Pulverized coal fineness online detection method based on adaptive filtering and laser diffraction method

Abstract

The invention discloses a pulverized coal fineness online detection method based on adaptive filtering and a laser diffraction method, and belongs to the technical field of pulverized coal fineness measurement. The method comprises the following steps: changing the compressed air flow in a pulverized coal conveying pipeline to ensure stable flowing of pulverized coal particle flow and realize constant-speed circulating sampling; the laser is properly adjusted to ensure that the laser can accurately irradiate the pulverized coal particle flow; the LMS algorithm is used to suppress environmental noise, and the noise influence is weakened after a certain number of iterations; diffracted light is collected through a charge-coupled device and converted into digital signals, diffraction rings are dispersedly received on pixels of a detector, and diffracted light at a specific angle is collected by each pixel point; and the collected diffraction ring is preprocessed, an extraction angular point is obtained, related data of the diffraction ring is calculated, and fineness information of the pulverized coal is obtained. The system has the advantages of real-time performance, high precision, automation and the like, and the size and distribution change of pulverized coal particles can be monitored in real time.

Description

Technical Field

[0001] The invention belongs to the technical field of coal powder fineness measurement, and in particular relates to an online detection method for coal powder fineness based on adaptive filtering and laser diffraction method. Background Art

[0002] The fineness of coal powder directly affects the combustion efficiency. Fine coal powder has a larger surface area and is more likely to come into contact with oxygen, thereby improving the combustion efficiency. Online detection can adjust the fineness of coal powder in real time to ensure the best combustion effect. By optimizing the fineness of coal powder, the consumption of coal can be reduced, the energy utilization rate can be improved, and energy can be saved. In industries such as metallurgy and cement, coal powder is used as a reducing agent or fuel, and its fineness directly affects the quality of the final product.

[0003] Coal powder that is too coarse or too fine will have an adverse effect on combustion equipment. Coarse coal powder may lead to incomplete combustion and increase mechanical wear; fine coal powder may cause burner coking and increased wear. By accurately controlling the fineness of coal powder, it is possible to reduce raw material waste, lower production costs, and improve economic benefits.

[0004] Cai Xiaoshu et al. proposed to use the optical pulsation method to detect the fineness of coal powder (Cai Xiaoshu, Ouyang Xin, Li Junfeng, et al. On-line measurement of coal powder in power plants [J]. Journal of Engineering Thermophysics, 2002 (06): 753-756). This method points out that the random change of the light intensity signal is related to the size and number of particles in the light beam at the moment of measurement. By measuring the random change sequence of the light signal, the optical pulsation method theory can be applied for analysis to obtain the average particle size and concentration of the particles. However, this method may not be sensitive enough when measuring low-concentration particles because its detection principle relies on the scattering of light by particles. The scattered light signal is weak at low concentrations, which may lead to inaccurate measurement results. Summary of the invention

[0005] The purpose of the present invention is to provide a method to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The method for online detection of coal powder fineness based on adaptive filtering and laser diffraction method comprises the following steps:

[0008] Step S1: in the pulverized coal conveying pipeline, the compressed air flow rate is changed by measuring the pressure to stabilize the flow of the pulverized coal particles, and the pulverized coal particles are sampled in a constant-speed cycle;

[0009] Step S2: performing a fineness detection operation on the sampled coal powder particles and outputting fineness information;

[0010] The fineness detection operation includes:

[0011] Step S21: generating a laser beam through a laser emitting device, and irradiating the laser beam onto the coal powder particle flow;

[0012] Step S22: minimizing the impact of environmental noise during fineness detection by iteration;

[0013] Step S23: collecting the diffraction light intensity of the diffraction rings formed by the diffraction light dispersed at different pixel points, wherein the diffraction light is generated by irradiating the laser beam onto the coal powder particle flow; changing the diffraction light intensity by adjusting the irradiation angle of the laser beam; and continuously adjusting the irradiation angle of the laser beam by observing the collected diffraction light intensity until the expected diffraction ring is obtained;

[0014] Step S24: preprocessing the obtained diffraction rings, wherein the preprocessing includes denoising, contrast enhancement and smoothing to generate high-quality diffraction rings;

[0015] Step S25: Based on the high-quality diffraction ring, a fineness information detection model is constructed to calculate the center coordinates and radius of the high-quality diffraction ring, and based on the radius, the fineness information is fitted.

