Coal fineness on-line detection method based on adaptive filtering and laser diffraction method

Abstract

The application discloses an online detection method for coal powder fineness based on adaptive filtering and laser diffraction, and belongs to the technical field of coal powder fineness measurement. The method comprises the following steps: changing the flow of compressed air in a coal powder conveying pipeline to ensure stable flow of the coal powder particle flow and realize isokinetic sampling; the laser is properly adjusted to ensure that the laser can accurately irradiate on the coal powder particle flow; the LMS algorithm is used to suppress environmental noise, and the noise influence is weakened after a certain number of iterations; a charge coupled device is used to collect diffraction light and convert the diffraction light into a digital signal; the diffraction ring is dispersedly received on the pixels of a detector, and each pixel point collects diffraction light of a specific angle; the collected diffraction ring is pretreated to obtain extracted corner points, relevant data of the diffraction ring is calculated, and the fineness information of the coal powder is obtained. The application has the advantages of real-time, high precision and automation, and can realize real-time monitoring of the size and distribution change of the coal powder particles.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of coal fineness measurement, and specifically relates to a coal fineness online detection method based on adaptive filtering and laser diffraction method. BACKGROUND

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

[0003] Too coarse or too fine coal will have an adverse effect on the combustion equipment. Too coarse coal may cause incomplete combustion and increase mechanical wear; too fine coal may cause the burner to coke and the wear to intensify. By precisely controlling the coal fineness, the waste of raw materials can be reduced, the production cost can be lowered, and the economic benefit can be improved.

[0004] Cai Xiaoshu et al. proposed a light fluctuation method to detect coal fineness (Cai Xiaoshu, Ouyang Xin, Li Junfeng, et al. Online measurement of coal in power plants [J]. Acta Thermophysics Sinica, 2002(06):753-756), which pointed out that the random changes of light intensity signal are related to the size and number of particles in the light beam at the measurement moment. By measuring the random change sequence of light signal, the average particle size and concentration can be obtained by applying the light fluctuation method theory for analysis. However, this method may not be sensitive enough when measuring low-concentration particles, because its detection principle relies on the scattering of particles on light. At low concentration, the scattered light signal is weak, which may lead to inaccurate measurement results. SUMMARY

[0005] The purpose of the present application is to provide a method that solves the problems raised in the background.

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

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

[0008] Step S1: In the coal conveying pipeline, the compressed air flow is changed by pressure measurement to make the coal particle flow stable, and the coal particles are sampled at a constant speed;

[0009] Step S2: Perform fineness detection operation on the sampled coal particles, and output the fineness information;

[0010] The fineness detection operation includes:

[0011] Step S21: Generate a laser beam using a laser emitting device and direct the laser beam onto the coal powder particle flow;

[0012] Step S22: Minimize the impact of environmental noise during the fineness detection process through iteration;

[0013] Step S23: Collect the intensity of the diffraction light formed by the diffraction ring dispersed at different pixel points. The diffraction light is generated by the laser beam irradiating the coal powder particle flow. The intensity of the diffraction light is changed by adjusting the irradiation angle of the laser beam. The irradiation angle of the laser beam is continuously adjusted by observing the collected diffraction light intensity until the expected diffraction ring is obtained.

[0014] Step S24: Preprocess the obtained diffraction rings, including noise reduction, contrast enhancement and smoothing, to generate high-quality diffraction rings;

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

[0016] Furthermore, the isokinetic cyclic sampling includes:

[0017] The pulverized coal particles are evenly distributed in the pulverized coal pipeline through the sieve plate and vibrator.

[0018] By changing the total pressure and static pressure inside the pulverized coal pipeline, the air flow rate inside the pulverized coal pipeline is compressed, thereby stabilizing the flow of pulverized coal particles.

