JP2020076543A - Boiler tube leakage diagnostic system and boiler tube leakage diagnosis method - Google Patents

Abstract

To provide a boiler tube leakage diagnosis system that further accurately detects the generation of tube leakage in a boiler, and specifies a position of the tube leakage.SOLUTION: A boiler tube leakage diagnosis system has an input data producing portion that inputs sensor information on a boiler, and produces input data used for the diagnosis of a tube leakage; an abnormality degree calculating portion that calculates an abnormality degree and an abnormality contribution degree; and an abnormality determining portion that diagnoses the tube leakage on the basis of the abnormality degree and the abnormality contribution degree calculated by the abnormality degree calculating portion.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a boiler tube leak diagnostic system that detects the occurrence of tube leak in a boiler and further identifies the position (occurrence site) of the tube leak.

  The boiler heats the feed water in a plurality of banks with high temperature gas generated by burning fuel such as coal to generate steam. The hot gas contains highly corrosive components produced from the sulfur component of fuels such as coal. Moreover, fatigue is repeatedly generated in the material by repeating start / stop and load changes.

  Under such an environment, the heat transfer tubes of the bank may be damaged due to creep, corrosion, thermal fatigue, and the like. The phenomenon in which the heat transfer tube is damaged and the steam flowing inside the heat transfer tube leaks (leaks) into the flow path of the high-temperature gas is called a tube leak.

  As a background art in this technical field, there is JP-A-2015-7509 (Patent Document 1). This publication describes a boiler tube leak detection device that realizes early detection of leak occurrence of tube leak of a boiler and early identification of the leak position, a measurement signal database that measures the state quantity of the boiler plant, and a boiler plant A state change detection unit that detects a change in the driving state and a detection content evaluation unit that evaluates the change detected by the state change detection unit are provided, and the state change detection unit includes the first measurement signal data of the measurement signal database. A monitoring data extraction unit that groups items into monitoring groups that include the metal temperatures of multiple heat exchangers in the boiler plant, an operation pattern evaluation unit that identifies the operation pattern of the boiler plant, and a monitoring group for each identified operation pattern. For each time, by comparing the diagnostic model and the second measurement signal with the classification unit that classifies the first measurement signal data belonging to the grouped data items and constructs the diagnostic model, it is possible to confirm that the operating state has changed. And a detection unit for detecting (see summary).

JP, 2005-7509, A

  Patent Document 1 describes a boiler tube leak detection device that realizes early detection of leak occurrence of tube leak of a boiler and early identification of the leak position. The boiler tube leak detection device described in Patent Document 1 detects the occurrence of a tube leak and identifies the position of the tube leak by processing the existing sensor information. Since the processing is performed, the fluctuation in the normal time that the sensor information has is not considered.

  Therefore, the present invention excludes the influence of the fluctuation at the normal time that the sensor information has, based on the change characteristic of the sensor information specific to the tube leak, more accurately detects the occurrence of the tube leak, and determines the position of the tube leak. Provide a boiler tube leak diagnostic system to identify.

  In order to solve the above problems, the boiler tube leak diagnosis system of the present invention inputs the sensor information of the boiler, and an input data creation unit that creates input data used in the diagnosis of the tube leak, and an input data creation unit. Anomaly degree calculation unit that calculates anomaly degree and anomaly contribution rate based on the created input data, and anomaly judgment that diagnoses tube leak based on the anomaly degree and anomaly contribution rate calculated by the anomaly degree calculation unit And a part.

  According to the present invention, based on the change characteristics of the sensor information peculiar to the tube leak, the influence of the fluctuation in the normal time of the sensor information is excluded, the occurrence of the tube leak is detected more accurately, and the position of the tube leak is detected. A boiler tube leak diagnosis system for specifying can be provided.

  The problems, configurations and effects other than those described above will be clarified by the following description of the embodiments.

It is explanatory drawing explaining the general structure of a boiler. It is explanatory drawing explaining the general structure of the bank which comprises a boiler. It is explanatory drawing explaining the structure of a boiler tube leak diagnostic system. It is explanatory drawing explaining the input / output relationship in whole bank behavior analysis. It is explanatory drawing explaining the input / output relationship in local behavior analysis of a bank. It is explanatory drawing explaining the structure in the whole behavior analysis of an input data database. It is explanatory drawing explaining the structure in the local behavior analysis of an input data database. It is explanatory drawing explaining the structure in the whole behavior analysis of an abnormality degree database. It is explanatory drawing explaining the structure in the local behavior analysis of an abnormality degree database. It is explanatory drawing explaining the logic for pinpointing the position of tube leak. It is explanatory drawing explaining the structure of a diagnostic result database. It is explanatory drawing explaining the structure of a sensor position information database. It is explanatory drawing explaining the display screen of a tube leak diagnostic information summary. It is explanatory drawing explaining the display screen of the whole behavior analysis result of a bank. It is explanatory drawing explaining the display screen of the local behavior analysis result of a bank.

  Embodiments of the present invention will be described below with reference to the drawings. It should be noted that the same components are denoted by the same reference numerals, and when the description is duplicated, the description may be omitted.

  FIG. 1 is an explanatory diagram illustrating a general configuration of a boiler.

  The boiler 100 described in the present embodiment includes a burner 101 that burns fuel such as coal to generate high-temperature gas, and a plurality of burners that heat feed water (or steam) to generate steam (or further heat steam). And a bank. The bank in this embodiment is a heat exchanger that exchanges heat between the high temperature gas and the feed water (or steam) to generate steam from the feed water (or further heat the steam).

  The thick solid lines shown in FIG. 1 indicate the heat transfer tubes of the bank, and may be referred to as tubes.

  The bank in this embodiment includes a economizer 109, a water wall 102, a cage wall 110, a primary heater 108, a secondary heater 103, a tertiary heater 104, a quaternary heater 105, a primary reheater 107, and a secondary heater. There are a next reheater 106 and a ceiling wall 113.

  The hot gas generated by the burner 101 flows from upstream to downstream while performing heat exchange (heat transfer to water supply or steam) in a plurality of banks in the boiler 100. For this reason, the temperature of the hot gas decreases toward the downstream side. Then, the high temperature gas is finally discharged from the gas damper 111 as exhaust gas.

