WO2022210827A1 - Procédé de commande pour dispositif de désulfuration de gaz de fumée humide, dispositif de commande pour dispositif de désulfuration de gaz de fumée humide, système de surveillance à distance comprenant ledit dispositif de commande pour dispositif de désulfuration de gaz de fumée humide, dispositif de traitement d'informations, et système de traitement d'informations - Google Patents

Procédé de commande pour dispositif de désulfuration de gaz de fumée humide, dispositif de commande pour dispositif de désulfuration de gaz de fumée humide, système de surveillance à distance comprenant ledit dispositif de commande pour dispositif de désulfuration de gaz de fumée humide, dispositif de traitement d'informations, et système de traitement d'informations Download PDF

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WO2022210827A1
WO2022210827A1 PCT/JP2022/015797 JP2022015797W WO2022210827A1 WO 2022210827 A1 WO2022210827 A1 WO 2022210827A1 JP 2022015797 W JP2022015797 W JP 2022015797W WO 2022210827 A1 WO2022210827 A1 WO 2022210827A1
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WIPO (PCT)
Prior art keywords
absorbent
concentration
flue gas
learning model
flow rate
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PCT/JP2022/015797
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English (en)
Japanese (ja)
Inventor
駿 郡司
仁 須藤
信弥 金森
一貴 吉田
Original Assignee
三菱重工業株式会社
三菱パワー株式会社
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Publication of WO2022210827A1 publication Critical patent/WO2022210827A1/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
    • B01D53/34—Chemical or biological purification of waste gases
    • B01D53/46—Removing components of defined structure
    • B01D53/48—Sulfur compounds
    • B01D53/50—Sulfur oxides
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
    • B01D53/34—Chemical or biological purification of waste gases
    • B01D53/74—General processes for purification of waste gases; Apparatus or devices specially adapted therefor
    • B01D53/77—Liquid phase processes
    • B01D53/78—Liquid phase processes with gas-liquid contact
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
    • F23J—REMOVAL OR TREATMENT OF COMBUSTION PRODUCTS OR COMBUSTION RESIDUES; FLUES 
    • F23J15/00—Arrangements of devices for treating smoke or fumes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • the present disclosure relates to a control method for a wet flue gas desulfurization apparatus, a control apparatus for a wet flue gas desulfurization apparatus, a remote monitoring system provided with the control apparatus for the wet flue gas desulfurization apparatus, an information processing device, and an information processing system.
  • exhaust gas generated by a combustion device such as a boiler is introduced into an absorption tower of the desulfurization system, and brought into gas-liquid contact with an absorption liquid circulating in the absorption tower.
  • the absorbent e.g., calcium carbonate
  • SO 2 sulfur dioxide
  • SO2 is removed ( exhaust gas is desulfurized).
  • the absorbent that has absorbed SO 2 drops and is stored in the storage tank below the absorption tower.
  • An absorbent is supplied to the storage tank, and the absorbent whose absorption performance has been restored by the supplied absorbent is supplied to the upper part of the absorption tower by a circulating pump, and is subjected to gas-liquid contact with exhaust gas ( SO2 absorption). be done. Since the circulation pump that circulates the absorbent consumes a large amount of power, conventionally, in order to suppress power consumption, the amount of absorbent required based on the flow rate of the flue gas flowing into the absorber and the SO2 concentration in the flue gas is used. The circulation flow rate is calculated and the number of operating circulation pumps is controlled.
  • Patent Literature 1 discloses a technique for appropriately adjusting operating conditions of a circulation pump for circulating an absorbent in an absorption tower of such a wet flue gas desulfurization apparatus.
  • a circulation pump for circulating an absorbent in an absorption tower of such a wet flue gas desulfurization apparatus.
  • the correlation between the operating data and the SO 2 concentration at the absorption tower outlet, and the operating data and the absorption liquid is modeled by machine learning, and based on the table obtained by these two learning models, the circulating flow rate of the absorbent and the absorbent concentration are controlled to be optimized.
  • Patent Document 1 does not specify parameters that are highly correlated with the SO 2 concentration and the absorbent concentration at the outlet of the absorption tower when constructing the learning model. Therefore, there is a risk that prediction disturbance will occur, and the widening of the learning range of the operating data may increase the computational load. In addition, it is necessary to construct two learning models by machine learning the correlation between the operating data and the SO 2 concentration at the absorption tower outlet, and the correlation between the operating data and the concentration of the absorbent contained in the absorbent. The calculation tends to be complicated because of the presence of
  • At least one embodiment of the present disclosure has been made in view of the above circumstances, and control of a wet flue gas desulfurization apparatus that can easily perform control for circulating an absorbent in an absorption tower of a wet flue gas desulfurization apparatus
  • An object is to provide a method, a control device, a remote monitoring system, an information processing device, and an information processing system.
