CN101698896B - System and method for steel-making online end-point control through furnace mouth radiation information fusion - Google Patents
System and method for steel-making online end-point control through furnace mouth radiation information fusion Download PDFInfo
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Abstract
The invention discloses a system and a method for steel-making online end-point control through furnace mouth radiation information fusion. The system comprises a furnace mouth radiation acquisition subsystem, an optical fiber spectrum division multiplexing subsystem, a multi-spectral combined detection subsystem, a furnace mouth flame video acquisition subsystem and a comprehensive determination subsystem, wherein the furnace mouth radiation acquisition subsystem is connected with the optical fiber spectrum division multiplexing subsystem; the optical fiber spectrum division multiplexing subsystem is connected with the multi-spectral combined detection subsystem; the multi-spectral combined detection subsystem is connected with the comprehensive determination subsystem through a serial port; and the furnace mouth flame video acquisition subsystem comprises a CCD camera provided with an optical lens and a video capture board, the CCD camera is arranged on the furnace mouth radiation acquisition subsystem and is connected with the video capture board, and the video capture board is connected with the comprehensive determination subsystem. The system and the method achieve a long-range detection (20 meters), so the system can work normally under a severe environment of steel-making without using a fussy cooling device and a dust collector.
Description
Technical field
The invention belongs to the terminal point control techniques for converter (Basic Oxygen Furnace, be called for short BOF) steel-making converting process, particularly a kind of furnace mouth radiation information merges and is used for making steel system and the method thereof of online terminal point control.
Background technology
The traditional method of judging about converter terminal now mainly contains the method for STEADYSTATE CONTROL MODEL and dynamic control model.Static model carry out quantitative Analysis according to material balance principle and heat balance principle to the starting condition of converting process, still, owing to not considering the information of converting process, can not carry out on-line tracing and revise in real time.If dynamic-control system sublance equipment, sublance can by sublance records in the converting process of converter result with after the blowing target of being scheduled to is compared, revise the blowing operation according to both deviations, but, be subjected to size and the high restriction of sublance cost of investment of sublance, now only use at large-scale converter steel factory, also can't be generalized to middle-size and small-size converter shed.
Thereby at present, the middle-size and small-size converter steel factory that accounts for main body in the domestic steel-making still mainly uses take the single static model as the basis, take the operator's console instrument show and artificial experience as the terminal point determining method of foundation.In actually operating, the thermometric of falling the stove is decided to carry out next step operation behind the carbon again, but generally speaking, be subjected to the impact of the complexity of converting process and workman's the mental status, the hit rate of a catch carbon is often lower, the operation that usually needs to re-blow, and this just reduces productivity, affect steel quality, strengthened workman's labour intensity.
Because traditional method or inaccurate to endpoint, perhaps the high suitability of cost is wideless, has engendered some novel end-point control methods that converter terminal is judged, wherein a kind of optical means is authorized in the Chinese patent 96197034 of husky profit A Luoke.
This Patent design a kind of optical sensor metering facility with cooling system and dust-collecting air brush, this equipment gathers the light intensity of the 560nm wavelength period of converter mouth visible light under the closer distance of 30 feet of distance 290 ton large-scale converters, by giving special-purpose programmable logic controller as the mode input variable and calculate recording the maximum light intensity of the process of bessemerizing blowing oxygen quantity poor and synchronization, what obtain molten steel this moment is blowing carbon content.This patent thinks that this technology is applicable to contain 0.06% or the prediction of the carbon content of low-carbon (LC) carbon content in steel still less and a catch carbon.
But the related device of this patent itself has caused the raising of cost, each sampling time in early stage is 60s, and each sampling time in later stage is 4s, and this has all brought certain impact to the continuity of data sample, and can lose valid data, especially at the crucial later stage of blowing.
In this patent, method therefor be only applicable at present for oxidation stability, the above mammoth conveter of 200t, carbon content be lower than the control of the interior molten steel endpoint carbon content of 0.05% scope, and in the device loaded down with trivial details refrigerating unit and cleaning apparatus are arranged, when judging the medium and high carbon steel converter, use this patent that larger error is arranged.
Summary of the invention
The object of the present invention is to provide a kind of for the converter steelmaking scene accurately, low-cost, noncontact is remote, easy to operate, the system of service-strong BOF Steelmaking Endpoint on-line measurement control and be used for the models and methods of converter terminal prediction, thereby can solve the problem that current converter steelmaking blow end point is difficult to Accurate Prediction.
