CN112503550B - Intelligent control method for eliminating black smoke of emptying torch based on image analysis - Google Patents
Intelligent control method for eliminating black smoke of emptying torch based on image analysis Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23G—CREMATION FURNACES; CONSUMING WASTE PRODUCTS BY COMBUSTION
- F23G7/00—Incinerators or other apparatus for consuming industrial waste, e.g. chemicals
- F23G7/06—Incinerators or other apparatus for consuming industrial waste, e.g. chemicals of waste gases or noxious gases, e.g. exhaust gases
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23G—CREMATION FURNACES; CONSUMING WASTE PRODUCTS BY COMBUSTION
- F23G5/00—Incineration of waste; Incinerator constructions; Details, accessories or control therefor
- F23G5/50—Control or safety arrangements
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23L—SUPPLYING AIR OR NON-COMBUSTIBLE LIQUIDS OR GASES TO COMBUSTION APPARATUS IN GENERAL ; VALVES OR DAMPERS SPECIALLY ADAPTED FOR CONTROLLING AIR SUPPLY OR DRAUGHT IN COMBUSTION APPARATUS; INDUCING DRAUGHT IN COMBUSTION APPARATUS; TOPS FOR CHIMNEYS OR VENTILATING SHAFTS; TERMINALS FOR FLUES
- F23L7/00—Supplying non-combustible liquids or gases, other than air, to the fire, e.g. oxygen, steam
- F23L7/002—Supplying water
- F23L7/005—Evaporated water; Steam
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23G—CREMATION FURNACES; CONSUMING WASTE PRODUCTS BY COMBUSTION
- F23G2207/00—Control
- F23G2207/60—Additives supply
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23G—CREMATION FURNACES; CONSUMING WASTE PRODUCTS BY COMBUSTION
- F23G2209/00—Specific waste
- F23G2209/14—Gaseous waste or fumes
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Abstract
The invention discloses an intelligent control method for eliminating black smoke of an emptying torch based on image analysis, which is based on an image analysis technology and an intelligent control technology, applies a visible light and infrared light high-definition camera, an upper computer, image analysis software, a PLC (programmable logic controller) and configuration software, shoots images of a torch combustion scene through a high-definition camera, transmits the images into the upper computer, detects the quality of the black smoke in MATLAB by using the image analysis technology, transmits a calculated black smoke quality value as a feedback value to the PLC, and finally outputs a signal to control the opening of a combustion-supporting steam valve so as to realize smoke elimination. The invention obtains the opening initial value of the combustion-supporting steam electric valve by calculation, and tracks the change of the torch gas flow in time by taking the torch gas flow as a feedforward value. Based on the image analysis technology, the black smoke quality data is used as a feedback signal, and great advancement is achieved in the aspects of efficient torch combustion, energy conservation and emission reduction.
Description
Technical Field
The invention belongs to a control system of an emptying torch of a petrochemical enterprise, and the control system utilizes an image analysis technology and an intelligent control method to control the flow of combustion-supporting steam of the emptying torch, thereby eliminating black smoke, improving the combustion efficiency of the torch and reducing energy consumption.
Background
The emptying torch is widely existed in industrial systems of enterprises such as petrochemical plants, oil refineries and the like, and is a special combustion facility for ensuring the safe production of the enterprises. When equipment failure, water cut, gas cut, power failure, fire, misoperation and other production accidents occur, the production device can discharge a large amount of waste gas, and most of the waste gas belongs to inflammable, explosive and toxic gas and cannot be directly discharged into the air. Therefore, the installation of a flare system is inevitable both from a safety standpoint and from an ecological standpoint. However, the composition of the flare discharge gas has the characteristics of complexity and time-varying property, and the flowing state has the characteristics of instantaneity, abruptness, large discharge capacity and the like, so that the flare discharge gas is difficult to be fully combusted, black smoke is generated, and new environmental pollution is caused; meanwhile, toxic gas slowly sinks and accumulates, and the safety of surrounding personnel is threatened.
The mainstream smoke abatement method at present is to inject combustion-supporting steam for smoke abatement. Currently, most domestic enterprises adopt manual control, and the flow of combustion-supporting steam is regulated and controlled by experienced technicians. However, manual control often causes problems of untimely combustion and excessive or insufficient flow of combustion-supporting steam due to experience of technicians, so that it is difficult to ensure that flare gas is sufficiently combusted, black smoke is generated to pollute the environment, a great deal of waste of combustion-supporting steam is caused, and the service life of equipment is shortened. At present, a novel automatic control method for combustion-supporting steam flow is urgently needed.