  • a method for controlling a wet flue gas desulfurization apparatus includes: A control method for a wet flue gas desulfurization apparatus for performing desulfurization by bringing exhaust gas generated in a combustion apparatus and an absorbing liquid into gas-liquid contact in an absorption tower, comprising: Explanatory variables including at least one parameter correlated with the absorbent concentration and circulation flow rate of the absorbent in the absorption tower, and the output of a generator driven by the gas produced in the combustion device; A step of building a learning model by machine learning about the relationship with the objective variable, which is the concentration of sulfur dioxide at the outlet of the absorption tower; calculating the predicted value of the sulfur dioxide concentration by the learning model for each output based on a reference table that defines the relationship between the parameter and the output, and the absorbent for the predicted value to satisfy the reference value; A step of creating a table showing the input amount of and the circulation flow rate of the absorbent; and determining control target values for the amount of the absorbent
  • a control device for a wet flue gas desulfurization apparatus includes: A control device for a wet flue gas desulfurization device that performs gas-liquid contact between an exhaust gas generated in a combustion device and an absorbing liquid in an absorption tower to perform desulfurization, Explanatory variables including at least one parameter correlated with the absorbent concentration and circulation flow rate of the absorbent in the absorption tower, and the output of a generator driven by the gas produced in the combustion device; A learning model building unit for building a learning model by machine learning about the relationship with the objective variable, which is the concentration of sulfur dioxide at the outlet of the absorption tower; calculating the predicted value of the sulfur dioxide concentration by the learning model for each output based on a reference table that defines the relationship between the parameter and the output, and the absorbent for the predicted value to satisfy the reference value; A table creation unit for creating a table showing the input amount of and the circulation flow rate of the absorbent; A control target value determination unit for determining control
  • the remote monitoring system according to at least one embodiment of the present disclosure, a control device for a wet flue gas desulfurization system according to at least one embodiment of the present disclosure; and a remote monitoring device electrically connected to the control device of the wet flue gas desulfurization system.
  • An information processing device that executes processing related to control of a wet flue gas desulfurization device that performs desulfurization by bringing exhaust gas generated in a combustion device and an absorbent into gas-liquid contact in an absorption tower, Explanatory variables including at least one parameter correlated with the absorbent concentration and circulation flow rate of the absorbent in the absorption tower, and the output of a generator driven by the gas produced in the combustion device;
  • a learning model building unit for building a learning model by machine learning about the relationship with the objective variable, which is the concentration of sulfur dioxide at the outlet of the absorption tower; calculating the predicted value of the sulfur dioxide concentration by the learning model for each output based on a reference table that defines the relationship between the parameter and the output, and the absorbent for the predicted value to satisfy the reference value;
  • a table creation unit for creating a table showing the input amount of and the circulation flow rate of the absorbent;
  • a control target value determination unit for determining control target values of the amount of the
  • Information processing consisting of an information processing device that executes processing related to the control of a wet flue gas desulfurization device that performs desulfurization by bringing the exhaust gas generated in the combustion device and the absorbing liquid into gas-liquid contact with the absorption liquid in the absorption tower, and a terminal capable of communication.
  • the information processing device is At least one parameter correlated with the absorbent concentration and circulation flow rate of the absorbent in the absorption tower and the output of a generator driven by the gas generated in the combustion device, according to the request from the terminal and a learning model building unit for building a learning model by machine learning about the relationship between the explanatory variable including the future absorption tower outlet and the objective variable, which is the sulfur dioxide concentration; calculating the predicted value of the sulfur dioxide concentration by the learning model for each output based on a reference table that defines the relationship between the parameter and the output, and the absorbent for the predicted value to satisfy the reference value; A table creation unit for creating a table showing the input amount of and the circulation flow rate of the absorbent; A control target value determination unit for determining control target values of the amount of the absorbent charged and the circulation flow rate of the absorbent corresponding to the state of the generator based on the table.
  • a control method, a control device, and a remote monitoring system for a wet flue gas desulfurization apparatus capable of easily performing control for circulating an absorbent in an absorption tower of a wet flue gas desulfurization apparatus , an information processing device, and an information processing system.
  • FIG. 1 is a configuration diagram of a wet flue gas desulfurization apparatus according to one embodiment
  • FIG. 1 is a configuration diagram of a remote monitoring system according to one embodiment
  • FIG. 4 is a flow chart showing a control method for a wet flue gas desulfurization system according to one embodiment.