  Hereinafter, each bank will be described along the flow of high-temperature gas.

  The water wall 102 is a heat transfer tube installed along the inner wall of the boiler 100. Further, the secondary superheater 103 is a heat transfer tube (aggregate) installed so as to be suspended in the hot gas flow path of the boiler 100. Similarly, the tertiary superheater 104, the quaternary superheater 105, and the secondary reheater 106 are also heat transfer tubes (aggregates) installed so as to be suspended in the hot gas passage of the boiler 100.

  The downstream (downstream from the secondary reheater 106), that is, from the downstream of the secondary reheater 106 to the gas damper 111 is referred to as a "rear transfer surface". On the rear transmission surface, the partition wall 112 divides the hot gas passage into two. The distribution of the flow rate of the high temperature gas in each flow path is adjusted by the opening degree of the gas damper 111.

  A primary reheater 107, which is a heat transfer tube, is installed on one of the rear heat transfer surfaces. A primary superheater 108, which is a heat transfer tube, and a economizer 109, which is a heat transfer tube, are installed on the other side of the rear surface. The cage wall 110 is a heat transfer tube installed along the inner wall of the rear heat transfer surface.

  The ceiling wall 113 is also a heat transfer tube installed along the upper part (ceiling) of the boiler 100.

  Also, each bank will be described along the flow of steam.

  Steam (water supply) to the boiler 100 flows through the economizer 109, the water wall 102, the cage wall 110, the primary superheater 108, the secondary superheater 103, the tertiary superheater 104, and the quaternary superheater 105.

  The outlet steam of the quaternary superheater 105 flows to a high pressure steam turbine (not shown). Since the thermal energy of the outlet steam of the quaternary superheater 105 is used for the rotational power of the high-pressure steam turbine, the outlet steam of the high-pressure steam turbine has a lower temperature than the inlet steam of the high-pressure steam turbine. The outlet steam of the high-pressure steam turbine flows again to the primary reheater 107 and the secondary reheater 106 and is heated.

  The outlet steam of the secondary reheater 106 flows to a medium and low pressure steam turbine (not shown). Since the thermal energy of the outlet steam of the secondary reheater 106 is used for the rotational power of the medium-low pressure steam turbine, the outlet steam of the medium-low pressure steam turbine has a lower temperature than the inlet steam of the medium-low pressure steam turbine. .. The outlet steam of the medium- and low-pressure steam turbine flows into the economizer 109 again and is heated.

  In FIG. 1, the steam pipes connecting the banks are omitted.

  Also, some banks, such as, for example, secondary superheater 103, tertiary superheater 104, quaternary superheater 105, secondary reheater 106, have steam headers at the inlet and outlet (inlet steam header and Outlet steam header) is installed. A plurality of heat transfer tubes are connected to the steam header, and the steam flowing from each heat transfer tube is mixed inside the steam header. As a result, the steam temperature difference between the heat transfer tubes can be made uniform.

  FIG. 2 is an explanatory diagram illustrating a general configuration of a bank that constitutes a boiler, and is a schematic diagram that schematically shows the relationship between the outlet steam header and the heat transfer tubes. Note that this schematic diagram is an overhead view of the secondary reheater 106 shown in FIG. 1 from the rear side.

  In this way, a large number of heat transfer tubes are installed in the furnace width direction of the boiler 100, and the outlet side of the heat transfer tubes is connected to the outlet steam header. The outlet steam header is located outside the furnace, and the heat transfer tube is installed so as to penetrate through the ceiling wall 113.

  High-temperature gas is flowing inside the furnace (inside the ceiling wall 113) of the heat transfer tube, and heats steam flowing inside the heat transfer tube (heat transfer area with the high-temperature gas).

  On the other hand, since the high temperature gas does not flow outside the furnace of the heat transfer tube (outside the ceiling wall 113), the outside temperature of the outside of the furnace is lower than that of the inside of the furnace. Therefore, a metal temperature sensor for measuring the metal temperature of the heat transfer tube is installed outside the furnace. Since the ambient temperature is high inside the furnace, the metal temperature sensor cannot be installed. The metal temperature sensor is installed on the surface of the heat transfer tube, and the heat insulating material is wound around the entire heat transfer tube installed on the outside of the furnace.

  In the present embodiment, the metal temperature measured by the metal temperature sensor changes little with the temperature of the steam flowing inside the heat transfer tube because the influence of the high-temperature gas is small. Because it is transmitted, there is some effect). The metal temperature sensor is installed in a plurality of heat transfer tubes so that the distribution of the steam temperature in the furnace width direction can be grasped.

  In this way, the metal temperature sensor is installed in multiple heat transfer tubes, and the distribution of the steam temperature in the furnace width direction can be grasped, so it is easy to change the steam temperature in the furnace width direction in the block where the metal temperature sensor is installed. Can be detected.

  The steam mixed in the outlet steam header flows from the left and right of the outlet steam header, and flows to the inlet steam header of the next bank (in this embodiment, the medium and low pressure steam turbine).

  The steam temperature sensor measures the steam temperature of the outlet steam of the outlet steam header (the outlet steam of the secondary reheater 106), and the outlet steam header to the inlet steam header of the next bank (in this embodiment, medium and low pressure). It is installed in the steam pipe (near the outlet of the outlet steam header) formed between the steam turbine and the steam turbine.

  In this embodiment, the steam temperature sensors are installed on the left and right (two places) of the outlet steam header.

  FIG. 3 is an explanatory diagram illustrating the configuration of the boiler tube leak diagnosis system.

  The boiler tube leak diagnosis system 1 described in the present embodiment receives sensor information of the boiler 100 from the control device 3 that controls the boiler 100 that is the target of the boiler tube leak diagnosis. The sensor information is a measurement value (sensor data) measured by various sensors.

  The input / output device 4 is also an input / output device for presenting the diagnosis result diagnosed by the boiler tube leak diagnosis system 1 to the user. The user can select the bank requested (input) by the input / output device 4.