The technical solution that realizes the object of the invention is: a kind of furnace mouth radiation information merges and is used for making steel the system of online terminal point control, comprises that furnace mouth radiation obtains sub-system, optical fiber spectrum division multiplexing sub-system, multispectral complex probe sub-system, fire door flame video acquisition sub-system and synthetic determination sub-system; Furnace mouth radiation obtains sub-system and connects optical fiber spectrum division multiplexing sub-system, and this optical fiber spectrum division multiplexing sub-system connects multispectral complex probe sub-system, and this multispectral complex probe sub-system is connected with the synthetic determination sub-system by serial ports; Fire door flame video acquisition sub-system comprises ccd video camera and the video frequency collection card that is being equipped with optical lens, and ccd video camera is erected at furnace mouth radiation and obtains on the sub-system, and this pick up camera connects video frequency collection card, and this video frequency collection card connects the synthetic determination sub-system;
Furnace mouth radiation obtains sub-system the spectral energy information of converter mouth flamew radiation is collected, and be imaged onto on the focal plane, the focal plane coupling that one end of optical fiber spectrum division multiplexing sub-system and furnace mouth radiation obtain sub-system, the other end is divided into four the road or four the tunnel to the spectral energy information that collects with upper channel, and be transferred to respectively in the photodetector corresponding with passage that is being equipped with the different wave length interference filter and go, these photodetectors are positioned at the receiving end of multispectral complex probe sub-system, by this multispectral complex probe sub-system optical signal is transformed into electrical signal and receives software by the serial ports that serial ports sends the synthetic determination sub-system to, carry out the reception of the spectrum intensity information of furnace mouth radiation, storage and mapping operation; The collection vision that ccd video camera adjustment and the furnace mouth radiation of fire door flame video acquisition sub-system obtains sub-system is coincide, after this ccd video camera collects the RGB color model information of converter mouth flamew video image, be transferred to video frequency collection card by video inputs, this video frequency collection card utilizes the image acquisition and processing software of synthetic determination sub-system [5], this RGB color model information exchange is crossed chrominance information storage in the hsv color model after the color model conversion, this chrominance information that obtains is carried out mapping operation as the characteristic information data of fire door flame image according to these data; Finally, the BP neural network end-point prediction model utilization of synthetic determination sub-system obtains the online end-point prediction control that spectrum intensity information and these two portions data of fire door flame image information are carried out converter steelmaking.
A kind of furnace mouth radiation information merges and is used for making steel the method for online terminal point control, and step is as follows:
The first step, obtain spectrum intensity information, the spectral energy information of converter mouth flamew radiation is collected, by optical fiber spectrum division multiplexing sub-system the spectral energy information that collects is divided into four the road or four the tunnel with upper channel, by multispectral complex probe sub-system optical signal is transformed into electrical signal and receives the software reception by serial ports, obtain the spectrum intensity information data at furnace mouth radiation place, carry out mapping operation according to these data;
Second step, obtain fire door flame image information, the RGB color model information of the converter mouth flamew video image that gathers, be transferred to video frequency collection card by video inputs, this video frequency collection card utilizes image acquisition and processing software, this RGB color model information exchange is crossed chrominance information storage in the hsv color model after the color model conversion, and this chrominance information that obtains is carried out mapping operation as the characteristic information data of fire door flame image according to these data;
The 3rd step, spectrum intensity information and fire door flame image information are carried out the benchmark adjustment, the magnification of the spectrum intensity value of less is adjusted, namely the corresponding constantly corresponding spectrum intensity value of blowing image maximum in latter stage is adjusted into onesize image value, with the adjusted basis of this magnification that obtains as global variable; The power central slide filtering methods such as simultaneously utilization carry out filtering and Denoising disposal to above-mentioned data, the spectrum intensity in finally obtaining bessemerizing and characteristics of image change curve;
The 4th step, the BP neural network model that makes up carries out quantitative judgement constantly to terminal point, at first select input variable in the BP neural network prediction model, suppose that selected whole variable numbers are m, the neuron number of hidden layer is h, and z is network output, and then hr is the input of each node of hidden layer, Hr is the output of each node of hidden layer, definition G
Rs=Cov (h
r, x
s) w
Rs/ Var (h
r) for inputting node s to the influence value of hidden node r, g
r=Cov (H
r, z) w
r/ Var (H
r) be that hidden node r is to the influence value of output, Q
s=∑ G
Rsg
rBe the influence value of input variable to overall output; If the sum that affects of n variable in the overall variable accounts for more than 90% of general impacts value, then choosing this n variable is the input variable of network model;
Secondly, finally the input and output parameter of selected model is:
(1) work output of BP network model: consider that whole model is the prediction that realizes the steel-making terminal time, so be chosen as converter steelmaking blow end point time T (unit: second) as the output of model;
(2) input of BP network model: because with the output of converter steelmaking blow end point time T as model, the value of considering the whole process middle and later periods is relatively stable, so after the comparative analysis, spectrum intensity average, image average, the ratio between the two of selected 400-500 time period second, the blowing corresponding time of image value maximum point in latter stage, the image value that this maximum point is corresponding, spectrum intensity value and between the two above seven variablees of ratio as the input parameter;
At last, set up BP neural network end-point prediction model based on the input/output variable of spectrum intensity and characteristics of image change curve and model, utilize a large amount of converter steelmaking blowing data of collection in worksite, after the training of BP neural network end-point prediction model, the terminal point of scene blowing heat is carried out the real-time estimate of network model, utilize end-point prediction value that this model obtains and the on-the-spot comparing result of terminal point actual value, realize the end-point prediction control of converter.
