Heat transfer surface cleaning system

Abstract

To provide a heat transfer surface cleaning system capable of making proper cleaning capacity act on a thickness of a deposit adhering to a heat transfer surface.SOLUTION: A heat transfer surface cleaning system comprises: an infrared camera that measures a surface temperature of a heat transfer surface by photographing the heat transfer surface exposed inside the boiler; a thermometer that measures a gas temperature in an internal space of the boiler; a cleaning device that cleans the heat transfer surface by injecting cleaning fluid; and a controller that controls an injection amount of the cleaning fluid. The controller performs machine learning of outputting the injection amount, acquiring state values including the measured surface temperature of the heat transfer surface and the gas temperature, acquiring a post-cleaning gas temperature measured by the thermometer after cleaning is performed by the cleaning device based on the injection amount, calculating a reward based on the post-cleaning gas temperature, and determining the injection amount to be output at current state values based on the injection amount, a state amount and the reward.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present disclosure relates to a heat transfer surface cleaning system that cleans the heat transfer surfaces of heat transfer tubes installed in a boiler to remove deposits from the heat transfer surfaces. [Background technology]

[0002] In waste heat boilers or coal-fired boilers that use high-temperature exhaust gas from waste incinerators, the exhaust gas contains a high concentration of dust. The furnace walls of such boilers are structured with heat transfer tubes through which cooling water flows. When such dust-rich exhaust gas is introduced into the boiler, the dust gradually adheres to the heat transfer surfaces of the heat transfer tubes.

[0003] As the thickness of deposits on the heat transfer surface increases, the amount of heat exchange between the furnace interior and the heat transfer tubes and cooling water decreases. Furthermore, the deposits can corrode the heat transfer surface. For this reason, regular inspections and cleaning work have traditionally been carried out. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2019-105393 [Patent Document 2] Japanese Patent Publication No. 62-299612 Summary of the Invention [Problem to be solved by the invention]

[0005] A known method for cleaning the heat transfer surfaces inside a boiler is to spray water onto them. However, the amount of deposits that adhere to the heat transfer surfaces in a boiler is not uniform across the entire surface. If the entire heat transfer surface is cleaned based on the deposits on only a portion of the heat transfer surface, the cleaning pressure may be excessive in areas with a small amount of deposits, potentially scraping the heat transfer surface. On the other hand, in areas with a large amount of deposits, the heat transfer surface may not be cleaned sufficiently.

[0006] In this regard, Patent Document 1 discloses a method for measuring the inlet steam temperature, outlet steam temperature, inlet exhaust gas temperature, and outlet exhaust gas temperature of a heat transfer tube, estimating the thickness of dust adhering to the heat transfer tube from these values, and continuing cleaning until the thickness estimated from each value becomes less than a threshold. However, the configuration of Patent Document 1 requires cleaning to be performed at a fixed cleaning power for a predetermined cleaning time, and then determining whether the thickness has become less than the threshold. Therefore, to prevent excessive cleaning, the cleaning power for the predetermined cleaning time must be set low in advance, and if the thickness is large, it will take a long time to complete cleaning.

[0007] Furthermore, Patent Document 2 discloses a configuration in which, after performing overall soot blowing, the temperature difference between the outlet and inlet of an economizer or the like, the feedwater temperature difference, or the above temperature differences is measured and compared to determine whether or not these differences have become larger than a predetermined value, thereby determining whether or not to perform individual soot blowing. However, like Patent Document 1, the configuration of Patent Document 2 only performs feedback control, and it is not clear what level of cleaning power is required for cleaning the thickness of deposits adhering to the heat transfer tubes.

[0008] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a heat transfer surface cleaning system that can apply appropriate cleaning power to the thickness of deposits attached to the heat transfer surface. [Means for solving the problem]

[0009] A heat transfer surface cleaning system according to one aspect of the present invention comprises an infrared camera that measures the surface temperature of a heat transfer surface exposed inside a boiler by photographing the heat transfer surface; a thermometer that measures the gas temperature in the internal space of the boiler; a cleaning device that cleans the heat transfer surface by spraying a cleaning fluid; and a controller that controls the amount of spray of the cleaning fluid, wherein the controller outputs the spray amount, acquires a state value including the measured surface temperature distribution of the heat transfer surface and the gas temperature, acquires a post-cleaning gas temperature measured by the thermometer after cleaning by the cleaning device based on the spray amount, calculates a reward based on the post-cleaning gas temperature, and performs machine learning to determine the injection amount to be output at the current state value based on the injection amount, the state quantity, and the reward. [Effects of the Invention]

[0010] According to the present disclosure, it is possible to apply an appropriate cleaning ability to the thickness of deposits adhering to the heat transfer surface. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a schematic cross-sectional view showing the internal structure of an incineration plant to which a heat transfer surface monitoring device and a heat transfer surface cleaning system according to this embodiment are applied. [Figure 2] FIG. 2 is a cross-sectional view of the radiation chamber shown in FIG. 1 taken along line II-II. [Figure 3] FIG. 3 is a diagram showing the infrared camera shown in FIG. 2 when rotated. [Figure 4] FIG. 4 is a block diagram showing a schematic configuration of a heat transfer surface monitoring device according to this embodiment. [Figure 5] FIG. 5 is a block diagram showing a schematic configuration of the heat transfer surface cleaning system according to this embodiment. [Figure 6] FIG. 6 is a flowchart showing the flow of machine learning performed by the controller shown in FIG. DETAILED DESCRIPTION OF THE INVENTION

[0012] A heat transfer surface monitoring device and a heat transfer surface cleaning system using the same according to an embodiment of the present disclosure will be described below with reference to the drawings. In this embodiment, a case where the heat transfer surface monitoring device and the heat transfer surface cleaning system are applied to an incineration plant having a waste heat boiler will be described as an example.

