Automatic monitoring of the melt flow leaving the waste heat boiler

Abstract

The present invention enables automatic monitoring of a melt stream exiting a waste heat boiler based on optical information. A processor is used to read at least one still imaging video sequence that includes digital image frames, and the digital image frames include an area to be inspected that represents at least a portion of the melt stream exiting the waste heat boiler. The processor is used to identify areas distinguishable based on color and / or intensity information in the area to be inspected. The processor is used to determine monitored flow characteristics of the melt stream based on the identified distinguishable areas.

Description

Technical Field

[0001] The present invention relates to the automatic monitoring of a melt stream leaving a waste heat boiler based on optical information. Background Art

[0002] A waste heat boiler has two main functions: chemical recovery, and the recovery of the combustion heat generated in the process as steam and electric energy. The chemical melt flowing out from the bottom of the waste heat boiler furnace through the melt nozzle contains sodium sulfide, sodium carbonate, and sodium sulfate.

[0003] The melt stream leaving the waste heat boiler has been monitored by training a monitoring camera, for example, on the area of the melt removal at the melt nozzle, and the operator has been able to use the images generated by the camera to monitor the melt stream and the changes therein. For example, this image information can be used to detect blockages and cleaning requirements. In addition, a sudden large inflow of melt that can cause an explosion of the melt in the dissolution tank can also be detected in the images generated by the camera. As described in Patent US10,012,616B2, a large inflow of melt can also be detected by observing the acoustic emission in the dissolution tank.

[0004] It has been possible to observe the amount of the melt stream by, for example, monitoring the concentration and amount of the green liquor leaving the dissolution tank. These quantitative data are integrated over a long period and include the total amount of the melt flowing through all the melt nozzles.

[0005] The prior art cannot monitor the melt stream leaving the waste heat boiler quantitatively in real time. The largest waste heat boilers can have more than 10 melt nozzles, and their flow rates may vary based on the combustion conditions.

[0006] However, it is clearly necessary to detect the differences and changes in the nozzle-specific melt stream of the waste heat boiler combustion event, as it helps to optimize chemical recovery and optimize power generation, and helps to predict a large inflow of melt that can damage the structure of the waste heat boiler equipment and is dangerous. Summary of the Invention

[0007] According to a first feature of the present invention, a method for automatically monitoring a melt stream leaving a waste heat boiler is proposed. The method includes the following steps:

[0008] Using a processor to read at least one still imaging video sequence, the still imaging video sequence including digital image frames, each digital image frame including at least one inspected area representing at least a part of the melt stream leaving the waste heat boiler;

[0009] Using a processor to identify at least one area distinguishable based on color and / or intensity information in the at least one inspected area; and

[0010] Determine at least one monitored flow characteristic of the melt flow based on the at least one distinguishable region identified by the processor.

[0011] According to a second aspect of the invention, there is provided a computer program product comprising at least one computer-readable storage medium having a set of instructions thereon which, when executed by one or more processors, cause a computer device to perform the method according to the first aspect.

[0012] According to a third aspect of the invention, there is provided a computer device comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are arranged to, with the at least one processor, cause the computer device to:

[0013] Read at least one still imaging video sequence comprising digital image frames, each digital image frame including at least one inspected region representative of at least a portion of a melt flow exiting a waste heat boiler;

[0014] In the at least one inspected region, identify at least one region distinguishable based on color and / or intensity information; and

[0015] Determine at least one monitored flow characteristic of the melt flow based on the at least one distinguishable region identified.

[0016] In one embodiment of the invention, a first inspected region represents a melt flow flowing into a melt nozzle having a known cross-sectional dimension, the first distinguishable region includes an edge of the surface of the melt flow, and the monitored flow characteristic includes at least one of: the melt flow width, or the melt flow height relative to the bottom of the melt nozzle, the melt flow width and / or the melt flow height being determined by the processor based on the identified edge of the surface of the melt flow.

[0017] In one embodiment of the invention, the monitored flow characteristic further includes the cross-sectional surface area of the melt flow, the cross-sectional surface area of the melt flow being determined by the processor based on the cross-sectional dimension of the melt nozzle and the determined melt flow width and / or height.

[0018] In one embodiment of the invention, a second distinguishable region includes a region moving in the flow direction of the melt flow, and the monitored flow characteristic further includes the flow velocity of the melt flow, the flow velocity being determined by the processor based on the position change of the second distinguishable region between at least two image frames of the video sequence.

[0019] In one embodiment of the present invention, the monitored flow characteristics further include the volumetric flow rate of the melt flow, which is determined based on the cross-sectional surface area and the flow velocity of the melt flow determined using a processor.

[0020] In one embodiment of the present invention, the monitored flow characteristics further include the mass flow rate of the melt flow, which is determined using a processor based on the melt flow density and the determined volumetric flow rate.

[0021] In one embodiment of the present invention, the second inspected area represents the melt flow exiting the melt nozzle, and the steam jet is directed to this melt flow to break the melt flow into droplets. The third distinguishable area includes at least some of the droplets, and the monitored flow characteristics further include the droplet distribution characteristics of at least some of the droplets.

[0022] In one embodiment of the present invention, a processor is used to read at least two still imaging video sequences imaged from different observation points of the melt flow to obtain the values of the monitored flow characteristics at the different observation points, and the processor is used to compare the values of the monitored flow characteristics thus obtained.

[0023] In one embodiment of the present invention, the area moving in the flow direction of the melt flow includes areas distinguishable due to deviations in the shape, composition, and / or temperature of the melt flow.