  Next, the boiler tube leak diagnosis system 1 includes an input data creation unit 11, an abnormality degree calculation unit 12, and an abnormality determination unit 13.

  The input data creation unit 11 inputs the sensor information of the boiler 100 via the control device 3 and creates the input data used in the tube leak diagnosis.

  The abnormality degree calculation unit 12 calculates the abnormality degree and the abnormality contribution rate based on the input data created by the input data creation unit 11. Here, the degree of abnormality is a numerical representation of the difference when the data pattern in the normal state is compared with the data pattern created based on the input data. Further, the degree of abnormality contribution represents how much each factor influences the variation of the degree of abnormality.

  It should be noted that the abnormality degree calculation unit 12 calculates the heat collection distribution of each bank from the heat collection amount of each bank constituting the boiler 100 and the metal temperatures at a plurality of points in each bank.

  The abnormality determination unit 13 diagnoses the tube leak based on the abnormality degree and the abnormality contribution rate calculated by the abnormality degree calculation unit 12. Here, the tube leak diagnosis is to detect the occurrence of the tube leak and to identify the position of the tube leak.

  Before describing the processing of each unit, an outline of a method of identifying the position of the tube leak based on the sensor information of the boiler 100 will be described.

  The boiler tube leak diagnosis system 1 described in the present embodiment determines an abnormality based on the data pattern recognition of the sensor information of the boiler 100. That is, regarding the sensor information of the boiler 100 that affects the tube leak, a data pattern of the sensor information of the boiler 100 in a normal state in advance (data pattern of heat collection amount of each bank, data pattern of heat collection distribution of each bank, station of each bank). Data pattern of the collected heat quantity) is learned.

  Then, in the process of determining abnormality, the input data pattern of the sensor information of the boiler 100 is compared with the previously learned normal data pattern of the sensor information of the boiler 100, and the difference is a predetermined value (preliminary value). If the value exceeds the setting value set in (1), it is determined to be abnormal.

  The abnormality determination unit 13 compares the heat collection amount of each bank in a normal state learned in advance with the heat collection amount of each bank calculated by the abnormality calculation unit 12, and calculates the heat collection amount of each bank. Is determined as the abnormality degree and the abnormality contribution degree, whether or not the amount of heat deviated from the normal heat collection amount of each bank.

  Further, the abnormality determination unit 13 compares the heat collection distribution of each bank in a normal state learned in advance with the heat collection distribution of each bank calculated by the abnormality degree calculation unit 12, and calculates the calculated heat collection distribution of each bank. Whether or not the heat collection distribution deviates from the normal heat collection distribution of each bank is determined as the abnormality degree and the abnormality contribution degree.

  In this embodiment, as such abnormality determination processing, for example, abnormality determination processing using clustering, which is one pattern recognition, described in International Publication WO2018 / 051568A1 can be used.

  In this abnormality determination processing, the input pattern is compared with the normal pattern, and the abnormality degree is obtained based on the difference. Further, it is also possible to obtain the abnormality contribution rate for each input information (data), for example, for each input sensor information (sensor data). This abnormality determination processing detects the occurrence of an abnormality due to an increase in the abnormality degree, and further determines which sensor information (sensor data) is abnormal based on the abnormality contribution rate for each sensor information (sensor data). You can

  Here, the abnormality contribution rate is standardized so that a value obtained by summing the abnormality contribution rates for each sensor information (sensor data) matches the abnormality degree. That is, the abnormality degree expresses a difference from the normality with respect to the entire set of a plurality of sensor information (sensor data), while the abnormality contribution rate indicates the sensor information (sensor data) for the abnormality degree. It is a breakdown.

  Next, a position identifying method (occurrence site identifying method) for identifying the position of the tube leak using pattern recognition will be described.

  The boiler tube leak diagnosis system 1 described in the present embodiment specifies the position of the tube leak based on the sensor information of each bank installed inside the boiler 100.

  Then, the boiler tube leak diagnosis system 1 performs an analysis for the purpose of catching a change tendency of the sensor information measured by the metal temperature sensor installed in the bank in which the tube leak has occurred. This analysis is done from two perspectives.

  The first (viewpoint 1) is to capture changes in mutual relationships among a plurality of banks installed inside the boiler 100. The heat collection amount of each bank influences each other not only when the tube leak occurs but also when the tube leak is normal.

  When viewed from the high temperature gas side, if the heat collection amount of a bank decreases, the heat transfer amount from the high temperature gas to the steam decreases. Along with this, the temperature of the hot gas downstream of this bank rises above normal. Therefore, in the bank downstream of this bank, a high temperature gas having a temperature higher than usual is supplied, and the heat collection amount tends to increase.

  Further, when viewed from the steam side, when the heat collection amount of a bank decreases, the outlet steam temperature of the bank decreases. For this reason, in the bank downstream of this bank, steam having a temperature lower than usual is supplied, the temperature difference between the steam and the high temperature gas becomes larger than usual, and the amount of heat collected tends to increase.

  In the boiler 100 described in the present embodiment, the flow direction of hot gas (flow direction of hot gas in contact with each bank) and the flow direction of steam (flow direction of steam flowing in each bank) are different. In other words, the direction in which the high-temperature gas that touches each bank flows while decreasing its temperature from a high temperature to a low temperature and the direction in which the steam flowing through each bank flows while increasing its temperature from a low temperature to a high temperature are different. Therefore, each bank influences each other, and the heat collection amount in each bank changes. This relationship is very complex.

  Then, when a tube leak occurs, the flow rate of steam flowing inside the heat transfer tube of the bank is reduced, and the bank downstream thereof is also affected. Similarly, the flow rate of steam flowing inside the heat transfer tube of this downstream bank is also reduced. ..

  Further, when the tube leak occurs, steam is mixed in the high temperature gas, so that the flow rate of the high temperature gas increases and the temperature of the high temperature gas decreases. Further, since steam is mixed in the high temperature gas, a large amount of water is included, and heat transfer characteristics such as a viscosity coefficient change.

  As described above, when the tube leak occurs, the steam is mixed with the high temperature gas, and the high temperature gas mixed with the steam also affects the bank installed downstream of the position of the tube leak.

