CN117772392B - Full intelligent control method for granularity of sintered fuel - Google Patents
Full intelligent control method for granularity of sintered fuel Download PDFInfo
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- CN117772392B CN117772392B CN202410205025.9A CN202410205025A CN117772392B CN 117772392 B CN117772392 B CN 117772392B CN 202410205025 A CN202410205025 A CN 202410205025A CN 117772392 B CN117772392 B CN 117772392B
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- 239000000446 fuel Substances 0.000 title claims abstract description 24
- 238000000034 method Methods 0.000 title claims abstract description 20
- 239000002245 particle Substances 0.000 claims abstract description 112
- 239000000463 material Substances 0.000 claims abstract description 65
- 238000009826 distribution Methods 0.000 claims abstract description 36
- 238000005516 engineering process Methods 0.000 claims abstract description 12
- 238000001035 drying Methods 0.000 claims abstract description 10
- 238000012545 processing Methods 0.000 claims abstract description 8
- 238000013473 artificial intelligence Methods 0.000 claims abstract description 5
- 238000001514 detection method Methods 0.000 claims description 20
- 230000002159 abnormal effect Effects 0.000 claims description 9
- 238000012216 screening Methods 0.000 claims description 8
- 239000000428 dust Substances 0.000 claims description 7
- 238000005303 weighing Methods 0.000 claims description 7
- 238000011084 recovery Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 4
- 238000005245 sintering Methods 0.000 description 9
- 239000004449 solid propellant Substances 0.000 description 8
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000571 coke Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000002737 fuel gas Substances 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
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Abstract
The invention relates to a full intelligent control method for the granularity of a sintered fuel, which is characterized in that the weight ratio of fuel to be detected with the granularity of more than 5 mm is calculated; readjusting four-roll crusher parameters to control the particle size distribution when the weight fraction exceeds a weight fraction threshold; when the weight ratio does not exceed the weight ratio threshold, drying the material to be tested, and shooting and collecting the falling movement data of the material to be tested by using a high-definition high-speed industrial camera; processing the motion data by utilizing a machine vision technology and an artificial intelligence technology to obtain the particle size grading distribution of the material to be detected; and transmitting the size grading distribution information to a four-roller crusher, and automatically adjusting parameters of the four-roller crusher according to the difference value between the weight ratio of ore particles with the particle size of more than 5 mm and the required ratio. The invention is difficult to adjust the interval between the lower rolls of the four-roll crusher by comparing the weight ratio and the required ratio of the large-grain-size ore grains in the prior art, so that the problem of poor control effect that the required ratio of the large-grain-size ore grains of the sintered fuel cannot be achieved is caused.
Description
Technical Field
The invention belongs to the technical field of sintered fuel, and relates to a full intelligent control method for granularity of sintered fuel.
Background
In the sintering process, the rationality of the particle size distribution of the sintering mixture influences various production parameters in the sintering production, including the air permeability index in the sintering ore preparation process, the crushing index in the raw material proportioning process, the rotating speed index of a mixer, the crushing and screening index of the final finished product sintering ore and the like. Therefore, there are strict control requirements on the particle size of the various solid raw materials entering the sintering machine.
At present, the particle size control of the sintered fuel is generally in a combination mode of double-smooth roll crushing and four-roll crushing, the operation of the particle size control equipment is mainly finished manually, data are visually checked or tested through experience, and then the particle size control equipment is manually adjusted to meet the requirement of sintering on the particle size of the fuel. The mode excessively relies on the manual work, can not realize automatic detection and control of the granularity of the sintered fuel, and has low working efficiency. In some fuel (e.g., coke breeze) sintering, a certain weight fraction of larger particle size (greater than 5 mm) ore particles is required to ensure fuel gas permeability. However, in the prior art, the weight ratio and the required ratio of the large-particle-size ore particles are difficult to adjust the distance between the lower rolls of the four-roll crusher, so that the control effect on the particle size of the sintered fuel is reduced, and the large-particle-size ore particles of the sintered fuel cannot reach the required ratio. Therefore, in the technical development of automatic detection and control of sintered fuel particle size, it is necessary to adjust four-roll crusher parameters to achieve accurate particle size automation and closed-loop control by utilizing the relation between the weight ratio of large-particle-size ore particles and the required ratio.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a full intelligent control method for the granularity of the sintered fuel.