[0024] In one embodiment of the present invention, the width and / or height of the melt flow is determined based on the pixel number dimensions of the first inspected area.

[0025] In one embodiment of the present invention, at least one of the monitored flow characteristics of the determined melt flow is used to control the waste heat boiler.

[0026] With the solution according to the present invention, the melt flow exiting the waste heat boiler can be automatically monitored. At least some solutions according to the present invention make it possible to detect differences and changes in the nozzle-specific melt flow of the waste heat boiler combustion event, thus contributing to optimizing chemical recovery and optimizing power generation, and contributing to predicting a large inflow of melt that will damage the structure of the waste heat boiler equipment and is dangerous. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention will be described below with reference to the accompanying drawings and by means of the attached exemplary embodiments, in which

[0028] Figure 1A The system according to the present invention is schematically shown;

[0029] Figure 1B The waste heat boiler according to the present invention is schematically shown;

[0030] Figure 2Schematically shows a device according to the present invention;

[0031] Figure 3 Schematically describes a method according to the present invention;

[0032] Figure 4A Is a cross-sectional view of a V-shaped melt nozzle according to the present invention;

[0033] Figure 4B Is a cross-sectional view of a U-shaped melt nozzle according to the present invention;

[0034] Figure 5 Schematically shows a digital image frame of a video sequence according to the present invention;

[0035] Figure 6 Schematically shows a camera arrangement according to the present invention; and

[0036] Figures 7A to 7C Depicts some monitored flow characteristics of a melt flow obtained according to the present invention.

[0037] The drawings always use the same reference numerals to refer to equivalent elements. Detailed Description of the Invention

[0038] The following is a detailed description of embodiments of the present invention, examples of which are described in the accompanying drawings. The following detailed description, together with the drawings, is intended to describe examples and not to show the only way to implement the provided examples or utilize them. The following focuses on example activities and a series of steps / operations for assembling and using the examples. However, the same or equivalent activities and steps / operations can also be achieved by other examples.

[0039] As an example, Figure 1A Depicts components of a system 100 according to the present invention, in which different embodiments of the present invention can be implemented. Figure 1A Examples of show: a waste heat boiler 110; an instrument 120 for generating a video sequence including digital image frames of a melt flow leaving the waste heat boiler 110; a computer device 200 for automatically monitoring the melt flow leaving the waste heat boiler 110; and an instrument 130 for further processing.

[0040] The waste heat boiler 110 is a steam boiler designed to burn black liquor and has a dual capacity to act as a chemical recovery device and a unit suitable for generating high-pressure steam and energy in a pulp mill. In combination Figure 1B The waste heat boiler 110 is described in more detail.

[0041] The apparatus 120 for generating a video sequence may include, for example, any suitable camera (such as a monitoring camera) that an operator uses to monitor the melt flow from a control room. The present invention does not require a separate dedicated camera, so existing and already installed cameras can be used. In one example, the camera is a visible area color camera.

[0042] Figure 6 The camera arrangement 600 according to the present invention is schematically shown laterally. In Figure 6 it, the melt spout 601 (corresponding to, for example, Figure 1B the melt spout 117) is positioned to slope downward such that the melt stream 602 eventually terminates in a dissolution tank ( Figure 6 not shown in). Figure 6 A steam nozzle 606 is also depicted, with which a steam jet is directed onto the melt stream 602 to break it into droplets 603. In addition, Figure 6 a first camera 604 mounted above the melt spout 601 is shown such that the area it images simultaneously covers the melt stream flowing into the melt spout 601 and the melt stream flowing out of the melt spout 601. As described below, this one camera 604 can alternatively be replaced by two cameras, one imaging the melt stream flowing into the melt spout 601 and the other imaging the melt stream flowing out of the melt spout 601. In addition, Figure 6 a second camera 605 is shown, which is positioned such that the area it images covers the droplet formation 603 caused by the steam blower 606.

[0043] Figure 4A A cross-sectional view of a V-shaped melt spout 410 according to the present invention (corresponding to, for example, Figure 1B the melt spout 117 and / or Figure 6 the melt spout 601) is shown. The top surface of the melt spout is represented by the line 411 in Figure 4A and the line 412 represents the top surface of the melt stream flowing into the melt spout 410. The surface 413 represents the cross-sectional area of the melt stream in the melt spout 410, which is determined as the monitored flow characteristics of the melt stream, as described below in conjunction with Figure 2 description.

[0044] Figure 4B Furthermore, a cross-sectional view of a U-shaped melt spout 420 according to the present invention (corresponding to, for example, Figure 1B the melt spout 117 and / or Figure 6 the melt spout 601) is shown. The top surface of the melt spout is represented by Figure 4BThe line 421 in [the figure] is shown, and the line 422 represents the top surface of the melt flow flowing into the melt nozzle 420. The surface 423 represents the cross-sectional surface area of the melt flow in the melt nozzle 420, which is determined as the monitored flow characteristics of the melt flow, as described below in connection with Figure 2 described.

[0045] In Figure 2 the description of [the figure], the computer device 200 for automatically monitoring the melt flow leaving the waste heat boiler is described in more detail.

[0046] The instrument 140 for further processing may include, for example, a workstation computer, a server computer, a database, and / or a communication connection, etc., which can be used to implement or initiate various further processing measures. The said further processing measures may include, for example: sending a command to the automatic device for cleaning the melt nozzle to clean the melt nozzle, making adjustments to prevent changes so as to prevent, for example, a large amount of melt from flowing in and / or making adjustments to adjust fuel injection and / or air injection, and / or adjusting the local conditions in the combustion chamber of the waste heat boiler based on the difference in the flow rates flowing out of different melt nozzles to achieve a laterally balanced combustion event.

