Plant operation optimization device and plant operation optimization method

Abstract

To provide a plant operation optimization device and a plant operation optimization method that can realize efficient energy recovery in a boiler.SOLUTION: A plant operation optimization device according to one embodiment comprises a receiving unit for receiving data related to a boiler in a power generation plant. The device also comprises a determining unit for determining a combustion state setting value that is a setting value for controlling a combustion state of the boiler, by a neural network on the basis of the data related to the boiler. The device also comprises an outputting unit for outputting the combustion state setting value to a display device.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] Embodiments of the present invention relate to a plant operation optimization device and a plant operation optimization method.

Background Art

[0002] In a steam turbine power generation plant, a boiler generates steam, a steam turbine is driven by the steam, and a generator is driven by the steam turbine to generate electricity. Specifically, the boiler boils water by the thermal energy generated by burning fuel to generate steam from the water. In this case, it is desirable to efficiently recover energy in the boiler.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Patent Document 4

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to efficiently recover energy in the boiler, for example, it is desirable to appropriately adjust the combustion state of the boiler and remove the soot accumulated in the boiler at an appropriate timing by soot blowing.

[0005] Therefore, embodiments of the present invention provide a plant operation optimization device and a plant operation optimization method capable of realizing efficient energy recovery in a boiler.

Means for Solving the Problems

[0006] According to one embodiment, a plant operation optimization device includes a receiving unit that receives data related to a boiler in a power generation plant. The device further includes a determining unit that determines, by a neural network, a combustion state setting value, which is a setting value for controlling the combustion state of the boiler, based on the data related to the boiler. The device further includes an output unit that outputs the combustion state setting value to a display device.

Brief Description of the Drawings

[0007]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Modes for Carrying Out the Invention

[0008] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In FIGS. 1 to 6, the same components are denoted by the same reference numerals, and redundant descriptions are omitted.

[0009] (First Embodiment) FIG. 1 is a schematic diagram showing the configuration of the plant operation optimization system of the first embodiment.

[0010] The plant operation optimization system shown in Fig. 1 includes a power generation plant 1, a plant operation optimization device 2, and a display device 3. The power generation plant 1 is the plant to be optimized in terms of operation. The plant operation optimization device 2 is a device that provides information for optimizing the operation of the power generation plant 1. The display device 3 is a device that displays the above information provided by the plant operation optimization device 2.

[0011] The power generation plant 1 is, for example, a steam turbine power generation plant, and includes a boiler 11, a high-pressure (HP) turbine 12, a reheater 13, an intermediate-pressure (IP) turbine 14, a low-pressure (LP) turbine 15, a generator 16, a condenser 17, and a feedwater heater 18. The power generation plant 1 may be other types of plants including the boiler 11.

[0012] The plant operation optimization device 2 may be installed inside the power generation plant 1 or outside the power generation plant 1. The plant operation optimization device 2 of this embodiment is installed outside the power generation plant 1, for example, in the form of being installed within the base of the company that designed the power generation plant 1. In this case, the plant operation optimization device 2 exchanges data with the power generation plant 1 via a network. The plant operation optimization device 2 is, for example, a PC (Personal Computer). As will be described later, the plant operation optimization device 2 of this embodiment performs, for example, processing for optimizing the combustion state of the boiler 11 and processing for optimizing soot blowing in the boiler 11.

[0013] The display device 3 may also be installed inside the power generation plant 1 or outside the power generation plant 1. The display device 3 of this embodiment is installed, for example, in a building constructed inside the power generation plant 1. In this case, the display device 3 exchanges data with other devices inside the power generation plant 1 and the plant operation optimization device 2 via a network. The display device 3 is, for example, a PC. In this embodiment, the display device 3 is provided separately from the plant operation optimization device 2, but it may be provided as a part of the plant operation optimization device 2.

[0014] The boiler 11 generates steam as the working fluid of the power plant 1. Specifically, the boiler 11 in this embodiment boils water by the thermal energy generated by burning fuel to generate steam from the water. The fuel is, for example, a fossil fuel such as coal. The high-pressure turbine 12 is driven by the steam supplied from the boiler 11.

[0015] The reheater 13 reheats the steam discharged from the high-pressure turbine 12. The reheater 13 may be provided as a part of the boiler 11 or may be provided separately from the boiler 11. When the reheater 13 is a part of the boiler 11, the reheater 13 heats the steam discharged from the high-pressure turbine 12, for example, by the thermal energy generated by burning the above-mentioned fuel. The intermediate-pressure turbine 14 is driven by the steam supplied from the reheater 13. The low-pressure turbine 15 is driven by the steam discharged from the intermediate-pressure turbine 14.