[0013] [Heat transfer surface monitoring device] First, the heat transfer surface monitoring device of this embodiment will be described. FIG. 1 is a schematic cross-sectional view showing the internal structure of an incineration plant to which the heat transfer surface monitoring device and heat transfer surface cleaning system of this embodiment are applied. The incineration plant 100 shown in FIG. 1 includes an incinerator 102 having a furnace chamber 103 for incinerating waste using an oxygen-containing gas, and a boiler 104, which is a steam recovery device that recovers exhaust heat as steam from the combustion exhaust gas discharged from the incinerator 102. A hopper 105 and a chute 106 are arranged on the opposite side of the incinerator 102 from the boiler 104, i.e., on the upstream side, and an exhaust path 107 for the combustion exhaust gas extends downstream from the boiler 104 to a chimney 108. For example, an economizer, a cooling tower, a dust collector, and a blower may be provided in the exhaust path 107, in that order from the upstream side.

[0014] The incinerator 102 has a stoker installed below the furnace chamber 103. The stoker functions as a waste transport means. The stoker has, in order from the side closest to the chute 106, a drying stoker 111, a combustion stoker 112, and a post-combustion stoker 113. That is, these stokers 111, 112, and 113 are arranged in the direction of waste movement. Wind boxes 114, 115, and 116 are installed below the drying, combustion, and post-combustion stokers 111, 112, and 113, respectively.

[0015] Furthermore, the incinerator 102 has a re-burning chamber 117 that is continuous with the furnace chamber 103 between the furnace chamber 103 and the boiler 104. Although the combustion stoker 112 has one stage in the example shown, two or more stages may be provided. Each of the stokers 111, 112, and 113 operates intermittently, for example, at different intervals.

[0016] In the furnace chamber 103, combustion gas is generated by the thermal decomposition and partial oxidation reaction of the waste, and this combustion gas is burned together with the waste. The re-burning chamber 117 is for completely burning the combustion gas that flows out of the furnace chamber 103. The dust after the combustion of the waste is discharged from an outlet 118 provided adjacent to the post-burning stoker 113.

[0017] In the boiler 104, steam is generated by waste heat from the combustion exhaust gas discharged from the incinerator 102. More specifically, as shown in Fig. 1, the boiler 104 is provided with an exhaust gas passage 109 through which the combustion exhaust gas passes. The exhaust gas passage 109 includes a radiation chamber 119 disposed above the re-burning chamber 117, a first flue 120 whose upper portion communicates with the radiation chamber 119, and a second flue 121 whose lower portion communicates with the first flue 120. In other words, the boiler 104 has the radiation chamber 119, the first flue 120, and the second flue 121 as its internal space.

[0018] A plurality of heat transfer tubes 123 are provided in each of the walls that define the radiant chamber 119 and the first flue 120 and the second flue 121. The material of the plurality of heat transfer tubes 123 is carbon steel, such as STB340. The boiler 104 includes a boiler drum 124 connected to the plurality of heat transfer tubes 123. Water sent from the boiler drum 124 flows through the plurality of heat transfer tubes 123. The water in the plurality of heat transfer tubes 123 recovers waste heat from the radiant chamber 119 and the first flue 120, and a portion of the water evaporates to become brackish water, which is then returned to the boiler drum 124. A portion of the brackish water returned to the boiler drum 124 vaporizes to become steam.

[0019] A superheater 125 is provided in the second flue 121. The superheater 125 has superheater tubes 126 for superheating the steam in the boiler drum 124 with the heat of combustion or the heat of the combustion exhaust gas. The superheated steam, which has been superheated by the superheater 125 and has become high temperature and high pressure, is sent to a turbine 128 connected to a generator 127 and used to generate electricity. Most of the combustion exhaust gas that has passed through the boiler 104 flows through the exhaust path 107 and is then released into the atmosphere from the chimney 108.

[0020] FIG. 2 is a cross-sectional view taken along line II-II of the radiation chamber shown in FIG. 1. As shown in FIG. 2, the radiation chamber 119 has a rectangular cross section defined by four heat transfer surfaces F1, F2, F3, and F4. Each of the four heat transfer surfaces F1, F2, F3, and F4 has a membrane wall to which a plurality of heat transfer tubes 123 are connected. Hereinafter, the four heat transfer surfaces will be referred to as the first heat transfer surface F1, the second heat transfer surface F2, the third heat transfer surface F3, and the fourth heat transfer surface F4. The second heat transfer surface F2 faces the first heat transfer surface F1, and the fourth heat transfer surface F4 faces the third heat transfer surface F3.

[0021] The first heat transfer surface F1 and the second heat transfer surface F2 are parallel to a first imaginary plane that includes the arrangement directions of the radiation chamber 119, the first flue 120, and the second flue 121, i.e., parallel to the plane of the paper in Figure 1, and the third heat transfer surface F3 and the fourth heat transfer surface F4 are planes that intersect with the first heat transfer surface F1 and the second heat transfer surface F2. The third heat transfer surface F3 is also a heat transfer surface that defines the first flue 120.

[0022] The first heat transfer surface F1 has a first window W1 through which infrared rays pass. The heat transfer surface monitoring device 1 is equipped with a first infrared camera 11 provided outside the first heat transfer surface F1 so as to photograph the second heat transfer surface F2 through the first window W1. The heat transfer surface monitoring device 1 further includes a second infrared camera 12 provided outside the second heat transfer surface F2 so as to photograph the first heat transfer surface F1 through the second window W2. Position P1 in FIG. 1 indicates the positions of the first window W1 and the second window W2. Position P1 is located, for example, in the center of the radiation chamber 119 in the direction of waste gas flow.

[0023] The first infrared camera 11 measures the surface temperature of the second heat transfer surface F2 by photographing the second heat transfer surface F2. Similarly, the second infrared camera 12 measures the surface temperature of the first heat transfer surface F1 by photographing the first heat transfer surface F1. The measurement results from the infrared cameras 11 and 12 are obtained as a surface temperature distribution within the photographing ranges of the infrared cameras 11 and 12. The surface temperature distribution is, for example, image data in which the photographing range is color-coded using a plurality of colors that differ according to a predetermined temperature range.

[0024] Furthermore, the heat transfer surface monitoring device 1 is equipped with a thermometer 13 that measures the gas temperature inside the radiation chamber 119. The thermometer 13 includes, for example, a temperature sensor having a thermocouple. The thermometer 13 is disposed inside the radiation chamber 119. The location where the thermometer 13 is disposed is not particularly limited, but it is disposed, for example, within the angle of view of the first infrared camera 11 or the second infrared camera 12.