The invention provides an intelligent control method for combustion of an emptying torch based on image analysis. The high-definition camera is used for obtaining a torch combustion scene image, the black smoke degree is identified by adopting an image analysis method and fed back to the PLC of the lower computer, and the controller automatically controls the opening of a combustion-supporting steam valve according to an intelligent control algorithm to eliminate smoke, so that efficient combustion of the torch is realized, environmental pollution is reduced, energy is saved, and emission is reduced.
Disclosure of Invention
The invention is based on an image analysis technology and an intelligent control technology, and realizes a novel intelligent torch smoke abatement control method by applying a visible light and infrared light high-definition camera, an upper computer, image analysis software, a PLC (programmable logic controller) and configuration software. The method takes a PLC and a control program as the core, adopts an image analysis technology to detect the quality of black smoke, and utilizes configuration software to design an upper computer operation interface and realize communication with the PLC and a camera. The invention is realized by the following technical scheme, which comprises the following steps:
the first step is as follows: high-definition camera type selection and image data acquisition.
The second step is that: torch image analysis programming and torch black smoke quality calculation.
The third step: and (4) hardware model selection design and configuration of the PLC.
The fourth step: and (5) designing a PLC control program.
The fifth step: and designing a configuration control program and a monitoring interface of the upper computer.
And a sixth step: designing and joint debugging of communication of each part.
The principle of the invention is as follows:
shooting a torch combustion scene image through a high-definition camera, transmitting the image to an upper computer, detecting the quality of black smoke in MATLAB by the upper computer through an image analysis technology, transmitting the calculated quality value of the black smoke as a feedback value to a PLC (programmable logic controller), and finally controlling the opening of a combustion-supporting steam valve through an output signal of the PLC so as to realize smoke elimination. The invention adopts a feedforward and feedback control strategy to carry out smoke abatement control. The flow, the average molecular weight and the combustion-supporting steam quantity of the flare gas are utilized, the opening initial value of the combustion-supporting steam electric valve is obtained through calculation, and the flare gas flow is used as a feedforward value to track the sudden change of the flare gas flow in time. The feedback control adopts a cascade control mode, the main variable of the system is the black smoke degree given by flare combustion scene image analysis, the auxiliary variable is the opening of a combustion-supporting steam valve, the main controller and the auxiliary controller work in series, and the output of the main controller is used as the given value of the auxiliary controller. The main and auxiliary controllers all adopt positive action mode, when the black smoke degree is increased, because the main controller is positive action so that the output is increased, namely the input of the auxiliary controller is also increased, and because the opening of the auxiliary loop is also positive action output valve is increased, the flow of combustion-supporting steam is increased to eliminate smoke of torch, and the black smoke degree is decreased and disappeared.
Drawings
FIG. 1: torch combustion scene image RGB color space separation map.
FIG. 2: and (5) carrying out torch combustion scene image black smoke extraction.
FIG. 3: and (5) communication flow of the configuration software and the PLC.
FIG. 4: and communicating the configuration software with the MATLAB.
FIG. 5: and (4) an upper computer interface of the intelligent controller.
FIG. 6: test field profiles.
FIG. 7: a graph was tested.
FIG. 8: experiment two graphs.
FIG. 9: comparing the smoke abatement before and after the first experiment and the second experiment.
Detailed Description
The following examples are given for the purpose of illustrating the present invention, and the detailed embodiments and specific procedures are given for the purpose of implementing the present invention as a premise of the technical solution of the present invention.
Example (b):
the first step is as follows: high-definition camera type selection and image data acquisition.
A cradle head camera in a DS-2TD6236 thermal imaging dual-spectrum network of Haekwover digital technology GmbH is selected but not limited, and monitoring and acquisition of the combustion scene image of the venting torch can be completed by utilizing SDK (software Development kit) software matched with a manufacturer.