The present invention compared with prior art, its remarkable advantage:
(1) by using telescopic system to combine with optical fiber spectrum division multiplexing system, realized long-range detection (20m), so just realized need not be loaded down with trivial details refrigerating unit and cleaning apparatus just can under the severe environment of making steel, work;
(2) proposed a kind of optical fiber spectrum division multiplexing technology, can extract the multispectral pattern of converter mouth optical radiation comprehensively, thereby more constitutionally obtains the inner link of radiation information, also improve measuring accuracy and reliability simultaneously.
(3) propose extraction with fire door flame video image characteristic information and be placed on method in the information analysis relevant with visual color, and in conjunction with spectrum intensity information, analyzed the converter optical radiation blowing change curve that has obtained blowing reaction process in the reflection stove.Test data in this optical radiation blowing change curve is obtained easy, feasible, accurate, real-time.
(4) the BP neural network end-point prediction model that makes up, the result shows the time of response less than 2s, the precision of prediction in 8s surpasses 85%, satisfies the requirement of the online fast judgement of converter terminal and precision of prediction.
(5) said system and method, its characteristic is that the measurement control method is online in real time, device is simple and with low cost, to improving the quality of products, reduce cost, promote the tooling modernization that obvious effect will be arranged, can provide reference frame to the control of the converter terminal in the present stage actual production process, have broad application prospects.
Below in conjunction with accompanying drawing the present invention is described in further detail.
Description of drawings
Fig. 1 is that furnace mouth radiation information of the present invention merges and is used for making steel the one-piece construction figure of online terminal point Controlling System.
Fig. 2 is the structure iron of multispectral radiation detection sub-system.
Fig. 3 is the three-dimensional plot of chromatic(ity)component.
Fig. 4 is the change curve of spectrum intensity and image feature value.
Fig. 5 is the BP neural network end-point prediction model structure figure that makes up.
Fig. 6 is that terminal point actual value and BP neural network are to the comparison diagram of end-point prediction value.
Embodiment
The present invention has made up a kind of furnace mouth radiation information theoretical based on spectral radiance and that experience steel-making is theoretical and has merged the system that is used for the online terminal point control of converter (BOF) steel-making, comprise that furnace mouth radiation obtains sub-system 1, optical fiber spectrum division multiplexing sub-system 3, multispectral complex probe sub-system 4, fire door flame video acquisition sub-system 2, synthetic determination sub-system 5, wherein synthetic determination sub-system 5 comprises that serial ports receives software, image acquisition and processing software, BP neural network end-point prediction model three parts.The one-piece construction figure of this system as shown in Figure 1.
Furnace mouth radiation obtains sub-system 1 and connects optical fiber spectrum division multiplexing sub-system 3, and this optical fiber spectrum division multiplexing sub-system 3 connects multispectral complex probe sub-system 4, and this multispectral complex probe sub-system 4 is connected with synthetic determination sub-system 5 by serial ports; Fire door flame video acquisition sub-system 2 comprises ccd video camera and the video frequency collection card that is being equipped with optical lens, and ccd video camera is erected at furnace mouth radiation and obtains on the sub-system 1, and this pick up camera connects video frequency collection card, and this video frequency collection card connects synthetic determination sub-system 5.
Workflow is as follows: furnace mouth radiation obtains sub-system 1 spectral energy information of converter mouth flamew radiation is collected, and be imaged onto on the focal plane, one end of optical fiber spectrum division multiplexing sub-system 3 and furnace mouth radiation obtain the focal plane coupling of sub-system 1, the other end is divided into four the road or four the tunnel to the spectral energy information that collects with upper channel, and be transferred to respectively in the photodetector corresponding with passage that is being equipped with the different wave length interference filter and go, these photodetectors are positioned at the receiving end of multispectral complex probe sub-system 4, by this multispectral complex probe sub-system 4 optical signal is transformed into electrical signal and receives software by the serial ports that serial ports sends synthetic determination sub-system 5 to, carry out the reception of the spectrum intensity information of furnace mouth radiation, storage and mapping operation; The collection vision that ccd video camera adjustment and the furnace mouth radiation of fire door flame video acquisition sub-system 2 obtains sub-system 1 is coincide, after this ccd video camera collects the RGB color model information of converter mouth flamew video image, be transferred to video frequency collection card by video inputs, this video frequency collection card utilizes the image acquisition and processing software of synthetic determination sub-system 5, this RGB color model information exchange is crossed chrominance information storage in the hsv color model after the color model conversion, this chrominance information that obtains is carried out mapping operation as the characteristic information data of fire door flame image according to these data; Finally, the BP neural network end-point prediction model utilization of synthetic determination sub-system 5 obtains the online end-point prediction control that spectrum intensity information and these two portions data of fire door flame image information are carried out converter steelmaking.