[0047] Figure 1B The waste heat boiler 110 according to the present invention is schematically shown. Figure 1B The waste heat boiler 110 includes, for example, a rectangular bottom 111, four furnace walls 112 1 -112 4 (The rear wall 112 Figure 1B is shown in [the figure] 1 and the front wall 112 2 ), a furnace chamber 113, a nozzle 114, and a heat transfer section 115. The bottom part of the furnace chamber 113 has a vent 116 and a melt nozzle 117. The heat transfer section 115 includes, for example, an economizer 115A, a reheating pipe 115B, and a superheater 115C.

[0048] In addition, Figure 1B an evaporator 151 for evaporating excess water from the black liquor and a liquid injector 152 for injecting the black liquor into the waste heat boiler 110 after evaporation are depicted. In other words, with the liquid injector 152, by means of the correct droplet size and corrected alignment, an attempt is made to form a preferred type of heap at the bottom 111 of the waste heat boiler 110.

[0049] The furnace walls 112 of the furnace chamber 113 of the waste heat boiler 110 1 -112 4 are generally composed of vertical tubes ( Figure 1Bis made (not shown in the figure) and is hermetically joined together to form a uniform cooking duct. The water flowing in the duct is vaporized by the heat energy released in the furnace 113, and finally the saturated water vapor mixture generated in the cycle is guided to the steam drum ( Figure 1B not shown in the figure), where the steam and water are separated and the steam is guided to the superheater 115C for superheating. When the waste heat boiler 110 operates, its bottom 111 is completely covered with a layer of melt, and an attempt is made to form a controlled pile containing inorganic materials and coke at the bottom of the boiler. The combustion of the organic matter in the black liquor and the chemical reduction under low oxygen conditions occur in the pile. The melt nozzle 117 is used to transport the melt at the bottom 111 of the boiler to the dissolving tank ( Figure 1B not shown in the figure).

[0050] For the combustion air supplied to the waste heat boiler 110, the waste heat boiler 110 generally has three air levels: primary, secondary, and tertiary, which are passed through the ventilation holes 116. They all have an impact on the supply of combustion air required for the combustion of the black liquor. Different from Figure 1B that, the melt nozzle and the ventilation holes are generally located on the front wall 112 1 and / or the rear wall 112 2 because they are wider than the end walls 112 3 、112 4 is wider.

[0051] The superheater 115C is generally protected by a protrusion or nozzle 114 located at the top of the waste heat boiler 110 to protect the superheater 115C from direct thermal radiation and guide the flue gas flow to the superheater 115C. After the superheater 115C, the flue gas generated during combustion is transported to the cooking duct 115B, where the heat of the flue gas is used for steam generation. The flue gas generally contains a large amount of ash, and an attempt is made to separate the ash from the heat transfer surface by regular steam soot blowing. These ashes separated from the flue gas channel ash hopper and the electrostatic filter are recovered, and the recovered ash is mixed with the black liquor and injected into the boiler furnace 113 for chemical recovery.

[0052] The waste heat boiler 110 generally has two feed water preheaters or economizers 115A located in the vertical flue gas channel. The feed water preheater 115A heats the feed water before supplying the feed water to the cooking duct 115B. The preheater 115A improves the coefficient of performance of the waste heat boiler 110 and cools its flue gas to a temperature close to that of the feed water. The economizer 115A in the flue gas flow also requires regular steam soot blowing to keep them open.

[0053] Figure 2 is a block diagram of a computer device 200 according to an embodiment.

[0054] The computer device 200 includes at least one processor 202 and at least one memory 204 containing computer program code 205. The computer device 200 may also include an input / output module 206 and / or a communication interface 208.

[0055] Although Figure 2 the computer device 200 in is shown as including only one processor 202, the computer device 200 may include multiple processors. In one embodiment, the commands 205 (e.g., operating system and / or different applications) may be stored in the memory 204. Additionally, the processor 202 may execute the stored commands. In one embodiment, the processor 202 may be implemented as a multi-core processor, a single-core processor, or a combination of one or more multi-core processors and one or more multi-core processors. The processor 202 may be implemented, for example, as one or more different processing devices, such as a coprocessor, a microprocessor, a controller, a DSP (digital signal processor), a processing circuit with or without a DSP, or various other processing devices including an ASIC (application-specific integrated circuit), an FPGA (field-programmable gate array), a microcontroller unit, a hardware accelerator, etc. In one embodiment, the processor 202 may be arranged to execute hard-coded functions. In one embodiment, the processor 202 has been implemented as an executor of software commands, where the processor 202 may be configured with commands to run the algorithms and / or operations described in this specification when running the commands.

[0056] The memory 204 may be implemented as one or more volatile storage devices, one or more non-volatile storage devices, and / or a combination of one or more volatile storage devices or one or more non-volatile storage devices. The memory 204 may be implemented, for example, as a semiconductor memory, such as a PROM (programmable ROM), an EPROM (erasable PROM), a flash ROM, a RAM (random access memory), etc.

[0057] The input / output module 206 has been arranged to assist in the organization of input and / or output. The input / output module 206 has been arranged to communicate with the processor 202 and the memory 204. Examples of the input / output module 206 include, but are not limited to, an input interface and / or an output interface. Examples of the input interface include, but are not limited to, a keyboard, a touch screen, a microphone, etc. Examples of the output interface include, but are not limited to, a speaker, a display, such as an LED display, a TFT display, a liquid crystal display, or an AMOLED display, etc.