  Further, the metal temperature effective for detecting the occurrence of tube leak also changes due to various factors. For example, the metal temperature effective for detecting the occurrence of tube leak has the following fluctuation factors.

  One factor is flame fluctuations in the burner 101. In particular, the burner 101, which burns fuel such as coal to generate high-temperature gas, has fluctuations in the coal crushing performance in the mill and fluctuations in the combustion characteristics due to changes in the coal type even when the output is constant. Always shakes. When the flame fluctuates, the temperature distribution of the hot gas also fluctuates, which changes the metal temperature.

  Also, one factor is sootblower. The soot blower is an operation of injecting high-temperature steam into the heat transfer tube of the bank during operation in order to remove ash attached to the surface of the heat transfer tube of the bank in the boiler 100 that burns fuel such as coal. In the bank in which the sootblower is operated, the heat collection amount increases instantaneously, and when ash adheres again, the heat collection amount decreases. The metal temperature also changes in accordance with the change in the amount of collected heat.

  That is, even at the metal temperature, it is not always effective for detecting the occurrence of the tube leak.

  The feed water flow rate for detecting the occurrence of the tube leak also has the following fluctuation factors, for example. The feedwater flow rate is controlled by feedback control of the steam temperature and steam pressure at the outlet of the boiler 100, in addition to the feedforward control at the load command value. For example, if the steam temperature or steam pressure fluctuates due to flame fluctuations, the feedwater flow rate also fluctuates.

  Further, the bank is composed of a large number of heat transfer tubes, and an initial tube leak, that is, a tube leak at the time when one heat transfer tube is broken, the feed water flow rate may be less than 1% of the whole. The change in the water supply flow rate caused by the tube leak is small and stays within the fluctuation range under normal conditions, so it is difficult to detect the occurrence of the tube leak in the water supply flow rate.

  That is, even if the supply water flow rate is detected, it is not always effective for detecting the occurrence of the tube leak.

  That is, the heat collection amount of each bank influences each other not only when the tube leak occurs but also when the tube leak is normal. Further, when the tube leak occurs, it affects not only the bank in which the tube leak occurs but also all the banks installed inside the boiler 100, and the heat collection balance of these banks changes.

  The change in the heat collection balance of all such banks is analyzed using pattern recognition.

  FIG. 4 is an explanatory diagram for explaining the input / output relationship in the overall behavior analysis of the bank, and shows the input / output data in the pattern recognition processing.

  As inputs, the amount of heat collected by the water wall 102, the amount of heat collected by the secondary superheater 103, the amount of heat collected by the third superheater 104, the amount of heat collected by the quaternary superheater 105, the amount of heat collected by the secondary reheater 106, and the primary reheat The heat collection amount of the container 107, the heat collection amount of the primary superheater 108, the heat collection amount of the economizer 109, and the cage wall 110.

  The heat collection amount (Q: kW) can be calculated based on the sensor information from the steam temperature sensor installed in each bank. The sensor information used is the inlet steam temperature (Tin: ° C), the outlet steam temperature (Tout: ° C), the steam flow rate (F: Kg / s), and the steam pressure (P: MPa). Calculate with (3).

  Note that Hout is the exit steam enthalpy (kJ / kg), Hin is the entrance steam enthalpy (kJ / kg), and Func () is the steam function of the enthalpy.

  On the other hand, the outputs are the degree of abnormality for all banks and the degree of abnormality contribution of each bank to the degree of abnormality. That is, as outputs, the abnormalities of the entire input and the abnormal contributions of each input (bank) (water wall 102, secondary superheater 103, tertiary superheater 104, quaternary superheater 105, secondary reheater) 106, primary reheater 107, primary superheater 108, economizer 109, cage wall 110).

  With this, it is possible to determine whether the heat absorption balance of all the banks deviates from the normal state or, if deviates from the normal state, which bank deviates. ..

  The second (viewpoint 2) is an analysis that captures the distribution (heat collection distribution) of the heat collection amount in each bank in the furnace width direction. Here, the heat collection distribution means the distribution of the heat collection amount in each bank shown in FIG. 2 in the furnace width direction.

  As described above, the metal temperature at the outlet of the heat transfer tube changes in conjunction with the outlet steam temperature inside the heat transfer tube. Therefore, the distribution of the outlet steam temperature in the furnace width direction, that is, the distribution of the heat collection amount in the furnace width direction can be grasped from the distribution of the metal temperature in the heat transfer tubes of each bank in the furnace width direction.

  In the actual boiler 100, the outlet steam temperature in the furnace width direction is often not uniform. This is because the distribution of the temperature of the high-temperature gas occurs as the flame spreads in the burner 101, and the flow rate of steam flowing from the inlet steam header into each heat transfer tube is not always uniform. This is because

  When a tube leak occurs, the heat transfer characteristics locally change in the furnace width direction. For example, when a leak occurs on the left side of the bank, the heat transfer characteristic changes on the left side of the bank, but the heat transfer characteristic does not change much on the right side of the bank. Therefore, the distribution of the outlet steam temperature in the furnace width direction is in a state in which only a part is locally changed as compared with the normal time.

  The distribution of the amount of heat collected in each bank in the width direction of the furnace is analyzed using pattern recognition.

  FIG. 5 is an explanatory diagram for explaining the input / output relationship in the local behavior analysis of the bank, and shows the input / output data in the pattern recognition processing. In this analysis, pattern recognition processing is performed for each bank.

  The (a) water wall shown in FIG. 5 is an analysis for the water wall 102 shown in FIG. 1, and the (b) secondary superheater shown in FIG. 5 is for the secondary superheater 103 shown in FIG. Analysis. Similarly, the pattern recognition process is also performed on the tertiary superheater 104 and the like. Here, the water wall 102 and the secondary superheater 103 will be described.

  (a) The input of the water wall is a metal temperature measured by a plurality of metal temperature sensors installed in the heat transfer tube of the water wall 102. For example, metal temperature sensors are installed at 20 locations (blocks) of the heat transfer tube of the water wall 102, such as the water wall outlet metal temperature 1, the water wall outlet metal temperature 2, ... Input the sensor information of each metal temperature sensor.