[0016] The generator 16 can be connected to the rotating shaft to which the high-pressure turbine 12, the intermediate-pressure turbine 14, and the low-pressure turbine 15 are connected, and is driven by the high-pressure turbine 12, the intermediate-pressure turbine 14, and the low-pressure turbine 15 when connected to this rotating shaft to generate electricity.

[0017] The condenser 17 returns the steam discharged from the low-pressure turbine 15 to water. The feedwater heater 18 heats the water discharged from the condenser 17 by the heat of the steam extracted from the high-pressure turbine 12, the intermediate-pressure turbine 14, or the low-pressure turbine 15. The water discharged from the feedwater heater 18 is supplied again into the boiler 11.

[0018] FIG. 2 is a block diagram showing the configuration of the plant operation optimization system according to the first embodiment.

[0019] As shown in FIG. 2, in addition to the above-mentioned boiler 11 and generator 16, etc., the power plant 1 includes a sensor 41, an actuator 42, an operator input device 43, a control system 44, and a soot blowing system 45.

[0020] The plant operation optimization device 2 includes a boiler combustion optimization unit 21, a mill flow rate distribution optimization unit 22, an air flow rate distribution optimization unit 23, a soot blow optimization unit 24, an efficiency calculation unit 25, a boiler model management unit 26, an NN (neural network) processing unit 27, an LR (linear regression) processing unit 28, and an EKF (extended Kalman filter) processing unit 29. The data reception function, data processing function, and data output function of the boiler combustion optimization unit 21, the mill flow rate distribution optimization unit 22, the air flow rate distribution optimization unit 23, and the soot blow optimization unit 24 are examples of a reception unit, a determination unit, and an output unit, respectively.

[0021] The display device 3 includes a dashboard processing unit 31, a database 32, a simulation UI (user interface) processing unit 33, an efficiency UI processing unit 34, an operator UI processing unit 35, and a proposal UI processing unit 36.

[0022] Hereinafter, with reference to FIG. 2, the details of the plant operation optimization system according to the first embodiment will be described. In this description, FIGS. 3 to 6 will also be referred to as appropriate. FIG. 3 is a graph for explaining the soot blow of the first embodiment. FIG. 4 is another graph for explaining the soot blow of the first embodiment. FIG. 5 is a diagram showing an example of the operator operation screen of the first embodiment. FIG. 6 is a diagram showing an example of the simulation screen of the first embodiment.

[0023] (1) Power generation plant 1 The sensor 41 detects various values in the power generation plant 1 and outputs the detection results of these values. Examples of these values are the temperature, pressure, and flow rate of the steam in the power generation plant 1, the temperature, oxygen concentration, and steam spray flow rate in the boiler 11 and the reheater 13. Another example of these values is the difference in the physical quantities measured between the left and right parts in the furnace of the boiler 11. Another example of these values is the physical quantity of a fluid other than steam (for example, exhaust gas) in the power generation plant 1, and the values related to the fuel and air supplied to the boiler 11. The values detected by the sensor 41 may be output to other devices in the power generation plant 1, or may be output to the plant operation optimization device 2 or the display device 3.

[0024] The actuator 42 is used to operate the operations of various devices in the power generation plant 1. An example of the actuator 42 is a damper used to adjust the exhaust gas temperature of the boiler 11. Data indicating the state of the actuator 42 may be output to other devices in the power generation plant 1, or may be output to the plant operation optimization device 2 or the display device 3.

[0025] The operator input device 43 is used by an operator who operates the operation of the power generation plant 1. For example, this operator can perform operations related to soot blowing for removing soot accumulated in the boiler 11 using the operator input device 43. The operator input device 43 may have a function of displaying the value output from the sensor 41 and the data output from the actuator 42.

[0026] The control system 44 controls various operations of the power generation plant 1. Examples of the control system 44 include a processor, an electric circuit, a PC, etc. The control system 44 controls, for example, the startup, operation, and stop of the power generation plant 1, and the opening and closing of valves for steam in the power generation plant 1. Further, the control system 44 may control the operation of the power generation plant 1 based on the value output from the sensor 41 and the data output from the actuator 42, and at this time, the actuator 42 may be used to control the power generation plant 1. The data acquired or generated by the control system 44 may be output to the plant operation optimization device 2 or the display device 3.

[0027] The soot blow system 45 performs soot blowing to remove the soot accumulated in the boiler 11. The soot blowing in this embodiment is carried out by the soot blower in the soot blow system 45 supplying steam for soot blowing into the boiler 11. The soot adhering to the boiler 11 is removed by this steam. The steam for soot blowing is, for example, steam generated by the boiler 11. The soot blow system 45 may perform soot blowing in response to a manual operation from a device such as the operator input device 43, or may perform soot blowing automatically without a manual operation. The data acquired or generated by the soot blow system 45 may be output to the plant operation optimization device 2 or the display device 3.