The second step is that: torch image analysis programming and torch black smoke quality calculation. The method comprises the following steps:
extracting black smoke from a torch combustion scene image:
firstly, the torch combustion scene image needs to be subjected to RGB color space separation, as shown in the attached drawing 1, under the background of clear weather, the difference degree between black smoke and black scenery of a B channel in RGB three channels is the largest, and the difference degree between a flame area of an R channel and other areas is the most obvious. Therefore, we can extract the flame area through the R channel, and further obtain the black smoke area after removing the flame area through the B channel, and the specific process effect can be seen in fig. 2. The specific algorithm for extracting the black smoke from the torch combustion scene image is as follows:
a. decomposing the original image F in the RGB color space to obtain F R F G F B Three single channel maps;
b. f is to be R The flame area in the graph is binarized, the flame area is extracted, and the calculation formula is as follows:
wherein λ is f In order to extract the threshold value of the flame, the setting is required according to a specific scene, and the lambda in the scene used in the experiment f ＝200。E f Extracting a binary image of the flame area;
c. binarizing image E of flame area f Obtained by median filtering
The algorithm realizes the realization of medfilt2 through matlab median filtering function;
d. f is to be B Flame regions are removed in the figure by dividing F by pixel B And
adding F B The flame area in the figure is covered by white to obtain the figure
And extracting a black smoke region through binarization and median filtering, wherein the calculation formula is as follows:
wherein λ is s In order to extract the threshold value of black smoke, the threshold value needs to be set according to specific scenes, and lambda is used in the scene used in the experiment s ＝50。E s Continuing to filter E by median to extract a binary image of the black smoke region s Is processed to obtain
Secondly, the black smoke quality is judged by integrating factors such as area, concentration and the like:
the process is carried out by extracting black smoke region map
Extracting black smoke pixel values in the original image, and comprehensively judging the black smoke quality by the number of the black smoke pixels and the size of the black smoke pixel values. According to the control requirement, the evaluation method of the black smoke quality comprises the following steps:
wherein, F s Is a graph of passing black smoke region
All black smoke pixel values extracted by the pixel index of the black smoke region in (1),
the number of black smoke pixels can be calculated, theta is a proportionality coefficient, and the value of the algorithm is 2. Q is the final black smoke quality result.
The third step: the PLC controller hardware model selection design and configuration method comprises the following steps:
according to the smoke abatement control requirement of the venting torch, a Siemens S7-200Smart PLC is selected but not limited, a CPU SR20 module is selected to be connected with a PC and a high-definition camera, an EM AI04 module is selected to read water vapor and torch gas flow data, and an EM AQ02 module is selected to be connected with an electric regulating valve. And then carrying out PLC hardware configuration design.
The fourth step: the PLC control program design method comprises the following steps:
(1) calculating the initial value of the flow of the combustion-supporting steam according to the standard of the petrochemical industry
Writing an analog quantity data acquisition program and an electric control valve control program, and calculating an initial value of the flow of combustion-supporting steam according to the petrochemical industry standard:
wherein G is st For supporting combustion, the amount of steam (kg/h), q cm The flow rate of the torch gas (kg/h), M c Is the average molecular mass of the hydrocarbons in the exhaust gas.
(2) A single neuron adaptive PID control algorithm is adopted to accurately adjust the flow of combustion-supporting steam, and the control algorithm and the learning algorithm are as follows
The following:
ω 1 (k+1)＝ω 1 (k)+η I z(k)u(k)x 1 (k) (7)
ω 2 (k+1)＝ω 2 (k)+η P z(k)u(k)x 2 (k) (8)
ω 3 (k+1)＝ω 3 (k)+η D z(k)u(k)x 3 (k) (9)
wherein u (K) and u (K-1) are respectively output by the controller at the time and the last time, K is the proportionality coefficient of single neuron, and x i (k) Is an incremental PID controller input,
is the controller weight coefficient;
w 1 (k)、w 2 (k)、w 3 (k) and w 1 (k+1)、w 2 (k+1)、w 3 (k +1) integral, proportional and differential weight coefficients, η, at this time and the next time, respectively P 、η I 、η D Respectively, proportional, integral, and differential learning rates, and z (k) is the difference between the expected value and the output value.
The fifth step: and designing a configuration control program and a monitoring interface of the upper computer. The method comprises the following steps:
the upper computer interface is compiled by configuration software and consists of two parts: a flare process flow chart and a system operation interface. The flare process flow chart comprises a flare gas tank, a water vapor tank, a liquid separation tank, a water seal tank, a flare head, an electric regulating valve and the like, and the system operation interface comprises a manual control interface, an automatic control interface and the like. As shown in fig. 3.
And a sixth step: designing and joint debugging of communication of each part.
The method for realizing the communication of the intelligent control station based on the FameView configuration software comprises the following steps:
firstly, configuring FameView configuration software, including a communication driver, an equipment data table and an operation database. Communicating the FameView with a PLC, transmitting analog quantity data such as flare gas, combustion-supporting steam and the like, configuring S7TCP drive and starting the drive to test, wherein the communication flow is shown in figure 4; communicating the FameView with the MATLAB, transmitting an image analysis result, connecting a camera, starting an MATLAB image analysis algorithm, writing a calculation result into a FameView text file, and then executing a global script by the FameView to read data in the text file in real time, wherein the communication process is shown in figure 5.
