CN108800916A - A kind of grate-cooler thickness of feed layer Model Predictive Control Algorithm based on machine vision - Google Patents
A kind of grate-cooler thickness of feed layer Model Predictive Control Algorithm based on machine vision Download PDFInfo
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- CN108800916A CN108800916A CN201810754696.5A CN201810754696A CN108800916A CN 108800916 A CN108800916 A CN 108800916A CN 201810754696 A CN201810754696 A CN 201810754696A CN 108800916 A CN108800916 A CN 108800916A
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- thickness
- feed layer
- grate
- cooler
- machine vision
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27B—FURNACES, KILNS, OVENS, OR RETORTS IN GENERAL; OPEN SINTERING OR LIKE APPARATUS
- F27B7/00—Rotary-drum furnaces, i.e. horizontal or slightly inclined
- F27B7/20—Details, accessories, or equipment peculiar to rotary-drum furnaces
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27B—FURNACES, KILNS, OVENS, OR RETORTS IN GENERAL; OPEN SINTERING OR LIKE APPARATUS
- F27B7/00—Rotary-drum furnaces, i.e. horizontal or slightly inclined
- F27B7/20—Details, accessories, or equipment peculiar to rotary-drum furnaces
- F27B7/42—Arrangement of controlling, monitoring, alarm or like devices
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
- F27D2019/0096—Arrangements of controlling devices involving simulation means, e.g. of the treating or charging step
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Feedback Control In General (AREA)
- Incineration Of Waste (AREA)
Abstract
The present invention relates to a kind of grate-cooler thickness of feed layer model predictive control method based on machine vision, includes the following steps；Step 1: software and hardware is disposed；Step 2: measuring thickness of feed layer using machine vision technique；Step 3: off-line identification；Step 4: On-line Control；The present invention directly measures thickness of feed layer by machine vision, avoids the problems such as indirect scale levies the inaccuracy brought, interference is big；Model Predictive Control Algorithm can stability contorting thickness of feed layer, keep the bed of material be uniformly distributed, improve heat exchange efficiency, is energy saving.Algorithm deployment is convenient, it is simple to safeguard, new approach is opened for the measurement and control of grate-cooler thickness of feed layer.
Description
Technical field
The grate-cooler thickness of feed layer Model Predictive Control Algorithm based on machine vision that the present invention relates to a kind of.
Background technology
Grate-cooler is the key equipment in manufacture of cement, is mainly made of grate plate and grate.High temperature chamotte is transported by rotary kiln
It send to Cooler Bed Driving, grate plate pushes clinker to spread out, travels forward along grate and push clinker, and grate plate return later is transported again
Clinker and so on moves.Cooling wind below grate is allowed to cool across clinker, and a cooling wind part is by thermosetting Secondary Air
Into rotary kiln, a part of wind enters device for generating power by waste heat.
The thickness of the grate-cooler bed of material is most important to cooling machine equipment, and the bed of material is too thin, then the time that cooling wind passes through the bed of material
It is too short, short circuit is formed, it is fast by the cooling wind wind speed of the bed of material, air quantity is big, it will also result in the reduction of secondary air temperature；The bed of material is too thick,
Cooling wind is impermeable and pressure under comb is made to increase, and then part occurs and blow through, and cooling wind is all blown away from blowing through position, other positions
" Red River " phenomenon is easily formed, grate plate hot-spot is caused, while thickness of feed layer increases so that lower pressure of combing increases therewith, for heat
The high pressure cold air of exchange is obstructed, and is reduced into comb room air quantity, causes secondary air temperature to reduce, heat exchange efficiency can also reduce.Therefore
The control of grate-cooler, it is most important that the state for making thickness of feed layer thickness as possible and grate boil generally in one.Thickness of feed layer
Adjusted by the speed (hereinafter referred to as comb speed) that grate plate moves, speed of combing is very fast, clinker quickly through grate, thickness of feed layer will under
Drop；Combing, speed is slower, and clinker is accumulated in grate, and thickness of feed layer will increase, and certain integral characteristic is presented in thickness of feed layer,
It can increase always.In addition, thickness of feed layer is also influenced by cooling wind air quantity, air quantity is big, then clinker is cooled soon, clinker viscosity
Reduce so that the bed of material is easy to be pushed out grate-cooler, and thickness of feed layer reduces, and air quantity is small, and clinker is cooled slowly, clinker viscosity compared with
It is high so that clinker is not easy to be pushed out, and thickness of feed layer increases.