[0058] The communication interface 208 can enable the computer device 200 to communicate with other devices. In one embodiment, different components of the computer device 200, such as the processor 202, the memory 204, the input / output module 206, and the communication interface 208, have been arranged to communicate with each other through a centralized circuit 210. The centralized circuit 210 can include, for example, a printed circuit board, such as a motherboard or the like.

[0059] The computer device 200 described and explained herein is only an example of a device that can benefit from the embodiments of the present invention and is not intended to limit the scope of protection of the present invention. Note that the computer device 200 can include components with a different number of components than those shown in Figure 2 . The computer device 200 can be divided into a plurality of physical units that communicate through appropriate communication links.

[0060] The at least one memory 204 and the computer program code 205 have been arranged to use the at least one processor 202 to cause the computer device 200 to read at least one still imaging video sequence containing digital image frames, each digital image frame including at least one inspected area representing at least a part of the melt flow leaving the waste heat boiler 110. Herein, the term "still imaging video sequence" means that the camera used for imaging the video is stationary during imaging, so that the only movement captured in the video sequence is the movement of the object being imaged. The video sequence can be substantially real-time.

[0061] The inspected area can include, for example, the area above the melt flow flowing into the melt spout 117, and another inspected area can include, for example, the area above the melt flow after the melt spout 117. In one example, the camera is positioned to cover these two inspected areas. In this case, the image taken by the camera above the melt spout 117 can show the width of the melt flow flowing into the melt spout 117 and / or the width of the melt flow after the melt spout 117. In another example, two cameras are used, one for imaging the area at the melt flow flowing into the melt spout 117 and the other for imaging the area at the melt flow after the melt spout 117.

[0062] Figure 5 A digital image frame 500 of a video sequence according to the present invention is schematically shown. The image frame 500 covers the melt spout 501 (corresponding to, for example, the melt spouts 117, 410, 420, and / or 601), the melt flow 502 flowing into the melt spout 501, and the melt flow 503 flowing out of the melt spout 501. The dashed line 504 represents the centerline of the melt flow. Figure 5 The example of shows the inspected area 505, which covers the area above the melt flow 502 flowing into the melt spout 501. In addition, Figure 5The example shows an area 506 to be inspected, which covers the area above the melt stream 503 flowing out of the melt nozzle 501.

[0063] The at least one memory 204 and the computer program code 205 are also arranged to use at least one processor 202 to cause the computer device 200 to identify at least one area distinguishable based on color and / or intensity information in the at least one area to be inspected.

[0064] The at least one memory 204 and the computer program code 205 are also arranged to use at least one processor 202 to cause the computer device 200 to determine at least one monitored flow characteristic of the melt stream based on the identified at least one distinguishable area. In one example, the flow characteristic is a quantitative variable. In one example, the flow characteristic is a characteristic indicating the melt flow rate, size (such as width or droplet size), and / or changes therein. In one example, the determined monitored flow characteristic can be saved for later use and can optionally be timestamped.

[0065] In one embodiment of the present invention, the first area to be inspected represents a melt stream flowing into a melt nozzle 117 with a known cross-sectional dimension, the first distinguishable area includes the edge of the surface of the melt stream, and the monitored flow characteristics include at least one of the following: the melt stream width, or the melt stream height relative to the bottom of the melt nozzle 117, and the melt stream width and / or the melt stream height are determined based on the identified edge of the surface of the melt stream. The melt stream width and / or the melt stream height can be determined based on, for example, the pixel number dimensions of the first area to be inspected. In this embodiment, the monitored flow characteristics can also include the cross-sectional surface area of the melt stream, which is determined based on the cross-sectional dimension of the melt nozzle 117 and the determined melt stream width and / or height.

[0066] In one embodiment, the second distinguishable area includes an area moving in the flow direction of the melt stream, and the monitored flow characteristics also include the flow velocity of the melt stream, which is determined based on the position change of the second distinguishable area between two or more image frames of the video sequence. The area moving in the flow direction of the melt stream can include, for example, an area distinguishable in terms of the composition and / or temperature of the melt stream. An example of an area distinguishable due to the composition of the melt stream is an impurity particle, which can be distinguished from the melt stream with a red tint, for example, an area darker or blacker than its surroundings. An example of an area distinguishable due to the temperature of the melt stream is an area hotter than the normal part of the melt stream, which can be distinguished from the melt stream with a red tint, for example, an area brighter than its surroundings. An example of an area distinguishable due to the shape of the melt stream is a wave or a rise protruding as a contour, for example.

[0067] In one embodiment, the monitored flow characteristics further include the volumetric flow rate of the melt flow, which is determined based on the cross-sectional surface area and flow velocity of the determined melt flow. In this embodiment, the monitored flow characteristics may further include the mass flow rate of the melt flow determined based on the melt flow density and the determined volumetric flow rate.

[0068] In one example, the volumetric flow rate or flow rate in the melt nozzle can be calculated as follows:

[0069] q = v x A,

[0070] where q is the amount of flowing melt in units of volume per unit time (e.g., liters per second), v is the velocity of the flowing melt (meters per second), and A is the cross-sectional surface area of the flowing melt (e.g., square meters).

[0071] In one example, the mass flow rate can be calculated as follows:

[0072]

[0073] where, is the mass flow rate, ρ is the melt density, and q is the amount of flowing melt in units of volume per unit time (e.g., liters per second).

