solving, and a coal source classification model is established.
The invention is further configured to: based on the principle of minimizing the structured risk, describing the evaluation problem as an optimization problem, and establishing an optimization function:
（2）；
in the formula (I), the compound is shown in the specification,
is a function of the regularization, and,
is a non-negative positiveParameters are normalized, a trade-off between accuracy and complexity of the model is determined,
representing the regression error between the actual and predicted values.
The invention is further configured to: the lagrange function is constructed as shown below:
（3）；
in the formula (I), the compound is shown in the specification,
(i =1, 2.... N.) represents the lagrangian multiplier, respectively for each pair
、
、
And
partial differentiation is carried out, all derivatives are set to be zero, and the optimal solution condition of the optimization problem is obtained as follows:
（8）；
in the formula (I), the compound is shown in the specification,
，
，
，
a symmetric matrix representing an N x N kernel function.
The invention is further configured to: the kernel function is shown as follows:
in the formula (I), the compound is shown in the specification,
。
the invention is further configured to: the coal source classification model is shown as the following formula:
in the formula (I), the compound is shown in the specification,
a vector of weights is represented by a vector of weights,
representing a bias term;
the Radial Basis Function (RBF) kernel is used as a kernel function, as shown in the following equation:
（11）；
in the formula (I), the compound is shown in the specification,
is the bandwidth of the kernel function.
In a second aspect, the above object of the present invention is achieved by the following technical solutions:
the X-ray coal quality multi-element-based coal blending classification system comprises a coal blending classifier, an X-ray coal quality multi-element analyzer and a control driving device, wherein the X-ray coal quality multi-element analyzer is used for collecting coal quality multi-element components of a coal source to be classified, the coal blending classifier is used for analyzing the collected coal quality multi-element components to obtain a coal source type, and the control driving device is used for transporting the coal source to be classified to a corresponding position according to the coal source type.
In a third aspect, the above object of the present invention is achieved by the following technical solutions:
an X-ray coal quality multi-element based coal blending classifier terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the method of the present application when executing the computer program.
In a fourth aspect, the above object of the present invention is achieved by the following technical solutions:
a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the method of the present application.
Compared with the prior art, the beneficial technical effects of this application do:
1. the method and the device establish the coal source classification model by performing algorithm processing on the multi-element component data of various coal sources, and ensure that the coal source classification can be realized;
2. furthermore, the LSSVM algorithm is adopted, the nonlinear problem is converted into an optimization problem, and the modeling process is simplified;
3. furthermore, the coal sources to be classified are classified through the coal source classification model, and real-time detection and coal source distinguishing of the coal quality of the coal sources are achieved.
Drawings
Fig. 1 is a schematic structural diagram of a coal source classification method according to an embodiment of the present application.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Detailed description of the preferred embodiment
The utility model provides a categorised modeling method of coal blending based on X ray coal quality multielement, at first gather the multielement component of N kinds of coal sources, constitute multielement group diversity, to the data of multielement group diversity, carry out algorithm processing, establish the coal source classification model.
The multielement component comprises Q elements, such as ash, sulfur, magnesium, aluminum, silicon, calcium, iron and the like.
According to the type of the coal source and the multi-element components, a multi-element component set is formed
As training data. Wherein the content of the first and second substances,
is an input vector of Q dimension, representing multi-element components;
the vector is an N-dimensional vector corresponding to the coal source type.
In order to realize the mapping from the input space to the feature space, a nonlinear function phi (xi) is adopted, and the form of the nonlinear function estimation modeling is as follows:
in the formula (I), the compound is shown in the specification,
a vector of weights is represented by a vector of weights,
a bias term is represented.
Based on the structured risk minimization principle, the evaluation problem is described as an optimization problem:
（2）；
in the formula (I), the compound is shown in the specification,
is a regularization function (penalty term),
is a non-negative regularization parameter, determines the trade-off between the accuracy and complexity of the model,
representing the regression error between the actual and predicted values.
In order to solve the optimization problem of the formula (2), the corresponding lagrangian function absorption boundary condition is adopted and expressed as:
（3）；
in the formula (I), the compound is shown in the specification,
(i =1, 2...., N) represents the lagrangian multiplier.