[0028] (2) Plant operation optimization device 2 The plant operation optimization device 2 receives various data regarding the boiler 11 in the power generation plant 1 from the power generation plant 1 etc., and based on the received data, determines various set values for controlling the boiler 11. For example, the plant operation optimization device 2 calculates the set value that the physical quantity regarding the boiler 11 should take in order to optimize the combustion state of the boiler 11, or calculates the set value that the physical quantity regarding soot blowing should take in order to optimize the soot blowing in the boiler 11. The plant operation optimization device 2 further outputs the determined set values to the display device 3. In this embodiment, these set values are transmitted from the plant operation optimization device 2 to the display device 3 via the network.

[0029] These processes in the plant operation optimization device 2 are performed by the boiler combustion optimization unit 21, the mill flow rate distribution optimization unit 22, the air flow rate distribution optimization unit 23, and the soot blow optimization unit 24. As will be described later, the boiler combustion optimization unit 21, the mill flow rate distribution optimization unit 22, the air flow rate distribution optimization unit 23, and the soot blow optimization unit 24 perform different types of optimization processes.

[0030] The plant operation optimization device 2 of this embodiment manages the existing set values that have already been used to control the boiler 11 as the set values of various physical quantities. When the plant operation optimization device 2 calculates a new set value of a certain physical quantity, it may calculate the difference between the existing set value and the new set value of that physical quantity (this is called the "bias value") and output this bias value as a set value to the display device 3. For example, regarding the set value of the fuel flow rate, if the existing set value is R1 and the new set value is R2, the plant operation optimization device 2 may output R2 to the display device 3, or alternatively output ΔR (=R2 - R1).

[0031] Hereinafter, the details of each block in the plant operation optimization device 2 will be described.

[0032] (2.1) Boiler Combustion Optimization Unit 21 The boiler combustion optimization unit 21 determines the combustion state set value, which is a set value for controlling the combustion state of the boiler 11, and outputs the determined combustion state set value to the display device 3. At this time, the boiler combustion optimization unit 21 may output the bias value between the existing combustion state set value and the new combustion state set value to the display device 3.

[0033] The values of variables and parameters representing the combustion state of the boiler 11 may deviate from the values in the design state. Examples of such variables and parameters are physical quantities such as the temperature, oxygen concentration, and steam spray flow rate inside the boiler 11, and the difference in physical quantities measured between the left and right parts inside the furnace of the boiler 11. If such deviations are left unaddressed, there is a risk that the combustion state of the boiler 11 will deteriorate, such as a decrease in the combustion efficiency of the boiler 11 or the exhaust gas emission conditions from the boiler 11 not being met.

[0034] Therefore, the boiler combustion optimization unit 21 determines a preferable combustion state set value for controlling the combustion state of the boiler 11, and outputs the determined combustion state set value to the display device 3. By referring to the combustion state set value displayed on the display device 3, the operator of the power generation plant 1 can improve the combustion state of the boiler 11. For example, the operator may actually use this combustion state set value for controlling the boiler 11, or may use this combustion state set value for simulating the operation of the boiler 11.

[0035] The boiler combustion optimization unit 21 of the present embodiment determines the combustion state set value so that the combustion state of the boiler 11 is improved. For example, the combustion state set value is determined so that the combustion efficiency of the boiler 11 is optimized. As a result, for example, waste of fuel used in the boiler 11 can be reduced, and the operation of the boiler 11 can be made technically and economically suitable.

[0036] The mode in which the combustion state set value and other set values are displayed on the display device 3 is called the monitoring mode. In the monitoring mode, bias values of various set values may be displayed on the display device 3. For example, the operator of the power generation plant 1 can know that the operation state of the boiler 11 is deteriorating because there is a bias value away from zero. The operator may manually change the existing set value used for controlling the boiler 11 by the amount of this bias value, or alternatively, may automatically change the existing set value by the amount of this bias value in the automatic control mode.

[0037] The boiler combustion optimization unit 21 performs such optimization processing, for example, during the trial operation of the power generation plant 1. During the trial operation of the power generation plant 1, for example, test coal is used as the fuel for the boiler 11. In this case, after the commercial operation of the power generation plant 1, the operation state of the boiler 11 may become unsuitable due to changes in the operation situation of the power generation plant 1 and the properties of coal. In this case, the boiler combustion optimization unit 21 may also perform the above-described optimization processing after the commercial operation of the power generation plant 1.