  • FIG. 4 is a flowchart showing a correction calculation for the objective variable of the learning model in step S3 of FIG. 3
  • FIG. 4 is a flowchart showing a correction calculation for explanatory variables of a learning model in step S3 of FIG. 3
  • FIG. 6 is a flowchart showing correction calculation of explanatory variables of a learning model using correction values calculated in FIG. 5 ;
  • FIG. 4 is a flowchart showing a correction calculation for the objective variable of the learning model in step S3 of FIG. 3
  • FIG. 4 is a flowchart showing a correction calculation for explanatory variables of a learning model in step S3 of FIG. 3
  • FIG. 6 is a flowchar
  • FIG. 7 is a diagram showing a determination flow of optimization conditions for performing a correction operation in FIG. 6;
  • FIG. 10 is a calculation example showing predicted values of SO 2 concentration calculated by a learning model when CaCO 3 concentration and the number of operating fixed displacement circulating pumps are changed for each generator output;
  • FIG. 9 is an example of a table created based on the calculation result of FIG. 8.
  • FIG. It is an example of computation showing predicted values of SO 2 concentration calculated by a learning model when the CaCO 3 concentration and the displacement of a variable displacement circulating pump are changed for each generator output.
  • 11 is an example of a table created based on the calculation result of FIG. 10;
  • 1 is a configuration diagram of an information processing system according to an embodiment;
  • FIG. 13 is a diagram showing an internal configuration of the information processing apparatus of FIG. 12 together with a control device;
  • FIG. 1 is a configuration diagram of a wet flue gas desulfurization apparatus 10 according to one embodiment.
  • the wet flue gas desulfurization device 10 is a device for desulfurizing the exhaust gas generated in the combustion device 1 .
  • the combustion device 1 is, for example, a boiler for generating steam, and is configured as part of a power plant capable of generating power by supplying the steam generated by the combustion device 1 to a power generator 5 .
  • the wet flue gas desulfurization apparatus 10 includes an absorption tower 11 communicating with a combustion apparatus 1 via a pipe 2, and a plurality of circulation pumps 12a, 12b provided in a circulation pipe 3 for absorbing liquid circulating in the absorption tower 11. 12c, .
  • An absorbent slurry supply unit 13 for supplying slurry (absorbent slurry) of calcium carbonate (CaCO 3 ), which is an absorbent contained in the liquid, into the absorption tower 11, and gypsum for recovering gypsum in the absorbent. and a recovery unit 14 .
  • the absorber 11 is provided with an outflow pipe 16 through which the exhaust gas desulfurized by the operation described later flows out from the absorber 11 as an outflow gas. gas analyzer 17 is provided.
  • the absorbent slurry supply unit 13 includes an absorbent slurry production facility 21 for producing absorbent slurry, an absorbent slurry supply pipe 22 communicating between the absorbent slurry production facility 21 and the absorption tower 11, and an absorbent slurry.
  • An absorbent slurry supply amount control valve 23 for controlling the flow rate of the absorbent slurry flowing through the supply pipe 22 is provided.
  • the gypsum recovery unit 14 includes a gypsum separator 25, a gypsum slurry withdrawal pipe 26 communicating between the gypsum separator 25 and the absorption tower 11, and a gypsum slurry withdrawal pump 27 provided in the gypsum slurry withdrawal pipe 26. I have it.
  • the wet type flue gas desulfurization device 10 is provided with a control device 15 for the wet type flue gas desulfurization device 10 .
  • the control device 15 acquires operation data including various detectors for acquiring various operation data (e.g., temperature and pressure at various parts, flow rates of various fluids, etc.) of the combustion device 1 and the wet flue gas desulfurization device 10.
  • a driving data receiving unit 30 electrically connected to the unit 20 is provided.
  • the operating data acquisition unit 20 includes a gas analyzer 17 .
  • the control device 15 includes a learning model construction unit 38 electrically connected to the operation data reception unit 30, a table generation unit 31 electrically connected to the learning model construction unit 38, and a table generation unit 31 electrically connected to the learning model construction unit 38.
  • 34 and a learning model correction unit 35 electrically connected to the learning model construction unit 38 .
  • the circulation pump adjustment unit 33 is electrically connected to each circulation pump 12 .
  • the absorbent slurry supply controller 34 is electrically connected to the absorbent slurry supply amount control valve 23 .
  • FIG. 2 shows the configuration of a remote monitoring system 40 for remotely monitoring the control state of the wet flue gas desulfurization equipment 10 (see FIG. 1).
  • the remote monitoring system 40 is electrically connected to a distributed control system (DCS) 41 of each device constituting the combustion device 1 (see FIG. 1) and the wet flue gas desulfurization device 10 (see FIG. 1), and to the DCS 41.
  • An edge server 42 equipped with a control device 15, and a remote monitoring device such as a desktop personal computer or tablet computer electrically communicatively connected to the edge server 42 via a cloud or a virtual private network (VPN). 43.
  • a control state of the wet flue gas desulfurization apparatus 10 can be remotely monitored by a remote monitoring device 43 which is normally located away from the edge server 42 .
  • the exhaust gas generated in the combustion device 1 flows through the pipe 2, flows into the absorption tower 11, and rises in the absorption tower 11.