[0025] The first infrared camera 11 is rotatable around a predetermined rotation axis R1 that is aligned in the vertical direction. The second infrared camera 12 is similarly rotatable around a predetermined rotation axis R2 that is aligned in the vertical direction. Both the rotation axis R1 and the rotation axis R2 are parallel to the first heat transfer surface F1 or the second heat transfer surface F2 and parallel to the third heat transfer surface F3 or the fourth heat transfer surface F4.

[0026] Fig. 3 is a diagram showing the infrared camera shown in Fig. 2 when rotated. In the example of Fig. 3, the first infrared camera 11 and the second infrared camera 12 are both rotated to face the third heat transfer surface F3. By rotating the first infrared camera 11 or the second infrared camera 12 in this way, it is possible to capture an image of the third heat transfer surface F3 and measure the surface temperature of the third heat transfer surface F3. Similarly, by rotating the first infrared camera 11 or the second infrared camera 12, it is possible to capture an image of the fourth heat transfer surface F4 and measure the surface temperature of the fourth heat transfer surface F4.

[0027] The angle of view of the first infrared camera 11 may be set, for example, so that the first infrared camera 11 is positioned directly opposite the second heat transfer surface F2 and includes both horizontal ends of the second heat transfer surface F2. However, a narrower angle of view may also be used. In this case, the surface temperature distribution across the entire horizontal area of ​​the second heat transfer surface F2 may be obtained by rotating the first infrared camera 11 by a predetermined angle around the rotation axis F1 as described above and taking multiple images. The same applies to the second infrared camera 12.

[0028] 4 is a block diagram showing a schematic configuration of a heat transfer surface monitoring device according to this embodiment. The heat transfer surface monitoring device 1 includes the above-described first infrared camera 11, second infrared camera 12, thermometer 13, and computing unit 14. The computing unit 14 is configured by a computer equipped with storage for storing various data. For example, the computing unit 14 includes a CPU, main memory (RAM), storage, a communication interface, etc.

[0029] It should be noted that the functions of the elements disclosed herein can be performed using circuits or processing circuits, including general-purpose processors, special-purpose processors, integrated circuits, ASICs (Application Specific Integrated Circuits), conventional circuits, or combinations thereof, configured or programmed to perform the disclosed functions. A processor is considered a processing circuit or circuit because it includes transistors and other circuitry. In this specification, a circuit, unit, or means (...part) is hardware that performs the recited functions or hardware that is programmed to perform the recited functions. The hardware may be hardware disclosed herein or other known hardware that is programmed or configured to perform the recited functions. Where the hardware is a processor, which is considered a type of circuit, the circuit, unit, or means is a combination of hardware and software, and the software is used to configure the hardware and / or processor.

[0030] The calculator 4 acquires the surface temperature distribution of the heat transfer surfaces F1, F2, F3, and F4 to be monitored from the corresponding infrared cameras 11 and 12. Furthermore, the calculator 14 acquires the gas temperature of the internal space of the boiler 104 partitioned by the heat transfer surfaces F1, F2, F3, and F4 to be monitored from the thermometer 13. The calculator 4 performs an estimation calculation to estimate the thickness of the deposit adhering to the corresponding heat transfer surface using the surface temperature and gas temperature of the heat transfer surface.

[0031] Here, the surface temperature Td of the dust, which is the adhered matter, is expressed by the following formula using the gas temperature Tg and the dust thickness td.

[0032]

number

[0033] All values ​​used in the above equations (1) to (4) other than the dust surface temperature Td, gas temperature Tg, and dust thickness td are given in advance from thermal calculations at the time of design, average measured values, etc. Therefore, the above equations (1) to (4) can be rewritten as follows to calculate the dust thickness td from the dust surface temperature Td and gas temperature Tg:

[0034]

number

[0035] The dust surface temperature Td corresponds to the surface temperature of the heat transfer surface acquired by the infrared cameras 11 and 12. The gas temperature Tg is a temperature acquired by the thermometer 13. The calculator 14 acquires the gas temperature Tg measured by the thermometer 13 and also acquires the temperature at a certain position coordinate from the surface temperature distribution of the heat transfer surface, and uses the acquired temperature as the dust surface temperature Td to calculate the dust thickness td from the above formula (5). That is, the first term of the above formula (5) includes variables Tg and Td, and the second term is a constant.

[0036] In the above formula (5), as the gas temperature Tg increases, the numerator of the first term becomes larger than the denominator, and therefore the dust thickness td increases. This means that as the dust thickness td increases, the heat exchange between the exhaust gas in the boiler 104 and the heat transfer tubes 123 decreases, thereby suppressing a decrease in the gas temperature Tg. Furthermore, in the above formula (5), as the dust surface temperature Td increases, the denominator of the first term becomes smaller, and therefore the dust thickness td increases. This means that as the dust thickness td increases, the heat exchange between the exhaust gas in the boiler 104 and the heat transfer tubes 123 decreases, thereby causing a rise in the dust surface temperature Td. Therefore, the higher the gas temperature Tg, the larger the dust thickness td, and the higher the dust surface temperature Td, the larger the dust thickness td.

[0037] The calculator 14 calculates the surface temperature distribution of the heat transfer surface, i.e., the dust thickness td for multiple position coordinates in the images captured by the infrared cameras 11 and 12. For example, the calculator 14 acquires temperatures at predetermined coordinate position intervals from the surface temperature distribution and calculates the dust thickness td for each acquired temperature. This allows the dust thickness distribution on the heat transfer surface within the image capture range of the infrared cameras 11 and 12 to be obtained.

[0038] As described above, with the heat transfer surface monitoring device 1 of this embodiment, the thickness td of dust adhering to the heat transfer surfaces F1, F2, F3, and F4 can be obtained as three-dimensional information by adding thickness information to the planar images captured by the infrared cameras 11 and 12, based on the surface temperatures of the heat transfer surfaces F1, F2, F3, and F4 obtained from the images captured by the infrared cameras 11 and 12 and the gas temperature inside the boiler 104. Therefore, the thickness distribution of dust adhering to the heat transfer surfaces F1, F2, F3, and F4 can be estimated quantitatively and in real time. Furthermore, by repeatedly capturing images over time, the temporal change in the thickness distribution of dust adhering to the heat transfer surfaces F1, F2, F3, and F4 can also be obtained.