[0038] The boiler combustion optimization unit 21 of this embodiment can perform optimization processing in cooperation with the boiler model management unit 26, the NN processing unit 27, and the LR processing unit 28. The boiler model management unit 26 manages a model for simulating the operation of the boiler 11. The boiler combustion optimization unit 21 may perform optimization processing using this model. The NN processing unit 27 performs information processing using a neural network. The LR processing unit 28 performs information processing using a linear regression algorithm. The boiler combustion optimization unit 21 can perform optimization processing using a neural network and a linear regression algorithm by cooperating with the NN processing unit 27 and the LR processing unit 28.

[0039] Generally, when attempting to optimize the operating state of the boiler 11, it is necessary to adjust a large number of variables and parameters. When such optimization is performed manually by a human, only a small number of variables and parameters can be adjusted, such as adjusting the flow rate of air supplied to the boiler 11. On the other hand, even when such optimization is automatically performed by a computer, although a larger number of variables and parameters can be adjusted compared to manual operation, there is still a possibility that only a small number of variables and parameters can be adjusted.

[0040] Therefore, the boiler combustion optimization unit 21 of this embodiment determines a combustion state set value for controlling the combustion state of the boiler 11 using a neural network. Thereby, even when adjusting a large number of variables and parameters, by having such adjustments learned by the neural network, it becomes possible to appropriately optimize the operating state of the boiler 11. According to this embodiment, by applying a neural network to the boiler combustion optimization unit 21, it becomes possible to, for example, adjust a large number of variables and parameters to determine a large number of combustion state set values. The boiler combustion optimization unit 21 of this embodiment may operate to constantly optimize each combustion state set value using a neural network.

[0041] For example, when it is desirable to increase the exhaust gas temperature of the boiler 11, the relationship between the actuator 42 (damper) used to adjust the exhaust gas temperature and the change in the combustion efficiency of the boiler 11 is learned by a neural network. Thereby, the boiler combustion optimization unit 21 can obtain the optimal solution of the combustion efficiency of the boiler 11 from the NN processing unit 27. The boiler combustion optimization unit 21 outputs this optimal solution and the combustion state set value (which may be a bias value) in this optimal solution to the display device 3. The operator of the power plant 1 can increase the exhaust gas temperature in a state where the combustion efficiency of the boiler 11 is good by manually or automatically adjusting the actuator 42 using these optimal solutions and combustion state set values.

[0042] The exhaust gas temperature of the boiler 11 can be changed by various methods. For example, the exhaust gas temperature of the boiler 11 can be changed by changing the fuel flow rate, changing the air flow rate, selecting whether to supply the coal, which is the fuel, in lumps or in powder, and selecting whether to supply the fuel to the upper part or the lower part in the boiler 11. The neural network can learn the optimal combination of the modes of using these methods. In addition, in this embodiment, physical quantities other than the exhaust gas temperature of the boiler 11 may be targets for change, and a neural network may be used for this processing.

[0043] In this embodiment, the operation state of the boiler 11 is optimized, for example, by minimizing a predetermined non-linear function. This non-linear function can, for example, minimize the combustion efficiency of the boiler 11, the coal consumption of the boiler 11, the operation cost of the boiler 11, etc. In this optimization process, it is possible to handle constraints such as the carbon dioxide emission amount, nitrogen oxide emission amount, and metal temperature of the boiler 11, and it is also possible to handle non-linear constraints. The boiler combustion optimization unit 21 of this embodiment can perform optimization using a feedforward, neural network, linear regression algorithm, etc., and can calculate solutions of an objective function and other functions. The weight coefficient of the objective function is calculated, for example, after a bias test performed in advance in the power generation plant 1. The result of the optimization process by the boiler combustion optimization unit 21 may be stored in the plant operation optimization device 2 or may be stored in the display device 3.

[0044] The neural network of this embodiment operates based on a plurality of input values and outputs a plurality of output values to the boiler combustion optimization unit 21. The input values are, for example, values detected by the sensor 41. The output values are used, for example, for the objective function and constraint conditions in the optimization process by the boiler combustion optimization unit 21.

[0045] (2.2) Mill Flow Distribution Optimization Unit 22 The mill flow distribution optimization unit 22 determines a fuel set value, which is a set value for controlling the fuel supplied to the boiler 11, and outputs the determined fuel set value to the display device 3. At this time, the mill flow distribution optimization unit 22 may output a bias value between the existing fuel set value and the new fuel set value to the display device 3.

