AU2021105929A4 - A Multi-Parameter Prediction Model For Coal Sample Hardiness - Google Patents
A Multi-Parameter Prediction Model For Coal Sample Hardiness Download PDFInfo
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Abstract
of Descriptions
The invention discloses a multi-parameter prediction model for coal sample hardiness,
including the following steps: Si, collecting the image information of coal particles and processing
the image information to obtain the diameter of the coal particles; S2, constructing a coal sample
particle size distribution curve; S3, performing regression linear processing on the coal sample
particle size distribution curve to get the fitting parameter of the coal sample particle size
distribution curve; S4, collecting the characteristic parameters of the coal particles; S5, evaluating
the hardiness of the coal samples according to the fitting parameter and the characteristic
parameters. A system for measuring coal sample hardiness based on image analysis, comprising a
hammering unit, a feeding unit, an imaging unit and a calculation unit. The method and system for
measuring coal sample hardiness based on image analysis can accurately measure the particle size
distribution of broken coal samples, and then effectively measure coal sample hardiness with small
measurement error and high accuracy.
Drawings of Descriptions
Collecting the image informationof S
coal particles and processing the
image information to obtain the
diameter of the coal particles
Costructi a oal sample S2
particle s iz e d is trinb ut ion c u rve
resfrmng regression linear proesin
n the coal sample parties size distributi
Mrva to get the fitting parameter of the
-a1 sample particle size distributioneur
Collecting the characteristic
parameters of the coal particles
Evaluating the hardiness of the coal
samples according to the fitting
parameter and the characteristic
parameters
Fig.1
Funnel
Vibratoy feeding chute
Sample particles
CCD camera
Q Particle photo Computer server Distaibutioncliagainof
C particle size and shape
Fig.2
1/2
Description
Drawings of Descriptions
Collecting the image informationof S coal particles and processing the image information to obtain the diameter of the coal particles
Costructi a oal sample S2 particle s ize distrinb ut ion c urve
resfrmng regression linear proesin n the coal sample parties size distributi Mrva to get the fitting parameter of the -a1 sample particle size distributioneur
Collecting the characteristic parameters of the coal particles
Evaluating the hardiness of the coal samples according to the fitting parameter and thecharacteristic parameters
Fig.1
Funnel
Vibratoy feeding chute
Sample particles
CCD camera
Q Particle photo Computer server Distaibutioncliagainof C particle size and shape
Fig.2
1/2
Descriptions A multi-parameter prediction model for coal sample hardiness
Technical Field The invention relates to the field of coal, in particular to a multi-parameter prediction model for coal sample hardiness.
Background Technology The coal industry usually uses the f value (coal hardiness coefficient) to characterize the hardiness of coal. Methods for Determining the Physical and Mechanical Propertiesof Coal and Rock-Part 12: Methodsfor Determining Coal Hardiness Coefficient (GB/T 23561.12-2010) is the standard method for measuring coal (rock) hardiness coefficient in the coal industry. This method only pays attention to the volume of crushed coal samples less than 0.5mm, and does not consider the grain distribution of crushed coal samples. Due to the lack of the above research, there is a large deviation in using coal hardiness coefficient to describe coal hardiness. Since most of the prior art is based on the above standard method and only modifies the experiment parameters in the above standard method, there is a large deviation in the hardiness of coal samples measured.
Summary of the invention On that account, the purpose of the invention is to overcome the defects in the prior art and provide a method and system for measuring coal sample hardiness based on image analysis. It can accurately measure the particle size distribution of broken coal samples, and then effectively measure coal sample hardiness with small measurement error and high accuracy. The method for measuring coal sample hardiness based on image analysis comprises the steps of: Sl, collecting the image information of coal particles and processing the image information to obtain the diameter d of the coal particles; S2, constructing coal sample particle size distribution curve F(d): F(d) = 1 - exp ( Where, F(d) is the proportion of the mass of coal particles with diameter less than d to the total mass of the coal samples, do and m are fitting parameters; S3, performing regression linear processing on the coal sample particle size distribution curve F(d) to get the fitting parameter do of the coal sample particle size distribution curve F(d) S4, collecting the characteristic parameters of the coal particles , including number of coal particles N, average particle size of coal particles D, particle size of coal particles D50, and particle size of coal particles D90; S5, evaluating the hardiness of the coal samples according to the fitting parameter do and the characteristic parameters. The higher the hardiness rating, the weaker the coal samples. Further, in SI, the coal particles required for each measurement are obtained by hammering, and the same hammering height is maintained during each hammering. Further, in Si, the diameter of the maximum inscribed circle of the coal particles is taken as the diameter of the coal particles.