[0039] Furthermore, because infrared cameras 11 and 12 are disposed opposite each other, it is possible to estimate the dust thickness distribution on both of the opposing heat transfer surfaces F1 and F2. Furthermore, because infrared cameras 11 and 12 rotate about rotation axes R1 and R2, it is also possible to estimate the dust thickness distribution on heat transfer surfaces F3 and F4 that are perpendicular to first heat transfer surface F1 and second heat transfer surface F2, on which first window W1 and second window W2 for infrared cameras 11 and 12 are installed. Therefore, it is possible to monitor the status of a wide range of heat transfer surfaces while reducing the number of parts required for heat transfer surface monitoring device 1.

[0040] The positions of the infrared cameras 11 and 12 can be set appropriately depending on the position of the heat transfer surface to be measured. For example, to obtain the dust thickness distribution on the heat transfer surface in the first flue 120, the infrared cameras 11 and 12 are installed at positions P2 and P3 in the first flue 120, or both of these positions. Furthermore, to obtain the dust thickness distribution on the heat transfer surface in the second flue 121, the infrared cameras 11 and 12 are installed at positions P4 and P5 in the second flue 121, or both of these positions.

[0041] In this way, the infrared cameras 11, 12 can be installed at one or more locations in each exhaust gas path partitioned by the heat transfer tubes 123. When the infrared cameras 11, 12 are installed at multiple locations, they may be installed in the flow direction of the exhaust gas path, for example, in the vertical direction, or in a direction intersecting the flow direction of the exhaust gas path, for example, in the horizontal direction. Furthermore, in this embodiment, the infrared cameras 11, 12 are rotatable in the horizontal direction, but in addition to this, or instead, they may be rotatable in the vertical direction. For example, the infrared cameras 11, 12 may be rotatable around a rotation axis parallel to a horizontal plane.

[0042] By installing infrared cameras 11 and 12 at multiple locations, rotating infrared cameras 11 and 12, or by combining these, it is possible to capture images of the entire desired heat transfer surface. In other words, the surface temperature distribution of the entire heat transfer surface can be obtained. Therefore, calculator 14 can estimate the thickness distribution of dust adhering to the entire heat transfer surface from the surface temperature distribution of the entire heat transfer surface.

[0043] Thermometers 13 for measuring the gas temperature in the boiler 104 may also be installed at multiple locations in the boiler 104. For example, when monitoring the heat transfer surface in the first flue 120, the thermometer 13 may be installed in the first flue 120. Furthermore, when monitoring the heat transfer surface in the second flue 121, the thermometer 13 may be installed in the second flue 121. Furthermore, thermometers 13 may be installed in each of the radiation chamber 119, the first flue 120, and the second flue 121. In this case, the gas temperature measured by the thermometer 13 in the radiation chamber 119 may be used to estimate the dust thickness distribution on the heat transfer surface of the radiation chamber 119, the gas temperature measured by the thermometer 13 in the first flue 120 may be used to estimate the dust thickness distribution in the first flue 120, and the gas temperature measured by the thermometer 13 in the second flue 121 may be used to estimate the dust thickness distribution in the second flue 121.

[0044] As described above, by obtaining the dust thickness distribution on the heat transfer surfaces F1, F2, F3, and F4, it is possible to set the maintenance timing for cleaning the inside of the boiler 104 at an efficient time. For example, the calculator 14 may display the dust thickness distribution on a predetermined monitor as image data in which different colors are used according to a predetermined thickness range. Furthermore, it is possible to pinpoint the location in the boiler 104 where cleaning should be performed.

[0045] For example, maintenance may be performed when the area of ​​the entire heat transfer surface where the dust thickness td exceeds a predetermined threshold is equal to or greater than a predetermined reference area. For example, the calculator 14 may display an image of the thickness distribution on a monitor and highlight the areas on the image where the dust thickness exceeds the threshold. Highlighting may include, for example, blinking the areas. Furthermore, the areas to be cleaned when performing maintenance may be within a predetermined range centered on the areas where the dust thickness exceeds the predetermined threshold. The maintenance or cleaning work may be performed manually by an operator entering the boiler 104, or may be performed by a cleaning device 21, which will be described later.

[0046] [Heat transfer surface cleaning system] Furthermore, in this embodiment, the incineration plant 100 is equipped with a heat transfer surface cleaning system 2 that cleans the inside of the boiler 104. Fig. 5 is a block diagram showing a schematic configuration of the heat transfer surface cleaning system in this embodiment. As shown in Fig. 5, the heat transfer surface cleaning system 2 also includes infrared cameras 11 and 12 and a thermometer 13, similar to the heat transfer surface monitoring device 1. The infrared cameras 11 and 12 and the thermometer 13 may be shared by the heat transfer surface monitoring device 1 and the heat transfer surface cleaning system 2, or the infrared cameras 11 and 12 and the thermometer 13 for the heat transfer surface monitoring device 1 and the infrared cameras 11 and 12 and the thermometer 13 for the heat transfer surface cleaning system 2 may be installed in the boiler 104.

[0047] The heat transfer surface cleaning system 2 further includes a cleaning device 21 and a controller 22. The cleaning device 21 is not particularly limited, but may be, for example, a water jet cleaning device having a water jet nozzle 21a that jets water as a cleaning fluid. The cleaning device 21 includes a main body 21b installed outside the boiler 104 and a water supply pipe 21c that extends into the boiler 104 and connects the main body 21b and the water jet nozzle 21a. The water supply pipe 21c is inserted into the boiler 104 through an insertion hole located in the ceiling of the boiler 104. The main body 21b includes a pump that pumps water. Water from the main body 21b flows through the water supply pipe 21c, and the water is sprayed into the boiler 104 from the water jet nozzle 21a. The main body 21b includes a drive unit that changes the direction or range of water jet from the water jet nozzle 21a and changes the vertical position of the water jet nozzle 21a.

[0048] The controller 22 is configured by a computer equipped with a storage for storing various data, and includes, for example, a CPU, a main memory (RAM), a storage, a communication interface, and the like.