[0019] A sampling point is set inside the pulverized coal pipeline, and the sampling point is connected to a sampling tube and a return tube. An ejector is installed in the sampling tube. Under the action of compressed air flow, the ejector generates negative pressure, and the pulverized coal particles are drawn into the sampling tube by the negative pressure. The return tube is used to send the pulverized coal particles back into the pulverized coal pipeline after sampling, so as to achieve constant velocity 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. The beam expander lens consists 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, minimizing the noise impact includes:

[0022] Set the weight vector of the filter at time n, denoted as ω(n); set the step size factor of the filter, denoted as μ; obtain the input signal with noise at time n, denoted as x(n); obtain the desired signal at time n, denoted as d(n); calculate the signal error value e(n) = d(n) - ω(n) at the nth iteration. T x(n) is used to update the weight vector ω(n+1) = ω(n) + μe(n)x(n) at the (n+1)th iteration; the iteration stops when the signal error value e(n) is less than the error threshold, and ω(n) is output. T x(n) and ω(n); where T represents the transpose symbol.

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

[0024] L numerical points are selected within the high-quality diffraction ring to describe its edge profile, which is denoted as S: {P} i |i∈[1,L]}, and P i =(x i y i ), where P i Let x represent the i-th numerical point, S represent the scale, and x represent the x-th numerical point. i and y i Representing the numerical point P respectively i The x and y coordinates of the numerical point P are obtained. i The numerical curvature at point K is denoted as K. S (P i Based on digital curvature K S (P i Based on the basic standard conditions, determine the numerical point P. i Is it a corner point? If it is a corner point, then change the number point P. i Pixels marked as edge contours are then smoothly connected sequentially to form an edge circle.

[0025] Furthermore, 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 the numerical point P i When the center is local, the encoded field corresponding to the formed digital point is defined, where i1 is the lower limit of the field, i2 is the upper limit of the field, and MAX{·} represents the maximum value symbol; the second basic standard condition is K. S (P i) >= u, u represents a preset curvature threshold value;

[0026] If the basic standard condition is met, the digital point P i is judged as a corner point.

[0027] Further, the construction fineness information detection model, the center coordinates and the radius of the high-quality diffraction ring are calculated, and the method further comprises the following steps of:

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

[0029] Further, the radius is used to fit the fineness information, and the method comprises the following steps of:

[0030] Based on the edge circle, the diameter D of the coal powder particle size is calculated And D = 2r.

[0031] Compared with the prior art, the present application has the following beneficial effects:

[0032] (1) The present application can provide high-precision particle size distribution data, with high resolution, suitable for fine particle size distribution of coal powder, especially in the case where the fineness needs to be accurately controlled;

[0033] (2) The coal powder fineness online detection method based on adaptive filtering and laser diffraction method provided by the present application can provide real-time coal powder fineness data, which helps to timely adjust the production process and improve the production efficiency;

[0034] (3) The structure of the LMS algorithm is relatively simple, easy to implement, and has small amount of calculation, which is suitable for use on devices with limited resources, and the LMS algorithm is not very sensitive to the initial conditions of the system, that is, it can work well even in the case where the statistical characteristics of the signal or noise are unknown. The LMS algorithm can automatically adjust the filter coefficients according to the changes of the input signal and noise, and the present application applies 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 DRAWINGS

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

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

[0037] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0038] Please refer to Figure 1 The present application provides technical solutions:

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

[0040] Step S1: In the coal powder conveying pipeline, the compressed air flow is changed by pressure measurement to stabilize the flow of the coal powder particle flow, and the coal powder particles are cyclically sampled at a constant speed.

[0041] For example, the constant speed cyclic sampling comprises:

[0042] The coal powder particles are uniformly distributed in the coal powder pipeline by the sieve plate and the vibrator.

[0043] The air flow in the coal powder pipeline is compressed by changing the total pressure and static pressure in the coal powder pipeline to stabilize the flow of the coal powder particle flow.

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

[0045] Step S2: Perform fineness detection operation on the sampled coal powder particles, and output the fineness information.

[0046] The fineness detection operation comprises:

[0047] Step S21: Generate a laser beam by a laser emitting device, and irradiate the laser beam onto the coal powder particle flow.

