CN113628171B - Pellet production method and device based on machine vision and data driving - Google Patents
Pellet production method and device based on machine vision and data driving Download PDFInfo
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Abstract
The invention discloses a pellet production method and device based on machine vision and data driving, wherein the method comprises the following steps: the method comprises the steps of completing acquisition of images of green pellets before roasting and finished pellets after roasting by using an industrial camera; acquiring green ball particle size and finished ball particle size based on the acquired green ball image and finished ball image; determining the pellet granularity change before and after roasting according to the green pellet granularity and the finished product pellet granularity; based on the dynamic regulation effect of the predetermined production process parameters on the pellet granularity change, a preset neural network model is adopted, and the production process parameters are regulated in real time according to the currently determined pellet granularity change before and after roasting, so that the pellet granularity change is kept within a preset granularity change range. The invention is suitable for the pellet production process, overcomes the defects of the existing granularity detection technology, improves the pellet quality and reduces the operation cost of production enterprises.
Description
Technical Field
The invention relates to the technical field of ferrous metallurgy, in particular to a pellet production method and device based on machine vision and data driving.
Background
In 2019, the yield of blast furnace pig iron in China is 7.7 hundred million tons and accounts for 62.2 percent of the world pig iron yield (12.4 hundred million tons). The high alkalinity agglomerate is added with acid pellet, which is the current furnace burden structure in China, the proportion of agglomerate in the national blast furnace burden structure in 2019 is 78%, and the pellet accounts for about 13% (1.2 hundred million tons). Compared with the sintered ore, the pellet ore has obvious advantages in the aspects of energy conservation, emission reduction and smelting performance. According to investigation, the best production index of the blast furnace in the world is European and American blast furnace mainly containing pellets, 100 percent of pellets are used for a long time by the blast furnace of SSAB factory in Sweden, and the utilization coefficient of the blast furnace can reach 3.5 t/(m) 3 D) the fuel consumption of blast furnace ton iron is 457kg, the slag quantity is only 146kg, and the slag quantity is far lowerThe average value of the fuel ratio of the blast furnace in China is 536kg/t, and the slag quantity is 353kg/t. Therefore, the development of the high-proportion pellet smelting technology has important significance for shortening the gap between the pellet smelting technology and developed countries and realizing low-carbon green smelting. However, the improvement of the charging proportion is limited by the productivity and the quality of the pellets in China, mainly because the iron ore resources in China have the characteristics of lean, impurity, fine and the like, and the control requirement on the production process parameters is more severe. Therefore, in view of the characteristics of energy conservation and environmental protection, smelting performance and iron ore resources in China, optimizing the production process parameters of the pellets, improving the production efficiency and improving the pellet performance are one of the key cores of the current development of the iron-making industry.
In order to fully exert the superiority and efficacy of the pellets in steel smelting, the quality requirements are increasingly strict. The pellet production process is a nonlinear system with large time lag, multiple variables and strong coupling, and the preheating temperature, the roasting temperature and the belt speed of a roasting machine are mutually coupled in the production process of a typical pellet production belt roasting machine, and the dynamic characteristics of the pellet production process are changed along with the change of the running conditions such as the granularity composition of green pellets, the quantity of green pellets, the moisture, the mineral types and the like. The particle size and distribution of the pellets are important indexes in quality detection. At present, the detection of the particle size distribution of raw pellet particles mainly adopts a screening sampling method, the method has the advantages of limited quantity of pellet particle samples, long time consumption, incapability of frequent operation, and adverse health of operators due to the severe working environment of high noise and strong dust in a production field. In addition, the green pellet has low hardness, and the measurement is inaccurate due to the brittleness in the screening process. The growth of the intergranular crystal between minerals in the pellet roasting process causes macroscopic volume shrinkage, the volume shrinkage rate is influenced by the technological parameters such as raw material types, roasting temperature and the like, is a core characteristic of pellet quality, and has obvious linear relation with pellet performance (compressive strength and reduction performance). For a long time, a control system consisting of a single-loop controller adopting a distributed control design is difficult to automatically operate, parameter regulation depends on manual experience, and the traditional production mode of pellet roasting, quality detection and parameter adjustment has serious hysteresis and blindness, and equipment parameters cannot be timely regulated according to raw material conditions, so that the pellets are over-burned or under-burned, and the production efficiency and quality stability are reduced. Therefore, the pellet granularity online detection technology is valued by domestic and foreign expert students, and a new detection way is provided for the particle granularity detection by using an image processing as a core detection technology at present of the vigorous development of artificial intelligence and computer technology. At present, a watershed algorithm and a morphological reconstruction algorithm are mainly adopted for pellet granularity identification and segmentation, but the accuracy of the algorithms in particle segmentation is required to be improved. Therefore, the realization of intelligent perception of pellet quality in the production process and the self-adaptive intelligent control of pellet production process equipment parameters are key to effectively solving the current technical problems.