Grate-cooler compares closing since internal environment is more severe, therefore is difficult the bed of material thickness inside directly measuring
Degree.How to judge that suitable thickness of feed layer is always the difficult point of grate-cooler optimization.It is industrial at present generally with thickness of feed layer phase
Indirect amount (comb lower pressure, secondary air temperature, grate-cooler hydraulic pressure signal etc.) the characterization thickness of feed layer closed.Traditional control strategy is thought
Amount meets linear relationship with thickness of feed layer indirectly, establishes the model indirectly between amount and speed of combing, by Model Predictive Control or
Control is implemented in fuzzy control.It is more complicated with the model of thickness of feed layer due to measuring indirectly, non-linear behavior is showed, and indirectly
Amount and thickness of feed layer relationship is affected by operating mode, is caused prodigious puzzlement to thickness of feed layer optimal control, is led to current market
Upper grate-cooler optimization cannot achieve benefit promotion, and need human intervention when operating mode changes.
Invention content
For overcome the deficiencies in the prior art, the present invention provides a kind of grate-cooler thickness of feed layer model based on machine vision
Predictive control algorithm, the algorithm measure thickness of feed layer in real time using machine vision technique, establish thickness of feed layer between speed of combing
Relationship model, while secondary air temperature and comb are pushed power as bound variable, pass through Model Predictive Control Algorithm and solves speed of combing, shape
At close loop control circuit.The algorithm can effectively solve the problems, such as that the indirect scale sign of grate-cooler thickness of feed layer is inaccurate, be the bed of material
Thickness provides precise information information, may finally reach stability contorting thickness of feed layer, promotes clinker quality, improves heat exchange effect
Rate saves the purpose of energy consumption.
Technical solution is used by the present invention solves above-mentioned technical problem：A kind of grate-cooler bed of material based on machine vision
Thickness model predictive control algorithm, includes the following steps；
(1) the mounting industrial camera above the side of grate-cooler bed of material exit；It installs and marks in grate-cooler bed of material exit side
Ruler, machine vision software is disposed in engineer station, and receiving industrial camera by industry communications protocol (TCP/IP, OPC etc.) transmits
Two-dimensional image data；The triumphant expert's optimization system software for containing independent research in Nanjing is installed；
(2) ROI region delimited, RGB image is changed into gray-value image, extracted by smooth, filtering, edge detection ripe
The edge contour of material can calculate the thickness of the bed of material by carrying out ratiometric conversion with the size of scale；
(3) thickness of feed layer being calculated is sent to the ends OPC Server by OPC, the history for recording thickness of feed layer becomes
Gesture；Speed of combing, the historical trend of secondary air temperature, lower pressure of combing are recorded simultaneously；Regard comb speed as manipulating variable (MV), thickness of feed layer is made
For controlled variable (CV), secondary air temperature and pushing power of combing establish the integral mould of an input three output as bound variable (CCV)
Type carries out System Discrimination using expert's optimization system；
(4) model for utilizing step (3) identification to obtain, implementation model PREDICTIVE CONTROL (MPC) in expert's optimization system,
MPC algorithm executes control by model prediction, rolling optimization, feedback compensation period；
Preferably, in the controlling cycle in each described step (4), rolling optimization is carried out to object function.
Preferably, feedback compensation is carried out to the calculated predicted value of the rolling optimization.
Compared with the prior art, the advantages of the present invention are as follows：
First, the present invention is by the algorithm of machine vision, and uses one camera, and the mode that scale compares measures grate-cooler
It is complicated, of high cost to avoid the processes such as common binocular camera three-dimensional modeling measurement and infrared survey in the market for thickness of feed layer
Measurement method.
Secondly, the present invention directly measures thickness of feed layer, and the model avoided when tradition is levied with indirect scale is inaccurate, environment
The influence of the uncertain factors such as interference so that measurement result is more accurate.