The aim of the invention can be achieved by the following technical scheme:
the application provides a full intelligent control method for the granularity of a sintered fuel, which comprises the following steps:
S1, detecting the moisture content of materials on a belt: when the moisture content does not exceed the moisture alarm threshold, executing the steps S2-S7; when the moisture content exceeds the moisture alarm threshold, the granularity detection operation is not performed;
S2, grabbing a certain amount of materials to be detected from the four-roller crusher according to a fixed time interval, classifying and screening the materials to be detected, weighing, and respectively calculating the weight ratio of ore particles with the particle sizes of more than 5mm and 10 mm;
S3, when the weight ratio of the ore particles with the particle size larger than 5mm exceeds a weight ratio threshold, the parameters of the four-roller crusher are readjusted to control the particle size distribution in combination with the actual requirement of the particle size distribution;
s4, when the weight ratio of the ore particles with the particle size being larger than 5mm does not exceed the weight ratio threshold, conveying the material to be tested into a material drying system, drying the material to be tested, and executing the operations of the steps S5-S6;
S5, placing the dried material to be tested in a granularity detection system, enabling the material to be tested to uniformly fall in a curtain-shaped and dispersed state through a vibration feeder, and shooting and collecting movement data of the falling material to be tested by using a high-definition high-speed industrial camera;
S6, processing the collected motion data by utilizing a machine vision technology and an artificial intelligence technology to obtain the particle size grading distribution of the material to be detected, and obtaining the weight ratio of ore particles with the particle size of more than 5mm according to the particle size grading distribution;
S7, automatically adjusting parameters of the four-roller crusher according to the difference value between the weight ratio of ore particles with the particle size larger than 5mm and the required ratio so as to intelligently control the particle size of the material to be tested;
s8, storing the control log, the abnormal information of the particle size distribution, the abnormal processing condition and the historical information of the particle size distribution of the four-roller crusher in the steps S1-S7;
S9, repeating the steps S2-S7 until the absolute value of the weight ratio deviation is smaller than 1.5%.
Further, in step S1, the moisture alarm threshold is 8%.
Further, in step S2, if the weight ratio of the ore particles with the particle size greater than 10mm is greater than 1%, it indicates that there are impurities or the four-roller equipment is abnormal, an alarm is started, and the subsequent operation is stopped.
Further, in step S3, the weight ratio threshold is 17.5% ± 0.5%.
Further, in step S5, after the material to be tested falls, the material is recovered onto the belt through the material recovery device, and at the same time, the dust removing device is utilized to remove the tiny dust.
Further, in step S7, the automatic adjustment of parameters of the four-roller crusher includes the following specific steps:
Calculating the difference between the weight ratio of ore particles with the particle size larger than 5mm and the required ratio to obtain the weight ratio deviation;
if the absolute value of the weight ratio deviation is within 1.5%, not adjusting the four-roller crusher;
if the absolute value interval of the weight ratio deviation is (1.5%, 2.5% ], reducing or expanding the lower roller spacing of the four-roller crusher by 0.7 mm;
If the absolute value interval of the weight ratio deviation is (2.5%, 3.5% ], reducing or expanding the lower roller spacing of the four-roller crusher by 1.2 mm;
If the absolute value interval of the weight ratio deviation is (3.5%, 4.5% ], reducing or expanding the lower roller spacing of the four-roller crusher by 1.6 mm;
And if the absolute value of the weight ratio deviation is greater than 4.5%, an alarm is sent out, and the weight ratio deviation is manually adjusted.
Further, the parameters of the four-roller crusher are automatically adjusted, and the adjusted lower roller spacing of the four-roller crusher is required to be ensured to be more than 0.5 mm.