Disclosure of Invention
The invention provides a pellet production method and device based on machine vision and data driving, which are used for solving the technical problems that in the prior art, pellet production process parameter regulation depends on manual experience, and the traditional pellet production mode has serious hysteresis and blindness, equipment parameters cannot be regulated in time according to raw material conditions, so that the pellets are over-burned or under-burned, and the production efficiency and quality stability are reduced.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the invention provides a method for producing pellets based on machine vision and data driving, which comprises the following steps:
the method comprises the steps of completing acquisition of images of green pellets before roasting and finished pellets after roasting by using an industrial camera;
acquiring green ball particle size and finished ball particle size based on the acquired green ball image and finished ball image;
determining the pellet granularity change before and after roasting according to the green pellet granularity and the finished product pellet granularity;
based on the dynamic regulation effect of the predetermined production process parameters on the pellet granularity change, a preset neural network model is adopted, and the production process parameters are regulated in real time according to the currently determined pellet granularity change before and after roasting, so that the pellet granularity change before and after roasting is kept within a preset granularity change range.
Further, the acquiring green ball particle size and finished ball particle size based on the acquired green ball image and finished ball image includes:
preprocessing the acquired green ball image and the acquired finished ball image respectively; wherein the preprocessing comprises the following steps: graying treatment is carried out on the image to be treated to obtain a gray image; carrying out smooth denoising treatment on the gray level image; enhancing the contrast of the image after the drying to obtain a preprocessed image;
and carrying out image recognition and image segmentation on the preprocessed image to obtain the pellet particle size in real time.
Further, the method for acquiring the pellet particle size in real time by carrying out image recognition and image segmentation on the preprocessed image comprises the following steps:
the method comprises the steps of accurately separating target pellets by using a preset image segmentation and feature extraction algorithm, solving the problem of segmentation of quasi-circular stacked images with targets shielded and adhered to each other in the images, and obtaining pellet particles and surface texture parameters.
Further, the determination process of the dynamic regulation effect of the production process parameters on the pellet granularity change comprises the following steps:
acquiring actual production process parameters and the granularity of corresponding green pellets and finished pellets;
according to the obtained actual production process parameters and the granularity of the corresponding green pellets and finished pellets, the dynamic regulation effect of the production process parameters on the pellet granularity change is clarified.
Further, the preset granularity variation range determining process includes:
constructing a mapping relation between pellet granularity change and pellet ore performance; wherein the pellet ore performance comprises pellet ore strength performance and pellet ore reduction performance;
and determining the corresponding granularity variation range of the pellets meeting the preset performance requirement based on the constructed mapping relation.
Further, the neural network model is an RBF neural network.
Further, the construction process of the RBF neural network comprises the following steps:
respectively adopting a K-mean value clustering algorithm, an ant colony algorithm and a genetic algorithm to optimize the RBF neural network structure;
and performing integrated learning and model training on the optimized RBF neural network to obtain a trained model.
Further, the adoption of the preset neural network model adjusts production process parameters in real time according to the currently determined pellet granularity change before and after roasting, and comprises the following steps:
and inputting the currently determined pellet granularity change before and after roasting into a preset neural network model, and outputting corresponding production process parameters through the preset neural network model to realize the adjustment of production equipment.
Further, the production process parameters comprise a preheating temperature, a roasting time and a belt speed of the sintering machine.