  • the absorbent flows through the circulation pipe 3 and into the absorption tower 11 , where the absorption liquid flows down.
  • the absorbent flowing down in the absorption tower 11 accumulates in the absorption tower 11 , flows out from the absorption tower 11 by the circulation pump 12 , and flows through the circulation pipe 3 .
  • the absorbent circulates within the absorption tower 11 .
  • SO 2 contained in the exhaust gas is expressed by the following reaction formula: SO 2 +CaCO 3 +2H 2 O+1/2O 2 ⁇ CaSO 4.2H 2 O+CO 2 , reacts with CaCO 3 in the absorbent to precipitate gypsum (CaSO 4 .2H 2 O) in the absorbent.
  • the SO 2 concentration in the effluent gas tends to decrease as the circulation flow rate of the absorbent circulating in the absorption tower 11 increases, unless the CaCO 3 concentration in the absorbent fluctuates greatly.
  • the control device 15 controls the number of operating circulation pumps 12 by a control method described later to control the circulation flow rate, thereby controlling the concentration of SO 2 in the outflow gas, for example, below a preset value. So the SO2 concentration in the effluent gas can be controlled.
  • the gypsum precipitated in the absorbing liquid in the absorption tower 11 is extracted as gypsum slurry from the absorption tower 11 by the gypsum slurry extraction pump 27 , and the gypsum slurry flows through the gypsum slurry extraction pipe 26 to the gypsum separator 25 . flow into Gypsum and water are separated in the gypsum separator 25, the gypsum is recovered, and the water is sent to a drainage facility (not shown).
  • the control device 15 controls the opening degree of the absorbent slurry supply amount control valve 23 by a control method to be described later, and absorbs the absorbent slurry produced in the absorbent slurry production facility 21 through the absorbent slurry supply pipe 22. Feed into column 11 .
  • the CaCO 3 concentration in the absorbent falls within a preset range, and large fluctuations in the CaCO 3 concentration during desulfurization of the flue gas are suppressed.
  • FIG. 3 is a flow chart showing a control method for the wet flue gas desulfurization system 10 according to one embodiment.
  • step S1 After collecting various operation data of the combustion device 1 and the wet-type flue gas desulfurization device 10 in step S1, in step S2, various operation data and the future SO2 concentration in the outflow gas flowing out from the absorber 11 Build a learning model for relationships using machine learning.
  • step S3 the learning model constructed in step S2 is corrected.
  • step S4 a table is created using the corrected learning model obtained in step S3.
  • step S5 based on the table created in step S4, the circulation flow rate of the absorbing liquid and the supply amount of the CaCO 3 absorbent slurry are controlled so that the SO 2 concentration in the outflow gas is equal to or lower than a preset value.
  • step S6 the operating conditions of the circulation pump 12 are adjusted based on the control target value of the circulation flow rate determined in step S5, and in step S7, the CaCO 3 absorbent determined in step S5
  • the absorbent slurry supply amount control valve 23 is controlled based on the control target value of the slurry supply amount. Thereby, the SO 2 concentration in the outflow gas is controlled so as to be equal to or lower than the preset value.
  • step S3 may be omitted if the accuracy of the learning model before correction is sufficient.
  • FIG. 3 illustrates the case where step S7 is performed after step S6, but step S7 may be performed before step S6, or steps S6 and S7 may be performed simultaneously.
  • step S1 as shown in FIG. 1, after the operation data acquisition unit 20 acquires various operation data of the combustion device 1 and the wet-type flue gas desulfurization device 10, the acquired various operation data are transmitted to the control device 15.
  • the control device 15 collects various types of operation data as the operation data receiving unit 30 receives the operation data.
  • various operating data include the SO2 concentration in the outflow gas.
  • step S2 the learning model building unit 38 builds a learning model by machine learning regarding the relationship between the various operating data collected by the operating data acquisition unit 20 and the future SO 2 concentration in the outflow gas.
  • the learning model is constructed as a regression model using a regression technique such as multiple regression, ridge regression, Lasso regression, or Elastic Net, for example.
  • n is an arbitrary natural number
  • k1 to kn are coefficients
  • b is an arbitrary intercept.
  • the learning model obtained by machine learning uses an explanatory variable consisting of a plurality of parameters included in the operating data acquired by the operating data acquisition unit 20 and the future SO 2 concentration in the outflow gas as objective variables, and calculates the correlation between the two. It is built as a model to show.
  • the combination of parameters included in the explanatory variables of the learning model can be arbitrarily selected from the following candidates.
  • the explanatory variables of the learning model are, among the above candidates, i) generator output command value (output command value from the outside for the generator 5), iii) boiler air flow rate or boiler exhaust gas flow rate (for the combustion device 1 supply air flow rate or flue gas flow rate from the combustion device 1 ), or iv) desulfurization inlet SO2 concentration or boiler outlet SO2 concentration ( SO2 concentration at the inlet of the absorption tower 11 or SO2 concentration at the combustion device 1 outlet).