For those in formula (3)
、
、
And
partial differentiation is performed, setting all derivatives to zero to obtain the condition for the optimal solution to the problem.
By
And deducing that:
（4）；
by
And deducing that:
（5）；
by
And deducing that:
（6）；
by
Deducing that:
the equations (4), (5), (6) and (7) are calculated to eliminate
And
the optimization process can be converted to the following linear equation:
（8）；
in the formula (I), the compound is shown in the specification,
， 
， 
， 
a symmetric matrix representing an N x N kernel function.
The kernel function is shown as follows:
in the formula (I), the compound is shown in the specification,
。
kernel function
The condition of Mercer is satisfied. The kernel function plays an important role in constructing a high-performance Least Square Support Vector Machine (LSSVM), and has the capability of reducing the computational complexity of a high-dimensional space.
Through the above processing, a coal source classification model, i.e. an LSSVM model, is obtained as shown in the following formula:
the Radial Basis Function (RBF) kernel is used as a kernel function, as shown in the following equation:
（11）；
in the formula (I), the compound is shown in the specification,
is the bandwidth of the kernel function.
Hyper-parameter
And
the parameters have a great influence on the performance of the LSSVM model and need to be determined carefully.
Detailed description of the preferred embodiment
A coal blending classification system based on X-ray coal quality multi-elements is shown in figure 1 and comprises a coal blending classifier, an X-ray coal quality multi-element analyzer and a control driving device, wherein the X-ray coal quality multi-element analyzer is used for collecting coal quality multi-element components of a coal source to be classified, the coal blending classifier is used for analyzing the collected coal quality multi-element components to obtain a coal source type, and the control driving device is used for transporting the coal source to be classified to a corresponding position according to the coal source type.
The X-ray coal quality multi-element analyzer emits a beam of low-energy X-ray to a measured object, namely coal, to excite the measured object to emit characteristic fluorescence, a detector reads the excited characteristic fluorescence to obtain a real-time energy spectrum of the measured object, and the obtained energy spectrum is analyzed through a deep learning algorithm to obtain the composition of the measured coal, wherein the composition comprises the information of the content of elements behind magnesium (Mg) in a periodic table, and the measurable elements in the coal comprise: ash, sulfur, magnesium, aluminum, silicon, calcium, iron.
The coal blending classifier is provided with a coal source classification model established by the method in the first embodiment, coal quality multi-element components of the coal source to be classified are input into the coal source classification model, and classification of the coal source to be classified is obtained through operation.
The control driving device comprises a controller and a driving device, the driving device comprises a belt transmission motor, and the controller controls the corresponding belt transmission motor to act according to the classification result so as to transport the coal source to be classified to the corresponding storage position.
Detailed description of the preferred embodiment
An embodiment of the present invention provides a coal blending classifier terminal device based on multiple elements of X-ray coal quality, where the terminal device of the embodiment includes: a processor, a memory and a computer program, such as a path weighted availability calculation program, stored in the memory and executable on the processor, the processor implementing the method of embodiment 1 when executing the computer program.
Alternatively, the processor implements the function of a coal blending classifier when executing the computer program.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of instruction segments of a computer program capable of performing specific functions, and the instruction segments are used for describing the execution process of the computer program in the terminal equipment of the X-ray coal quality multi-element-based coal blending classifier. For example, the computer program may be divided into a plurality of modules, each module having the following specific functions:
1. the model establishing module is used for establishing a coal source classification model;
2. and the classification module is used for classifying the coal sources according to the coal source data to be classified.
The X-ray coal quality multi-element-based coal blending classifier terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The X-ray coal quality multi-element-based coal blending classifier terminal equipment can comprise, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that the foregoing examples are merely examples of the terminal device of the X-ray coal multi-element-based coal blending classifier, and do not constitute a limitation of the terminal device of the X-ray coal multi-element-based coal blending classifier, and may include more or less components than those shown in the drawings, or combine some components, or different components, for example, the terminal device of the X-ray coal multi-element-based coal blending classifier may further include an input/output device, a network access device, a bus, and the like.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general processor may be a microprocessor or the processor may be any conventional processor or the like, the processor is a control center of the terminal device of the X-ray coal multi-element based coal blending classifier, and various interfaces and lines are used for connecting various parts of the whole terminal device of the X-ray coal multi-element based coal blending classifier.