  On the other hand, (a) the output of the water wall is the degree of abnormality with respect to the entire metal temperature data, and the degree of abnormality contribution of each metal temperature sensor to the degree of abnormality. That is, the abnormalities of the entire input and the abnormal contributions of each input (metal temperature sensor) (water wall outlet metal temperature 1, water wall outlet metal temperature 2, ..., Water wall outlet metal temperature 20).

  Further, (b) the input of the secondary superheater is the metal temperature measured by a plurality of metal temperature sensors installed in the heat transfer tube of the secondary superheater 103. For example, a secondary superheater metal temperature 1, a secondary superheater metal temperature 2, ..., A secondary superheater metal temperature 10, such as a secondary superheater metal temperature 10, is provided at 10 locations (blocks) of the heat transfer tube of the secondary superheater 103. Is installed, and sensor information of each metal temperature sensor is input.

  On the other hand, (b) the output of the secondary superheater is the degree of abnormality with respect to the entire metal temperature data, and the degree of abnormality contribution of each metal temperature sensor to the degree of abnormality. That is, the abnormalities of the entire input and the abnormal contributions of each input (metal temperature sensor) (secondary superheater metal temperature 1, secondary superheater metal temperature 2, ..., Secondary superheater metal temperature 10) is there.

  As a result, the distribution of the metal temperature in the width direction of the furnace deviates from the normal state, and if it deviates from the normal state, which metal temperature sensor sensor information deviates? Can be determined.

  The metal temperature sensors are preferably installed at equal intervals in the furnace width direction. Since the sensor information of the metal temperature sensor close to the position of the tube leak is more affected by the leaking steam, the position of the tube leak in the furnace width direction can be specified from the abnormal contribution of each metal temperature sensor. ..

  As a result, it is possible to specify the rough tube leak position such as left, center, or right of a predetermined bank. As described above, the boiler tube leak diagnosis described in the present embodiment is particularly effective for specifying the position of the tube leak in the furnace width direction of the bank.

  By performing the same analysis for each bank, it is possible to determine whether the distribution of the metal temperature in the width direction of the furnace deviates from the normal state. If so, it is possible to determine which metal temperature sensor the sensor information deviates from.

  As described above, the boiler tube diagnosis system described in the present embodiment analyzes sensor information from two viewpoints. The former analyzes the overall behavior of all the banks composing the boiler 100, while the latter analyzes the local behavior of each bank composing the boiler 100.

  Based on these two viewpoints, the processing contents of the boiler tube leak diagnosis system described in this embodiment will be described.

  In the boiler tube leak diagnosis system 1 shown in FIG. 3, the following processing is executed.

  The input data creation unit 11 inputs sensor information via the control device 3, creates input data to be used in pattern recognition processing, and stores it in an input data database (hereinafter, the database may be referred to as DB) 14. Store.

  FIG. 6A is an explanatory diagram illustrating a configuration in the overall behavior analysis of the input data database. Further, FIG. 6B is an explanatory diagram illustrating a configuration in the local behavior analysis of the input data database.

  There are two types of input data. The input data shown in FIG. 6A is used for the overall behavior analysis of the entire bank, and the input data shown in FIG. 6B is used for the local behavior analysis of each bank. Is. Since the boiler tube leak diagnosis processing described in this embodiment is executed at a constant cycle (every 1 second in this embodiment), input data is also stored in time series. It should be noted that this input data is created by the input data creation unit 11.

  The input data described in FIG. 6A is the heat collection amount of each bank such as the water wall 102 and the secondary superheater 103.

  Note that the heat collection amount is calculated by using the steam temperature sensor installed in the inlet steam header, the steam temperature sensor installed in the outlet steam header, and the steam flow rate of each bank, as shown in equations (1) to (3). It is calculated using sensor information from each of the steam flow sensor to measure and the steam pressure sensor to measure steam pressure.

  The input data creation unit 11 inputs the sensor information necessary for calculating the heat absorption amount of each bank, calculates the heat absorption amount, and stores the result in the input data DB 14.

  On the other hand, the input data shown in FIG. 6B is the metal temperature of each bank such as the water wall 102 and the secondary superheater 103. At this time, the input data is sensor information of each block corresponding to the plurality of installed metal temperature sensors.

  In this way, the input data creation unit 11 inputs the sensor information, calculates the heat absorption amount of each bank, and creates the input data used for the pattern recognition processing.

  The abnormality degree calculation unit 12 calculates an abnormality degree and an abnormality contribution rate by using pattern recognition processing based on the input data (heat absorption amount and metal temperature) created by the input data creation unit 11, and calculates the abnormality degree. Store in DB15.

  FIG. 7A is an explanatory diagram illustrating a configuration in the overall behavior analysis of the abnormality degree database. Further, FIG. 7B is an explanatory diagram illustrating a configuration in the local behavior analysis of the abnormality degree database.

  There are two types of analysis results. The analysis result shown in FIG. 7A is used for the overall behavior analysis of the entire bank, and the analysis result shown in FIG. 7B is used for the local behavior analysis of each bank. Is. Since the boiler tube leak diagnosis processing described in this embodiment is executed at a constant cycle (every 1 second in this embodiment), the analysis result is also stored in time series. The analysis result is calculated (analyzed) by the abnormality degree calculation unit 12.

  The analysis results described in FIG. 7A are the degree of abnormality of the boiler 100, the degree of abnormality contribution of each bank such as the water wall 102 and the secondary superheater 103.

  On the other hand, the analysis results shown in FIG. 7B show that the degree of abnormality of the water wall 102, the degree of abnormality contribution to the sensor information of each of the plurality of metal temperature sensors installed on the water wall 102, the degree of abnormality of the secondary superheater 103, It is the degree of abnormality contribution to the sensor information of each of the plurality of metal temperature sensors installed in the next superheater 103. At this time, the analysis result corresponds to the input data, and is for the sensor information of each block corresponding to the plurality of metal temperature sensors to be installed.