  • generator output command value output command value from the outside for the generator 5
  • boiler air flow rate or boiler exhaust gas flow rate for the combustion device 1 supply air flow rate or flue gas flow rate from the combustion device 1
  • desulfurization inlet SO2 concentration or boiler outlet SO2 concentration SO2 concentration at the inlet of the absorption tower 11 or SO2 concentration at the combustion device 1 outlet
  • the explanatory variables of the learning model are, among the above candidates, iii) boiler air flow rate or boiler exhaust gas flow rate (supply air flow rate to combustion device 1 or exhaust gas flow rate from combustion device 1), and iv) desulfurization inlet SO 2 concentration or boiler exit SO2 concentration ( SO2 concentration at absorber 11 inlet or SO2 concentration at combustor 1 exit).
  • iii) boiler air flow rate or boiler exhaust gas flow rate supply air flow rate to combustion device 1 or exhaust gas flow rate from combustion device 1
  • desulfurization inlet SO 2 concentration or boiler exit SO2 concentration SO2 concentration at absorber 11 inlet or SO2 concentration at combustor 1 exit
  • step S3 the learning model correction unit 35 corrects the learning model constructed in step S2.
  • the correction of the learning model is performed based on the error between the SO 2 concentration predicted value calculated by the learning model and the measured value, and is performed for the explanatory variable and the objective function of the learning model.
  • FIG. 4 is a flowchart showing the correction calculation for the objective variable of the learning model in step S3 of FIG.
  • the correction time ⁇ Vp corresponding to the measurement delay is output after the predicted value Vp calculated using the learning model to be corrected.
  • the predicted value Vp calculated using the learning model does not include the time required for the gas analyzer 17 to measure the measured value Vm.
  • the correction time ⁇ Vp corresponds to the time required for the gas analyzer 17 to measure the actual measurement value Vm. It can be made comparable to the value Vm.
  • the corrected predicted value Vp′ is obtained.
  • the ratio (Vm/Vp') between the predicted value Vp' after correction and the measured value Vm of the gas analyzer 17 is calculated, and the moving average thereof is calculated to obtain the correction value A related to the objective function.
  • the correction value A calculated in this manner is applied to the learning model.
  • the learning model after correction is expressed by the following formula.
  • SO 2 concentration at the outlet of the absorption tower (k1 x explanatory variable 1 + k2 x explanatory variable 2 + ... + kn x explanatory variable n + b) x A (2)
  • FIG. 5 is a flowchart showing the correction calculation for the explanatory variables of the learning model in step S3 of FIG.
  • a case of correcting the explanatory variable SG which is a parameter having a correlation with the output of the generator 5 (the generator output Y), will be described.
  • a reference table Tr that defines the correlation between the generator output Y and the values X1 to X4 of the explanatory variable SG is prepared in advance.
  • the reference table Tr prescribes values X1 to X4 of the explanatory variable SG for each value of the generator output Y, as shown in FIG.
  • the values X1 to A reference table Tr is shown containing characteristic functions defining X4.
  • the learning model correction unit 35 outputs the corresponding explanatory variable SG by inputting the generator output Y obtained from the outside to such a reference table Tr. On the other hand, the learning model correction unit 35 obtains the actual measurement value SGm of the explanatory variable based on the result obtained by the operation data receiving unit 30, and the ratio (SGm/SG) to the value SG of the explanatory variable output from the reference table Tr. Ask for Then, by calculating the moving average of the ratio, the correction value B for the explanatory function is obtained.
  • FIG. 6 is a flowchart showing correction calculation of the explanatory variable SG of the learning model using the correction value B calculated in FIG.
  • the optimization condition C includes at least one (for example, all) conditions for determining whether or not the environment for correcting the reference table Tr with the correction value B is in place. It can be determined from various points of view that the operation state is stable in the control device 15 and that no abnormality has occurred.
  • FIG. 7 is a diagram showing a determination flow of the optimization condition C for performing correction calculation in FIG.
  • determination is made based on the following five conditions.
  • Condition 1 Abnormalities are not monitored in explanatory variables.
  • Condition 2 The instrument for acquiring the actual measurement value SGm of the explanatory variable is not being adjusted.
  • Condition 3) The generator output (or boiler load) is above a predetermined value (for example, 0% to 50%).
  • the plant must be in a smoking state.
  • Condition 5 Initialization of the computing software must be completed.
  • the learning model correction unit 35 multiplies the correction value B by the explanatory variable SG value corresponding to the generator output for the characteristic function defined in the reference table Tr. By doing so, the explanatory variables are corrected. As a result of such correction, the characteristic functions defined in the reference table Tr are updated as shown in FIG. ing).
  • a table T is created based on the learning model corrected at step S3.