Consider from security standpoint, framework plays system in the operation room of the on-the-spot approximately 20m of steel-making.The operation steps of system's key instrument is: set up trivet; Furnace mouth radiation is obtained sub-system and ccd video camera is installed on the trivet; Obtain the focal plane of sub-system at furnace mouth radiation focalizer is installed; Regulate orientation and pitching that furnace mouth radiation obtains sub-system, make the center superposition of flame kernel and focalizer; Regulate focusing handwheel, make imaging combustion clear; Gun sight or the ccd video camera of focalizer are aimed at flame, flame kernel is overlapped with the gun sight of focalizer or the field of view center of ccd video camera; Take out focusing lens, load onto the jumbo fiber bundle of optical fiber spectrum division multiplexing sub-system; If in use trivet moves, as long as regulate the instrument position of host machine take gun sight or the ccd video camera of focalizer as benchmark, needn't use again focalizer; Aforesaid operations is preferably take the gun sight of focalizer as benchmark adjustment instrument host.After adjusting furnace mouth radiation and obtaining sub-system, can use that the image in the video window carries out fine tuning to ccd video camera on the nut of support of ccd video camera and the observer computer.
1.1 furnace mouth radiation obtains sub-system 1 for the severe environment at converter steelmaking scene, in order to incite somebody to action fire door imaging combustion at a distance to the focal plane, selected the heavy caliber camera lens in two not cemented objectives, obtained the structural parameter table (seeing Table 1) that furnace mouth radiation obtains sub-system by calculation of parameter, this furnace mouth radiation obtains sub-system and is fixed, supports with trivet, height and degree of turning by adjusting the tripod pallet obtain to gather preferably the vision orientation.This furnace mouth radiation obtains sub-system and comprises by the designed heavy caliber camera lens of table 1, be positioned at furnace mouth radiation obtain the sub-system focal plane outside, can before and after stretch and be used for regulating the focusing handwheel of imaging combustion sharpness, and external, can be installed in furnace mouth radiation and obtain the focalizer that is used for the aiming flame kernel on the sub-system focal plane.This furnace mouth radiation obtains sub-system in the face of converter mouth, to gather the spectral energy information of converter mouth radiation; Obtain focusing handwheel on the sub-system by regulating this furnace mouth radiation, spectral energy information can clearly be imaged onto on the focal plane that this furnace mouth radiation obtains sub-system.The design this furnace mouth radiation obtain sub-system have bore unrestricted, can utilize the advantages such as airspace correct residual error and relative aperture can increase, can reach the visual effect of artificial low coverage, high temperature and pollution can be kept away from again to the infringement of detection system, the collecting work of remote (approximately 20m) condition can be satisfied.
Table 1 furnace mouth radiation obtains the structural parameter (mm) of sub-system
1.2 it is many part that optical fiber spectrum division multiplexing sub-system 3 can be divided into after receiving spectral energy equably, and be transferred to respectively in the ten tunnel different photodetectors that are being equipped with different spectral filters and go.The present invention selects the ladder index fiber of jumbo fiber core diameter, use correlation theory and the technology of optical fiber " wavelength-division multiplex ", design optical fiber spectrum sub-system realizes " optical fiber spectrum division multiplexing ", adopt the corresponding Optic transmission fiber bundle of the subchannels such as bundle number and spectral energy information, the performance of these Optic transmission fiber bundles is identical, diameter is the same, from an end of every bundle, take out an optical fiber, with the synthetic unit spot of the optical fiber that takes out, then all unit spot random alignment are formed a jumbo fiber bundle, behind this jumbo fiber bundle interface gummed, cut out smooth even curface, form the entrance pupil face of optical fiber spectrum division multiplexing sub-system 3; Thereby the other end of all Optic transmission fiber bundles and the interference filter of different wave length are close to the effect that has realized that spectrum is divided.
1.3 multispectral complex probe sub-system 4 receives the spectral energy information that optical fiber spectrum division multiplexing sub-system transmits, selecting Fa Buli---uncle Luo Gan relates to spectral filter light is filtered into arrowband monochromatic ray information, then this multispectral complex probe sub-system conversion of monochromatic ray information and warp is output as the numerary signal of reciprocity passage, sends to computer by serial ports.The interference filter wavelength parameter of design is shown in Table 2.
Table 2 spectral filter Specifeca tion speeification
This multispectral complex probe sub-system 4 will satisfy quick response, process large information capacity and the requirement such as work in real time under the severe environment such as high temperature, flue dust, main being responsible for becomes ten road spectral energy signals that the transmission of optical fiber spectrum division multiplexing sub-system comes into ten railway digital signals, and send to the task of computer, thereby selected to be formed (any suitable device that may measure the class likelihood data all can use) by photodetector and the Circuits System take micro-chip as core in native system.