On the other hand, the invention also provides a pellet production device based on machine vision and data driving, which comprises:
the machine vision system comprises a green ball identification module and a finished ball identification module; the green ball identification module is used for completing acquisition of green ball images before roasting by using an industrial camera; the finished ball identification module is used for completing acquisition of the baked finished ball image by using an industrial camera;
the control system is used for acquiring the green ball particle size and the finished ball particle size based on the acquired green ball image and the finished ball image; determining the pellet granularity change before and after roasting according to the green pellet granularity and the finished product pellet granularity; based on the dynamic regulation effect of the predetermined production process parameters on the pellet granularity change, a preset neural network model is adopted, and the production process parameters are regulated in real time according to the currently determined pellet granularity change before and after roasting, so that the pellet granularity change before and after roasting is kept within a preset granularity change range.
In yet another aspect, the present invention also provides an electronic device including a processor and a memory; wherein the memory stores at least one instruction that is loaded and executed by the processor to implement the above-described method.
In yet another aspect, the present invention also provides a computer readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the above method.
The technical scheme provided by the invention has the beneficial effects that at least:
the invention can realize accurate online detection on the granularity and defect of green pellets and finished pellets, obtain the influence of a preheating roasting temperature system and production operation parameters on pellet granularity change, establish the mapping relation between pellet granularity change and pellet ore strength performance and reduction performance, and realize intelligent control of the roasting process temperature and operation parameters by using a proper pellet production process intelligent control neural network algorithm. Can effectively improve the quality and the yield of pellets, reduce the production and operation cost, and has important significance for low-carbon and green production of iron and steel enterprises.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an execution flow of a method for producing pellets based on machine vision and data driving according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a machine vision system for a belt roasting machine provided by an embodiment of the present invention;
FIG. 3 is a schematic illustration of a machine vision system for a shaft furnace provided by an embodiment of the present invention;
fig. 4 is a schematic diagram of a machine vision system for a grate-rotary kiln, according to an embodiment of the present invention.
Reference numerals illustrate:
1. a green ball identification module; 2. a finished ball identification module; 3. a control system; 4. a belt roasting machine;
5. a shaft furnace; 6. a chain grate; 7. a rotary kiln; 8. and (5) a ring cooling machine.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
First embodiment
Firstly, it should be noted that the pellet production process is a nonlinear system with large time lag, multiple variables and strong coupling, and the preheating temperature, the roasting temperature and the belt speed of the roasting machine are mutually coupled in the production process of a typical pellet production belt roasting machine, and the dynamic characteristics of the pellet production process are changed along with the change of the running conditions such as the granularity composition of green pellets, the quantity of green pellets, the moisture, the mineral types and the like. The particle size and distribution of the pellets are important indexes in quality detection. The growth of the intergranular crystal between minerals in the pellet roasting process causes macroscopic volume shrinkage, the volume shrinkage rate is influenced by the technological parameters such as raw material types, roasting temperature and the like, is a core characteristic of pellet quality, and has obvious linear relation with pellet performance (compressive strength and reduction performance). For a long time, a control system consisting of a single-loop controller adopting a distributed control design is difficult to automatically operate, parameter regulation depends on manual experience, and the traditional production mode of pellet roasting, quality detection and parameter adjustment has serious hysteresis and blindness, and equipment parameters cannot be timely regulated according to raw material conditions, so that the pellets are over-burned or under-burned, and the production efficiency and quality stability are reduced.
Therefore, aiming at the problems that in the traditional pellet production mode, pellet characteristic parameters cannot be timely and accurately detected, serious hysteresis and blindness exist in adjusting roasting process parameters, and the pellet production efficiency is greatly reduced. The embodiment provides a pellet production method based on machine vision and data driving, which is used for obtaining high-precision and high-accuracy particle size detection through accurate pellet image segmentation, reducing overburning or undercooling of pellets and increasing pellet quality and yield. Specifically, the method execution flow is shown in fig. 1, and comprises the following steps:
s1, acquiring images of green pellets before roasting and finished pellets after roasting by using an industrial camera;
s2, acquiring the particle size of green pellets and the particle size of finished pellets based on the acquired green pellets and the acquired finished pellet images;
specifically, in this embodiment, the implementation procedure of S2 is specifically as follows:
preprocessing the acquired green ball image and the acquired finished ball image respectively; wherein, the pretreatment process comprises the following steps: firstly, carrying out graying treatment on an image to be treated to obtain a gray image; then carrying out smoothing denoising treatment on the obtained gray level image, ensuring maximization of variable information entering the model and minimization of noise, and reserving edges of pellet particles as much as possible during denoising; finally, enhancing the contrast of the image after the drying;
and carrying out image recognition and image segmentation on the preprocessed image to obtain the pellet particle size in real time.