[0016] Further, the isokinetic cyclic sampling includes:

[0017] The sieve plate and vibrator are used to make the coal powder particles evenly distributed in the coal powder pipeline;

[0018] By changing the total pressure and static pressure in the pulverized coal pipeline, the air flow in the pulverized coal pipeline is compressed to stabilize the flow of pulverized coal particles;

[0019] A sampling point is arranged in the pulverized coal pipeline, and the sampling point is connected with a sampling tube and a powder return tube; an ejector is arranged in the sampling tube, and the ejector generates negative pressure under the action of compressed air flow, and the pulverized coal particles are sucked into the sampling tube through the negative pressure; the powder return tube is used to send the pulverized coal particles back to the pulverized coal pipeline through the ejector after sampling the pulverized coal particles, so as to realize constant-speed circulation sampling.

[0020] Furthermore, the laser emitting device includes: a blue diode laser power supply, a blue diode laser, a collimating lens, a beam expander lens and a beam expander mirror, wherein the beam expander lens is composed of a front lens and a rear lens, wherein the front lens is a convex lens and the rear lens is a concave lens.

[0021] Furthermore, the noise impact minimization includes:

[0022] Set the weight vector of the filter at the nth moment, recorded as ω(n), set the step size factor of the filter, recorded as μ; obtain the input signal with noise at the nth moment, recorded as x(n), obtain the expected signal at the nth moment, recorded as d(n); calculate the signal error value at the nth iteration e(n) = d(n)-ω(n) T x(n), update the weight vector ω(n+1)=ω(n)+μe(n)x(n) at the n+1th iteration; when the signal error value e(n) is less than the error threshold, the iteration stops and outputs ω(n) T x(n) and ω(n); where T represents the transposed sign.

[0023] Furthermore, the construction of the fineness information detection model to calculate the center coordinates and radius of the high-quality diffraction ring includes:

[0024] Select L digital points in the high-quality diffraction ring to describe the edge profile of the high-quality diffraction ring, and record the edge profile as S: {P i |i∈[1,L]}, and P i =(x i ,y i ), where P i represents the i-th digital point, S represents the scale, x i and i Represents the digital point P i The horizontal and vertical coordinates of the digital point P i The digital curvature at S (P i ); Based on the digital curvature K S (P i ), through the basic standard conditions, determine the digital point P i Is it a corner point? If it is a corner point, then the digital point P i Pixel points marked as edge contours; smoothly connect each pixel point in sequence to form an edge circle.

[0025] Furthermore, the basic standard condition includes: a first basic standard condition and a second basic standard condition; the first basic standard condition is: |K S (P i )|≥MAX{|K S (P i )||i∈[i 1 ,i 2 ]}, where [i 1 ,i 2 ] indicates the digital point P i When it is the local center, the code number domain corresponding to the digital point formed, and i 1 is the lower limit of the domain, i 2is the upper limit of the domain, MAX{·} represents the maximum value symbol; the second basic standard condition is K S (P i )≥u, where u represents the preset curvature threshold;

[0026] If the basic standard conditions are met at the same time, the digital point P is judged i For corner points.

[0027] Furthermore, the construction of the fineness information detection model to calculate the center coordinates and radius of the high-quality diffraction ring also includes:

[0028] Based on the edge circle, calculate the horizontal coordinate of the circle center respectively The vertical coordinate of the center of the circle The center of the circle is represented as (h, k); calculate the radius of the edge circle And r i 2 =(x i -h) 2 +(y i -k) 2 , where N represents the total number of pixels.

[0029] Furthermore, fitting fineness information based on the radius includes:

[0030] Calculate the diameter of coal powder particle size based on the edge circle And D=2r.