  As described above, in the pattern recognition processing described in the present embodiment, the degree of abnormality of the boiler 100, the degree of abnormality of each bank such as the water wall 102 and the secondary superheater 103, and the degree of abnormality such as the water wall 102 and the secondary superheater 103. The degree of abnormal contribution of the bank, the degree of abnormal contribution to the sensor information of each of the plurality of metal temperature sensors installed in the water wall 102, the secondary superheater 103, etc. are analyzed.

  Then, these analysis results are stored in the abnormality degree DB 15.

  The input data stored in the input data DB 14 and the analysis result stored in the abnormality degree DB 15 are output to the input / output device 4 via the input / output control unit 18.

  FIG. 8 is an explanatory diagram illustrating a logic for specifying the position of the tube leak.

  The abnormality determination unit 13 detects the occurrence of the tube leak based on the abnormality degree and the abnormality contribution rate calculated by the abnormality degree calculation unit 12, and specifies the position of the tube leak.

  The boiler tube leak diagnosis system described in the present embodiment includes a process 201 for analyzing the overall behavior of all banks composing the boiler 100, and a process for analyzing the local behavior of each bank composing the boiler 100. 202 and two processes.

  In process 201, the following process is executed.

  By pattern recognition processing, the degree of abnormality of the boiler 100 and the degree of abnormality contribution of each bank such as the water wall 102, the secondary superheater 103, etc. are analyzed (S1).

  Next, it is determined whether the degree of abnormality exceeds a predetermined threshold value (S2). If the degree of abnormality does not exceed the predetermined threshold, the process ends.

  On the other hand, if the degree of abnormality exceeds the threshold value (YES), the bank having the highest degree of abnormality contribution is determined to be the bank in which the tube leak has occurred, and that bank is output (S3).

  On the other hand, in the process 202, the following processes are executed.

  By the pattern recognition processing, for example, the degree of abnormality of the water wall 102 and the degree of abnormality contribution (local behavior analysis of the bank 1) to the sensor information of each of the plurality of metal temperature sensors installed on the water wall 102 are analyzed (S4).

  Next, it is determined whether the degree of abnormality exceeds a predetermined threshold value (S5). If the degree of abnormality does not exceed the predetermined threshold, the process ends.

  On the other hand, when the degree of abnormality exceeds the threshold value (YES), it is determined that the bank has a tube leak in the water wall 102, and the bank is output.

  Similarly, by pattern recognition processing, for example, the degree of abnormality of the secondary superheater 103 and the degree of abnormality contribution to the sensor information of each of the plurality of metal temperature sensors installed in the secondary superheater 103 (local behavior analysis of the bank 2) Is analyzed (S6).

  Next, it is determined whether the degree of abnormality exceeds a predetermined threshold value (S7). If the degree of abnormality does not exceed the predetermined threshold, the process ends.

  On the other hand, if the degree of abnormality exceeds the threshold value (YES), it is determined that the bank has a tube leak in the secondary superheater 103, and that bank is output.

  Similarly, the tertiary superheater 104 and the like are also analyzed by pattern recognition processing. Here, the water wall 102 and the secondary superheater 103 will be described.

  Then, the bank output by the process 201 is compared with the bank output by the process 202, and if the same bank exists, the occurrence of the tube leak can be detected and the position of the tube leak can be specified. Is output as "a bank in which a tube leak has occurred (diagnosis result)" (S8).

  As described above, the abnormality determination unit 13 detects the occurrence of the tube leak by executing the above two processes based on the abnormality degree and the abnormality contribution rate stored in the abnormality degree DB 15, and detects the position of the tube leak. The diagnosis result is specified and stored in the diagnosis result DB 17.

  As described above, the abnormality determination unit 13 includes a bank deviating from the normal heat collection amount (determined in process 201) and a bank deviating from the normal heat collection distribution (determined in process 202). When it is determined that the same, the banks determined to be the same are determined to be the banks in which the tube leak has occurred.

  Further, the diagnosis result is output to the input / output device 4 via the input / output control unit 18.

  FIG. 9 is an explanatory diagram illustrating the configuration of the diagnosis result database.

  The diagnosis result DB 17 stores the diagnosis result in a text format in time series. A flag "1" or "2" is stored in the "information type" column.

  The flag “1” or “2” stored in the “information type” column will be described.

  The flag "1" is information output when the degree of abnormality exceeds the threshold value. Therefore, although the occurrence of tube leak is detected, the position of tube leak is not specified. The information stored in the diagnosis result DB under this condition is the bank whose abnormality degree exceeds the threshold value in the case of local behavior analysis. In the case of the whole behavior analysis, when the degree of abnormality exceeds the threshold value, the bank having the largest degree of abnormality contribution is the bank.

  On the other hand, the flag “2” is information output at the time when the position of the tube leak is specified. At this time, information about the position of the bank in the furnace width direction is also output as a diagnosis result. For example, at date and time 2018/1/1 12:02, “2” is output as the information type and “secondary superheater left side” is output as the diagnosis result. This "left side" corresponds to the information regarding the position of the bank in the furnace width direction.

  The position of the tube leak in the furnace width direction of the bank is the metal temperature sensor that measures the metal temperature at which the anomalous contribution is the maximum when the anomaly exceeds the threshold in the local behavior analysis of each bank. The position (block) where is installed is determined as the position of the tube leak.

  The sensor position information DB 16 stores position information where the metal temperature sensor is installed in each bank.

  FIG. 10 is an explanatory diagram illustrating the configuration of the sensor position information database.

  The sensor position information DB 16 stores each metal temperature sensor and a position (installation position information) at which the metal temperature sensor is installed, corresponding to each bank.

  For example, the metal temperature sensor for measuring the metal temperature 1 of the water wall 102 is "left side of the front wall", the metal temperature sensor for measuring metal temperature 2 of the water wall 102 is "left side of the front wall" ..., of the secondary superheater 103. The metal temperature sensor for measuring the metal temperature 1 is stored on the “left side”, the metal temperature sensor for measuring the metal temperature 2 of the secondary superheater 103 is stored on the “left side”, and so on.

  Similarly, the tertiary superheater 104 and the like are also stored.