Further, S5 comprises: Building a coal sample hardiness rating model:
A, do ai Ldo= B, a1<do<az C, a2 do
L5 = B, A, Y1 <Nc D Nc<Y2
L= < B y<D<Y2 C, Y2 A,D50561 LDo = B, 1 <D50 <6 2 C, (62 D50 A, D90 E1 LD O 9 = B, E 1 < D90< E2 D90 C, E2 Where, Ldo is the coal sample hardiness rating level corresponding to fitting parameter do; LNc is the coal sample hardiness rating level corresponding to number of coal particles Nc; L5 is the coal sample hardiness rating level corresponding to average particle size of coal particles D; LD5 is the coal sample hardiness rating level corresponding to particle size of coal particles D50; LD 9O is the coal sample hardiness rating level corresponding to particle size of coal particles D90; ai and a2 are set thresholds for the fitting parameter do; #1 and #2 are set thresholds for number of coal particles Nc; Y1 and Y2 are set thresholds for average particle size of coal particles D; 51 and (2 are set thresholds for particle size of coal particles D50; E1 and E2 are set thresholds for particle size of coal particles D90; A, B, C are the three coal sample hardiness rating levels in descending order; The obtained fitting parameter do, the number of coal particles N, the average particle size of coal particles D, the particle size of coal particles D50 and the particle size of coal particles D90 are brought into the coal sample hardiness rating model to obtain the coal sample hardiness rating levels Ldo, LNc, L LD50 and LD90. The highest of these levels is used as the coal sample hardiness level; The higher the coal sample hardiness level, the weaker the coal samples; A multi-parameter prediction model for coal sample hardiness comprises a hammering unit, a feeding unit, an imaging unit and a calculation unit. The hammering unit is configured to hammer the original coal sample with the same hammering height to obtain coal particles; The feeding unit is configured to shake the coal particles so that they disperse and fall; The imaging unit is configured to capture the dispersed coal particles to obtain coal particle images; The calculation unit is configured to perform statistical analysis on the coal particle images to obtain the diameter of the coal particle d, the number of the coal particles N, the average particle diameter of the coal particles D, the particle diameter of the coal particles D50 and the particle diameter of the coal particles D90, and to calculate the hardiness of the coal samples. Further, the feeding unit comprises a feeding hopper and a feeding chute inclined along the axial direction, and one end of the feeding chute is provided with a vibrator for controlling the vibration of the feeding chute. Further, the imaging unit comprises a camera and a laser emitter arranged directly above the camera, and the laser emitter provides a monochromatic source for the camera. The beneficial effects of the invention are as follows: the multi-parameter prediction model and system for coal sample hardiness disclosed by the present invention can obtain the distribution curve and characteristic parameters of broken coal samples by statistically analyzing the images of broken coal samples. Based on the fitting parameter of the distribution curve and the characteristic parameters, the model can be used to evaluate the hardiness of coal samples, and then the hardiness of coal samples can be measured effectively, with small measurement error and high accuracy.