[0074] In one embodiment, the second inspected area represents the melt flow flowing out of the melt nozzle 117. For example, a steam jet is directed to this melt flow to break the melt flow into droplets. The third distinguishable area includes at least some of the droplets, and the monitored flow characteristics further include droplet distribution characteristics that affect the droplet distribution of the at least some droplets, such as droplet size (e.g., minimum and / or maximum size) and / or droplet distribution (e.g., median and / or average). For example, the droplet distribution characteristics can be used to adjust and optimize steam purging, thereby saving steam. For example, droplet size monitoring can be used to check whether there are unbroken oversized melt chunks left. Droplet sizes that are one or several orders of magnitude larger than the average may also indicate or suggest a large influx of melt.

[0075] In one embodiment, the at least one memory 204 and the computer program code 205 can also be arranged to cause the computer device 200 to read at least two still imaging video sequences imaged at different melt flow observation points by using at least one processor 202, so as to obtain the values of the monitored flow characteristics at different observation points; and compare the values of the monitored flow characteristics thus obtained.

[0076] In one embodiment, the at least one monitored flow characteristic of the determined melt flow is used to control the waste heat boiler 110. The control measures can include, for example: issuing a command to clean the melt nozzle to the automatic equipment for cleaning the melt nozzle, making adjustments to prevent changes so as to prevent, for example, a large amount of melt from flowing in, and / or making adjustments to adjust fuel injection and / or air injection, and / or adjusting the local conditions in the combustion chamber of the waste heat boiler based on the flow rate differences of the melt flowing from different melt nozzles to achieve a laterally balanced combustion event.

[0077] In one embodiment, an imaged video sequence is saved before and after (e.g., 30 seconds) a disruptive event. In this case, the operator can view the video sequence later (in slow motion if needed), for example, for analysis purposes.

[0078] When the cross-section of the melt nozzle 117 is known, the width of the melt flow observed in the imaging can be used to estimate the cross-sectional surface area of the melt flow. It can be assumed that the cross-sectional surface area of the melt flow is proportional to the volume of the melt flow per unit time, especially when the temperature of the melt flow remains constant. When velocity information of the soluble item (such as particles or other color and / or intensity regions) moving with the flow in the melt nozzle 117 is obtained based on the position differences between the images of the soluble item taken at specific time intervals, even more accurate information about the flow rate of the melt flow can be obtained. In this case, absolute flow rate information can be obtained instead of information based on relative information (based on width information) or an evaluation completed through calibration.

[0079] Observation of the flow width after the melt nozzle 117 gives an indication of the relative amount and changes of the melt flow, but it does not give absolute flow rate information. Using this observation point, in the case of stable flow, reference information can be obtained on whether the melt flow width information observed from the melt flow from the melt nozzle 117 is reliable, because these width information are highly correlated. When the ratio of these widths deviates from normal, we can, for example, conclude that the melt nozzle 117 obviously needs to be cleaned. For this purpose, a command to clean the melt nozzle 117 can be given to the automatic equipment for cleaning the melt nozzle 117 based on such an indication here.

[0080] Large (e.g., greater than 30%) instantaneous deviations in the melt flow can be observed from any observation point. Other thresholds can also be used, and there may be several of the above, e.g., changes (decreases and / or exceedances) such as 25%, 50%, 100%, and / or 200% etc. compared to the flow rate designated as normal can be reported separately and used as a basis for adjustment measures to prevent such changes, e.g., to prevent a large influx of melt, and / or to adjust fuel injection and / or air injection. The flow rate can be determined as normal by default, e.g., proportional to the fuel injection amount, or determined from the flow rate in the case of maximum capacity operation. Generally, the waste heat boiler 110 is continuously used at a standard power, and the power is basically unchanged, e.g., according to the power demand, because the main purpose of the equipment is to maintain the chemical recovery cycle.

[0081] The differences between the flow rates flowing out of different melt nozzles 117 can be expressed, and based on these expressions, the conditions in the combustion chamber of the waste heat boiler 110 can be adjusted to achieve a balanced combustion event. The differences in flow velocity and / or the changes in melt color can indicate, for example, local temperature differences between the melt in the combustion chamber and / or at the bottom 111 of the waste heat boiler 110. The combustion conditions also affect the percentage of salt recovery or reduction sought to be maximized. A locally weaker reduction percentage may indicate differences in nozzle-specific flow rates.

[0082] The width of the melt flow in the nozzle can be observed as, for example, a very narrow linear inspection area or an inspection area covering a large area. If the inspection area is large (e.g., representing a length of 100 mm or 200 mm in the nozzle direction), local and instantaneous deviations at the edge of the melt can be filtered out from a single image, e.g., by determining the average center line and using it as width information. If this edge line significantly deviates from the longitudinal straight line, it can also be considered a deviation in flow characteristics, and a deviation indication can be created for it. The number of pixels at the melt can also be calculated from the image, which can be used to calculate the surface area of the melt in the inspection area. When the surface area and length of the detected area are known, they can be used to calculate the average width of the melt flow. The inspection area may be asymmetrical, in which case only the area on the other side of the symmetric melt nozzle is inspected, and the other side is assumed to be symmetric. In this case, the width between the edge of the melt and the center line of the nozzle can be multiplied by 2 to obtain width information.