The invention has the beneficial effects that:
(1) The automatic detection and control of the sintered fuel particle size distribution are realized, the labor cost is reduced, and the working efficiency is improved by integrating an automatic sampling, screening and weighing control algorithm, a solid fuel particle size distribution intelligent detection model based on a machine vision technology and a knowledge-guided solid fuel particle size intelligent control algorithm;
(2) Establishing an intelligent detection and closed-loop control system database of the granularity of the sintered fuel, and storing important data such as a control log of a four-roller crusher, abnormal information of granularity distribution, abnormal processing conditions, historical information of granularity distribution and the like, so that key data can be traced back conveniently;
(3) The weight ratio deviation is obtained by detecting the weight ratio of the ore particles with the particle size larger than 5mm and calculating the difference between the weight ratio of the ore particles with the particle size larger than 5mm and the required ratio; the space between the lower rolls of the four-roll crusher is adjusted according to the specific value of the weight ratio deviation, so that the automatic control of the granularity in the sintering process is more accurate; and taking whether the absolute value of the weight ratio deviation is smaller than 1.5% as a node of closed-loop control, so as to realize the accurate closed-loop control of granularity.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a flow chart of a method for fully intelligent control of sintered fuel particle size in accordance with the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention for achieving the intended purpose, the following detailed description will refer to the specific implementation, structure, characteristics and effects according to the present invention with reference to the accompanying drawings and preferred embodiments.
Referring to fig. 1, the application provides a full intelligent control method for sintered fuel granularity, which comprises the following steps:
S1, detecting the moisture content of materials on a belt: when the moisture content does not exceed the moisture alarm threshold, executing the steps S2-S7; when the moisture content exceeds the moisture alarm threshold, the granularity detection operation is not performed;
S2, grabbing a certain amount of materials to be detected from the four-roller crusher according to a fixed time interval, classifying and screening the materials to be detected, weighing, and respectively calculating the weight ratio of ore particles with the particle sizes of more than 5mm and 10 mm;
In this embodiment, step S2 is to perform preliminary judgment and analysis on the weight ratio of the ore particles with the particle sizes of greater than 5mm and 10mm, and the weight ratio is not required to be acquired precisely, so in order to improve the efficiency of the workflow, the material to be tested is not dried before weighing after classified screening under the condition that the moisture content exceeds the moisture alarm threshold.
S3, when the weight ratio of the ore particles with the particle size larger than 5mm exceeds a weight ratio threshold, the parameters of the four-roller crusher are readjusted to control the particle size distribution in combination with the actual requirement of the particle size distribution;
s4, when the weight ratio of the ore particles with the particle size being larger than 5mm does not exceed the weight ratio threshold, conveying the material to be tested into a material drying system, drying the material to be tested, and executing the operations of the steps S5-S6;
S5, placing the dried material to be tested in a granularity detection system, enabling the material to be tested to uniformly fall in a curtain-shaped and dispersed state through a vibration feeder, and shooting and collecting movement data of the falling material to be tested by using a high-definition high-speed industrial camera;
S6, processing the collected motion data by utilizing a machine vision technology and an artificial intelligence technology to obtain the particle size grading distribution of the material to be detected, and obtaining the weight ratio of ore particles with the particle size of more than 5mm according to the particle size grading distribution;
In this embodiment, the particle size classification distribution in step S6 refers to the volume ratio of different particle sizes of the material to be tested, and because the densities of the same material are the same, the weight ratio of the ore particles with different particle sizes can be obtained through the particle size classification distribution.
S7, automatically adjusting parameters of the four-roller crusher according to the difference value between the weight ratio of ore particles with the particle size larger than 5mm and the required ratio so as to intelligently control the particle size of the material to be tested;
s8, storing the control log, the abnormal information of the particle size distribution, the abnormal processing condition and the historical information of the particle size distribution of the four-roller crusher in the steps S1-S7;
S9, repeating the steps S2-S7 until the absolute value of the weight ratio deviation is smaller than 1.5%.
In this embodiment, the solid fuel based sampling and inspection integrated hardware system includes an automatic sampling system, a material screening and weighing system, a material drying system, a particle size detection system, a moisture detection system, and other accessories in a relatively closed space. Firstly, a moisture detection system detects the moisture content of materials on a belt, when the moisture content does not exceed a moisture alarm threshold value, an automatic sampling system grabs a certain amount of materials to be detected from the inside of a four-roller crusher to a material screening weighing system according to a fixed time interval, and the materials to be detected are classified, screened and weighed to obtain the weight ratio of ore particles above 5mm to ore particles above 10 mm. Based on the detected ore particle duty ratio information of more than 5mm and the actual requirement of the particle size distribution, the parameters of the four-roller crusher are adjusted, so that the particle size distribution is controlled, and related information is output to an upper computer system. And for the materials to be tested below 5mm, conveying the materials to a material drying system, and drying the materials to be tested. And then placing the dried material to be tested in a granularity detection system, uniformly dropping the material in a curtain-shaped and dispersed state by a vibration feeder, and collecting the dropping motion data of the material by a high-definition high-speed industrial camera. Based on the machine vision technology and the artificial intelligence technology, relevant motion data are processed to obtain the particle size grading distribution. When the moisture content exceeds the moisture alarm threshold, the system automatically alarms and does not perform operations such as granularity detection.