Again, the present invention carries out Model Predictive Control by the thickness of feed layer directly measured, thickness of feed layer can be made to keep
It is unlikely to the state for occurring locally erupting again thick as possible, the bed of material is maintained at best boiling form, makes secondary air temperature and comb to push
Power meets process requirements, improves heat exchange efficiency, reaches the target of quality environmental protection two-win.
Finally, it is not necessary to modify field devices in addition to industrial camera by the present invention, therefore dispose conveniently, which is grate-cooler material
Layer thickness is measured opens new approach with optimal control.
Description of the drawings
Fig. 1 is the industrial camera scheme of installation of the present invention；
Fig. 2 is Nanjing Kai Sheng expert's optimization system surface chart in the present invention；
Fig. 3 a are the original image of the image processing process figure of the present invention；
Fig. 3 b are the ROI region image of the image processing process figure of the present invention；
Fig. 3 c are gray processing treated the image of the image processing process figure of the present invention；
Fig. 3 d are the image after the extraction profile of the image processing process figure of the present invention；
Fig. 4 is communication and the control flow chart of the present invention.
Specific implementation mode
A kind of grate-cooler thickness of feed layer model predictive control method based on machine vision, includes the following steps；
Step 1: software and hardware is disposed；The mounting industrial camera above the side of grate-cooler bed of material exit, is protected by protective cover
Camera is interfered from high temperature；Scale is installed in grate-cooler bed of material exit side, for demarcating thickness of feed layer, industrial camera and mark
Industrial camera is installed on comb by the scheme of installation of ruler as shown in Figure 1, in order to collect bed of material image inside accurate grate-cooler
Above the side of cold exit, in order to observe grate-cooler internal state with large viewing, additionally by mounting industrial protective cover
Avoid industrial camera by the interference of high temperature and dust etc.；Installation scale is exported in the grate-cooler bed of material, such as blue vertical bar institute in Fig. 1
Show；It is generally believed that thickness of feed layer is linear with scale, by the bed of material image information of acquisition, after image procossing with mark
Ruler conversion can be obtained practical thickness of feed layer；Machine vision software is disposed in engineer station, passes through industry communications protocol (TCP/
IP, OPC etc.) receive the two-dimensional image data that industrial camera transmits；It is soft that the triumphant expert's optimization system for containing independent research in Nanjing is installed
Part, the industrial common control algolithm such as the Integrated Simulation identification, Model Predictive Control Algorithm, the interface of expert's optimization system
As shown in Figure 2；Nanjing Kai Sheng expert's optimization system covers the controls such as Model Predictive Control, fuzzy control, generalized predictive control
Algorithm has the script technologies such as smooth, filtering, can (the manipulated variable MV in such as figure, controlled volume CV become with displaying live view controlling curve
Gesture), facilitate adjustment control parameter, setting variate-value etc..When actually using Model Predictive Control Algorithm, by manipulating variable MV,
Controlled variable CV etc. is configured to related OPC variables, and system optimization control can be realized in incorporating parametric configuration feature.
Step 2: measuring thickness of feed layer using machine vision technique；Since top temperature is higher in grate-cooler, top section
Clinker is in molten condition, therefore is in crimson color in the picture, and the insufficiently burnt clinker of bottom is in furvous；Therefore can pass through
ROI region delimited, RGB image is changed into gray-value image, the edge wheel of clinker is extracted by smooth, filtering, edge detection
Exterior feature, image processing process are shown in Fig. 3 (a~d)；Clinker can be approximately considered, and equal proportion is distributed in the camera with scale, therefore is passed through
The thickness of the bed of material can be calculated by carrying out ratiometric conversion with the size of scale；Grate-cooler thickness of feed layer image procossing includes mainly four
Part.First, original image collecting part, original figure are acquired by industrial camera, and is reached and be mounted on by industry communications protocol
The machine vision software of engineer station；Second is that the two-dimensional image information of acquisition is carried out analyzing processing by machine vision software, obtain
ROI region；Third, the gray processing processing procedure of ROI region；Fourth, extracting bed of material profile from gray level image.Obtain bed of material profile
It can convert to obtain practical thickness of feed layer with engineer's scale later.