The memory can be used for storing the computer program and/or the module, and the processor can realize various functions of the X-ray coal multi-element-based coal blending classifier terminal device by operating or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Detailed description of the preferred embodiment
The module/unit integrated on the terminal device of the X-ray coal quality multi-element-based coal blending classifier can be stored in a computer readable storage medium if the module/unit is realized in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc.
The embodiments of the present invention are preferred embodiments of the present invention, and the scope of the present invention is not limited by these embodiments, so: all equivalent changes made according to the structure, shape and principle of the invention are covered by the protection scope of the invention.
Claims (10)
1. A coal blending classification modeling method based on X-ray coal quality multi-elements is characterized by comprising the following steps: the method comprises the steps of obtaining coal quality multi-element components of at least one coal source, forming the coal quality multi-element components of all the coal sources into multi-element group diversity, carrying out algorithm processing on multi-element group diversity data, and establishing a coal source classification model.
2. The X-ray coal multi-element based coal blending classification modeling method according to claim 1; the method is characterized in that: and obtaining coal element components of each coal source by adopting an X-ray coal multi-element analyzer, and carrying out LSSVM algorithm processing on the element component data to establish a coal source classification model.
3. The X-ray coal multi-element based coal blending classification modeling method according to claim 1 or 2; the method is characterized in that: setting coal quality multi-element components to comprise Q elements, setting the type of a coal source to be N, taking the coal quality multi-element components as Q-dimensional input vectors, establishing a nonlinear function, and mapping an input space to a feature space; based on a structured risk minimization principle, a Lagrange function is constructed, an optimization problem is solved, conditions of an optimal solution of the problem are set, a kernel function is obtained through solving, and a coal source classification model is established.
4. The X-ray coal multi-element based coal blending classification modeling method according to claim 3; the method is characterized in that: based on the principle of minimizing the structured risk, describing the evaluation problem as an optimization problem, and establishing an optimization function:
（2）；
in the formula (I), the compound is shown in the specification,
is a function of the regularization of the image,
is a non-negative regularization parameter, determines the trade-off between the accuracy and complexity of the model,
representing the regression error between the actual and predicted values.
5. The X-ray coal multi-element based coal blending classification modeling method according to claim 3; the method is characterized in that: the lagrange function is constructed as shown below:
in the formula (I), the compound is shown in the specification,
(i =1, 2.... N.) represents the lagrangian multiplier, respectively for each pair
、
、
And
partial differentiation is carried out, all derivatives are set to be zero, and the optimal solution condition of the optimization problem is obtained as follows:
in the formula (I), the compound is shown in the specification,
，
，
，
a symmetric matrix representing an N x N kernel function.
6. The X-ray coal multi-element based coal blending classification modeling method according to claim 3; the method is characterized in that: the kernel function is shown as follows:
in the formula (I), the compound is shown in the specification,
。
7. the X-ray coal multi-element based coal blending classification modeling method according to claim 3; the method is characterized in that: the coal source classification model is shown as the following formula:
in the formula (I), the compound is shown in the specification,
a vector of weights is represented by a vector of weights,
representing a bias term;
the Radial Basis Function (RBF) kernel is used as a kernel function, as shown in the following equation:
in the formula (I), the compound is shown in the specification,
is the bandwidth of the kernel function.
8. A coal blending classification system based on X-ray coal quality multi-elements is characterized in that: the coal source classification device comprises a coal blending classifier, an X-ray coal quality multi-element analyzer and a control driving device, wherein the X-ray coal quality multi-element analyzer is used for collecting coal quality multi-element components of a coal source to be classified, the coal blending classifier is used for analyzing the collected coal quality multi-element components to obtain a coal source type, and the control driving device is used for transporting the coal source to be classified to a corresponding position according to the coal source type.
9. An X-ray coal multi-element based coal blending classifier terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: the processor, when executing the computer program, implements the method of any of claims 1-7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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