[0046] The mill flow distribution optimization unit 22 determines the fuel set value so that, for example, the flow rate of the fuel supplied to the boiler 11 is optimized. Examples of the fuel set value in this case are the set value of the fuel flow rate itself and the set value related to the operation of the actuator 42 that adjusts the fuel flow rate.

[0047] In the boiler 11 of this embodiment, fuel is supplied from a plurality of mills to the boiler 11. The mill flow rate distribution optimization unit 22 of this embodiment may receive data on the flow rates of fuel supplied from these mills and data on the soundness of these mills, and determine the fuel set values for each mill based on the received data. The mill flow rate distribution optimization unit 22 of this embodiment may perform an optimization process so that the center of the fuel fireball in the boiler 11 is optimized with the minimum setting change. The result of the optimization process by the mill flow rate distribution optimization unit 22 may be stored in the plant operation optimization device 2 or may be stored in the display device 3.

[0048] The system architecture of the plant operation optimization device 2 of this embodiment is divided into a first layer and a second layer. The first layer is a higher-level optimization level that supplies calculation results to the second layer, and includes a boiler combustion optimization unit 21 and an EKF processing unit 29 for the soot blow optimization unit 24 described later. The second layer includes a mill flow rate distribution optimization unit 22 and an air flow rate distribution optimization unit 23 described later. The plant operation optimization device 2 of this embodiment may determine various set values by the optimization processes of the first layer and the second layer, or may determine various set values by the optimization process of only the first layer depending on the purpose of optimization. For example, when optimizing the oxygen concentration and burner tilt without changing the fuel flow rate and air flow rate, the set values may be determined by the optimization process of only the first layer.

[0049] (2.3) Air flow rate distribution optimization unit 23 The air flow rate distribution optimization unit 23 determines an air set value, which is a set value for controlling the air supplied to the boiler 11, and outputs the determined air set value to the display device 3. At this time, the air flow rate distribution optimization unit 23 may output a bias value between the existing air set value and the new air set value to the display device 3.

[0050] The air flow rate distribution optimization unit 23 determines, for example, an air set value so that the flow rate of the air supplied to the boiler 11 is optimized. Examples of the air set value in this case are a set value of the air flow rate itself and a set value related to the operation of the actuator 42 that adjusts the air flow rate.

[0051] In the boiler 11 of the present embodiment, in order to maintain appropriate combustion, it is desirable to maintain the ratio of fuel to air. For example, the air flow rate distribution optimization unit 23 of the present embodiment performs an optimization process for maintaining this ratio by secondary air damper control (SADC). The air flow rate distribution optimization unit 23 of the present embodiment may receive data on the opening degrees of a plurality of dampers and data on the soundness of these plurality of dampers, and determine the air setting values of each damper based on the received data. Similar to the mill flow rate distribution optimization unit 22, the air flow rate distribution optimization unit 23 of the present embodiment may also perform an optimization process so that the center of the fuel fireball in the boiler 11 is optimized with the minimum setting change. The result of the optimization process by the air flow rate distribution optimization unit 23 may be stored in the plant operation optimization device 2 or may be stored in the display device 3.

[0052] (2.4) Soot blow optimization unit 24 The soot blow optimization unit 24 determines a soot blow setting value, which is a setting value for controlling soot blow in the boiler 11, and outputs the determined soot blow setting value to the display device 3. At this time, the soot blow optimization unit 24 may output a new soot blow setting value that replaces the existing soot blow setting value to the display device 3.

[0053] The soot blow optimization unit 24 determines the soot blow setting value, for example, so that the timing of performing soot blow in the boiler 11 is optimized. Examples of the soot blow setting value in this case are the start time, duration, end time, etc. of performing soot blow. The soot blow optimization unit 24 may output a new time that replaces the existing time to the display device 3 regarding these times.

[0054] As described above, the boiler combustion optimization unit 21 performs an optimization process for optimizing the combustion state of the boiler 11. As a result, it becomes possible to efficiently recover energy in the boiler 11. However, when soot accumulates in the boiler 11, efficient energy recovery in the boiler 11 is hindered. Furthermore, when soot accumulates in the boiler 11, an error may occur in the optimization of the combustion state of the boiler 11 by the neural network. Therefore, it is desirable to perform soot blowing in the boiler 11.

[0055] Soot blowing can be considered to be performed at regular intervals, for example, once every 8 hours. However, when soot blowing is performed using steam, if soot blowing is performed when there is little soot, the steam is wasted. For example, when the steam generated by the boiler 11 is used for soot blowing, if soot blowing is performed when there is little soot, the fuel in the boiler 11 is wasted. On the other hand, if soot blowing is not performed when there is a lot of soot, efficient energy recovery in the boiler 11 is hindered. Also, when powdered coal is used as fuel, the problem is that soot tends to accumulate.