  • Table T is created by the amount of absorbent added so that the predicted value of SO 2 concentration calculated by the learning model satisfies a preset reference value (for example, becomes equal to or lower than the reference value) for each generator output. and by calculating the circulation flow rate of the absorbent.
  • FIG. 8 is a calculation example showing predicted values of SO 2 concentration calculated by the learning model when changing the CaCO 3 concentration and the number of fixed capacity circulation pumps 12 in operation for each generator output.
  • 9 is an example of a table T created based on the calculation result of FIG.
  • the CaCO 3 concentration is a parameter corresponding to the amount of absorbent charged, and can take a value of "2 to 5" as an allowable range.
  • the number of circulating pumps 12 in operation is a parameter corresponding to the circulating amount of the absorbent, and can take a value of "8 to 10" as an allowable range.
  • the table creating unit 31 changes the CaCO 3 concentration to "4" by increasing by one step the CaCO 3 concentration, which has less impact on the operation cost than the number of operating circulation pumps. Then, the predicted value of the SO 2 concentration by the learning model is 100 ppm, which is below the reference value (100 ppm). Therefore, the table creating unit 31 specifies that the optimum CaCO 3 concentration at the generator output of "50%” is "4" and the number of operating circulation pumps is "8".
  • the table creation unit 31 changes the CaCO 3 concentration to “5” by increasing by one step the CaCO 3 concentration, which has less influence on the operation cost than the number of operating circulation pumps.
  • the SO 2 concentration predicted by the learning model is 110 ppm, still exceeding the reference value (100 ppm).
  • the table creation unit 31 specifies that the optimum CaCO 3 concentration is "3" and the number of operating pumps is "9” when the generator output is "60%".
  • the table creation unit 31 creates the table T shown in FIG. 9 by specifying the optimum CaCO 3 concentration and the number of operating circulation pumps for each generator output in this way.
  • the table T is created so that the CaCO 3 concentration decreases and increases at the timing when the number of operating circulation pumps 12 increases stepwise as the generator output increases.
  • FIG. 10 is a calculation example showing predicted values of the SO 2 concentration calculated by the learning model when the CaCO 3 concentration and the capacity of the variable displacement circulation pump 12 are changed for each generator output
  • FIG. is an example of a table T created based on the calculation result of FIG.
  • the CaCO 3 concentration is a parameter corresponding to the amount of absorbent charged, and can take a value of "2 to 5" as an allowable range.
  • the circulating pump capacity is a parameter corresponding to the circulating amount of the absorbent, and can take a value of "10 to 100" as an allowable range.
  • the table creating unit 31 changes the CaCO 3 concentration to "4" by increasing by one step the CaCO 3 concentration, which has less influence on the operation cost than the capacity of the circulating pump. Then, the predicted value of the SO 2 concentration by the learning model is 100 ppm, which is below the reference value (100 ppm). Therefore, the table creating unit 31 specifies that the optimum CaCO 3 concentration at the generator output of "50%” is "4" and the circulation pump capacity is "10".
  • the table creating unit 31 changes the pump displacement to "15".
  • the SO 2 concentration predicted by the learning model is 110 ppm, still exceeding the reference value (100 ppm).
  • the table generator 31 changes the circulating pump capacity to "18”.
  • the predicted value of SO 2 concentration by the learning model is 100 ppm, which is below the reference value (100 ppm). Therefore, the table creation unit 31 specifies that the optimum CaCO 3 concentration at the generator output of "60%" is "5" and the circulation pump capacity is "18".
  • the table creating unit 31 creates the table shown in FIG. 11 by specifying the optimum CaCO 3 concentration and circulation pump capacity for each generator output in this way.
  • the table T is created so that the pump displacement increases at the timing when the increase in the CaCO 3 concentration becomes insufficient as the generator output increases.
  • the search for the optimum CaCO 3 concentration and the number of operating circulation pumps (or circulation pump capacity) for creating the tables is performed to minimize the operation cost.
  • the search may be performed from other viewpoints.
  • the graph-format table T as shown in FIGS. 9 and 11 is illustrated, but the table T does not necessarily have to be in such a form, and may be in the form of a matrix, formula, or the like.
  • step S5 the control target value determination unit 32 acquires the command value of the generator output, and based on the table T created in step S4, the control target value (absorbent input amount and Each control target value) of the circulation flow rate of the absorbing liquid is determined.
  • the circulation pump adjustment unit 33 controls the circulation pumps 12a to 12c based on the control target value determined in step S5, and the absorbent slurry supply control unit 34 adjusts the supply amount of the absorbent. Control.
  • step S5 the circulation flow rate is adjusted by controlling the number of circulation pumps based on the table shown in FIG. , the pump capacity is controlled based on the table T shown in FIG.