[0048] For example, the laser emitting device comprises a blue diode laser power supply, a blue diode laser, a collimating lens, an expansion lens and an expansion mirror, and the expansion 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: Minimize the impact of environmental noise during the fineness detection process through iteration;

[0050] For example, minimizing the noise impact includes:

[0051] Set the weight vector of the filter at time n, denoted as ω(n); set the step size factor of the filter, denoted as μ; obtain the input signal with noise at time n, denoted as x(n); obtain the desired signal at time n, denoted as d(n); calculate the signal error value e(n) = d(n) - ω(n) at the nth iteration. T x(n) is used to update the weight vector ω(n+1) = ω(n) + μe(n)x(n) at the (n+1)th iteration; the iteration stops when the signal error value e(n) is less than the error threshold, and ω(n) is output. T x(n) and ω(n); where T denotes the transpose sign;

[0052] Step S23: Collect the intensity of the diffraction light formed by the diffraction ring dispersed at different pixel points. The diffraction light is generated by the laser beam irradiating the coal powder particle flow. The intensity of the diffraction light is changed by adjusting the irradiation angle of the laser beam. The irradiation angle of the laser beam is continuously adjusted by observing the collected diffraction light intensity until the expected diffraction ring is obtained.

[0053] Step S24: Preprocess the obtained diffraction rings, including noise reduction, contrast enhancement and smoothing, to generate high-quality diffraction rings;

[0054] Step S25: Based on the high-quality diffraction ring, construct a fineness information detection model, calculate the center coordinates and radius of the high-quality diffraction ring, and fit the fineness information based on the radius;

[0055] For example, the construction of the fineness information detection model, and the calculation of the center coordinates and radius of the high-quality diffraction ring, includes:

[0056] L numerical points are selected within the high-quality diffraction ring to describe its edge profile, which is denoted as S: {P} i |i∈[1,L]}, and P i =(x i y i ), where P i Let x represent the i-th numerical point, S represent the scale, and x represent the x-th numerical point. i and y i Representing the numerical point P respectively i The x and y coordinates of the numerical point P are obtained. i The numerical curvature at point K is denoted as K.S (P i ); based on the digital curvature K S (P i ), by the basic standard condition, judge whether the digital point P i is a corner point, if it is a corner point, mark the digital point P i as a pixel point of the edge contour; sequentially smooth connect each pixel point to form an edge circle;

[0057] Wherein, the basic standard condition includes: the first basic standard condition and the second basic standard condition; the first basic standard condition is: |K S (P i )|≥MAX{|K S (P i )||i∈[i1,i2]}, wherein, [i1,i2] represents the code number domain of the digital point corresponding to the digital point P i , 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, u represents a preset curvature threshold;

[0058] If the basic standard condition is satisfied at the same time, the digital point P i is judged as a corner point;

[0059] Wherein, based on the edge circle, the horizontal coordinate of the circle center x The vertical coordinate of the circle center k is calculated, then the circle center is represented as (h, k); the radius of the edge circle r is calculated, and r i 2 =(x i -h) 2 +(y i -k) 2 , wherein N represents the total number of pixel points;

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

[0061] Based on the edge circle, the diameter of the coal powder particle size D is calculated, and D=2r.