The schematic circuit of this multispectral complex probe sub-system 4 as shown in Figure 2, its chief component is photodetector, filtering circuit, amplifier, sampling hold circuit, A/D change-over circuit, micro-chip, clock circuit, communicating circuit etc.Send the furnace mouth radiation spectral energy of ten road five equilibriums of multispectral complex probe sub-system to through optical fiber spectrum division multiplexing sub-system, conversion outputting ten railway digital signal in this multispectral complex probe sub-system, this ten railway digitals signal represents respectively the radiation light intensity of different wave length, according to there are differences between different wave length flashlight yield of radiation, through needing after the filtering by the different amplifying circuit of gain, reach and enter the A/D change-over circuit behind the requirement voltage amplitude and change, data send to computer by serial ports after treatment behind the micro-chip Collect conversion, and utilize serial ports reception software that spectrum intensity information is received, storage and mapping operation.
This serial ports receives the MSComm control that software utilizes the VC software platform, the frequency parameter of having set baud rate, data bit, stop bit, parity checking and the reception signal of serial ports transmission is 10ms/ time, multispectral complex probe sub-system 4 every transmission one secondary data, this serial ports receives software and just receives and storage work, again the light intensity data of the 546nm wavelength respective channel that obtains is carried out mapping operation.
Here carry out lower explanation, the light intensity that collects is relative light intensity, as long as light intensity data unit of measure self-consistentency, rule that just can the reaction converter converting process, unit choose on this variation tendency without any impact, any unit of measure arbitrarily or fixing, can be used for measuring the spectrum intensity information of furnace mouth radiation in native system.
1.4 the ccd video camera that is being equipped with optical lens of fire door flame video acquisition sub-system 2 is erected at furnace mouth radiation and obtains on the sub-system, and the collection vision that adjustment and furnace mouth radiation obtain sub-system is coincide.
After this ccd video camera collects the RGB color model information of converter mouth flamew video image, be transferred to video frequency collection card by video inputs, this video frequency collection card utilizes the image acquisition and processing software of synthetic determination sub-system, this RGB color model information exchange is crossed chrominance information storage in the hsv color model after the color model conversion, this chrominance information that obtains is carried out mapping operation as the characteristic information data of fire door flame image according to these data.This image acquisition and processing software workflow of Processing tasks while gathering of realizing fire door flame video image is:
(1) creates the window of catching of fire door flame image information: call the capCreateCaptureWindow function, the generating video acquisition window.
(2) gather fire door flame image video information to buffer zone and carry out analyzing and processing, it is the core content of system's real time implementation, need to utilize callback mechanism, three call back functions that need Accreditation System to use: mistake call back function capSetCallbackOnError, state call back function capSetCallbackOnStatus and video flowing call back function capSetCallbackOnVideoStream, setting by the video flowing call back function and the structure processing mode of tabling look-up, make the RGB color model information of the fire door flame image that collects, through obtaining the chrominance information in the corresponding hsv color model after the color model conversion, namely first the corresponding data sheet of the change over condition of two kinds of color model is built, in call back function, add the software code that the RGB color model is converted to the hsv color model; Then each two field picture that gathers is according to pixels extracted, and search position in the corresponding HSV table of this rgb value; Then the essential numerical value of all pixels of this two field picture superposeed, obtain and draw the three-dimensional plot of chromatic(ity)component, as shown in Figure 3; Last in this three-dimensional plot, find in the converting process certain colourimetric number corresponding reach home the time change obvious curve, and draw.What time followingly note simultaneously: get rid of the more violent variation that occurs once in a while, this is to disturb the possibility that causes larger; Select the number of pixels value to change metastable that colourity rank; Should not select background more colourity rank to occur; Destination county is bessemerized in corresponding closing under this colourity rank obvious variation, changes obviously relatively being beneficial to endpoint.What adopt here is that look-up method can for speed up processing, reach the purpose of processing while gathering.
(3) call function capCaptureGetSetup arranges the correlation parameter of acquisition window, and the default per second gathers 15 frames.
(4) window and video capture device are caught in call function capDriveConnect connection, and use the capDriverGetCaps function to return the function of sampler.
(5) for the better differentiation situation of observation flame, system call function capPreview, reach good preview effect with frame number faster.
(6) do not carry out the preservation of video flowing in the above step, if some other requirement is arranged, can preserve by call function capFileSetCaptureFile, but the video file without compression is very large, can use video compress manager (Video Compression Mananger) to compress rear preservation.
(7) call function capCaptureAbort finishes video.
This important evidence that different integral color variations is arranged with reference to the different blowing fire door flame constantly of experience steel-making, system becomes the hsv color model data to the image transitions of the rgb format that collects, obtained as shown in Figure 3 chromatic(ity)component three-dimensional plot, X-axis is the colourity rank among the figure, Y-axis is single-frame images number, and Z axis is corresponding number of pixels.Because the histogram of single-frame images can not reflect the Changing Pattern of whole image/video, thereby from the 3-D view that all HSV image histograms form, find colourity H comprising more potential information 17 times, when closing on terminal point with compare before value and obvious variation occurred.