Effects of the implementation
According to the steps, the intelligent control test for eliminating the black smoke of the emptying torch is carried out in the Huashan experimental base of Qingdao's institute of safety and engineering, and the test field is shown in figure 6. The experiment is carried out by adopting ethylene to simulate the components of the torch gas, the ethylene is used as a hydrocarbon compound, the carbon content is high, black smoke is easily generated when the combustion-supporting gas is insufficient, and various states of torch combustion can be conveniently simulated.
Starting ignition, introducing ethylene and combustion-supporting steam to enable a torch to stably burn without smoke; secondly, reducing the flow of combustion-supporting steam to generate black smoke; thirdly, starting a smoke abatement control system of the fire extinguishing torch until black smoke disappears; and fourthly, closing the valve after finishing. And collecting a torch combustion image and recording experimental data.
The experiments are respectively carried out twice, and the curves of the experiment I and the experiment II are respectively shown in attached figures 7 and 8, wherein the red curve is the black smoke degree, and the green curve is the valve opening degree. The experimental data are shown in table 1.
In the first experiment, 3 tanks of ethylene are selected as the consumption of torch gas, after the system normally operates, the opening of a steam valve is adjusted to be about 31 degrees, black smoke is generated, the neuron proportion coefficient is 0.3, the learning rate is 1, the initial value of the proportion weight coefficient is 0, and finally the learning is 11.6. As can be seen from fig. 7, as the curve, i.e., the soot level, suddenly rises to reach a peak value of 28%, the opening of the steam valve is increased, and then the soot level significantly decreases, and finally, the soot level is stabilized to about 6%, i.e., a smokeless state. The opening of the steam valve is finally stabilized to be about 37 degrees.
Experiment two selects 2 jars of ethylene as the quantity of torch gas, and the rest is unchangeable, after the system normal operating, adjusts steam valve opening degree about 23, produces black cigarette, and neuron proportionality coefficient is 0.3, and the initial value of the weight coefficient of proportion is 0, and final study is 11.5. As can be seen from the attached figure 8, the black smoke degree and the steam valve opening degree variation trend are consistent with the first experiment, wherein the maximum opening degree of the steam valve reaches 45 degrees and is finally stabilized to be near 29 degrees, the peak value of the black smoke degree reaches 18 percent and is finally stabilized to be near 5 percent.
FIG. 9 is a photograph of the smoke before and after the smoke is eliminated in the first experiment, which is about 15 seconds, and in the second experiment, which is about 11 seconds. In the field experiment, based on PLC's unloading torch intelligent control station test is successful, has improved torch combustion efficiency, satisfies the emission requirement, has practiced thrift steam.
TABLE 1
Claims (5)
1. The intelligent control method for eliminating the black smoke of the emptying torch based on image analysis is characterized by comprising the following steps: shooting a torch combustion scene image through a high-definition camera, and calculating the black smoke quality through an upper computer image analysis program; the calculated black smoke quality value is transmitted to a PLC (programmable logic controller) as a feedback value, and finally the PLC calculates and outputs a signal to control the opening of a combustion-supporting steam valve by using an intelligent control algorithm so as to realize smoke abatement control, and meanwhile, monitoring is realized on an upper computer by using configuration software; the method comprises the following steps:
the first step is as follows: selecting a high-definition camera and acquiring image data;
the second step is that: designing a torch image analysis program and calculating the quality of torch black smoke;
the third step: the hardware model selection design and configuration of the PLC controller;
the fourth step: designing a PLC control program;
the fifth step: designing a configuration control program and a monitoring interface of an upper computer;
and a sixth step: designing and joint debugging of communication of each part;
in the first step of high-definition camera model selection and image data acquisition, selecting a thermal imaging double-spectrum network mid-load pan-tilt camera, and completing monitoring and acquisition of an image of a combustion scene of an emptying torch by using matched SDK software;
the second step of torch image analysis program design and torch black smoke quality calculation is as follows:
extracting black smoke from a torch combustion scene image:
firstly, performing RGB color space separation on a torch combustion scene image, wherein the difference degree between black smoke and a black scene of a B channel in RGB three channels is the largest, and the difference degree between a flame area of an R channel and other areas is the most obvious under the background of clear weather; therefore, a flame area is extracted through the R channel, and a black smoke area is obtained after the flame area is removed through the B channel; the specific algorithm for extracting the black smoke from the flame combustion image is as follows:
a. decomposing the original image F in the RGB color space to obtain F R F G F B Three single channel maps;
b. f is to be R The flame area in the graph is binarized, the flame area is extracted, and the calculation formula is as follows:
wherein λ is f For extracting the threshold value of the flame, the lambda in the used scene is set according to a specific scene f ＝200；E f Extracting a binary image of the flame area;
c. binarizing image E of flame area f Obtaining E by median filtering f Realizing the realization of the filtering function medfilt2 by matlab median;
d. f is to be B Flame regions are removed in the figure by dividing F by pixel B And
adding F B The flame area in the figure is covered by white to obtain the figure
And extracting a black smoke region through binarization and median filtering, wherein the calculation formula is as follows:
wherein λ is s In order to extract the threshold value of black smoke, the used scene needs to be set according to the specific scene s ＝50；
E s Continuing to filter E by median to extract a binary image of the black smoke region s Is processed to obtain E s ；
And secondly, judging the quality of the black smoke by integrating factors such as area, concentration and the like:
the process is carried out by extracting black smoke region map
Extracting black smoke pixel values in the original image, and comprehensively judging the black smoke quality through the number of the black smoke pixels and the size of the black smoke pixel values; according to the control requirement, the evaluation method of the black smoke quality comprises the following steps:
wherein, F s Is a graph of passing black smoke region
All black smoke pixel values extracted by the pixel index of the black smoke region in (1),
the number of black smoke pixels can be calculated, θ is the scaling factor, and Q is the final black smoke quality result.