[0049] The controller 22 controls the operation of the cleaning device 21. For example, the controller 22 controls the vertical position of the water jet nozzle 21a. The controller 22 also controls the direction or range of water jet from the water jet nozzle 21a. This controls the position at which water is jetted from the water jet nozzle 21a relative to the heat transfer surface. The controller 22 also controls the amount of water jetted from the water jet nozzle 21a.

[0050] The controller 22 controls the position of the water injection nozzle 21a and the amount of water injected based on the dust thickness distribution estimated by the calculator 14 of the heat transfer surface monitoring device 1. For example, the controller 22 sets a position where the dust thickness is equal to or greater than a predetermined threshold based on the dust thickness distribution as a target position, and controls the position of the water injection nozzle 21a so that water is injected toward the target position. The controller 22 also controls the amount of water injected according to the dust thickness at the target position. The amount of water injected is adjusted, for example, by changing the injection time, changing the amount injected per unit time, or changing the number of injections when a predetermined amount is injected per injection.

[0051] Regarding control of the amount of water sprayed, the controller 22 may determine the amount of sprayed using a trained model based on machine learning. The following example illustrates a mode in which the controller 22 generates a trained model through reinforcement learning. In reinforcement learning, the agent, which is the subject of learning, progresses in learning through interactions between the agent and the environment to be controlled. In this embodiment, the agent is the controller 22, and the environment to be controlled is the boiler 104 and the cleaning device 21. More specifically, when the agent learns, the following steps (A) to (D) are repeated. (A) The agent is in the state S of the environment at time T. t To observe. (B) Action A that the agent can take based on its observations and past learning. t Select and take action A t To carry out the following. (C) Action A t By executing t is the next state S t+1Based on this state change, the agent determines the reward R t+1 To receive. (D) The agent is in state S t , action A t , reward R t+1 and building on past learning outcomes.

[0052] In the learning process in (D) above, the agent uses the state S as the reference information for determining the amount of reward R that can be obtained in the future. t , action A t , reward R t+1 For example, if the number of possible states at each time is m and the number of possible actions is n, then by repeating the actions, the state S t and Action A t The reward for the pair R t+1 A two-dimensional array of m × n is obtained, which stores the values ​​of the current state and actions. Then, a value function or evaluation function, which indicates how good the current state or action is based on the mapping obtained above, is used, and the optimal action for the state is learned by updating the value function while repeating the action.

[0053] The state-action value function Q(S t ,A t ) is a state S t In Action A t indicates how good an action is. The state-action value function Q(S t ,A t ) is expressed as a function that takes states and actions as arguments. The state-action value function Q(S t ,A t ) is updated based on the reward obtained for an action in a certain state and the value of the action in a future state to which the action will lead during learning as the action is repeated. t ,A t ) is defined according to the reinforcement learning algorithm. For example, in Q-learning, which is one of the most common reinforcement learning algorithms, the state-action value function Q(S t ,At The update formula for ) is defined as the following formula (6).

[0054]

number

[0055] And, Action A in (B) above t In selecting the current state S, a value function created by past learning is used. t Future rewards (R t+1 +R t+2 +…) is optimal. t , i.e., state S t The most valuable action in t In Q-learning, the reward (R t+1 +R t+2 +…) is optimal. t is the reward (R t+1 +R t+2 +…) is maximized by action A. t is equivalent to

[0056] Various reinforcement learning algorithms are well known, such as Q-learning, SARSA, TD learning, and AC, and any of these reinforcement learning algorithms may be adopted as the method applied to the present invention. Because these reinforcement learning algorithms are well known, further detailed explanations of each algorithm will be omitted in this specification.

[0057] In addition, as a way to update the value function, the current state S t Action A t By applying the state S tis the new state S t+1 It is not necessary to adopt a learning method that updates the value function every time the current state S t Action A t By applying the state S t is the new state S t+1 The state and action are repeatedly transferred to the next state, and the state and action are accumulated as learning data. The accumulated learning data is used to update the value function, so-called batch learning or mini-batch learning may be adopted.

[0058] For this purpose, the controller 22 includes an injection amount output unit 221 , a state value acquisition unit 222 , a reward calculation unit 223 , and a learning unit 224 .

[0059] The injection amount output unit 221 outputs the injection amount as a command value for the cleaning device 21. The injection amount becomes an operating value, which will be described later. The state value acquisition unit 222 acquires, as state values, the surface temperature of the heat transfer surface obtained from images captured by the infrared cameras 11 and 12 and the gas temperature Tg measured by the thermometer 13. The acquired state values ​​are stored in the storage of the controller 22. The storage stores state values ​​for each predetermined time period as history. Furthermore, the state value acquisition unit 222 also acquires an injection amount command value and stores it in the storage. The storage stores, as history, the position of the heat transfer surface cleaned by the cleaning device 21, the time of cleaning, and the amount of water sprayed. As a result, the reward calculation unit 223 regards the surface temperature Td and gas temperature Tg of the heat transfer surface at a specified heat transfer surface position included in the period from the time cleaning was performed at that heat transfer surface position to a specified time before as the surface temperature before cleaning and the gas temperature before cleaning, and regards the surface temperature Td and gas temperature Tg of the heat transfer surface at that heat transfer surface position included in the period from the time cleaning was performed to a specified time after as the surface temperature after cleaning and the gas temperature after cleaning, and performs the following calculations.

[0060] The reward calculation unit 223 calculates a reward based on a preset reward condition by analyzing the state value acquired by the state value acquisition unit 222. The reward calculation unit 223 outputs the calculated reward to the learning unit 224.

[0061] The reward condition is a condition for providing a reward in reinforcement learning. The reward condition is stored in advance in a storage. For example, the reward condition includes providing a larger reward when the post-cleaning gas temperature is determined to be low than when the post-cleaning gas temperature is determined to be high. Also, for example, the reward condition includes providing a larger reward when the post-cleaning surface temperature is determined to be high than when the post-cleaning surface temperature is determined to be low. The reward may be, for example, a positive reward, a negative reward, or no reward, i.e., zero reward.