1.5 being spectrum intensity and image feature values that a spectrum intensity information that collects and fire door flame image information differ larger, synthetic determination sub-system 5 carries out the benchmark adjustment, the principle of benchmark adjustment is that the magnification of the spectrum intensity value of less is adjusted, namely the corresponding constantly corresponding spectrum intensity value of blowing image maximum in latter stage is adjusted into onesize image value, with the adjusted basis of this magnification that obtains as global variable; The power central slide filtering methods such as simultaneously utilization carry out filtering and Denoising disposal to above-mentioned data, and its mathematical expression form is
K=n+1, n+2, Λ, N-n has finally obtained spectrum intensity in the bessemerizing as shown in Figure 4 and the change curve of image feature value.
In this change curve, can find, in this polynary heterogeneous high-temperature physics chemical reaction process of converter steelmaking, the spectrum intensity value progressively becomes large with the converter steelmaking converting process, the blowing mid-term since the reaction violent interference increase the phenomenon that fluctuation is arranged, light intensity curve slow decreasing during near blow end point; Meanwhile image feature value exists violent vibration mid-term at the initial stage rising of blowing, significantly improves during near terminal point latter stage and arrive, and near blow end point the time, image curve has the appearance of a maximum value.These variation characteristic real time reactions the variation of the spectrum picture characteristic quantity in the steelmaking process.But because the difference of the factors such as the starting material that different heat adds and blowing condition, caused the terminal point of each heat constantly to have larger fluctuation, thereby this description qualitatively can not be judged terminal point exactly, on this basis, made up BP neural network end-point prediction model terminal point has been carried out quantitative judgement constantly.
The model construction of the BP neural network prediction model that the present invention makes up as shown in Figure 5.Owing to now the selection of input variable in the BP neural network prediction model also there is not a kind of method of comparison system, all be to choose according to planner's experience generally.The present invention to the method that BP neural network prediction model Input variable selection adopts is: the selected whole variable numbers of supposition are m, the neuron number of hidden layer is h, and z is network output, and then hr is the input of each node of hidden layer, Hr is the output of each node of hidden layer, definition G
Rs=Cov (h
r, x
s) w
Rs/ Var (h
r) for inputting node s to the influence value of hidden node r, g
r=Cov (H
r, z) w
r/ Var (H
r) be that hidden node r is to the influence value of output, Q
s=∑ G
Rsg
rBe the influence value of input variable to overall output; If the sum that affects of n variable in the overall variable accounts for more than 90% of general impacts value, then choosing this n variable is the input variable of network model;
Finally the input and output parameter of selected model is:
(1) work output of BP network model: consider that whole model is the prediction that realizes the steel-making terminal time, so be chosen as converter steelmaking blow end point time T (unit: second) as the output of model.
(2) input of BP network model: because with the output of converter steelmaking blow end point time T as model, the value of considering the whole process middle and later periods is relatively stable, so after the comparative analysis, spectrum intensity average, image average, the ratio between the two of selected 400-500 time period second, the blowing corresponding time of image value maximum point in latter stage, the image value that this maximum point is corresponding, spectrum intensity value and between the two above seven variablees of ratio as the input parameter.
Set up BP neural network end-point prediction model based on the input/output variable of spectrum intensity and characteristics of image change curve and model.Utilize a large amount of blowing data of collection in worksite, after the training of network model, the terminal point of on-the-spot 49 groups of heats is carried out the real-time estimate of network model, the model prediction time of response is 1.2s, and Fig. 6 has provided current experimental result.From the experimental result comparison diagram that utilizes end-point prediction value that this model obtains and terminal point actual value, can find out, 42 groups predicated error is arranged in 8s, that is to say that this neural network model has reached 85.7% at the precision of prediction of 8s, satisfy the requirement of the online fast judgement of converter terminal and precision of prediction.