  • the amount of absorbent introduced into the absorption tower 11 and the inside of the absorption tower 11 are controlled within a range in which the SO 2 concentration in the future outflow gas is equal to or lower than the preset reference value.
  • the circulation flow rate of the circulating absorbent can be adjusted appropriately.
  • Such a control calculation can obtain control target values for the amount of absorbent introduced into the absorber 11 and the circulation flow rate of the absorbent circulating in the absorber 11 based on a single learning model. Therefore, the computational burden is small.
  • Each processing executed by the control device 15 in the above embodiment is executed by the information processing device 44 (see FIG. 12) electrically communicably connected to the edge server 42 in the cloud environment or via VPN. Configurations are possible.
  • the information processing device 44 includes an operation data reception unit 30, a learning model construction unit 38, a learning model correction unit 35, a table creation unit 31, and a control target value determination unit 32.
  • the control target value determination unit 32 By communicating the determined control target value to the circulation pump adjustment section 33 and the absorbent slurry supply control section 34 in the control device 15, the circulation pump and the absorbent supply amount may be controlled.
  • the operation data receiving unit 30 may receive various kinds of operation data via the operation data relay unit 39 (FIG. 13) of the control device 15, or may receive various kinds of operation data from the operation data acquisition unit 20 as described above. may be received.
  • operation data relay unit 39 FIG. 13
  • the control target values of the circulating pump and absorbent are not directly controlled, but only displayed.
  • a driving index map generated in a cloud environment is sent to a customer-owned device (terminal 45) and illustrated through a dedicated application, and the local driving index map is updated by the customer.
  • the information processing device 44 may also include a circulation pump control section 33 and an absorbent slurry supply control section 34 to remotely control the circulation pump and the supply amount of absorbent. Furthermore, the information processing device 44 may be configured to execute each process in response to a request from the terminal 45 .
  • a method for controlling a wet flue gas desulfurization apparatus includes: In the absorption tower (for example, the absorption tower 11 of the above-described embodiment), the wet-type flue gas desulfurization device (for example, the wet-type flue gas desulfurization device of the above-described embodiment) performs desulfurization by bringing the exhaust gas generated in the combustion device and the absorbing liquid into gas-liquid contact.
  • the wet-type flue gas desulfurization device for example, the wet-type flue gas desulfurization device of the above-described embodiment
  • the amount of absorbent introduced into the absorption tower and the amount of absorbent circulated in the absorption tower are within the range where the SO 2 concentration in the future outflow gas is equal to or lower than the preset reference value. It is possible to appropriately adjust the circulation flow rate of the absorbent. Since such a control calculation can obtain control target values for the amount of absorbent introduced into the absorption tower and the circulation flow rate of the absorbent circulating in the absorption tower based on a single learning model, Low computational load.
  • At least one circulation pump e.g., In order to reduce the operation cost of the circulation pump 12
  • a search is made for the amount of the absorbent to be supplied and the circulation flow rate of the absorbent so that the predicted value satisfies the reference value.
  • the amount of absorbent input and the circulation flow rate of the absorbent are adjusted so as to reduce the operating cost. is searched for.
  • the operating cost of the plant can be effectively reduced while suppressing the sulfur dioxide discharged from the absorption tower within a necessary range.
  • a step of correcting the learning model based on an error between the predicted value of the concentration of sulfur dioxide in the exhaust gas at the outlet of the absorber by the learning model and the measured value is further provided.
  • control accuracy can be improved by correcting the learning model based on the error between the predicted value obtained by the learning model and the actual measurement value.
  • the at least one parameter includes at least one of an external command value of the output, the output, the air flow rate of the combustion device, and the sulfur dioxide concentration at the absorption tower inlet.
  • the data to be learned is efficiently narrowed down, thereby reducing the computational burden of machine learning and improving the performance. It is possible to build a learning model that can predict with high accuracy.
  • the at least one parameter includes the combustor air flow rate and the absorber inlet sulfur dioxide concentration.
  • the learning target data can be efficiently narrowed down to further reduce the computational burden of machine learning, while achieving better accuracy. Build predictable learning models.
  • the learning model is represented by a linear polynomial.
  • the reference table is defined as a function indicating the relationship between the previously specified parameters and the output.
  • the parameters having a correlation with the generator output are defined as a reference table indicating the correlation.
  • the at least one circulation pump is of fixed displacement type.
  • the amount of absorbent to the absorption tower is within the range where the concentration of sulfur dioxide in the future effluent gas is equal to or lower than a preset reference value.
  • the amount of input and the circulation flow rate of the absorbent circulating in the absorption tower can be adjusted appropriately.
  • the at least one circulation pump is of variable displacement type.
  • the amount of absorbent to the absorption tower is within the range where the concentration of sulfur dioxide in the future effluent gas is equal to or lower than a preset reference value.
  • the amount of input and the circulation flow rate of the absorbent circulating in the absorption tower can be adjusted appropriately.