  That is, for example, when the abnormal contribution of the metal temperature sensor that measures the metal temperature 1 is maximum as a result of the local behavior analysis in the secondary superheater 103 (judged result), it is stored in the sensor position information DB 16. The position of the tube leak is specified on the left side of the secondary superheater 103 on the basis of the position information.

  In this way, the abnormality determination unit 13 identifies the position of the tube leak in the furnace width direction of each bank from the installation position information of the metal temperature sensor in each bank and the determined result.

  As described above, the boiler tube leak diagnosis system described in the present embodiment detects the occurrence of tube leak and identifies the position of tube leak.

  In order to further improve the accuracy of the tube leak diagnosis, when the input data creating unit 11 creates the input data, for example, the rate of change of the heat absorption amount and the metal temperature used (change per unit time). Amount) may be used. As a result, it is possible to improve the accuracy of diagnosis even when the measured value of the metal temperature sensor has a characteristic in a temporal change such as a sudden change.

  In other words, it is possible to calculate the time change rate of the heat collection amount and the metal temperature, and analyze it by inputting this.

  Regarding the metal temperature, an average value or a median value of the measured values of a plurality of metal temperature sensors installed in the furnace width direction of the bank may be calculated, and a difference from the average value or the median value may be used. As a result, the relative magnitude relationship between the metal temperatures can be grasped, and the deviation from the normal state can be determined based on the distribution in which the temperature increase / decrease in the entire furnace width direction is excluded.

  Further, the metal temperature can also be converted into a heat absorption amount by the method described below. This is calculated using the following equations (4)-(8).

  Here, TMa is the average value (° C) of the metal temperature in the furnace width direction, TM (i) is the measured value (° C) of the i-th metal temperature sensor, N is the number of installed metal temperature sensors, and Tout. (I) is the estimated value (° C) of the outlet steam temperature of the i-th block, Tout is the measured value of the outlet steam temperature (° C), and Hout (i) is the estimated value of the outlet steam enthalpy of the i-th block (kJ / kg), Hin is the inlet vapor enthalpy (kJ / kg), Func () is the enthalpy vapor function, P is the vapor pressure (MPa), Tin is the inlet vapor temperature (° C), and Q (i) is the i-th block. Is an estimated value (kW) of the heat collection amount, and F is a steam flow rate (kg / s).

  In this calculation, the relative temperature distribution in the furnace width direction is the same between the metal temperature and the outlet steam temperature of each heat transfer tube (outlet steam temperature from the steam temperature sensor), and It is assumed that the flowing steam flow rate is uniform. That is, the furnace width direction is divided into blocks according to the number of metal temperature sensors in each bank, and the heat collection amount (local heat collection amount) in each block is estimated. The metal temperature follows only the change of the outlet steam temperature. On the other hand, the heat collection amount of each block obtained by this calculation can also take into consideration changes in the steam flow rate and the inlet steam temperature.

  That is, the abnormality calculating unit 12 divides each bank in the furnace width direction according to the number of metal temperature sensors, and increases or decreases the furnace width direction of the metal temperature sensors (a difference value with respect to an average value or a median value) and steam. The outlet steam temperature from the temperature sensor is used to calculate the local heat absorption of each divided block.

  Then, the abnormality determination unit 13 uses the local heat absorption amount to compare the local heat absorption amount of each bank in a normal state learned in advance with the local heat amount amount of each bank calculated by the abnormality degree calculation unit 13. Then, it is determined whether or not the calculated local heat absorption amount of each bank deviates from the normal heat absorption amount of each bank as the abnormality degree and the abnormality contribution degree.

  Similar to the metal temperature, the local heat absorption amount is also calculated by calculating the difference value with respect to the average value and the median value in the furnace width direction of the bank, and using this as an input, the deviation from the normal state is determined. can do.

  Further, as with the heat absorption amount and the metal temperature, the temporal change rate of the local heat absorption amount can be calculated and analyzed with this as an input.

  In addition, the boiler tube leak diagnosis method described in the present embodiment inputs the sensor information of the boiler, creates the input data used in the diagnosis of the tube leak, and based on the created input data, the abnormality degree and the abnormal contribution. The degree is calculated, and the tube leak is diagnosed based on the calculated degree of abnormality and degree of abnormality contribution.

  As described above, according to the present embodiment, it is possible to detect the occurrence of the tube leak earlier and to identify the position of the tube leak. Thereby, the period until the operation of the boiler 100 is restarted can be shortened, and the economical loss can be reduced.

  According to the present embodiment, it is possible to exclude the influence of the fluctuation of the sensor information (sensor data) at the normal time (normal time) based on the change characteristic of the sensor information (sensor data) peculiar to the tube leak. Highly accurate diagnosis is possible.

  Although the number and arrangement of banks and the number and arrangement of heat transfer tubes installed in the banks differ depending on the boiler, the boiler tube leak diagnosis system described in this embodiment can be applied.

  Next, a display screen when the user uses the boiler tube leak diagnosis system described in the present embodiment will be described. The processing related to the display screen is executed by the input / output control unit 18 displaying various information (various data) stored in the input data DB 14, the abnormality degree DB 15, and the diagnosis result DB 17 on the input / output device 4.

  FIG. 11 is an explanatory diagram illustrating a display screen of the tube leak diagnostic information summary.

  As the tube leak diagnostic information summary, the data 301 stored in the diagnostic result DB 17 is displayed. At this time, in the information type flag stored in the diagnosis result DB 17, "1" is called "abnormality detection" (detection of tube leak occurrence), and "2" is called "site identification" (tube leak position identification). Replaced by display.

  Further, the position of the tube leak is displayed in the schematic diagram 302 of the boiler 100. In this embodiment, since it is determined that the tube leak has occurred in the secondary superheater 103, the color of the secondary superheater 103 is changed and displayed. Note that, for example, the secondary superheater 103 may be displayed by blinking or the like.

  Further, a trend 303 of the degree of abnormality is displayed as a result of the overall behavior analysis of the bank. In this embodiment, the degree of abnormality is rapidly increased with the occurrence of tube leak. Further, as a result of the local behavior analysis of the bank, the trend 304 of the degree of abnormality in the secondary superheater 103 is displayed. The trend 303 and the trend 304 are rapidly increasing at the same timing, and it is identified from the analysis results of both that the tube leak has occurred in the secondary superheater 103, and the basis for the identification can be confirmed.