Further, the method for obtaining the pellet particle size in real time in this embodiment specifically includes: the method comprises the steps of accurately separating target pellets by using a preset image segmentation and feature extraction algorithm, solving the problem of segmentation of similar circular stacked images such as mutual shielding and adhesion of targets in the images, and obtaining pellet particles and surface texture parameters.
S3, determining pellet particle size change before and after roasting according to the particle size of the green pellets and the particle size of the finished pellets;
s4, based on the dynamic regulation effect of the predetermined production process parameters on the pellet granularity change, adopting a preset neural network model, and adjusting the production process parameters in real time according to the currently determined pellet granularity change before and after roasting, so that the pellet granularity change before and after roasting is kept within a preset granularity change range.
The method for determining the dynamic regulation effect of the production process parameters on the pellet granularity change comprises the following steps: acquiring actual production data and the granularity of corresponding green pellets and finished pellets; according to the obtained actual data, the dynamic regulation effect of the preheating roasting temperature system, the operation parameters of the production process and the like on the pellet granularity change is clear.
The preset granularity change range determining mode is as follows: constructing a mapping relation between the particle size shrinkage change of the pellets in the roasting process and the pellet ore performance (strength and reducibility) according to actual data; and (3) defining the corresponding granularity change range of the high-quality finished pellets based on the constructed mapping relation so as to adaptively adjust and control the parameters of the output system.
The neural network model is an RBF neural network, and the construction process is as follows:
respectively adopting a K-mean value clustering algorithm, an ant colony algorithm and a genetic algorithm to optimize the RBF neural network structure;
and performing integrated learning and model training on the optimized RBF neural network to obtain a neural network optimization algorithm suitable for intelligent control of the pellet production process so as to construct an intelligent pellet production control system.
The process of adopting the model to adjust the production process parameters in real time comprises the following steps:
based on the main control computer for processing production data, variable screening and the granularity of the obtained green pellets and finished pellets, the pellet granularity change before and after the roasting is currently determined is input into an RBF neural network, and corresponding production process parameters are output through the RBF neural network, so that the adjustment of the output parameters is timely and accurately controlled. In this embodiment, the parameters to be adjusted include preheating temperature, baking time, and belt speed of the sintering machine.
In summary, the embodiment realizes intelligent control of the pellet production process through online detection and intelligent control development research of the pellet production process, obtains high-precision and high-accuracy particle size detection through accurate pellet image segmentation, and determines the particle size control range of high-quality pellets by constructing the mapping relation between pellet particle size change and pellet ore strength performance and reduction performance. Finally, by optimizing RBF neural network parameters, an intelligent control neural network algorithm suitable for pellet production technology is provided, intelligent online control of the temperature and the belt speed of the roasting process facing pellet granularity optimization is realized, the defects of the existing granularity detection technology are overcome, the pellet quality is improved, and the operation cost of a production enterprise is reduced. The production stability is improved, the pellet quality is improved, a foundation is laid for smelting high-proportion pellets, and new power is injected for low-carbon and green production of iron and steel enterprises.
Second embodiment
The embodiment provides a pellet production device based on machine vision and data drive, which comprises the following modules:
the machine vision system comprises a green ball identification module and a finished ball identification module; the green ball identification module is used for completing acquisition of green ball images before roasting by using an industrial camera; the finished ball identification module is used for completing acquisition of the baked finished ball image by using an industrial camera;
the control system is used for acquiring the green ball particle size and the finished ball particle size based on the acquired green ball image and the finished ball image; determining the pellet granularity change before and after roasting according to the green pellet granularity and the finished product pellet granularity; based on the dynamic regulation effect of the predetermined production process parameters on the pellet granularity change, a preset neural network model is adopted, and the production process parameters are regulated in real time according to the currently determined pellet granularity change before and after roasting, so that the pellet granularity change before and after roasting is kept within a preset granularity change range.
The pellet production device based on machine vision and data driving of the present embodiment corresponds to the pellet production method based on machine vision and data driving of the above-described first embodiment; the functions realized by the functional modules in the pellet production device based on machine vision and data driving in the embodiment are in one-to-one correspondence with the flow steps in the pellet production method based on machine vision and data driving; therefore, the description is omitted here.