Further, in step S1, the moisture alarm threshold is 8%.
Further, in step S2, if the weight ratio of the ore particles with the particle size greater than 10 mm is greater than 1%, an alarm is started, and the subsequent operation is stopped.
Further, in step S3, the weight ratio threshold is 17.5% ± 0.5%.
In the process of material falling, ore particles are in a stacking form and motion smear exists, so that the characteristics of each ore particle are difficult to clearly distinguish, and the real area of the ore particle is difficult to extract. Meanwhile, in the material falling process, dynamic phenomena of collision, adhesion and separation and spin movement exist, and the granularity detection precision is affected. Aiming at the problems, the invention aims to build an intelligent detection model of the solid fuel particle size distribution based on the machine vision technology, on the basis of controlling materials to uniformly fall in a curtain-shaped and dispersed state, a high-definition high-speed industrial camera is adopted to shoot the falling movement data of ore particles, the movement data is input into the intelligent detection model of the solid fuel particle size distribution, the detection result of the particle size distribution is obtained, and relevant information is sent to a four-roller crusher in comparison with actual requirements.
Further, in step S5, after the material to be tested falls, the material is recovered onto the belt through the material recovery device, and at the same time, the dust removing device is utilized to remove the tiny dust.
Further, in step S7, the automatic adjustment of parameters of the four-roller crusher includes the following specific steps:
Calculating the difference between the weight ratio of ore particles with the particle size larger than 5mm and the required ratio to obtain the weight ratio deviation;
if the absolute value of the weight ratio deviation is within 1.5%, not adjusting the four-roller crusher;
if the absolute value interval of the weight ratio deviation is (1.5%, 2.5% ], reducing or expanding the lower roller spacing of the four-roller crusher by 0.7 mm;
If the absolute value interval of the weight ratio deviation is (2.5%, 3.5% ], reducing or expanding the lower roller spacing of the four-roller crusher by 1.2 mm;
If the absolute value interval of the weight ratio deviation is (3.5%, 4.5% ], reducing or expanding the lower roller spacing of the four-roller crusher by 1.6 mm;
And if the absolute value of the weight ratio deviation is greater than 4.5%, an alarm is sent out, and the weight ratio deviation is manually adjusted.
Further, the parameters of the four-roller crusher are automatically adjusted, and the adjusted lower roller spacing of the four-roller crusher is required to be ensured to be more than 0.5 mm.
It should be noted that the removal of minute dust can prevent it from affecting the controls of industrial cameras and other equipment.
From on-site investigation and current analysis, the granularity control has the following difficulties: 1) At present, a set of system and scientific control method is not formed in the solid fuel granularity control mode; 2) How to adjust the parameters of the four-roller crusher based on the detected particle size distribution to meet the production requirements; 3) Before and after the parameters of the four-roller crusher are adjusted, the change rule of the solid fuel particle size distribution has no quantitative conclusion yet. Aiming at the problems, the application aims to construct a set of knowledge-guided solid fuel granularity intelligent control algorithm, firstly, the control experience of field experts on the four-roller crusher is arranged and integrated, and then, the four-roller crusher is combined based on the material granularity characteristic information, the implementation operation parameters and the like, so that a set of scientific, systematic and robust granularity intelligent control method is formed, and the production operation is guided.
The present invention is not limited to the above embodiments, but is capable of modification and variation in detail, and other modifications and variations can be made by those skilled in the art without departing from the scope of the present invention.