Description of Drawings The invention is further explained below with reference to the drawings and the specific embodiments: Fig. 1 is a schematic diagram of the method of the present invention; Fig. 2 is a schematic diagram of the system structure of the present invention; Fig. 3 is a schematic diagram of coal sample particles of the present invention; Fig. 4 is a schematic diagram of the distribution curve of coal sample particles of the present invention;
Detailed Description of the Presently Preferred Embodiments The invention is further explained with reference to the drawings attached to the specification as shown in the figures below: The method for measuring coal sample hardiness based on image analysis, as shown in Fig.1, comprises the steps of: Si, obtaining the coal sample particles by hammering, collecting, scattering and shooting the coal sample particles to obtain the images; By using the image algorithm based on non-standard spherical particles, the images of the coal sample particles are analyzed and processed, and then the diameter d of coal sample particles is obtained. The coal sample particles with width of 20um-30mm can be identified by the image algorithm. In this embodiment, the image algorithm based on non-standard spherical particles is the prior art, so it is not repeated here; S2, if the distribution of the coal sample particles can conform to the Rosin-Rammler distribution model, the coal sample particle size distribution curve F(d) can be constructed: F(d) = 1 - exp ( Where, F(d) is the proportion of the mass of coal particles with diameter less than d to the total mass of the coal samples, do and m arefitting parameters; S3, performing regression linear processing on the coal sample particle size distribution curve F(d) to get the fitting parameter do of the coal sample particle size distribution curve F(d) S4, by using the image analysis software, the characteristic statistics of the images of the coal sample particles are carried out, to obtain the characteristic parameters of the coal sample particles, comprising the number of coal sample particles N, the average particle size of coal sample particles D, the particle size of coal sample particles D50 and the particle size of coal sample particles D90. The image analysis software is prior art and will not be described here. S5, evaluating the hardiness of the coal samples according to the fitting parameter do and the characteristic parameters. The higher the hardiness rating level, the weaker the coal samples. In this embodiment, in Si, a number of coal samples are selected and hammered respectively to obtain coal sample particles; the same hammering height is maintained each time to ensure the same hammering force, eliminating errors due to different hammering forces. In this embodiment, in Sl, as shown in Fig. 3, the coal sample particles formed by pulverizing the coal samples are generally non-standard sphere, and the diameter of the maximum inscribed circle of the coal particles is taken as the diameter of the coal particles, thus, the diameter of coal particles can be calculated efficiently and rapidly. In this embodiment, in S3, as shown in Fig. 4, the coal sample particle size distribution curve F(d) = 1 - exp (-()) of the coal sample particles, F(d) is the proportion of the mass of coal particles with diameter less than d to the total mass of the coal samples, do and m are fitting parameters; take the double logarithm of both sides of the coal sample particle size distribution curve equation F(d), and the following formula is obtained: In{- In[1 - F(d)]} = mind - mndo Replace the left side of the equation with y, replace the right side Ind of the equation with x, obtain the linear equation y = mx - mndo. The linear equation is the linear regression equation of the coal sample particle size distribution curve F(d). A number of coal sample particles are set in the rectangular coordinate system corresponding to y and x, to get m (corresponding to straight slope) and -mindo (corresponding to linear intercept), then get do based on m. In this embodiment, S5 comprises: Building a coal sample hardiness rating model:
A, do ai Ldo= B, a1 <do< a2 C, az ! do A, fl2 ! Nc LNc = B, fl1< Nc < f2 C, Nc :f1 A, D y1 Lb= B, y1 <D<Y2 C, Y2 D A,D505 1 LDso = B, 51 <D50 <( 2 C, (2S D50 A, D90 E1 LD O 9 = B, E1 < D90< E2 C, E2 D90 Where, Ldo is the coal sample hardiness rating level corresponding to fitting parameter do; LNc is the coal sample hardiness rating level corresponding to number of coal particles Nc; L5 is the coal sample hardiness rating level corresponding to average particle size of coal particles D; LD5O is the coal sample hardiness rating level corresponding to particle size of coal particles D50; LDO 9 is the coal sample hardiness rating level corresponding to particle size of coal particles D90; ai and a 2 are set thresholds for the fitting parameter do; in this embodiment, the value of a1 is 2,350tm and the value of a 2 is 3,000 m; #1 and #2 are set thresholds for number of coal particles Nc; in this embodiment, the value of #1 is 500,000 and the value of #2 is 2000,000; y1 and Y2 are set thresholds for average particle size of coal particles D; in this embodiment, the value of yi is 850tm and the value of Y2 is 2,800[tm; 51 and (2 are set thresholds for particle size of coal particles D50; in this embodiment, the value of 51 