[0031] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0032] (1) The present invention can provide high-precision particle size distribution data with high resolution, and is suitable for coal powder with fine particle size distribution, especially in situations where precise control of fineness is required;

[0033] (2) The method for online detection of coal powder fineness based on adaptive filtering and laser diffraction provided by the present invention can provide coal powder fineness data in real time, which is helpful to adjust the production process in time and improve production efficiency;

[0034] (3) The structure of the LMS algorithm is relatively simple, easy to implement, and has a small amount of calculation. It is suitable for use on resource-constrained devices. In addition, the LMS algorithm is not very sensitive to the initial conditions of the system and can work well even when the statistical characteristics of the signal or noise are unknown. The LMS algorithm can automatically adjust the filter coefficients according to the changes in the input signal and noise. The present invention uses the LMS algorithm to suppress environmental noise, which can minimize the mean square error between the output signal and the expected signal, thereby effectively suppressing noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0036] Figure 1 is a schematic diagram of steps of an embodiment of the present invention; DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1 , the present invention provides a technical solution:

[0039] The method for online detection of coal powder fineness based on adaptive filtering and laser diffraction method comprises the following steps:

[0040] Step S1: in the pulverized coal conveying pipeline, the compressed air flow rate is changed by measuring the pressure to stabilize the flow of the pulverized coal particles, and the pulverized coal particles are sampled in a constant-speed cycle;

[0041] Exemplarily, the isokinetic cyclic sampling includes:

[0042] The sieve plate and vibrator are used to make the coal powder particles evenly distributed in the coal powder pipeline;

[0043] By changing the total pressure and static pressure in the pulverized coal pipeline, the air flow in the pulverized coal pipeline is compressed to stabilize the flow of pulverized coal particles;

[0044] A sampling point is set in the pulverized coal pipeline, and the sampling point is connected to a sampling tube and a powder return tube; an ejector is set in the sampling tube, and the ejector generates negative pressure under the action of compressed air flow, and the pulverized coal particles are sucked into the sampling tube through the negative pressure; the powder return tube is used to send the pulverized coal particles back to the pulverized coal pipeline through the ejector after sampling the pulverized coal particles, so as to realize constant-speed circulation sampling;

[0045] Step S2: performing a fineness detection operation on the sampled coal powder particles and outputting fineness information;

[0046] The fineness detection operation includes:

[0047] Step S21: generating a laser beam through a laser emitting device, and irradiating the laser beam onto the coal powder particle flow;

[0048] Exemplarily, the laser emitting device comprises: a blue diode laser power supply, a blue diode laser, a collimating lens, a beam expander lens and a beam expander, wherein the beam expander lens is composed of a front lens and a rear lens, wherein the front lens is a convex lens and the rear lens is a concave lens;

[0049] Step S22: minimizing the impact of environmental noise during fineness detection by iteration;

[0050] Exemplarily, the minimization of noise impact includes:

[0051] Set the weight vector of the filter at the nth moment, recorded as ω(n), set the step size factor of the filter, recorded as μ; obtain the input signal with noise at the nth moment, recorded as x(n), obtain the expected signal at the nth moment, recorded as d(n); calculate the signal error value at the nth iteration e(n) = d(n)-ω(n) T x(n), update the weight vector ω(n+1)=ω(n)+μe(n)x(n) at the n+1th iteration; when the signal error value e(n) is less than the error threshold, the iteration stops and outputs ω(n) T x(n) and ω(n); where T represents the transposition sign;

[0052] Step S23: collecting the diffraction light intensity of the diffraction rings formed by the diffraction light dispersed at different pixel points, wherein the diffraction light is generated by irradiating the laser beam onto the coal powder particle flow; changing the diffraction light intensity by adjusting the irradiation angle of the laser beam; and continuously adjusting the irradiation angle of the laser beam by observing the collected diffraction light intensity until the expected diffraction ring is obtained;

[0053] Step S24: preprocessing the obtained diffraction rings, wherein the preprocessing includes denoising, contrast enhancement and smoothing to generate high-quality diffraction rings;