[0056] Therefore, the soot blowing optimization unit 24 of the present embodiment is configured to be able to perform an optimization process in cooperation with the EKF processing unit 29. The EKF processing unit 29 performs information processing using an extended Kalman filter. The soot blowing optimization unit 24 can perform an optimization process using an extended Kalman filter by cooperating with the EKF processing unit 29. The soot blowing optimization unit 24 of the present embodiment can present an appropriate timing for performing soot blowing by determining the soot blowing set value by the extended Kalman filter so that the timing for performing soot blowing is optimized. As a result, it becomes possible to suppress the waste of steam and suppress the decrease in energy recovery efficiency due to soot. The soot blowing optimization unit 24 of the present embodiment may operate to constantly optimize each soot blowing set value using an extended Kalman filter.

[0057] The soot blow optimization unit 24 of this embodiment uses a soft sensor for estimating the accumulation of soot in the boiler 11 for the optimization process. This soft sensor estimates, for example, the accumulation of soot in the furnace of the boiler 11 and individual superheaters (heat exchangers). The soft sensor of this embodiment includes three components: a thermodynamic model or first principle model (1), an extended Kalman filter (2), and a feedback signal (3) from the sensor 41 which is a temperature sensor. The soot blow optimization unit 24 can determine a soot blow set value for optimizing the timing of performing the soot blow by estimating the accumulation of soot with a soft sensor such as an extended Kalman filter. In this optimization process, for example, physical quantities (such as temperature) measured in the furnace of the boiler 11 and individual superheaters are used as input values. Also, in this optimization process, for a plurality of parts in the boiler 11, the timing of performing the soot blow may be determined for each part.

[0058] The graph in Fig. 3 shows the change over time in the heat transfer efficiency of the boiler 11. The soot blow optimization unit 24 of this embodiment can acquire the heat transfer efficiency estimated by the extended Kalman filter. In this case, the soot blow optimization unit 24 determines the soot blow set value so as to perform the soot blow at the timing when the heat transfer efficiency has decreased to the threshold value. Thereby, it becomes possible to perform the soot blow at an appropriate timing and suppress an excessive decrease in the heat transfer efficiency of the boiler 11.

[0059] The graph in Fig. 4 shows the relationship between the soot blow interval of the boiler 11 and the annual operation cost. According to this graph, when the soot blow interval is about 9 hours, the operation cost of the boiler 11 becomes the maximum. Therefore, the soot blow optimization unit 24 may determine the soot blow set value in consideration of such characteristics of the operation cost. Thereby, it becomes possible to optimize the timing of performing the soot blow while also considering the cost.

[0060] (2.5) Efficiency calculation unit 25 The efficiency calculation unit 25 receives data related to the boiler 11 from the power generation plant 1 or the like, and calculates the efficiency related to the boiler 11 based on the received data. For example, the efficiency calculation unit 25 calculates the combustion efficiency of the boiler 11. The efficiency calculated by the efficiency calculation unit 25 may be output to the display device 3, or may be used in processing (for example, optimization processing) by other blocks in the plant operation optimization device 2.

[0061] (3) Display device 3 In the present embodiment, the plant operation optimization device 2 provides information for optimizing the operation of the power generation plant 1, and the display device 3 displays the information provided from the plant operation optimization device 2. As shown in FIG. 2, the display device 3 includes a dashboard processing unit 31, a database 32, a simulation UI processing unit 33, an efficiency UI processing unit 34, an operator UI processing unit 35, and a proposal UI processing unit 36. The GUI (Graphical User Interface) displayed on the screen by the display device 3 may be created by the display device 3 or may be created by the plant operation optimization device 2. In the latter case, the display device 3 may display the GUI using a browser.

[0062] The dashboard processing unit 31 displays a dashboard on the screen of the display device 3, and displays the information provided from the plant operation optimization device 2 in the dashboard. The information displayed in the dashboard is, for example, various set values output from the boiler combustion optimization unit 21, the mill flow rate distribution optimization unit 22, the air flow rate distribution optimization unit 23, or the soot blow optimization unit 24, and the efficiency output from the efficiency calculation unit 25 (for example, the combustion efficiency of the boiler 11). The set value displayed in the dashboard may be a new soot blow set value replacing the above-mentioned bias value or the existing soot blow set value. The dashboard processing unit 31 may display the dashboard on the simulation UI, the efficiency UI, the operator UI, or the proposal UI described later.

[0063] The database 32 stores various information used by the display device 3. For example, the information provided by the plant operation optimization device 2 is stored in the database 32.