[0062] The reward calculation unit 223 calculates a positive reward if the post-cleaning gas temperature is smaller than a predetermined first reference value, and calculates a negative or zero reward if the post-cleaning gas temperature is larger than the first reference value. Furthermore, the reward calculation unit 223 calculates a positive reward if the post-cleaning surface temperature is smaller than a predetermined second reference value, and calculates a negative or zero reward if the post-cleaning gas temperature is larger than the second reference value. The first and second reference values ​​are determined by simulations, past operating results, etc., and are stored in advance in the storage of the controller 22.

[0063] Instead of comparing the post-cleaning gas temperatures themselves, the reward calculation unit 223 may compare the value obtained by subtracting the post-cleaning gas temperature from the pre-cleaning gas temperature with a predetermined third reference value, and calculate a positive reward if the value is greater than the third reference value, and a negative or zero reward if the value is less than the third reference value. Similarly, instead of comparing the post-cleaning surface temperatures themselves, the reward calculation unit 223 may compare the value obtained by subtracting the post-cleaning surface temperature from the pre-cleaning surface temperature with a predetermined fourth reference value, and calculate a positive reward if the value is greater than the fourth reference value, and a negative or zero reward if the value is less than the fourth reference value.

[0064] Here, the reward calculation does not need to be performed for cleaning at all heat transfer surface positions. For example, when the cleaning device 21 cleans a heat transfer surface position predetermined as a calculation target because the dust thickness estimated by the calculator 14 is equal to or greater than a predetermined threshold, the reward calculation unit 223 may calculate the reward from the amount of water sprayed, the surface temperature after cleaning, and the gas temperature after cleaning related to the cleaning.

[0065] The learning unit 224 performs machine learning to determine an operation value, i.e., an injection amount to be output by the injection amount output unit 221, from the current state value based on the injection amount command value to the cleaning device 21, the state value acquired from the state value acquisition unit 222, and the reward calculated by the reward calculation unit 223. More specifically, the learning unit 224 performs machine learning to determine an operation value, i.e., an injection amount to be output by the injection amount output unit 221, from the current state value based on the combination of the state values. t and the decision of the driving value in the state are expressed as arguments, for example, the state-action value function Q(S t ,A t ) to update the value function so that the reward obtained is maximized.

[0066] The results of learning by the learning unit 224 are stored in storage as a learned model. Note that methods of storing a value function as a learning result generally use an approximation function or an array. However, in addition to these methods, for example, when the state takes on many states, a method using a state S t , action A t Alternatively, a method using a supervised learning machine such as a neural network or a multi-value output SVM that takes the input and outputs a value may be used.

[0067] The injection amount output unit 221 outputs the injection amount, which is an operating value, based on the learned model and the current state value.

[0068] The trained model may be a common trained model that is independent of the position on the heat transfer surface, or may include multiple trained models that differ depending on the position or region of the heat transfer surface. For example, the trained models used to determine the injection amount may be different for the radiation chamber 119, the first flue 120, and the second flue 121. In this case, the learning unit 224 performs machine learning based on the state value in the radiation chamber 119 and the injection amount in the radiation chamber 119. The same applies to the first flue 120 and the second flue 121. Furthermore, for example, the trained models used to determine the injection amount may be different for the first heat transfer surface F1, the second heat transfer surface F2, the third heat transfer surface F3, and the fourth heat transfer surface F4.

[0069] Next, the flow of machine learning in this embodiment will be described. Fig. 6 is a flowchart showing the flow of machine learning performed by the controller shown in Fig. 5.

[0070] When machine learning starts, in step S1, the state value acquisition unit 222 acquires the state value at time t as the state S t The state value acquisition unit 222 stores the acquired state values ​​in a storage. In step S2, the learning unit 224 learns the current state S based on the state values ​​acquired by the state value acquisition unit 222. t In step S3, the learning unit 224 identifies the state S t and act based on A t Select Action A. t is a defined state S t Here, for example, a plurality of driving values ​​may be prepared as selectable actions, and the action A that will maximize the reward R to be obtained in the future may be selected based on the past learning results. The injection amount output unit 221 determines the driving value according to the selected action A. t In step S4, the cleaning device 21 executes the action A t is carried out and the inside of the boiler 104 is cleaned.

[0071] Action A t After the state is changed by executing the above, the state value acquisition unit 222 acquires the state S t+1 At this stage, the state of the boiler 104 is determined by the time transition from time t to time t+1, and the data for identifying the executed action A is acquired as a state value. t In step S6, the reward calculation unit 223 calculates the reward R from the state value at time t+1 based on the set reward condition. t+1 In step S7, the learning unit 224 calculates the state S t , action A t , and reward R t+1 The value function used in learning is determined according to the learning algorithm to be applied. In step S8, the learning unit 224 stores the learning results in storage. Learning at time t+1 can be performed in the same manner as above, with time t in the flowchart shown in FIG. 6 replaced with time t+1.

[0072] In this way, the learning unit 224 repeats machine learning. Learning by the learning unit 224 may be completed when it is confirmed that an optimal system for injection amount control has been realized. For example, learning by the learning unit 224 may be completed when the reward calculated by the reward calculation unit 223 becomes positive by a predetermined percentage or more. After learning is completed, no further learning is performed using newly obtained state values ​​and operating values, and the learned model is not updated.

[0073] On the other hand, machine learning may be continued without being completed in the learning unit 224. The optimal operating values ​​may change due to aging of the cleaning device 21 and the boiler 104. By continuing machine learning, it becomes possible to respond to changes in the optimal operating values.

[0074] As described above, according to the heat transfer surface cleaning system 2 of this embodiment, the surface temperatures of the heat transfer surfaces F1, F2, F3, and F4 obtained from images captured by the infrared cameras 11 and 12 and the gas temperature Tg inside the boiler 104 are used as state values, and a reward is calculated based on the gas temperature Tg after cleaning by the cleaning device 21, thereby learning the optimal amount of water to be sprayed from the cleaning device 21, which is an operating value. This makes it possible to pinpoint the correlation between the dust thickness td estimated from the surface temperature Td of the heat transfer surface and the gas temperature Tg and the amount of water sprayed to clean the dust for each position on the heat transfer surface according to the surface temperature distribution of the heat transfer surface. Therefore, it is possible to apply a cleaning ability appropriate to the thickness of dust adhering to the heat transfer surface.