The following describes the above technical solution in detail with specific application examples:
application example 1: belt roasting machine technology. When the pellet production device based on machine vision and data driving of the embodiment is applied to the process of the belt roasting machine, a schematic layout of the green pellet identification module 1, the finished pellet identification module 2 and the control system 3 around the belt roasting machine 4 is shown in fig. 2.
Step one, raw material preparation, wherein the main chemical components are shown in table 1:
TABLE 1 application example 1 Using the principal chemical Components of the raw materials
Step two, proportioning and balling
Pelletizing the ore prepared by 98.5 percent of concentrate powder and 1.5 percent of composite bentonite, adding water, uniformly mixing, and pelletizing on a disc pelletizer to obtain green pellets; the dropping strength of the green balls is 5.1 times per ball, the compression strength of the green balls is 13.2N per ball, and the granularity of the green balls is 10-15 mm detected by a green ball identification system.
Step three, preheating and roasting
The green pellets are sent to a bottom-paving and edge-paving tank on a roasting machine through a belt conveying system, and then are respectively sent to a trolley through a valve, wherein the thickness of the bottom-paving layer is 75-100 mm; air-blast drying for 5-6 min, wherein the air temperature is 200-400 ℃, and the air speed (standard meter) is controlled to be 1.5-2.0 m/s; air draft drying for 1-3 min, wherein the air temperature is 150-340 ℃, and the air speed (standard meter) is controlled to be 1.5-2.0 m/s; the preheating time is controlled to be 1-3 min, and the preheating temperature is about 980 ℃; roasting time is 5-8 min, and temperature is about 1280 ℃; the return air temperature of the high temperature section (first cooling) is 800-1200 ℃; the return air temperature of the low-temperature section (secondary cooling) is 250-350 ℃; finally, the baked finished ball is obtained, the compressive strength of the baked ball is 3220N/ball, and the granularity of the finished ball is 8-14 mm detected by a finished ball identification system.
Application example 2: a shaft furnace production process. When the pellet production device based on machine vision and data driving of the embodiment is applied to the shaft furnace production process, a schematic diagram of the arrangement of the green pellet recognition module 1, the finished pellet recognition module 2 and the control system 3 around the shaft furnace 5 is shown in fig. 3.
Step one, raw material preparation, wherein the main chemical components are shown in table 2:
TABLE 2 application example 2 Using the principal chemical Components of the raw materials
Step two, proportioning and balling
Pelletizing the concentrate powder 98.2 percent and the composite bentonite 1.8 percent by mixing with water, and pelletizing on a disc pelletizer to obtain green pellets; the dropping strength of the green balls is 5.2 times per ball, the compression strength of the green balls is 13.1N per ball, and the granularity of the green balls is 10-16 mm detected by a green ball identification system.
Step three, preheating and roasting
Smoothly feeding the green pellets into a drying belt of the shaft furnace through a material distribution device, enabling the green pellets to move from top to bottom in the shaft furnace, and enabling the feeding thickness of a drying bed to be 150-200 mm; the wet balls stay on the drying bed for 5-6 min, the drying air temperature is about 450 ℃, and the flow rate is about 1.8 m/s; the green pellets are heated to about 1000 ℃ by a preheating zone of the shaft furnace; the roasting temperature is kept between 1230 and 1280 ℃; cooling the roasted high-temperature pellets, and controlling cooling air to 25500-34000 m 3 /h; finally, the baked finished ball is obtained, the compressive strength of the baked ball is 3240N/ball, and the granularity of the finished ball is 8-14 mm detected by a finished ball identification system.
Application example 3: a grate-rotary kiln process, wherein the grate-rotary kiln process equipment comprises a grate 6, a rotary kiln 7 and a circular cooler 8; when the pellet production device based on machine vision and data driving of the embodiment is applied to a grate-rotary kiln process, a schematic diagram of the arrangement of the green pellet identification module 1, the finished pellet identification module 2 and the control system 3 around the grate-rotary kiln process equipment is shown in fig. 4.