[0083] The edge of the melt flow can be indicated in a pixel-specific manner, for example, based on thresholds of color or intensity, because the molten salt emits a very distinguishable bright red. The color and intensity of the melt also depend on its temperature. The melt nozzle 117 can also be illuminated linearly, for example, with a laser from a different direction than the camera, which highlights the shape contours of the nozzle and the melt in the form of the angle of the light.

[0084] In one example, measurement data obtained from an image frame of a video sequence as the number of pixels in the image is such that the distance between the camera and the target affects the measured value. These numbers of pixels can be calibrated to correspond to a physical measurement length, for example, during debugging. Measurement information from a V-shaped melt nozzle 117 can be reliably obtained using a camera, for example, above the centerline of the melt nozzle 117 or above its extension. If the melt nozzle 117 is, for example, U-shaped, the camera is preferably located on one side of the centerline, which allows for a more precise observation of the change in the height of the melt flow in the melt nozzle 117. In this case, assuming the melt height is symmetric, only the edge of the melt flow can be monitored on one side of the centerline. An image taken from this side can give information about the vertical width of the melt flow, for example, after the melt nozzle 117. A second camera can also be used for this purpose, for example, in combination with the V-shaped melt nozzle 117. Two or more cameras can be used for different longitudinal, vertical, and lateral positioning and alignment of the melt nozzle 117 at different measurement points, such as at the melt nozzle 117 and at the flow after the melt nozzle 117. Using different cameras at different targets also helps to better shield the cameras from contamination, since the shielding can be used to limit the imaging to only the area being inspected.

[0085] It can be set as a condition for the flow rate measurement that there must be an observable flow in the inspected area after the melt nozzle 117. If no flow exits the melt nozzle 117, this situation can be interpreted as a blockage, or that the combustion event is not producing melt temporarily, for example, during startup or shutdown. If the indication of the fuel injection value and other combustion parameters over an appropriate time period given by the operating conditions of the waste heat boiler 110 indicates that the combustion should produce molten salt entering the melt nozzle 117, an alarm can be sent to the operator and / or a fault indication regarding the observed lack of flow can be sent to the boiler control system, for example.

[0086] Next, an example embodiment of the present invention related to determining the flow velocity of the melt flow will be described in more detail. Some steps of this example embodiment are optional. This example embodiment uses the ARPS (Adaptive Rood Pattern Search) algorithm.

[0087] The same measurement area or inspected area is selected from two consecutive video image frames or two video image frames obtained at a specific time period between them. In the first video image frame, the measurement area is divided into square blocks, where the block size is given as a parameter of n x n pixels. For these blocks, the vertical direction is the flow direction in the nozzle.

[0088] For each block, a new position is sought in the same inspected area in the subsequent video image frame. The cost function is used to find the new position of the block, i.e., by moving the block horizontally and vertically on the image (in a cross shape, which is the origin of "Rood"), the block is fitted into the second video image frame. At each point, the error is calculated between the blocks for the cost function as the difference. For example, the mean absolute difference function can be used as the cost function. The new position of the block is determined based on the point in the image where the lowest possible cost or the best equivalent is obtained for the block. Similar comparisons can also be made based on more than two image frames to improve the calculation accuracy.

[0089] In the next stage, the value of the cost function is specified by finding the equivalence of the block with the semi-base point. In the inspection of the semi-base point, based on the assumption that the assumed motion of the inspected block may be parallel to the blocks in its vicinity, the step size and direction are determined by the found position of the previous block. The calculation of the error is the same as in the previous point.

[0090] The newly found position of each block is the horizontal and vertical displacement resulting from the minimum value in the previous cost function. The displacements are saved in vectors. One vector has the horizontal motion, and the other vector has the vertical motion. The motion of all blocks between the video image frames is compiled into vectors.

[0091] Finally, zero velocities are removed from the vertical motion direction, and the average value is calculated. The horizontal motion direction is not considered. When the time between the video image frames and the pixel size in the International System of Units or SI system are known, the displacement gives the flow velocity, so velocity = (average displacement in pixels) * pixel size between video image frames / time. In this example, the pixel size is in m (meters), and the time between the video image frames is in s (seconds).

[0092] In the evaluation, blocks with no detected motion can be filtered from the motion direction, and the dispersion of the motion can be examined. If the dispersion (displacement) of the motion is too low, no reliable displacement is observed. However, if the dispersion is very large, the motion is more random, and a reliable velocity cannot be calculated. For example, in these cases, a warning can be issued.

[0093] In this example, the measurement of the flow rate focuses on the downward vertical motion, so this motion direction can be examined in the filtering.

[0094] Figures 7A to 7C Some monitored flow characteristics of the melt flow obtained according to the present invention are depicted. Figure 7A The graph 710 in shows the variation of the width (in millimeters) of the melt flow in the melt nozzle over time (in seconds). Figure 7BThe graph 720 therein shows the volumetric flow rate (in liters per second) of the melt flow in the melt spinneret. Figure 7C The graph 730 therein shows the flow velocity (in meters per second) of the melt flow in the melt spinneret. In Figures 7A to 7C the example, the average flow velocity is 0.53 m / s, the average volumetric flow rate is 0.88 L / s, and the average melt flow width is 52 mm. It can be clearly seen from Figures 7A to 7C that these monitored flow characteristics can vary significantly.

[0095] Figure 3 The example flowchart of method 300 for determining the relative particle group cross-section of one or more flue gases of a waste heat boiler according to an example embodiment is shown.

[0096] Operation 301 involves using a processor to read at least one still imaging video sequence containing digital image frames, each image frame including at least one inspected area representing at least a portion of the melt flow exiting the waste heat boiler.