Step 3: off-line identification；The thickness of feed layer being calculated is sent to the ends OPCServer by OPC, records the bed of material
The historical trend of thickness；Speed of combing, the historical trend of secondary air temperature, lower pressure of combing are recorded simultaneously；Regard comb speed as manipulating variable
(MV), it is defeated to establish one as bound variable (CCV) as controlled variable (CV), secondary air temperature and pushing power of combing for thickness of feed layer
The integral model for entering three outputs carries out System Discrimination using expert's optimization system.
Step 4: On-line Control；The model recognized using step 3, implementation model is predicted in expert's optimization system
It controls (MPC), MPC algorithm executes control by model prediction, rolling optimization, feedback compensation period.
In each controlling cycle, rolling optimization is carried out to object function；The objective of institute's optimality criterion is：It should protect
It demonstrate,proves thickness of feed layer and is in bound restriction range as close as setting value, secondary air temperature and lower pressure of combing, while to avoid
Speed of combing acute variation.
The calculated predicted value of rolling optimization is subjected to feedback compensation；Since there are model mismatch, environment in actual motion
The X factors such as interference, may deviate from actual value by the predicted value that rolling optimization calculates, if utilizing letter in real time not in time
Breath carries out feedback compensation, and the optimization of next step will establish on the basis of inaccurate model prediction, with the progress of process, prediction
Output is possible to increasingly deviate reality output.
The communication part of the present invention relates generally to industrial camera and machine vision software, machine vision software and OPC
Communication between Server, OPC Client and OPC Server.Standard is followed between industrial camera and machine vision software
Industry communications protocol, such as TCP/IP, OPC DA, OPC UA etc.；Machine vision software and OPC Client and OPC Server it
Between be all made of OPC agreements and communicated.And control flow part is the prediction mould established between manipulating variable and controlled volume first
Type, this process can recognize to obtain by off-line model；Followed by each controlling cycle, controlled system is optimized,
It should ensure that thickness of feed layer maintains thick as possible and equally distributed state, avoid the frequent strenuous vibration of speed of combing again；It is finally
To predicting the feedback compensation of output valve, the problem of decline with the control accuracy for making up environmental disturbances, prediction model distortion is brought.
Grate-cooler compares closing since internal environment is more severe, therefore is difficult the bed of material thickness inside directly measuring
Degree.How to judge that suitable thickness of feed layer is always the difficult point of grate-cooler optimization.It is industrial at present generally with thickness of feed layer phase
Indirect amount (comb lower pressure, secondary air temperature, grate-cooler hydraulic pressure signal etc.) the characterization thickness of feed layer closed.Traditional control strategy is thought
Amount meets linear relationship with thickness of feed layer indirectly, establishes the model indirectly between amount and speed of combing, by Model Predictive Control or
Control is implemented in fuzzy control.It is more complicated with the model of thickness of feed layer due to measuring indirectly, non-linear behavior is showed, and indirectly
Amount and thickness of feed layer relationship is affected by operating mode, is caused prodigious puzzlement to thickness of feed layer optimal control, is led to current market
Upper grate-cooler optimization cannot achieve benefit promotion, and need human intervention when operating mode changes.
The grate-cooler thickness of feed layer Model Predictive Control Algorithm based on machine vision that the invention discloses a kind of.The algorithm is adopted
The two dimensional image inside grate-cooler is shot with industrial camera, and is transmitted to machine vision software.Machine vision software is from X-Y scheme
Obtain ROI region as in, bed of material edge contour extracted by gray processing, smooth, filtering, edge detection, and with scale in image
It converts, obtains bed of material actual (real) thickness.Become thickness of feed layer as controlled volume, secondary air temperature and pushing power of combing as constraint
Amount, speed of combing are used as manipulating variable, system model are obtained by off-line calculation.The system model established offline is introduced into Nanjing
The triumphant expert's optimization system for containing independent research implements on-time model PREDICTIVE CONTROL.Thickness of feed layer is directly measured by machine vision,
Avoid the problems such as indirect scale levies the inaccuracy brought, interference is big.Model Predictive Control Algorithm being capable of stability contorting bed of material thickness
Degree keeps the bed of material to be uniformly distributed, improve heat exchange efficiency, is energy saving.Algorithm deployment is convenient, it is simple to safeguard, is grate-cooler material
The measurement and control of layer thickness open new approach.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, it will be understood by those of skill in the art that it still can be right
Technical solution recorded in foregoing embodiments is modified, or is replaced on an equal basis to which part technical characteristic；And this
A little modification or replacements, the spirit and model of various embodiments of the present invention technical solution that it does not separate the essence of the corresponding technical solution
It encloses.