Claims (2)
1. a furnace mouth radiation information merges and is used for making steel the method for online terminal point control, it is characterized in that: undertaken by online terminal point Controlling System, this online terminal point Controlling System comprises that furnace mouth radiation obtains sub-system [1], optical fiber spectrum division multiplexing sub-system [3], multispectral complex probe sub-system [4], fire door flame video acquisition sub-system [2] and synthetic determination sub-system [5]; Furnace mouth radiation obtains sub-system [1] and connects optical fiber spectrum division multiplexing sub-system [3], and this optical fiber spectrum division multiplexing sub-system [3] connects multispectral complex probe sub-system [4], and this multispectral complex probe sub-system [4] is connected with synthetic determination sub-system [5] by serial ports; Fire door flame video acquisition sub-system [2] comprises ccd video camera and the video frequency collection card that is being equipped with optical lens, ccd video camera is erected at furnace mouth radiation and obtains on the sub-system [1], this pick up camera connects video frequency collection card, and this video frequency collection card connects synthetic determination sub-system [5]; The step of line terminal point control is as follows:
The first step, obtain spectrum intensity information, the spectral energy information of converter mouth flamew radiation is collected, by optical fiber spectrum division multiplexing sub-system [3] spectral energy information that collects is divided into four the tunnel with upper channel, by multispectral complex probe sub-system [4] optical signal is transformed into electrical signal and receives the software reception by serial ports, obtain the spectrum intensity information data at furnace mouth radiation place, carry out mapping operation according to these data;
Second step, obtain fire door flame image information, the RGB color model information of the converter mouth flamew video image that gathers, be transferred to video frequency collection card by video inputs, this video frequency collection card utilizes image acquisition and processing software, this RGB color model information exchange is crossed chrominance information storage in the hsv color model after the color model conversion, and this chrominance information that obtains is carried out mapping operation as the characteristic information data of fire door flame image according to these data;
The 3rd step, spectrum intensity information and fire door flame image information are carried out the benchmark adjustment, the magnification of the spectrum intensity value of less is adjusted, namely the corresponding constantly corresponding spectrum intensity value of blowing image maximum in latter stage is adjusted into onesize image value, with the adjusted basis of this magnification that obtains as global variable; The power central slide filtering methods such as simultaneously utilization carry out filtering and Denoising disposal to above-mentioned data, the spectrum intensity in finally obtaining bessemerizing and characteristics of image change curve;
The 4th step, the BP neural network model that makes up carries out quantitative judgement constantly to terminal point, at first select input variable in the BP neural network prediction model, suppose that selected whole variable numbers are m, the neuron number of hidden layer is h, and z is network output, and then hr is the input of each node of hidden layer, Hr is the output of each node of hidden layer, definition G
Rs=Cov (h
r, x
s) w
Rs/ Var (h
r) for inputting node s to the influence value of hidden node r, g
r=Cov (H
r, z) wr/Var (H
r) be that hidden node r is to the influence value of output, Q
s=∑ G
Rsg
rBe the influence value of input variable to overall output; If the sum that affects of n variable in the overall variable accounts for more than 90% of general impacts value, then choosing this n variable is the input variable of network model;
Secondly, finally the input and output parameter of selected model is:
(1) work output of BP network model: consider that whole model is the prediction that realizes the steel-making terminal time, so be chosen as converter steelmaking blow end point time T (unit: second) as the output of model;
(2) input of BP network model: because with the output of converter steelmaking blow end point time T as model, the value of considering the whole process middle and later periods is relatively stable, so after the comparative analysis, spectrum intensity average, image average, the ratio between the two of selected 400-500 time period second, the blowing corresponding time of image value maximum point in latter stage, the image value that this maximum point is corresponding, spectrum intensity value and between the two above seven variablees of ratio as the input parameter;
At last, set up BP neural network end-point prediction model based on the input/output variable of spectrum intensity and characteristics of image change curve and model, utilize a large amount of converter steelmaking blowing data of collection in worksite, after the training of BP neural network end-point prediction model, the terminal point of scene blowing heat is carried out the real-time estimate of network model, utilize end-point prediction value that this model obtains and the on-the-spot comparing result of terminal point actual value, realize the end-point prediction control of converter;
Described furnace mouth radiation obtains sub-system [1] spectral energy information of converter mouth flamew radiation is collected, and be imaged onto on the focal plane, one end of optical fiber spectrum division multiplexing sub-system [3] and furnace mouth radiation obtain the focal plane coupling of sub-system [1], the other end is divided into four the road or four the tunnel to the spectral energy information that collects with upper channel, and be transferred to respectively in the photodetector corresponding with passage that is being equipped with the different wave length interference filter and go, these photodetectors are positioned at the receiving end of multispectral complex probe sub-system [4], by this multispectral complex probe sub-system [4] optical signal is transformed into electrical signal and receives software by the serial ports that serial ports sends synthetic determination sub-system [5] to, carry out the reception of the spectrum intensity information of furnace mouth radiation, storage and mapping operation; The collection vision that ccd video camera adjustment and the furnace mouth radiation of fire door flame video acquisition sub-system [2] obtains sub-system [1] is coincide, after this ccd video camera collects the RGB color model information of converter mouth flamew video image, be transferred to video frequency collection card by video inputs, this video frequency collection card utilizes the image acquisition and processing software of synthetic determination sub-system [5], this RGB color model information exchange is crossed chrominance information storage in the hsv color model after the color model conversion, this chrominance information that obtains is carried out mapping operation as the characteristic information data of fire door flame image according to these data; Finally, the BP neural network end-point prediction model utilization of synthetic determination sub-system [5] obtains the online end-point prediction control that spectrum intensity information and these two portions data of fire door flame image information are carried out converter steelmaking;
Obtain in the sub-system [1] at furnace mouth radiation, select the heavy caliber camera lens in two not cemented objectives and calculate suitable structural parameter table and make up furnace mouth radiation and obtain sub-system, this furnace mouth radiation obtains sub-system in the face of converter mouth, to gather the spectral energy information of converter mouth radiation, obtain focusing handwheel on the sub-system by regulating this furnace mouth radiation, spectral energy information clearly is imaged onto on the focal plane that this furnace mouth radiation obtains sub-system, simultaneously, this furnace mouth radiation obtains sub-system and is fixed with trivet, support, height and degree of turning by adjusting the tripod pallet obtain to gather preferably the vision orientation;
In optical fiber spectrum division multiplexing sub-system [3], adopt the corresponding Optic transmission fiber bundle of the subchannels such as bundle number and spectral energy information, from an end of every bundle, take out an optical fiber, with the synthetic unit spot of the optical fiber that takes out, then all unit spot random alignment are formed a jumbo fiber bundle, behind this jumbo fiber bundle interface gummed, cut out smooth even curface, form the entrance pupil face of optical fiber spectrum division multiplexing sub-system; The other end of all Optic transmission fiber bundles and the interference filter of different wave length are close to;
In multispectral complex probe sub-system [4], at first the spectral energy information that sends of the subchannel such as spectral energy information is through Fa Buli---and uncle Luo Gan relates to spectral filter light is filtered into arrowband monochromatic ray information, then this multispectral complex probe sub-system conversion of monochromatic ray information and warp is output as the numerary signal of reciprocity passage, sends to computer by serial ports.