[0097] Operation 302 involves using a processor to identify at least one area distinguishable based on color and / or intensity information in the at least one inspected area.

[0098] Operation 303 involves determining at least one monitored flow characteristic of the melt flow based on the at least one distinguishable area identified using the processor.

[0099] In optional operation 304, the processor reads at least two still imaging video sequences imaged from different observation points of the melt flow to obtain values of the flow characteristics monitored at the different observation points, and the processor compares the values of the monitored flow characteristics thus obtained.

[0100] In optional operation 305, if necessary, the processor is used to issue an alarm (e.g., an alarm to an operator). For example, an alarm can be issued when the comparison result of operation 304 exceeds a pre-specified threshold.

[0101] Method 300 can be performed using Figure 2 device 200. The additional features of method 300 are a direct result of the operation and parameters of device 200 and are therefore not repeated here. Method 300 can be executed using one or more computer programs.

[0102] An example embodiment can include, for example, any suitable computer device and equivalents capable of running the processes of the example embodiment. The devices and subsystems of the example embodiment can communicate with each other using any suitable protocol, and they can be implemented using one or more programmed computer systems or devices.

[0103] One or more connection mechanisms (including Internet connections, electrical communications in any suitable form (voice, modem, etc.), wireless communication media, and equivalents) can be used with the exemplary embodiments. The communication network or connection can include, for example, one or more satellite communication networks, wireless communication networks, cellular communication networks, 3G communication networks, 4G communication networks, 5G communication networks, general switched telephone networks, packet data networks, the Internet, intranets, or combinations thereof.

[0104] It should be understood that the exemplary embodiments are merely examples, as many variations of the specific devices used to implement the exemplary embodiments are possible, as understood by those skilled in the art. For example, the functions of one or more components of the exemplary embodiments can be implemented by hardware and / or software.

[0105] The exemplary embodiments can store information related to the different processes described in this specification. This information can be stored in one or more memories, such as hard disks, optical disks, magneto-optical disks, RAM memories, etc. The information for implementing the exemplary embodiments of the present invention can be stored in one or more databases. Data structures (e.g., data records, tables, boards, fields, graphics, trees, or lists) including one or more of the memories or storage media listed here can be used to organize the databases. Regarding the exemplary embodiments, the described processes can include appropriate data structures for saving the data collected and / or generated by the processes of the devices and subsystems of the exemplary embodiments into one or more databases.

[0106] As understood by those skilled in the art, the exemplary embodiments can be implemented in whole or in part using one or more general-purpose processors, microprocessors, DSP processors, microcontrollers, etc., programmed according to the teachings of the exemplary embodiments of the present invention. As understood by those skilled in the software field, an ordinary programmer can easily generate suitable software based on the teachings of the exemplary embodiments. In addition, as understood by those skilled in the electronics field, the exemplary embodiments can be implemented using application-specific integrated circuits or conventional component circuits that combine appropriate networks. Therefore, the exemplary embodiments are not limited to any specific combination of hardware and / or software.

[0107] Stored in any computer-readable medium or combination thereof, example embodiments of the present invention may include software for controlling components of the example embodiments, operating components of the example embodiments, implementing interactions between components of the example embodiments and human users, etc. Such software may include, but is not limited to, device drivers, firmware, operating systems, software development tools, application software, etc. These computer-readable media may include computer program products of the embodiments of the present invention for performing processes in the embodiments of the present invention in whole or in part (if the processing is distributed). The computer code devices of the example embodiments of the present invention may include any suitable interpretable or executable code mechanisms, including, but not limited to, command scripts, interpretable programs, dynamic link libraries, Java classes and applets, fully executable programs, etc. In addition, for improving performance, reliability, cost, etc., part of the processing of the example embodiments of the present invention may be distributed.

[0108] As described above, the components of the example embodiments may include a computer-readable medium or memory to store commands programmed according to the teachings of the present invention and data structures, tables, data records, and / or other data described in this specification. The computer-readable medium may include any suitable medium involved in organizing commands to be executed by a processor. Such a medium may have various forms, including, but not limited to, non-volatile or permanent storage media, volatile or non-permanent storage media, etc. The non-volatile storage media may include optical discs or magnetic disks, etc. The volatile storage media may include dynamic memories, etc. The general form of the computer-readable medium may include floppy disks, hard disk drives, or any other medium readable by a computer.

[0109] The present invention is not limited to only the above example embodiments; within the framework of the inventive concept defined in the patent claims, many variations are possible.

Claims

1. A method (300) for automatically monitoring a melt flow exiting a waste heat boiler (110), characterized in that, the method (300) comprises the following steps: using a processor (202) to read (301) at least one still imaging video sequence, the still imaging video sequence comprising digital image frames, each digital image frame comprising at least one inspected area representing at least a portion of the melt flow exiting the waste heat boiler (110); using the processor (202) to identify (302) at least one area distinguishable based on color and / or intensity information in the at least one inspected area; and based on the at least one distinguishable area identified by the processor (202), determining (303) at least one monitored flow characteristic of the melt flow, wherein a first inspected area represents the melt flow flowing into a melt spout (117) having a known cross-sectional dimension, the first distinguishable area comprises an edge of the surface of the melt flow, and wherein a second inspected area represents the melt flow flowing out of the melt spout (117), a steam jet is directed onto the melt flow to break the melt flow into droplets, a third distinguishable area comprises at least some of the droplets, and the monitored flow characteristic comprises a droplet distribution characteristic of the at least some of the droplets.