2. The intelligent control method for eliminating the black smoke of the evacuated torch based on the image analysis as claimed in claim 1, wherein the third step is a PLC (programmable logic controller) hardware model selection design and configuration method, and the method comprises the following steps:
according to the smoke abatement control requirement of the venting torch, Siemens S7-200Smart PLC is selected, a CPU SR20 module is selected to be connected with a PC and a high-definition camera, an EMAI04 module is selected to read the flow data of combustion-supporting steam and torch gas, and an EMAQ02 module is selected to be connected with an electric regulating valve; and then carrying out PLC hardware configuration design.
3. The intelligent control method for eliminating the black smoke of the evacuated torch based on the image analysis as claimed in claim 1, wherein the fourth step of PLC control program design comprises the following steps:
(1) calculating the initial value of the flow of the combustion-supporting steam according to the standard of the petrochemical industry
Writing an analog quantity data acquisition program and an electric control valve control program, and calculating an initial value of the flow of combustion-supporting steam according to the petrochemical industry standard:
wherein G is st To assist the combustion of steam, q cm For the amount of flare gas flow, M c Is the average molecular mass of hydrocarbons in the exhaust gas;
(2) a single neuron self-adaptive PID control algorithm is adopted to accurately adjust the flow of combustion-supporting steam, and the control algorithm and the learning algorithm are as follows:
ω 1 (k+1)＝ω 1 (k)+η I z(k)u(k)x 1 (k) (7)
ω 2 (k+1)＝ω 2 (k)+η P z(k)u(k)x 2 (k) (8)
ω 3 (k+1)＝ω 3 (k)+η D z(k)u(k)x 3 (k) (9)
wherein u (K) and u (K-1) are respectively output by the controller at the time and the last time, K is the proportionality coefficient of single neuron, and x i (k) Is an incremental PID controller input,
is the controller weight coefficient;
w 1 (k)、w 2 (k)、w 3 (k) and w 1 (k+1)、w 2 (k+1)、w 3 (k +1) integral, proportional and differential weight coefficients, η, at this time and the next time, respectively P 、η I 、η D Respectively, proportional, integral, and differential learning rates, and z (k) is the difference between the expected value and the output value.
4. The intelligent control method for eliminating the black smoke of the evacuated torch based on the image analysis as claimed in claim 1, wherein in the fifth step, the upper computer interface is written by configuration software and comprises two parts: a torch process flow chart and a system operation interface; the flare process flow chart comprises a flare gas tank, a water vapor tank, a liquid separating tank, a water seal tank, a flare head and an electric regulating valve, and the system operation interface comprises a manual control interface and an automatic control interface.
5. The intelligent control method for eliminating the black smoke of the evacuated torch based on the image analysis as claimed in claim 1, wherein in the design and joint debugging of the communication of each part in the sixth step, firstly, FameView configuration software is configured, and the configuration software comprises a communication driver, an equipment data table and an operation database; communicating the FameView with a PLC, transmitting torch gas and combustion-supporting steam analog quantity data, configuring an S7TCP driver, starting the driver, and testing;
communicating the FameView with the MATLAB, transmitting an image analysis result, connecting a camera, starting an MATLAB image analysis algorithm, writing a calculation result into a FameView text file, and then executing a global script by the FameView to read data in the text file in real time.
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