[0054] Step S25: constructing a fineness information detection model based on the high-quality diffraction ring, calculating the center coordinates and radius of the high-quality diffraction ring, and fitting the fineness information based on the radius;

[0055] Exemplarily, the step of constructing a fineness information detection model and calculating the center coordinates and radius of a high-quality diffraction ring includes:

[0056] Select L digital points in the high-quality diffraction ring to describe the edge profile of the high-quality diffraction ring, and record the edge profile as S: {P i |i∈[1,L]}, and P i =(x i ,y i ), where P i represents the i-th digital point, S represents the scale, x iand i Represents the digital point P i The horizontal and vertical coordinates of the digital point P i The digital curvature at S (P i ); Based on the digital curvature K S (P i ), through the basic standard conditions, determine the digital point P i Is it a corner point? If it is a corner point, then the digital point P i Mark the pixel points as edge contours; smoothly connect the pixel points in sequence to form an edge circle;

[0057] The basic standard conditions include: a first basic standard condition and a second basic standard condition; the first basic standard condition is: |K S (P i )|≥MAX{|K S (P i )||i∈[i 1 ,i 2 ]}, where [i 1 ,i 2 ] indicates the digital point P i When it is the local center, the code number domain corresponding to the digital point formed, and i 1 is the lower limit of the domain, i 2 is the upper limit of the domain, MAX{·} represents the maximum value symbol; the second basic standard condition is K S (P i )≥u, where u represents the preset curvature threshold;

[0058] If the basic standard conditions are met at the same time, the digital point P is judged i is the corner point;

[0059] Among them, based on the edge circle, the horizontal coordinates of the circle center are calculated respectively The vertical coordinate of the center of the circle The center of the circle is represented as (h, k); calculate the radius of the edge circle And r i 2 =(x i -h) 2 +(y i -k) 2 , where N represents the total number of pixels;

[0060] Among them, based on the radius, the fineness information is fitted, including:

[0061] Calculate the diameter of coal powder particle size based on the edge circle And D=2r.

[0062] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0063] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An online detection method for coal powder fineness based on adaptive filtering and laser diffraction method, characterized in that: The method includes: Step S1: in the pulverized coal conveying pipeline, the compressed air flow rate is changed by measuring the pressure to stabilize the flow of the pulverized coal particles, and the pulverized coal particles are sampled in a constant-speed cycle; Step S2: performing a fineness detection operation on the sampled coal powder particles and outputting fineness information; The fineness detection operation includes: Step S21: generating a laser beam through a laser emitting device, and irradiating the laser beam onto the coal powder particle flow; Step S22: minimizing the impact of environmental noise during fineness detection by iteration; Step S23: collecting the diffraction light intensity of the diffraction rings formed by the diffraction light dispersed at different pixel points, wherein the diffraction light is generated by irradiating the laser beam onto the coal powder particle flow; changing the diffraction light intensity by adjusting the irradiation angle of the laser beam; and continuously adjusting the irradiation angle of the laser beam by observing the collected diffraction light intensity until the expected diffraction ring is obtained; Step S24: preprocessing the obtained diffraction rings, wherein the preprocessing includes denoising, contrast enhancement and smoothing to generate high-quality diffraction rings; Step S25: Based on the high-quality diffraction ring, a fineness information detection model is constructed to calculate the center coordinates and radius of the high-quality diffraction ring, and based on the radius, the fineness information is fitted.

2. The method for online detection of coal powder fineness based on adaptive filtering and laser diffraction method according to claim 1 is characterized in that: The isokinetic cyclic sampling comprises: The sieve plate and vibrator are used to make the coal powder particles evenly distributed in the coal powder pipeline; By changing the total pressure and static pressure in the pulverized coal pipeline, the air flow in the pulverized coal pipeline is compressed to stabilize the flow of pulverized coal particles; A sampling point is arranged in the pulverized coal pipeline, and the sampling point is connected with a sampling tube and a powder return tube; an ejector is arranged in the sampling tube, and the ejector generates negative pressure under the action of compressed air flow, and the pulverized coal particles are sucked into the sampling tube through the negative pressure; the powder return tube is used to send the pulverized coal particles back to the pulverized coal pipeline through the ejector after sampling the pulverized coal particles, so as to realize constant-speed circulation sampling.