Step one, raw material preparation, wherein the main chemical components are shown in table 3:
TABLE 3 application example 3 Using the principal chemical Components of the raw materials
Step two, proportioning and balling
Pelletizing the ore prepared by 98.0 percent of concentrate powder and 2.0 percent of composite bentonite, adding water, uniformly mixing, and pelletizing on a disc pelletizer to obtain green pellets; the dropping strength of the green balls is 5.3 times per ball, the compression strength of the green balls is 13.2N per ball, and the granularity of the green balls is 10-16 mm detected by a green ball identification system.
Step three, preheating and roasting
The green pellets are sent into a grate machine, the drying time of a blast drying section is 3.5min, the temperature of a wind box is 200-250 ℃, and the temperature of a smoke hood is 70-80 ℃; the drying time of the section I is 3.5min, and the temperature of the fume hood is 300-400 ℃; the drying time of the pumping II stage is 4.5-5 min, the temperature of the fume hood is 500-650 ℃, and the air speed is controlled to be about 1.8 m/s; the drying time of the preheating section is 7min, the temperature of the fume hood is 900-1000 ℃, and the temperature of the air box is 450-550 ℃.
The pellets are sent into a rotary kiln, the kiln head temperature is 1000-1100 ℃ (kiln head cover), the kiln temperature is 1100-1200 ℃, the kiln tail temperature is 950-1050 ℃ (kiln tail cover), and the flame temperature is controlled above 1300 ℃; the rotation speed of the kiln is adjusted (0.9-1.3 r/min); controlling the coal supply air quantity to 2500-3000 m 3 And/h, the coal amount is 3.5 t/h+/-0.5 t/h; finally, unloading the oxidized pellets from the rotary kiln to a cooler for cooling to obtain finished pellets, wherein the compressive strength of the roasted pellets is 3250N/each pellet, and the granularity of the finished pellets is 8-14 mm through detection of a finished pellet identification system.
In summary, the embodiment realizes the online detection of the pellet particles by constructing a machine vision system, acquires characteristic parameters such as granularity and the like, outputs a detection result to a control system, and determines the granularity control range of the high-quality pellets by constructing the mapping relation between the pellet granularity change, the pellet ore strength performance and the reduction performance. Finally, the RBF neural network parameters are optimized, an intelligent control neural network algorithm suitable for the pellet production process is provided, intelligent online control of the temperature and the belt speed in the roasting process facing pellet granularity optimization is realized, the defects of the existing granularity detection technology are overcome, the pellet quality is improved, and the operation cost of a production enterprise is reduced.
Third embodiment
The embodiment provides an electronic device, which comprises a processor and a memory; wherein the memory stores at least one instruction that is loaded and executed by the processor to implement the method of the first embodiment.
The electronic device may vary considerably in configuration or performance and may include one or more processors (central processing units, CPU) and one or more memories having at least one instruction stored therein that is loaded by the processors and performs the methods described above.
Fourth embodiment
The present embodiment provides a computer readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the method of the first embodiment described above. The computer readable storage medium may be, among other things, ROM, random access memory, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. The instructions stored therein may be loaded by a processor in the terminal and perform the methods described above.
Furthermore, it should be noted that the present invention can be provided as a method, an apparatus, or a computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable storage media having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
It is finally pointed out that the above description of the preferred embodiments of the invention, it being understood that although preferred embodiments of the invention have been described, it will be obvious to those skilled in the art that, once the basic inventive concepts of the invention are known, several modifications and adaptations can be made without departing from the principles of the invention, and these modifications and adaptations are intended to be within the scope of the invention. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Claims (6)
1. The pellet production method based on machine vision and data driving is characterized by comprising the following steps of:
the method comprises the steps of completing acquisition of images of green pellets before roasting and finished pellets after roasting by using an industrial camera;
acquiring green ball particle size and finished ball particle size based on the acquired green ball image and finished ball image;
determining the pellet granularity change before and after roasting according to the green pellet granularity and the finished product pellet granularity;
based on the dynamic regulation effect of the predetermined production process parameters on the pellet granularity change, adopting a preset neural network model, and adjusting the production process parameters in real time according to the currently determined pellet granularity change before and after roasting so as to ensure that the pellet granularity change before and after roasting is kept within a preset granularity change range;
based on the acquired green ball image and the finished ball image, the method for acquiring the green ball particle size and the finished ball particle size comprises the following steps:
preprocessing the acquired green ball image and the acquired finished ball image respectively; wherein the preprocessing comprises the following steps: graying treatment is carried out on the image to be treated to obtain a gray image; carrying out smooth denoising treatment on the gray level image; enhancing the contrast of the image after the drying to obtain a preprocessed image;
carrying out image recognition and image segmentation on the preprocessed image to obtain pellet particle size in real time;
the method for acquiring the pellet particle size in real time by carrying out image recognition and image segmentation on the preprocessed image comprises the following steps:
accurately separating the target pellets by using a preset image segmentation and feature extraction algorithm, solving the problem of segmentation of quasi-circular stacked images with mutually blocked and adhered targets in the images, and obtaining pellet particles and surface texture parameters;
the neural network model is an RBF neural network;
the construction process of the RBF neural network comprises the following steps:
respectively adopting a K-mean value clustering algorithm, an ant colony algorithm and a genetic algorithm to optimize the RBF neural network structure;
and performing integrated learning and model training on the optimized RBF neural network to obtain a trained model.