[0075] Furthermore, in this embodiment, as described above, the reward calculation unit 223 calculates a positive reward if the post-cleaning gas temperature is smaller than a predetermined first reference value, and calculates a negative or zero reward if the post-cleaning gas temperature is greater than the first reference value. Alternatively, the reward calculation unit 223 calculates a positive reward if the value obtained by subtracting the pre-cleaning gas temperature from the post-cleaning gas temperature is smaller than a predetermined third reference value, and calculates a negative or zero reward if the value is greater than the third reference value. In this way, a decrease in the gas temperature Tg due to cleaning can be evaluated as a decrease in the dust thickness td and an increase in the thermal efficiency between the exhaust gas in the boiler 104 and the heat transfer tube 123.

[0076] Furthermore, in this embodiment, the reward is calculated based on the post-cleaning surface temperature in addition to the post-cleaning gas temperature. This allows for more appropriate learning using multiple evaluation criteria. In this case, the reward calculation unit 223 calculates a positive reward if the post-cleaning surface temperature is greater than a predetermined second reference value, and calculates a negative or zero reward if the post-cleaning surface temperature is less than the second reference value. Alternatively, the reward calculation unit 223 calculates a positive reward if the value obtained by subtracting the pre-cleaning surface temperature from the post-cleaning surface temperature is greater than a predetermined fourth reference value, and calculates a negative or zero reward if the value is less than the fourth reference value. This allows for evaluation of the increase in the surface temperature of the heat transfer surface due to cleaning as a reduction in the dust thickness td and an increase in the thermal efficiency between the exhaust gas in the boiler 104 and the heat transfer tube 123.

[0077] Furthermore, in this embodiment, a water jet cleaning device that sprays water is used as the cleaning device 21. This makes it possible to clean a small area unit, allowing for more pinpoint cleaning.

[0078] [Variations] Although the embodiments of the present disclosure have been described above, the present disclosure is not limited to the above-described embodiments, and various improvements, changes, and modifications are possible within the scope of the spirit of the present disclosure.

[0079] For example, in the above embodiment, an example was shown in which the present disclosure is applied to a waste heat boiler that uses exhaust gas from an incineration plant 100, but the configuration of the present disclosure can be widely applied to solid fuel-fired boilers that generate dust.

[0080] In the above embodiment, the infrared cameras 11 and 12 are installed on the first heat transfer surface F1 and the second heat transfer surface F2, but in addition to or instead of this, an infrared camera may be installed on the third heat transfer surface F3 or the fourth heat transfer surface F4. Also, at least one infrared camera may be installed to obtain the surface temperature distribution on at least one heat transfer surface.

[0081] In the above embodiment, the computing unit 14 of the heat transfer surface monitoring device 1 and the controller 22 of the heat transfer surface cleaning system 2 are configured by separate computers, but this is not limiting. For example, the controller 22 of the heat transfer surface cleaning system 2 may include the computing unit 14 of the heat transfer surface monitoring device 1. Furthermore, for example, the control device of the incineration plant 100 may include the computing unit 14 and the controller 22.

[0082] Furthermore, in the above embodiment, an example has been given in which the controller 22 has functional blocks or circuits 222, 223, and 224 for performing machine learning, but the heat transfer surface cleaning system 2 may also be provided with a separate learning device for performing machine learning.

[0083] In addition, in the above embodiment, an example was given in which the remuneration calculation unit 223 calculates the remuneration based on the post-cleaning surface temperature in addition to the post-cleaning gas temperature, but the remuneration calculation unit 223 may also calculate the remuneration based only on the post-cleaning gas temperature.

[0084] In the above embodiment, the cleaning device 21 is a water jet cleaning device equipped with a water jet nozzle 21a that sprays water as a cleaning fluid. However, the present invention is not limited to this. For example, the cleaning device 21 may be a steam cleaning device equipped with a steam jet nozzle that sprays steam as a cleaning fluid. Furthermore, for example, the cleaning device 21 may be a pressure wave cleaning device equipped with an exhaust duct that sprays pressure waves as a cleaning fluid. Multiple types of cleaning devices may be combined. For example, the cleaning device 21 that cleans the radiation chamber 119 and the first flue 120 may be a water jet cleaning device, and the cleaning device 21 that cleans the second flue 121, into which the superheater protrudes, may be a pressure wave cleaning device or a steam cleaning device.

[0085] Furthermore, in the above embodiment, the cleaning device 21 supplies the cleaning fluid into the boiler 104 through the ceiling of the boiler 104, but the present invention is not limited to this. For example, the cleaning device 21 may supply the cleaning fluid into the boiler 104 through a sidewall of the boiler 104. For example, at least one of the heat transfer surfaces F1, F2, F3, and F4 may have an insertion hole through which the water supply pipe 21c is inserted. In this case, the heat transfer surface may have multiple insertion holes so that multiple heat transfer surface areas can be cleaned.

[0086] Furthermore, in the above embodiment, an example has been given of a configuration in which the incineration plant 100 is equipped with both the heat transfer surface monitoring device 1 and the heat transfer surface cleaning system 2, but the heat transfer surface cleaning system 2 can also be applied to boilers in incineration plants and the like that do not have the heat transfer surface monitoring system 1. That is, the heat transfer surface cleaning system 2 may be applied to cleaning the heat transfer surface in the boiler 104 without estimating the thickness of dust adhering to the heat transfer surface from the surface temperature distribution of the heat transfer surface and the gas temperature. For example, the cleaning device 21 may perform cleaning at a predetermined timing. The trained model described above may be used to control the amount of water sprayed at this time, and machine learning may be performed using the spray amount.

[0087] Summary of this disclosure A heat transfer surface cleaning system (2) according to one aspect of the present disclosure includes infrared cameras (11, 12) that measure surface temperatures of heat transfer surfaces (F1, F2, F3, F4) exposed inside a boiler (104) by photographing the heat transfer surfaces (F1, F2, F3, F4), a thermometer (13) that measures a gas temperature in an internal space of the boiler (104), a cleaning device (21) that cleans the heat transfer surfaces (F1, F2, F3, F4) by spraying a cleaning fluid, and a controller (22) that controls the amount of spray of the cleaning fluid. ), wherein the controller (22) outputs the injection amount, acquires state values ​​including the measured surface temperatures of the heat transfer surfaces (F1, F2, F3, F4) and the gas temperature, acquires a post-cleaning gas temperature measured by the thermometer (13) after cleaning by the cleaning device (21) based on the injection amount, calculates a reward based on the post-cleaning gas temperature, and performs machine learning to determine the injection amount to be output for the current state value based on the injection amount, the state amount, and the reward.