2. furnace mouth radiation information according to claim 1 merges and is used for making steel the method for online terminal point control, and the image acquisition and processing software in the second step is characterized in that:
(1) creates the window of catching of fire door flame image information: call the capCreateCaptureWindow function, the generating video acquisition window;
(2) three call back functions using of Accreditation System: mistake call back function capSetCallbackOnError, state call back function capSetCallbackOnStatus and video flowing call back function capSetCallbackOnVideoStream, setting by the video flowing call back function, make the RGB color model information of the fire door flame image that collects, through obtaining the chrominance information in the corresponding hsv color model after the color model conversion, namely first the corresponding data sheet of the change over condition of two kinds of color model is built, in call back function, add the software code that the RGB color model is converted to the hsv color model; Then each two field picture that gathers is according to pixels extracted, and search position in the corresponding HSV table of this rgb value; Then the essential numerical value of all pixels of this two field picture superposeed, obtain and draw the three-dimensional plot of chromatic(ity)component; Last in this three-dimensional plot, find in the converting process certain colourimetric number corresponding reach home the time change obvious curve, and draw; What time followingly note simultaneously: get rid of the more violent variation that occurs once in a while, this is to disturb the possibility that causes larger; Select the number of pixels value to change metastable that colourity rank; Should not select background more colourity rank to occur; Destination county is bessemerized in corresponding closing under this colourity rank obvious variation, changes obviously relatively being beneficial to endpoint;
(3) call function capCaptureGetSetup arranges the frame number that acquisition window and per second gather;
(4) window and video capture device are caught in call function capDriveConnect connection, and use the capDriverGetCaps function to return the function of sampler;
(5) call function capCaptureAbort finishes video.
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| AT509866B1 (en) * | 2010-06-02 | 2011-12-15 | Siemens Vai Metals Tech Gmbh | METHOD FOR DETERMINING THE TIME OF IGNITION IN THE INFLATION METHOD |
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| CN102392095B (en) * | 2011-10-21 | 2013-09-11 | 湖南镭目科技有限公司 | Termination point prediction method and system for converter steelmaking |
| CN104531936B (en) * | 2014-12-01 | 2016-08-31 | 南华大学 | Converter molten steel carbon content On-line Measuring Method based on Flame Image Characteristics |
| CN106153550A (en) * | 2015-04-10 | 2016-11-23 | 南京理工大学 | Converter steel-smelting molten steel carbon content based on SVM online Real-time and Dynamic Detection method |
| CN106153553B (en) * | 2015-04-10 | 2019-11-15 | 南京理工技术转移中心有限公司 | The online Real-time and Dynamic Detection system of converter steel-smelting molten steel carbon content |
| CN106153551A (en) * | 2015-04-10 | 2016-11-23 | 南京理工大学 | Converter steel-smelting molten steel carbon content based on SVM online Real-time and Dynamic Detection system |
| CN106153556A (en) * | 2015-04-10 | 2016-11-23 | 南京理工大学 | Pneumatic steelmaking carbon content dynamic detection system |
| CN106148637B (en) * | 2015-04-10 | 2018-11-13 | 南京理工大学 | The pneumatic steelmaking carbon content dynamic detection system of high stable |
| CN106153552A (en) * | 2015-04-10 | 2016-11-23 | 南京理工大学 | Converter steel-smelting molten steel carbon content online Real-time and Dynamic Detection system |
| CN105678332B (en) * | 2016-01-08 | 2020-01-10 | 昆明理工大学 | Converter steelmaking end point judgment method and system based on flame image CNN recognition modeling |
| CN109711004B (en) * | 2018-12-11 | 2023-03-28 | 重庆邮电大学 | Optical fiber refractive index big data prediction method |
| CN112734722B (en) * | 2021-01-08 | 2022-09-13 | 昆明理工大学 | Flame endpoint carbon content prediction method based on improved complete local binary pattern |
| CN113718082A (en) * | 2021-08-17 | 2021-11-30 | 河南省冶金研究所有限责任公司 | Prediction system and method for judging steelmaking end point temperature of converter based on flame image |
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