3. The method for online detection of coal powder fineness based on adaptive filtering and laser diffraction method according to claim 1 is characterized in that: The laser emitting device comprises: a blue diode laser power supply, a blue diode laser, a collimating lens, a beam expanding lens and a beam expanding mirror, wherein the beam expanding lens is composed of a front lens and a rear lens, wherein the front lens is a convex lens and the rear lens is a concave lens.

4. The method for online detection of coal powder fineness based on adaptive filtering and laser diffraction method according to claim 1 is characterized in that: The noise impact minimization includes: Set the weight vector of the filter at the nth moment, recorded as ω(n), set the step size factor of the filter, recorded as μ; obtain the input signal with noise at the nth moment, recorded as x(n), obtain the expected signal at the nth moment, recorded as d(n); calculate the signal error value at the nth iteration e(n) = d(n)-ω(n) T x(n), update the weight vector ω(n+1)=ω(n)+μe(n)x(n) at the n+1th iteration; when the signal error value e(n) is less than the error threshold, the iteration stops and outputs ω(n) T x(n) and ω(n); where T represents the transposed sign.

5. The method for online detection of coal powder fineness based on adaptive filtering and laser diffraction method according to claim 1 is characterized in that: The method of constructing a fineness information detection model and calculating the center coordinates and radius of a high-quality diffraction ring includes: Select L digital points in the high-quality diffraction ring to describe the edge profile of the high-quality diffraction ring, and record the edge profile as S: {P i |i∈[1,L]}, and P i =(x i ,y i ), where P i represents the i-th digital point, S represents the scale, x i and i Represents the digital point P i The horizontal and vertical coordinates of the digital point P i The digital curvature at S (P i ); Based on the digital curvature K S (P i ), through the basic standard conditions, determine the digital point P i Is it a corner point? If it is a corner point, then the digital point P i Pixel points marked as edge contours; smoothly connect each pixel point in sequence to form an edge circle.

6. The method for online detection of coal powder fineness based on adaptive filtering and laser diffraction method according to claim 5 is characterized in that: The basic standard conditions include: a first basic standard condition and a second basic standard condition; the first basic standard condition is: |K S (P i )|≥MAX{|K S (P i )||i∈[i1,i2]}, where [i1,i2] represents a digital point P i When it is the local center, the code number domain corresponding to the digital point formed, and i1 is the lower limit of the domain, i2 is the upper limit of the domain, and MAX{·} represents the maximum value symbol; the second basic standard condition is K S (P i )≥u, where u represents the preset curvature threshold; If the basic standard conditions are met at the same time, the digital point P is judged i For corner points.

7. The method for online detection of coal powder fineness based on adaptive filtering and laser diffraction method according to claim 6 is characterized in that: The construction of the fineness information detection model and calculation of the center coordinates and radius of the high-quality diffraction ring also includes: Based on the edge circle, calculate the horizontal coordinate of the circle center respectively The vertical coordinate of the center of the circle The center of the circle is represented as (h, k); calculate the radius of the edge circle And r i 2 =(x i -h) 2 +(y i -k) 2 , where N represents the total number of pixels.

8. The method for online detection of coal powder fineness based on adaptive filtering and laser diffraction method according to claim 7 is characterized in that: The step of fitting fineness information based on the radius includes: Calculate the diameter of coal powder particle size based on the edge circle And D=2r.

9. A computer-readable storage medium, characterized in that: The readable storage medium is used to carry a computer program, and the computer program can be executed by a processor to implement the steps of the method according to any one of claims 1 to 8.

10. The online detection mechanism of coal powder fineness based on adaptive filtering and laser diffraction method is characterized by: include: one or more processors; A system device, used for enabling the online detection mechanism of coal powder fineness based on adaptive filtering and laser diffraction method to implement the steps of the method as described in any one of claims 1 to 8 when the one or more computer programs are executed by the one or more processors.

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