is 635tm and the value of 62 is 1,650[tm; E1 and E 2 are set thresholds for particle size of coal particles D90; in this embodiment, the value of El is 1,850pm and the value of E 2 is 7,000pm; A, B, C are the three coal sample hardiness rating levels in descending order; The obtained fitting parameter do, the number of coal particles N, the average particle size of coal particles D, the particle size of coal particles D50 and the particle size of coal particles D90 are brought into the coal sample hardiness rating model to obtain the coal sample hardiness rating levels Ld, LNc, LI LD5and LD 9 0. The highest of these levels is used as the coal sample hardiness level; The higher the coal sample hardiness level, the easier they are to be crushed, the weaker the coal samples, and vice versa. A multi-parameter prediction model for coal sample hardiness comprises a hammering unit, a feeding unit, an imaging unit and a calculation unit. The hammering unit is configured to hammer the original coal sample with the same hammering height to obtain coal particles; the hammering unit comprises an automatic hammering device; the automatic hammering device hammers the original coal sample according to the set hammering height, thereby forming coal sample particles, and eliminating errors caused by human factors. The automatic hammering device adopts the prior art and is not described in detail herein. The feeding unit is configured to shake the coal particles so that they disperse and fall; The imaging unit is configured to capture the dispersed coal particles to obtain coal particle images; The calculation unit is configured to perform statistical analysis on the coal particle images to obtain the diameter of the coal particles d, the number of the coal particles Nc, the average particle diameter of the coal particles D, the particle diameter of the coal particles D50 and the particle diameter of the coal particles D90, and to calculate the hardiness of the coal samples. The calculation unit comprises a computer, an image statistics and analysis software and a control system for controlling the work of the feeding unit and the imaging unit; The image statistics and analysis software and the control system both adopt the prior art and are not described here. It should be noted that there are many coal sample powder particles remaining in the system after the measurement test, and a vacuum cleaner can be used to remove these coal sample powder particles, so as to ensure the standard measurement test environment. In this embodiment, as shown in Fig. 2, the feeding unit comprises a feeding hopper and a feeding chute inclined along the axial direction; the feeding hopper receives coal sample particles hammered by the hammering unit and dumps the coal sample particles into the feeding chute below the feeding funnel; a vibrator for controlling the vibration of the feeding chute is arranged at the edge of one end of the feeding chute. The vibrator vibrates automatically under a certain amplitude to disperse the coal sample particles in the feeding chute under the condition that the coal sample particles are not broken. Meanwhile, the coal sample particles incline and slide down along the axis direction of the feeding chute, which realizes intelligent operation and saves labor cost. In this embodiment, as shown in Fig. 2, the imaging unit comprises a camera and a laser emitter arranged directly above the camera, and the laser emitter provides a monochromatic source for the camera. The camera is a high-resolution CCD industrial camera, the laser emitter can provide a stable monochromatic source for the camera; the unit light source has a narrow wave and is not easy to be disturbed. It ensures that the camera can take a better image under the environment of the monochromatic source. Finally, it should be noted that the above embodiments are only for explaining, but not limiting, the technical solutions of the present invention; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understood that the technical solutions described in the foregoing embodiments may be modified, or some of the technical features may be equivalently substituted; and such modifications or substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the present invention.
Claims (7)
1. A multi-parameter prediction model for coal sample hardiness, wherein, the model comprises the following steps: Si, collecting the image information of coal particles and processing the image information to obtain the diameter d of the coal particles; S2, constructing coal sample particle size distribution curve F(d): F(d) = 1 - exp ( Where, F(d)is the proportion of the mass of coal particles with diameter less than d to the total mass of the coal samples, do and m are fitting parameters; S3, performing regression linear processing on the coal sample particle size distribution curve F(d) to get the fitting parameter do of the coal sample particle size distribution curve F(d) S4, collecting the characteristic parameters of the coal particles , including number of coal particles N, average particle size of coal particles D, particle size of coal particles D50, and particle size of coal particles D90; S5, evaluating the hardiness of the coal samples according to the fitting parameter do and the characteristic parameters, the higher the hardiness rating level, the weaker the coal samples.