[0062] It is to be noted that, in the present text, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0063] Finally, it should be noted that the above-mentioned only constitutes preferred embodiments of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications, equivalent replacements, improvements and the like of the technical solutions described in the foregoing embodiments can still be made. Any modifications, equivalent replacements, improvements and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An online method for detecting the fineness of pulverized coal based on adaptive filtering and laser diffraction, characterized in that, The method includes: Step S1: In the pulverized coal conveying pipeline, the compressed air flow rate is changed by pressure measurement to stabilize the flow of pulverized coal particles, and the pulverized coal particles are sampled in a constant-speed circulation. Step S2: Perform a fineness test on the sampled coal powder particles and output the fineness information; The fineness detection operation includes: Step S21: Generate a laser beam using a laser emitting device and direct the laser beam onto the coal powder particle flow; Step S22: Minimize the impact of environmental noise during the fineness detection process through iteration; Step S23: Collect the intensity of the diffraction light formed by the diffraction ring dispersed at different pixel points. The diffraction light is generated by the laser beam irradiating the coal powder particle flow. The intensity of the diffraction light is changed by adjusting the irradiation angle of the laser beam. The irradiation angle of the laser beam is continuously adjusted by observing the collected diffraction light intensity until the expected diffraction ring is obtained. Step S24: Preprocess the obtained diffraction rings, including noise reduction, contrast enhancement and smoothing, to generate high-quality diffraction rings; Step S25: Based on the high-quality diffraction ring, construct a fineness information detection model, calculate the center coordinates and radius of the high-quality diffraction ring, and fit the fineness information based on the radius; The construction of the fineness information detection model, which calculates the center coordinates and radius of the high-quality diffraction ring, includes: L numerical points are selected within the high-quality diffraction ring to describe its edge profile, which is denoted as S: {P} i |i∈[1,L]}, and P i =(x i y i ), where P i Let x represent the i-th numerical point, S represent the scale, and x represent the x-th numerical point. i and y i Representing the numerical point P respectively i The x and y coordinates of the numerical point P are obtained. i The numerical curvature at point K is denoted as K. S (P i Based on digital curvature K S (P i Based on the basic standard conditions, determine the numerical point P. i Is it a corner point? If it is a corner point, then change the number point P. i Pixels marked as edge contours; smoothly connect each pixel in sequence to form an edge circle; 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 the numerical point P i When the center is local, the encoded field corresponding to the formed digital point is defined, where i1 is the lower limit of the field, i2 is the upper limit of the field, 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 aforementioned basic criteria are met simultaneously, then the numerical point P is determined. i Corner point; The construction of the fineness information detection model, which calculates the center coordinates and radius of the high-quality diffraction ring, also includes: Based on the edge circle, calculate the x-coordinate of the circle center respectively. The ordinate 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; The process of fitting fineness information based on radius includes: Calculate the diameter of pulverized coal particles based on the edge circle. And D = 2r.

2. The online coal powder fineness detection method based on adaptive filtering and laser diffraction as described in claim 1, characterized in that, The constant-rate cyclic sampling includes: The pulverized coal particles are evenly distributed in the pulverized coal pipeline through the sieve plate and vibrator. By changing the total pressure and static pressure in the pulverized coal pipeline, the air flow rate in the pulverized coal pipeline is compressed, thereby stabilizing the flow of pulverized coal particles. A sampling point is set inside the pulverized coal pipeline, and the sampling point is connected to a sampling tube and a return tube. An ejector is installed in the sampling tube. Under the action of compressed air flow, the ejector generates negative pressure, which causes pulverized coal particles to be drawn into the sampling tube. The return tube is used to send the pulverized coal particles back into the pulverized coal pipeline through the ejector after sampling, so as to achieve constant velocity cyclic sampling.

3. The online coal powder fineness detection method based on adaptive filtering and laser diffraction as described in claim 1, characterized in that, 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. The beam expander lens consists 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 online coal powder fineness detection method based on adaptive filtering and laser diffraction as described in claim 1, characterized in that, The noise impact minimization includes: Set the weight vector of the filter at time n, denoted as ω(n); set the step size factor of the filter, denoted as μ; obtain the input signal with noise at time n, denoted as x(n); obtain the desired signal at time n, denoted as d(n); calculate the signal error value e(n) = d(n) - ω(n) at the nth iteration. T x(n) is used to update the weight vector ω(n+1) = ω(n) + μe(n)x(n) at the (n+1)th iteration; the iteration stops when the signal error value e(n) is less than the error threshold, and ω(n) is output. T x(n) and ω(n); where T represents the transpose symbol.

5. A computer-readable storage medium, characterized in that, The readable storage medium is used to carry a computer program that can be executed by a processor to implement the steps of the method as described in any one of claims 1 to 4.

6. An online coal powder fineness detection mechanism based on adaptive filtering and laser diffraction, characterized in that, include: One or more processors; A system apparatus for causing the online coal powder fineness detection mechanism based on adaptive filtering and laser diffraction to implement the steps of the method as described in any one of claims 1 to 4 when the one or more computer programs are executed by the one or more processors.

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