2. The method for producing pellets based on machine vision and data driving according to claim 1, wherein the determination of the dynamic adjustment of the pellet size variation by the production process parameters comprises:
acquiring actual production process parameters and the granularity of corresponding green pellets and finished pellets;
according to the obtained actual production process parameters and the granularity of the corresponding green pellets and finished pellets, the dynamic regulation effect of the production process parameters on the pellet granularity change is clarified.
3. The machine vision and data driven pellet production method of claim 1, wherein the predetermined particle size variation range determination process comprises:
constructing a mapping relation between pellet granularity change and pellet ore performance; wherein the pellet ore performance comprises pellet ore strength performance and pellet ore reduction performance;
and determining the corresponding granularity variation range of the pellets meeting the preset performance requirement based on the constructed mapping relation.
4. The method for producing pellets based on machine vision and data driving according to claim 1, wherein the step of adopting a preset neural network model to adjust production process parameters in real time according to currently determined pellet particle size changes before and after firing comprises the steps of:
and inputting the currently determined pellet granularity change before and after roasting into a preset neural network model, and outputting corresponding production process parameters through the preset neural network model to realize the adjustment of production equipment.
5. The machine vision and data driven pellet production method of claim 4 wherein the production process parameters include preheat temperature, bake time, and sintering machine belt speed.
6. Pellet apparatus for producing based on machine vision and data drive, characterized by comprising:
the machine vision system comprises a green ball identification module and a finished ball identification module; the green ball identification module is used for completing acquisition of green ball images before roasting by using an industrial camera; the finished ball identification module is used for completing acquisition of the baked finished ball image by using an industrial camera;
the control system is used for acquiring the green ball particle size and the finished ball particle size based on the acquired green ball image and the finished ball image; determining the pellet granularity change before and after roasting according to the green pellet granularity and the finished product pellet granularity; based on the dynamic regulation effect of the predetermined production process parameters on the pellet granularity change, adopting a preset neural network model, and adjusting the production process parameters in real time according to the currently determined pellet granularity change before and after roasting so as to ensure that the pellet granularity change before and after roasting is kept within a preset granularity change range;
based on the acquired green ball image and the finished ball image, the method for acquiring the green ball particle size and the finished ball particle size comprises the following steps:
preprocessing the acquired green ball image and the acquired finished ball image respectively; wherein the preprocessing comprises the following steps: graying treatment is carried out on the image to be treated to obtain a gray image; carrying out smooth denoising treatment on the gray level image; enhancing the contrast of the image after the drying to obtain a preprocessed image;
carrying out image recognition and image segmentation on the preprocessed image to obtain pellet particle size in real time;
the method for acquiring the pellet particle size in real time by carrying out image recognition and image segmentation on the preprocessed image comprises the following steps:
accurately separating the target pellets by using a preset image segmentation and feature extraction algorithm, solving the problem of segmentation of quasi-circular stacked images with mutually blocked and adhered targets in the images, and obtaining pellet particles and surface texture parameters;
the neural network model is an RBF neural network;
the construction process of the RBF neural network comprises the following steps:
respectively adopting a K-mean value clustering algorithm, an ant colony algorithm and a genetic algorithm to optimize the RBF neural network structure;
and performing integrated learning and model training on the optimized RBF neural network to obtain a trained model.
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