2. The method for measuring coal sample hardiness based on image analysis according to claim 1, wherein, in SI, the coal particles required for each measurement are obtained by hammering, and the same hammering height is maintained during each hammering.
3. The multi-parameter prediction model for coal sample hardiness according to claim 1, wherein, in Si, the diameter of the maximum inscribed circle of the coal particles is taken as the diameter of the coal particles.
4. The multi-parameter prediction model for coal sample hardiness according to claim 1, wherein, S5 comprises the following steps: Building a coal sample hardiness rating model:
A, do ai Ldo = B, a1 <do< a2 C, az! do A, 2 Nc LNc= B, 1 <Nc<Y 2 'C, Nc 1l A, 5 y1 < L5 B, y1 < D< Y2 C, yz 5 ! 5 LDo = B, 51 < D50 <( 2 C, D50 (52 A, D90 E1 LD 9O = B, Ei < D90 < E2 C,E 2 D90 Where, Ldo is the coal sample hardiness rating level corresponding to fitting parameter do; LNc is the coal sample hardiness rating level corresponding to number of coal particles Nc; L5 is the coal sample hardiness rating level corresponding to average particle size of coal particles D; LDSO is the coal sample hardiness rating level corresponding to particle size of coal particles D50;
LDO9 is the coal sample hardiness rating level corresponding to particle size of coal particles D90; a, and a 2 are set thresholds for the fitting parameter do; fl and #2 are set thresholds for number of coal particles Nc; Y1 and Y2 are set thresholds for average particle size of coal particles D; 51 and (2 are set thresholds for particle size of coal particles D50; El and E 2 are set thresholds for particle size of coal particles D90; A, B, C are the three coal sample hardiness rating levels in descending order; The obtained fitting parameter do, the number of coal particles N, the average particle size of coal particles D, the particle size of coal particles D50 and the particle size of coal particles D90 are brought into the coal sample hardiness rating model to obtain the coal sample hardiness rating levels Ld, LNc, L LD5o and LD90. The highest of these levels is used as the coal sample hardiness level; The higher the coal sample hardiness level, the weaker the coal samples;
5. A system for measuring the coal sample hardiness based on image analysis, wherein, it comprises a hammering unit, a feeding unit, an imaging unit and a calculation unit. The hammering unit is configured to hammer the original coal sample with the same hammering height to obtain coal particles; The feeding unit is configured to shake the coal particles so that they disperse and fall; The imaging unit is configured to capture the dispersed coal particles to obtain coal particle images; The calculation unit is configured to perform statistical analysis on the coal particle images to obtain the diameter of the coal particles d, the number of the coal particles Nc, the average particle diameter of the coal particles D, the particle diameter of the coal particles D50 and the particle diameter of the coal particles D90, and to calculate the hardiness of the coal samples.
6. The system for measuring the coal sample hardiness based on image analysis according to claim 5, the feeding unit comprises a feeding hopper and a feeding chute inclined along the axial direction, and one end of the feeding chute is provided with a vibrator for controlling the vibration of the feeding chute.
7. The system for measuring the coal sample hardiness based on image analysis according to claim 5, wherein, the imaging unit comprises a camera and a laser emitter arranged directly above the camera, and the laser emitter provides a monochromatic source for the camera.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114842015A (en) * | 2022-07-04 | 2022-08-02 | 煤炭科学技术研究院有限公司 | Coal flow detection method and training method for generating countermeasure network under condition |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114842015A (en) * | 2022-07-04 | 2022-08-02 | 煤炭科学技术研究院有限公司 | Coal flow detection method and training method for generating countermeasure network under condition |
| CN114842015B (en) * | 2022-07-04 | 2022-09-20 | 煤炭科学技术研究院有限公司 | Coal flow detection method and training method for generating countermeasure network under condition |
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| FGI | Letters patent sealed or granted (innovation patent) | ||
| MK22 | Patent ceased section 143a(d), or expired - non payment of renewal fee or expiry |