[0064] The simulation UI processing unit 33 displays a simulation UI for simulating the operation of the power generation plant 1. The simulation UI processing unit 33 can simulate the operation of the power generation plant 1, for example, the operation of the boiler 11, based on the set values, efficiency output from the plant operation optimization device 2, and the information input on the simulation UI by the operator of the power generation plant 1. The simulation UI processing unit 33 can, for example, simulate the combustion state of the boiler 11 based on the combustion state set value. The results of the simulation performed by the simulation UI processing unit 33 are displayed on the simulation UI. Thereby, the operator can determine how to operate the boiler 11. The simulation UI is an example of a simulation screen.

[0065] The efficiency UI processing unit 34 displays an efficiency UI for providing information regarding the efficiency of the power generation plant 1, such as the combustion efficiency of the boiler 11. The efficiency output from the efficiency calculation unit 25 is displayed, for example, on the efficiency UI.

[0066] The operator UI processing unit 35 displays an operator UI for an operator of the power generation plant 1 to operate the power generation plant 1 and the plant operation optimization device 2. The operator UI may display various bias values output from the plant operation optimization device 2 or new soot blow setting values replacing existing soot blow setting values. In this case, the operator UI processing unit 35 allows the operator to change a bias value or a new soot blow setting value replacing an existing soot blow setting value from one numerical value to another on the operator UI, and then transmits the changed bias value (or changed setting value) or a new soot blow setting value replacing the existing soot blow setting value to the power generation plant 1 or the plant operation optimization device 2. Thereby, it becomes possible to change the setting value used by the power generation plant 1 to control the boiler 11 or to change the setting value used by the plant operation optimization device 2 as an existing setting value. The operator UI is an example of an operator operation screen.

[0067] The proposal UI processing unit 36 displays a proposal UI that displays various setting values output from the plant operation optimization device 2 as proposed values (recommended values) from the display device 3. The proposal UI processing unit 36 may display all the setting values output from the plant operation optimization device 2 as proposed values, or may display some of the setting values output from the plant operation optimization device 2 as proposed values. By checking the proposal UI, the operator of the power generation plant 1 can know the existence of suitable setting values. The setting values displayed on the proposal UI may be bias values or new soot blow setting values replacing existing soot blow setting values.

[0068] FIG. 5 shows an example of the operator UI. The operator UI shown in FIG. 5 can display various bias values or new soot blow setting values replacing existing soot blow setting values related to three mills A, B, C and secondary air damper control (SADC), and the operator can change these bias values or new soot blow setting values replacing existing soot blow setting values on the operator UI.

[0069] FIG. 6 shows an example of a simulation UI. In the simulation UI shown in FIG. 6, it is possible to specify bias values used in the simulation and to view simulation results. The specification of the bias values may be performed, for example, in the form of specifying the upper or lower limit of the bias value. FIG. 6 shows simulation results regarding the reheat temperature, nitrogen oxides, reheater spray, and metal temperature.

[0070] As described above, the plant operation optimization device 2 of the present embodiment determines a set value (combustion state set value) for controlling the combustion state of the boiler 11 by a neural network, and determines a set value (soot blow setting value) for controlling soot blow in the boiler 11 by an extended Kalman filter. Therefore, according to the present embodiment, it is possible to optimize the combustion state of the boiler 11 in consideration of various factors and to optimize the timing for removing soot that hinders the optimization of the combustion state of the boiler 11, thereby enabling efficient energy recovery in the boiler 11.

[0071] Note that the plant operation optimization device 2 of the present embodiment may perform only one of the determination of the combustion state set value by the neural network and the determination of the soot blow set value by the extended Kalman filter, or may perform both of them.

[0072] As described above, several embodiments have been described, but these embodiments are presented only as examples and are not intended to limit the scope of the invention. The novel devices and methods described in this specification can be implemented in various other forms. Also, various omissions, substitutions, and changes can be made to the forms of the devices and methods described in this specification without departing from the gist of the invention. The appended claims and equivalents thereof are intended to include such forms and modifications within the scope and gist of the invention.