2. The method (300) according to claim 1, wherein, the monitored flow characteristic further comprises at least one of the following: a melt flow width, or a melt flow height relative to the bottom of the melt spout (117), the melt flow width and / or the melt flow height being determined by the processor (202) based on the edge of the surface of the identified melt flow.

3. The method (300) according to claim 2, wherein, the monitored flow characteristic further comprises a cross-sectional surface area of the melt flow, the cross-sectional surface area of the melt flow being determined by the processor (202) based on the cross-sectional dimension of the melt spout (117) and the determined melt flow width and / or melt flow height.

4. The method (300) according to any one of claims 2 to 3, wherein, a second distinguishable area comprises an area moving in the flow direction of the melt flow, and the monitored flow characteristic further comprises a flow velocity of the melt flow, the flow velocity being determined by the processor (202) based on a change in position of the second distinguishable area between at least two image frames of the video sequence.

5. The method (300) according to claim 4, wherein, the monitored flow characteristic further comprises a volume flow rate of the melt flow, the volume flow rate being determined based on the cross-sectional surface area and the flow velocity of the melt flow determined by the processor (202).

6. The method (300) according to claim 5, wherein, The monitored flow characteristics further include the mass flow rate of the melt flow, which is determined by the processor (202) based on the melt flow density and the determined volume flow rate.

7. The method (300) according to any one of claims 2 to 6, wherein, the processor (202) reads (301) at least two stationary imaging video sequences imaged from different observation points of the melt flow to obtain values of the monitored flow characteristics at the different observation points, and the processor (202) compares (304) the values of the monitored flow characteristics thus obtained.

8. The method (300) according to any one of claims 4 to 7, wherein, the region moving in the flow direction of the melt flow includes regions distinguishable due to deviations in the shape, composition, and / or temperature of the melt flow.

9. The method (300) according to any one of claims 2 to 8, wherein, the melt flow width and / or the melt flow height are determined based on the pixel number dimensions of the first inspected region.

10. The method (300) according to any one of claims 1 to 9, wherein, the determined at least one monitored flow characteristic of the melt flow is used to control the waste heat boiler (110).

11. A computer program product comprising at least one computer-readable storage medium containing a set of instructions that are run by one or more processors (202) to cause a computer device (200) to perform the method according to any one of claims 1 to 10.

12. A computer device (200) comprising at least one processor (202) ； and at least one memory (204), the at least one memory (204) including computer program code (205), characterized in that the at least one memory (204) and the computer program code (205) are arranged to use at least one processor (202) to cause the computer device (200) to: read at least one stationary imaging video sequence, the stationary imaging video sequence including digital image frames, each digital image frame including at least one inspected region representing at least a part of the melt flow leaving the waste heat boiler (110); in the at least one inspected region, identify at least one region distinguishable based on color and / or intensity information; and based on the identified at least one distinguishable region, determine at least one monitored flow characteristic of the melt flow, wherein the first inspected region represents the melt flow flowing into a melt spout (117) having a known cross-sectional dimension, the first distinguishable region includes the edge of the surface of the melt flow, and Wherein, the second inspected area represents the melt flow flowing out of the melt nozzle (117), and a steam jet is guided to the melt flow to break the melt flow into droplets. The third distinguishable area includes at least some of the droplets, and the monitored flow characteristics further include droplet distribution characteristics of at least some of the droplets in the droplets.

13. The computer device (200) according to claim 12, Wherein, the monitored flow characteristics further include at least one of the following: the melt flow width, or the melt flow height relative to the bottom of the melt nozzle (117), and the melt flow width and / or the melt flow height are determined based on the edges of the surface of the identified melt flow.

14. The computer device (200) according to claim 13, Wherein, the monitored flow characteristics further include the cross-sectional surface area of the melt flow, and the cross-sectional surface area of the melt flow is determined based on the cross-sectional dimensions of the melt nozzle (117) and the determined melt flow width and / or melt flow height.

15. The computer device (200) according to any one of claims 13 to 14, Wherein, the second distinguishable area includes an area moving in the flow direction of the melt flow, and the monitored flow characteristics further include the flow velocity of the melt flow, and the flow velocity is determined by the processor (200) based on the position change of the second distinguishable area between at least two image frames of the video sequence.

16. The computer device (200) according to claim 15, Wherein, the monitored flow characteristics further include the volume flow rate of the melt flow, and the volume flow rate is determined based on the cross-sectional surface area and the flow velocity of the determined melt flow.

17. The computer device (200) according to claim 16, Wherein, the monitored flow characteristics further include the mass flow rate of the melt flow, and the mass flow rate is determined based on the melt flow density and the determined volume flow rate.

18. The computer device (200) according to any one of claims 13 to 17, Wherein, at least one memory (204) and computer program code (205) are further arranged to cause the computer device (200) by using at least one processor (202): read at least two still imaging video sequences imaged from different observation points of the melt flow to obtain values of the monitored flow characteristics at the different observation points, and compare the values of the monitored flow characteristics thus obtained.

19. The computer device (200) according to any one of claims 15 to 18, Wherein, the area moving in the flow direction of the melt flow includes areas distinguishable due to deviations in the shape, composition, and / or temperature of the melt flow.

20. The computer device (200) according to any one of claims 13 to 19, Wherein, the melt flow width and / or the melt flow height are determined based on the pixel number dimensions of the first inspected area.

21. The computer device (200) according to any one of claims 12 to 20, wherein, the at least one monitored flow characteristic of the melt flow is used to control the waste heat boiler (110).

Images