  The display screen has a bank selection window 309 for displaying the states of other banks. The user can confirm the status of another bank by selecting (inputting) another bank in the bank selection window 309.

  FIG. 12 is an explanatory diagram illustrating a display screen of the entire bank behavior analysis result.

  As the display screen of the detailed information of the boiler tube leak diagnosis, the trend 305 (303) of the abnormality degree displayed on the display screen of the tube leak diagnosis information summary shown in FIG. 11 is displayed as a result of the overall behavior analysis of the bank. It

  Further, the trend 306 of the abnormal contribution degree of each bank (for example, the water wall 102, the secondary superheater 103, the tertiary superheater 104, ...) Is displayed. In particular, it can be confirmed that the degree of abnormal contribution of the secondary superheater 103 is rapidly increasing.

  FIG. 13 is an explanatory diagram illustrating a display screen of the local behavior analysis result of the bank.

  As the display screen of the detailed information of the boiler tube leak diagnosis, as a result of the local behavior analysis of the bank, the trend 307 (304) of the abnormality degree displayed on the display screen of the tube leak diagnosis information summary shown in FIG. 11 is displayed. It In this example, the analysis result for the secondary superheater 103 is displayed.

  Further, the trend 308 of the degree of abnormality contribution of the metal temperature (eg, metal temperature 1, metal temperature 2, metal temperature 3, ...) Measured by each metal temperature sensor is displayed. In particular, it can be confirmed that the abnormal contributions of the metal temperature 1 and the metal temperature 2 are rapidly increasing. Since the metal temperature sensors are numbered from the left, it is possible to confirm that the position where the tube leak has occurred is the left side of the secondary superheater 103.

  As described above, according to the present embodiment, in consideration of the change characteristic of the sensor information (sensor data) peculiar to the tube leak, the influence of the fluctuation in the normal time (normal time) of the sensor information (sensor data) is excluded, and It is possible to accurately detect the occurrence of a tube leak and specify the position of the tube leak.

  It should be noted that the present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments have been described in detail in order to explain the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the configurations described.

  1: Boiler tube leak diagnosis system, 3: Control device, 4: Input / output device, 11: Input data creation unit, 12: Abnormality degree calculation unit, 13: Abnormality determination unit, 14: Input data DB, 15: Abnormality degree DB , 16: sensor position information DB, 17: diagnosis result DB, 18: input / output control unit, 100: boiler, 101: burner, 102: water wall, 103: secondary superheater, 104: tertiary superheater, 105: four Next superheater, 106: secondary reheater, 107 primary reheater, 108: primary superheater, 109: economizer, 110: cage wall, 111: gas damper, 112: isolation wall, 113: ceiling wall.

Claims (9)

  Input the sensor information of the boiler, and calculate the anomaly degree and the anomalous contribution rate based on the input data creation section that creates the input data used in the tube leak diagnosis and the input data created by the input data creation section. A boiler tube leak diagnosis system, comprising: an abnormality degree calculation unit that performs the above; and an abnormality determination unit that diagnoses a tube leak based on the abnormality degree and the abnormality contribution rate calculated by the abnormality degree calculation unit. The abnormality degree calculation unit calculates the heat collection amount of each bank constituting the boiler,
The abnormality determination unit compares the heat collection amount of each bank at the time of normal learned in advance and the heat collection amount of each bank calculated by the abnormality degree calculation unit, and the heat collection amount of each bank calculated is The boiler tube leak diagnosis system according to claim 1, wherein whether or not the heat collection amount of each bank is normal is determined as an abnormality degree and an abnormality contribution degree. The anomaly degree calculation unit calculates the heat collection distribution of each bank from the metal temperatures at a plurality of locations in each bank,
The abnormality determination unit compares the heat collection distribution of each bank at a normal time learned in advance with the heat collection distribution of each bank calculated by the abnormality calculation unit, and calculates the heat collection of each bank. The boiler tube leak diagnosis system according to claim 1, wherein whether or not the distribution deviates from the heat collection distribution of each bank in a normal state is determined as the degree of abnormality and the degree of contribution of abnormality.   The abnormality determination unit, when it is determined that the bank deviating from the normal heat collection amount and the bank deviating from the normal heat collection distribution are the same, a tube leak occurs in the bank. The boiler tube leak diagnosis system according to claim 1, wherein it is determined that the bank is a bank.   The said abnormality determination part pinpoints the position of the tube leak with respect to the furnace width direction of each bank from the installation position information of the metal temperature sensor in each bank, and the said determination result, It is characterized by the above-mentioned. Boiler tube leak diagnosis system. The abnormality degree calculation unit divides each bank in the furnace width direction according to the number of metal temperature sensors, and uses the increase / decrease value in the furnace width direction of the metal temperature sensor and the steam temperature from the steam temperature sensor, Calculate the local heat absorption of each divided block,
The abnormality determination unit uses the local heat absorption amount and compares the local heat absorption amount of each bank in a normal state learned in advance with the local heat amount amount of each bank calculated by the abnormality degree calculation unit. The abnormality locality and the abnormal contribution are determined as to whether or not the calculated local heat absorption amount of each bank deviates from the normal heat absorption amount of each bank. Boiler tube leak diagnosis system.   The metal temperature or the local heat absorption amount is calculated by calculating a difference value with respect to an average value or a median value in the furnace width direction, and analyzing the difference value as an input, the boiler according to claim 3 or 6. Tube leak diagnostic system.   The heat collection amount, the metal temperature, or the local heat collection amount is calculated by calculating the time rate of change of each, and analyzing it with the calculated rate of change as an input. Boiler tube leak diagnostic system to describe.   Input the sensor information of the boiler, create the input data to be used in the diagnosis of tube leak, calculate the abnormality degree and the abnormality contribution rate based on the created input data, and calculate the calculated abnormality degree and abnormality contribution degree. A boiler tube leak diagnosis method, which comprises diagnosing a tube leak based on the above.

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