Claims (3)
1. a kind of grate-cooler thickness of feed layer Model Predictive Control Algorithm based on machine vision, includes the following steps；
(1) the mounting industrial camera above the side of grate-cooler bed of material exit；Scale is installed in grate-cooler bed of material exit side,
Engineer station disposes machine vision software, and the two dimension that industrial camera transmits is received by industry communications protocol (TCP/IP, OPC etc.)
Image data；The triumphant expert's optimization system software for containing independent research in Nanjing is installed；
(2) ROI region delimited, RGB image is changed into gray-value image, clinker is extracted by smooth, filtering, edge detection
Edge contour can calculate the thickness of the bed of material by carrying out ratiometric conversion with the size of scale；
(3) thickness of feed layer being calculated is sent to the ends OPC Server by OPC, records the historical trend of thickness of feed layer；
Speed of combing, the historical trend of secondary air temperature, lower pressure of combing are recorded simultaneously；Regard comb speed as manipulating variable (MV), thickness of feed layer conduct
Controlled variable (CV), secondary air temperature and pushing power of combing establish the integral mould of an input three output as bound variable (CCV)
Type carries out System Discrimination using expert's optimization system；
(4) step (3) is utilized to recognize obtained model, implementation model PREDICTIVE CONTROL (MPC) in expert's optimization system, MPC are calculated
Method executes control by model prediction, rolling optimization, feedback compensation period.
2. a kind of grate-cooler thickness of feed layer Model Predictive Control Algorithm based on machine vision according to claim 1,
It is characterized in that, in the controlling cycle in each described step (4), rolling optimization is carried out to object function.
3. a kind of grate-cooler thickness of feed layer Model Predictive Control Algorithm based on machine vision according to claim 2,
It is characterized in that, feedback compensation is carried out to the calculated predicted value of the rolling optimization.
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| CN201810754696.5A CN108800916B (en) | 2018-07-11 | 2018-07-11 | A kind of grate-cooler thickness of feed layer Model Predictive Control Algorithm based on machine vision |
| PCT/CN2019/088672 WO2020010937A1 (en) | 2018-07-11 | 2019-05-27 | Machine vision-based model predictive control algorithm for material layer thickness of grate cooler |
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| CN201810754696.5A CN108800916B (en) | 2018-07-11 | 2018-07-11 | A kind of grate-cooler thickness of feed layer Model Predictive Control Algorithm based on machine vision |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110595397A (en) * | 2019-10-10 | 2019-12-20 | 南京凯盛国际工程有限公司 | Grate cooler working condition monitoring method based on image recognition |
| WO2020010937A1 (en) * | 2018-07-11 | 2020-01-16 | 南京凯盛国际工程有限公司 | Machine vision-based model predictive control algorithm for material layer thickness of grate cooler |
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| CN103363812A (en) * | 2013-07-03 | 2013-10-23 | 中国科学院沈阳自动化研究所 | Control method of cement clinker grate cooler |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020010937A1 (en) * | 2018-07-11 | 2020-01-16 | 南京凯盛国际工程有限公司 | Machine vision-based model predictive control algorithm for material layer thickness of grate cooler |
| CN110595397A (en) * | 2019-10-10 | 2019-12-20 | 南京凯盛国际工程有限公司 | Grate cooler working condition monitoring method based on image recognition |
| WO2021068497A1 (en) * | 2019-10-10 | 2021-04-15 | 南京凯盛国际工程有限公司 | Grate cooler working state monitoring method based on image recognition |
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| CN108800916B (en) | 2019-07-05 |
| WO2020010937A1 (en) | 2020-01-16 |
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