Explanation of Reference Numerals

[0073] 1: Power generation plant, 2: Plant operation optimization device, 3: Display device, 11: Boiler, 12: High-pressure turbine, 13: Reheater, 14: Intermediate-pressure turbine, 15: Low-pressure turbine, 16: Generator, 17: Condenser, 18: Feedwater heater, 21: Boiler combustion optimization unit, 22: Mill flow rate distribution optimization unit, 23: Air flow rate distribution optimization unit, 24: Soot blow optimization unit, 25: Efficiency calculation unit, 26: Boiler model management unit, 27: NN processing unit, 28: LR processing unit, 29: EKF processing unit, 31: Dashboard processing unit, 32: Database, 33: Simulation UI processing unit, 34: Efficiency UI processing unit, 35: Operator UI processing unit, 36: Proposal UI processing unit, 41: Sensor, 42: Actuator, 43: Operator input device, 44: Control system, 45: Soot blow system

Claims

1. a receiving unit that receives data related to a boiler in a power generation plant; a determination unit that determines, by means of a neural network, a combustion state setting value, which is a setting value for controlling the combustion state of the boiler, based on the data related to the boiler; an output unit that outputs the combustion state setting value to a display device; comprising; the determination unit further determines, based on the data related to the boiler, a fuel setting value, which is a setting value for controlling the fuel supplied to the boiler, or an air setting value, which is a setting value for controlling the air supplied to the boiler; the output unit further outputs the fuel setting value or the air setting value to the display device; the determination unit operates to determine the combustion state setting value and the fuel setting value or the air setting value such that both the combustion efficiency of the boiler and the flow rate of the fuel or air supplied to the boiler are optimized; operates to determine the combustion state setting value such that the combustion efficiency of the boiler is optimized without changing the flow rate of the fuel or air supplied to the boiler; A plant operation optimization device capable of the above.

2. The plant operation optimization device according to claim 1, wherein the combustion state setting value is a bias value between an existing setting value and a new setting value of a physical quantity representing the combustion state of the boiler.

3. the determination unit further determines, based on the data related to the boiler, a soot blow setting value, which is a setting value for controlling soot blow in the boiler, by means of an extended Kalman filter; the output unit further outputs the soot blow setting value to the display device; The plant operation optimization device according to claim 1 or 2.

4. the determination unit operates to determine the soot blow setting value and the fuel setting value or the air setting value such that both the timing of performing soot blow in the boiler and the flow rate of the fuel or air supplied to the boiler are optimized; operates to determine the soot blow setting value such that the timing of performing soot blow in the boiler is optimized without changing the flow rate of the fuel or air supplied to the boiler; The plant operation optimization device according to claim 3, capable of the above.

5. The plant operation optimization device according to claim 3 or 4, wherein the soot blow setting value is a new setting value replacing an existing setting value of the time for performing soot blow in the boiler.

6. The plant operation optimization device according to any one of claims 1 to 5, wherein the output unit transmits the combustion state setting value to the display device different from the plant operation optimization device.

7. The plant operation optimization device according to any one of claims 1 to 6, wherein the display device displays an operator operation screen for operating the power generation plant or the plant operation optimization device.

8. The plant operation optimization device according to any one of claims 1 to 7, wherein the display device displays a simulation screen for simulating the operation of the boiler based on the combustion state setting value.

9. A receiving unit that receives data related to a boiler in a power generation plant; A determination unit that determines, by an extended Kalman filter, a soot blow setting value, which is a setting value for controlling soot blow in the boiler, based on the data related to the boiler; An output unit that outputs the soot blow setting value to a display device; comprising The determination unit further determines a fuel setting value, which is a setting value for controlling the fuel supplied to the boiler, or an air setting value, which is a setting value for controlling the air supplied to the boiler, based on the data related to the boiler; The output unit further outputs the fuel setting value or the air setting value to the display device; The determination unit operates to determine the soot blow setting value and the fuel setting value or the air setting value such that both the timing for performing soot blow in the boiler and the flow rate of the fuel or air supplied to the boiler are optimized; operates to determine the soot blow setting value such that the timing for performing soot blow in the boiler is optimized without changing the flow rate of the fuel or air supplied to the boiler; A plant operation optimization device capable of the above.

10. receives data related to a boiler in a power generation plant Based on the data related to the boiler, determine a combustion state set value, which is a set value for controlling the combustion state of the boiler, by a neural network, or determine a soot blow set value, which is a set value for controlling soot blow in the boiler, by an extended Kalman filter. Output the combustion state set value or the soot blow set value to a display device. Including Based on the data related to the boiler, determine a fuel set value, which is a set value for controlling the fuel supplied to the boiler, or an air set value, which is a set value for controlling the air supplied to the boiler. Output the fuel set value or the air set value to the display device. Further including Determine the combustion state set value or the soot blow set value and the fuel set value or the air set value so that both the combustion efficiency of the boiler or the timing of performing soot blow in the boiler and the flow rate of the fuel or air supplied to the boiler are optimized. Determine the combustion state set value or the soot blow set value so that the combustion efficiency of the boiler or the timing of performing soot blow in the boiler is optimized without changing the flow rate of the fuel or air supplied to the boiler. A plant operation optimization method further including this.

Images