[0088] With the above configuration, the surface temperatures of the heat transfer surfaces F1, F2, F3, and F4 obtained from images captured by the infrared cameras 11 and 12 and the gas temperature Tg inside the boiler 104 are used as state values, and a reward is calculated based on the gas temperature Tg after cleaning by the cleaning device 21, thereby learning the optimal amount of water to be sprayed from the cleaning device 21, which is an operating value. This makes it possible to pinpoint the correlation between the dust thickness td estimated from the surface temperature Td of the heat transfer surface and the gas temperature Tg and the amount of water sprayed to clean the dust for each position on the heat transfer surface according to the surface temperature distribution of the heat transfer surface. Therefore, it is possible to apply a cleaning ability appropriate to the thickness of dust adhering to the heat transfer surface.

[0089] The controller (22) may calculate a positive reward if the post-cleaning gas temperature is less than a predetermined first reference value, and may calculate a negative or zero reward if the post-cleaning gas temperature is greater than the first reference value. This allows the reduction in the gas temperature Tg due to cleaning to be evaluated as a reduction in the thickness of dust and an increase in the thermal efficiency between the exhaust gas in the boiler 104 and the heat transfer tubes 123.

[0090] The controller (22) may acquire, in addition to the post-cleaning gas temperature, post-cleaning surface temperatures of the heat transfer surfaces (F1, F2, F3, F4) measured by the infrared cameras (11, 12) after cleaning by the cleaning device (21) based on the injection amount, and calculate the reward based on the post-cleaning gas temperature and the post-cleaning surface temperatures. This enables more appropriate learning using multiple evaluation criteria.

[0091] The controller (22) may calculate a positive reward if the surface temperature after cleaning is less than a predetermined second reference value, and may calculate a negative or zero reward if the surface temperature after cleaning is greater than the second reference value. In this way, the decrease in the surface temperature of the heat transfer surface due to cleaning can be evaluated as a decrease in the thickness of dust and an increase in heat exchange between the exhaust gas in the boiler 104 and the heat transfer tubes 123.

[0092] The controller (22) may calculate a positive reward if the difference between the gas temperature before cleaning and the gas temperature after cleaning is greater than a predetermined third reference value, and may calculate a negative or zero reward if the difference is less than the third reference value. This allows the reduction in the gas temperature Tg due to cleaning to be evaluated as a reduction in the thickness of dust and an increase in heat exchange between the exhaust gas in the boiler 104 and the heat transfer tubes 123.

[0093] The reward calculation unit (223) may calculate a positive reward when the value obtained by subtracting the surface temperature after cleaning from the surface temperature of the heat transfer surfaces (F1, F2, F3, F4) before cleaning is greater than a predetermined fourth reference value, and may calculate a negative or zero reward when the value is less than the fourth reference value. In this way, a decrease in the surface temperature of the heat transfer surfaces due to cleaning can be evaluated as a decrease in the thickness of dust and an increase in heat exchange between the exhaust gas in the boiler 104 and the heat transfer tubes 123.

[0094] The cleaning device (21) may include a water injection nozzle (21a) for injecting water as the cleaning fluid. [Explanation of symbols]

[0095] 2 Heat transfer surface cleaning system 11,12 Infrared camera 13 Thermometer 21 Cleaning equipment 21a Water injection nozzle 22 Controller 104 Boiler F1, F2, F3, F4 heat transfer surfaces

Claims

1. an infrared camera that measures the surface temperature of a heat transfer surface exposed inside the boiler by photographing the heat transfer surface; a thermometer for measuring a gas temperature in the internal space of the boiler; a cleaning device that cleans the heat transfer surface by spraying a cleaning fluid; a controller that controls the amount of spray of the cleaning fluid, The controller outputting the injection amount; acquiring state values ​​including the measured surface temperature distribution of the heat transfer surface and the gas temperature; After cleaning by the cleaning device based on the injection amount, a post-cleaning gas temperature measured by the thermometer is acquired, and a reward is calculated based on the post-cleaning gas temperature. A heat transfer surface cleaning system that performs machine learning to determine the injection amount to be output at the current state value based on the injection amount, the state value, and the reward.

2. 2. The heat transfer surface cleaning system of claim 1, wherein the controller calculates a positive reward if the post-cleaning gas temperature is less than a predetermined first reference value, and a negative or zero reward if the post-cleaning gas temperature is greater than the first reference value.

3. 3. The heat transfer surface cleaning system according to claim 1, wherein the controller acquires, in addition to the post-cleaning gas temperature, a post-cleaning surface temperature of the heat transfer surface measured by the infrared camera after cleaning by the cleaning device based on the injection amount, and calculates the reward based on the post-cleaning gas temperature and the post-cleaning surface temperature.

4. 4. The heat transfer surface cleaning system of claim 3, wherein the controller calculates a positive reward if the post-cleaning surface temperature is less than a predetermined second reference value, and a negative or zero reward if the post-cleaning surface temperature is greater than the second reference value.

5. 2. The heat transfer surface cleaning system of claim 1, wherein the controller calculates a positive reward if the difference between the gas temperature before cleaning and the gas temperature after cleaning is greater than a predetermined third reference value, and calculates a negative or zero reward if the difference is less than the third reference value.

6. 4. The heat transfer surface cleaning system of claim 3, wherein the controller calculates a positive reward when a value obtained by subtracting the surface temperature after cleaning from the surface temperature of the heat transfer surface before cleaning is greater than a predetermined fourth reference value, and calculates a negative or zero reward when the value obtained by subtracting the surface temperature after cleaning is less than the fourth reference value.

7. The heat transfer surface cleaning system according to claim 1 , wherein the cleaning device includes a water injection nozzle that injects water as the cleaning fluid.

Images