Claims (4)
1. A full intelligent control method for the granularity of sintered fuel is characterized in that: the method comprises the following steps:
S1, detecting the moisture content of materials on a belt: when the moisture content does not exceed the moisture alarm threshold, executing the operations of the steps S2-S7; when the moisture content exceeds the moisture alarm threshold, the granularity detection operation is not performed;
S2, grabbing a certain amount of materials to be detected from the four-roller crusher according to a fixed time interval, classifying and screening the materials to be detected, weighing, and respectively calculating the weight ratio of ore particles with the particle sizes of more than 5mm and 10 mm;
S3, when the weight ratio of ore particles with the particle size of more than 5mm exceeds a weight ratio threshold, the parameters of the four-roller crusher are readjusted to control the particle size distribution in combination with the actual requirement of the particle size distribution, wherein the weight ratio threshold is 17.5%;
s4, when the weight ratio of the ore particles with the particle size being larger than 5mm does not exceed the weight ratio threshold, conveying the material to be tested into a material drying system, drying the material to be tested, and executing the operations of the steps S5-S6;
S5, placing the dried material to be tested in a granularity detection system, enabling the material to be tested to uniformly fall in a curtain-shaped and dispersed state through a vibration feeder, and shooting and collecting movement data of the falling material to be tested by using a high-definition high-speed industrial camera;
S6, processing the collected motion data by utilizing a machine vision technology and an artificial intelligence technology to obtain the particle size grading distribution of the material to be detected, and obtaining the weight ratio of ore particles with the particle size of more than 5mm according to the particle size grading distribution;
S7, automatically adjusting parameters of the four-roller crusher according to the difference value between the weight ratio of ore particles with the particle size larger than 5mm and the required ratio so as to intelligently control the particle size of the material to be tested;
s8, storing the control log, the abnormal information of the particle size distribution, the abnormal processing condition and the historical information of the particle size distribution of the four-roller crusher in the steps S1-S7;
in step S7, according to the difference between the ore grain weight ratio with the grain diameter greater than 5 mm and the required ratio, parameters of the four-roller crusher are automatically adjusted, and the specific flow is as follows:
Calculating the difference between the weight ratio of ore particles with the particle size larger than 5mm and the required ratio to obtain the weight ratio deviation;
if the absolute value of the weight ratio deviation is within 1.5%, not adjusting the four-roller crusher;
if the absolute value interval of the weight ratio deviation is (1.5%, 2.5% ], reducing or expanding the lower roller spacing of the four-roller crusher by 0.7 mm;
If the absolute value interval of the weight ratio deviation is (2.5%, 3.5% ], reducing or expanding the lower roller spacing of the four-roller crusher by 1.2 mm;
If the absolute value interval of the weight ratio deviation is (3.5%, 4.5% ], reducing or expanding the lower roller spacing of the four-roller crusher by 1.6 mm;
If the absolute value of the weight ratio deviation is greater than 4.5%, an alarm is sent out, and the weight ratio deviation is manually adjusted;
the parameters of the four-roller crusher are automatically adjusted, and the lower roller spacing of the four-roller crusher after adjustment is required to be ensured to be larger than 0.5 mm;
S9, repeating the steps S2-S7 until the absolute value of the weight ratio deviation is smaller than 1.5%.
2. The method for fully intelligently controlling the granularity of the sintered fuel according to claim 1, which is characterized by comprising the following steps: in step S1, the moisture alarm threshold is 8%.
3. The method for fully intelligently controlling the granularity of the sintered fuel according to claim 1, which is characterized by comprising the following steps: in the step S2, if the weight ratio of ore particles with the particle size of more than 10 mm is more than 1%, an alarm is started, and the subsequent operation is stopped.
4. The method for fully intelligently controlling the granularity of the sintered fuel according to claim 1, which is characterized by comprising the following steps: in step S5, after the material to be tested falls, the material is recovered to the belt through the material recovery device, and dust is removed by using the dust removing device.
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| WO2023279556A1 (en) * | 2021-07-09 | 2023-01-12 | 福建南方路面机械股份有限公司 | Aggregate particle size sampling and monitoring method |
| CN114471924A (en) * | 2021-12-22 | 2022-05-13 | 陕西神渭煤炭管道运输有限责任公司 | Automatic coal powder sampling and screening device and control method for gap adjustment of crusher |
| CN115493959A (en) * | 2022-10-13 | 2022-12-20 | 江苏省镔鑫钢铁集团有限公司 | Online automatic test method for sinter |
| CN115957880A (en) * | 2023-01-05 | 2023-04-14 | 湖南中科电气股份有限公司 | Automatic granularity control method for shaping machine |
| CN116680558A (en) * | 2023-06-06 | 2023-09-01 | 重庆钢铁股份有限公司 | Method for evaluating grain size qualification rate of sintered solid fuel |
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