  • the wet-type flue gas desulfurization device performs desulfurization by bringing the exhaust gas generated in the combustion device and the absorbing liquid into gas-liquid contact.
  • control device for example, the control device 15 of the above embodiment
  • Explanatory variables including at least one parameter correlated with the absorbent concentration and circulation flow rate of the absorbent in the absorption tower, and the output of a generator driven by the gas produced in the combustion device
  • a learning model construction unit for example, the learning model construction unit 38 of the above embodiment
  • a predicted value of the sulfur dioxide concentration by the learning model is calculated for each output based on a reference table that defines the relationship between the parameter and the output (for example, the reference table Tr of the above embodiment), and the predicted value
  • a table creating unit for example, the table creating unit 31
  • a control target value determination unit for example, the control target value decision unit 32.
  • the amount of absorbent introduced into the absorption tower and the amount of absorbent circulated in the absorption tower are within the range where the SO 2 concentration in the future outflow gas is equal to or lower than the preset reference value. It is possible to appropriately adjust the circulation flow rate of the absorbent. Such a control calculation can obtain control target values for the amount of absorbent introduced into the absorber 11 and the circulation flow rate of the absorbent circulating in the absorber based on a single learning model. , the computational burden is small.
  • a remote monitoring system includes: a control device for a wet flue gas desulfurization system according to the above aspect (10); and a remote monitoring device electrically connected to the control device of the wet flue gas desulfurization system.
  • control state of the wet flue gas desulfurization system can be remotely monitored.
  • the wet-type flue gas desulfurization device performs desulfurization by bringing the exhaust gas generated in the combustion device and the absorbing liquid into gas-liquid contact.
  • an information processing device for example, the information processing device 44 of the above embodiment
  • Explanatory variables including at least one parameter correlated with the absorbent concentration and circulation flow rate of the absorbent in the absorption tower, and the output of a generator driven by the gas produced in the combustion device
  • a learning model construction unit for example, the learning model construction unit 38 of the above embodiment
  • a predicted value of the sulfur dioxide concentration by the learning model is calculated for each output based on a reference table that defines the relationship between the parameter and the output (for example, the reference table Tr of the above embodiment), and the predicted value
  • a table creating unit for example, the table creating unit 31
  • a control target value determination unit for example, the control target value decision unit 32.
  • the amount of absorbent introduced into the absorption tower and the amount of absorbent circulated in the absorption tower are within the range where the SO 2 concentration in the future outflow gas is equal to or lower than the preset reference value. It is possible to appropriately adjust the circulation flow rate of the absorbent. Since such a control calculation can obtain control target values for the amount of absorbent introduced into the absorption tower and the circulation flow rate of the absorbent circulating in the absorption tower based on a single learning model, Low computational load.
  • the wet-type flue gas desulfurization device performs desulfurization by bringing the exhaust gas generated in the combustion device and the absorbing liquid into gas-liquid contact.
  • An information processing system for example, the an information processing system 46
  • the information processing device is At least one parameter correlated with the absorbent concentration and circulation flow rate of the absorbent in the absorption tower and the output of a generator driven by the gas generated in the combustion device, according to the request from the terminal and a learning model construction unit (for example, the learning model construction unit 38 of the above embodiment) for constructing a learning model by machine learning about the relationship between the explanatory variable including the future absorption tower outlet and the objective variable, which is the sulfur dioxide concentration ,
  • a predicted value of the sulfur dioxide concentration by the learning model is calculated for each output based on a reference table that defines the relationship between the parameter and the output (for example, the reference table Tr of the above embodiment), and the predicted value
  • a table creating unit for example, the table creating unit 31
  • a control target value determination unit for example, the control target value decision unit 32.
  • the amount of absorbent introduced into the absorption tower and the amount of absorbent circulated in the absorption tower are within the range where the SO 2 concentration in the future outflow gas is equal to or lower than the preset reference value. It is possible to appropriately adjust the circulation flow rate of the absorbent. Since such a control calculation can obtain control target values for the amount of absorbent introduced into the absorption tower and the circulation flow rate of the absorbent circulating in the absorption tower based on a single learning model, Low computational load.

Abstract

Un dispositif de désulfuration de gaz de fumée humide comprend une colonne d'absorption, une pompe de circulation et une unité d'alimentation en suspension d'agent absorbant. Dans ce procédé de commande pour ledit dispositif, un modèle d'apprentissage est construit, par apprentissage automatique, pour la relation entre : des variables explicatives qui comprennent la concentration en agent absorbant dans un liquide d'absorption, le débit de circulation du liquide d'absorption et au moins un paramètre qui est en corrélation avec la sortie d'un générateur; et une variable cible qui est une future concentration en dioxyde de soufre à la sortie de la colonne d'absorption. Pour chaque sortie de générateur, une valeur prédite de la concentration en dioxyde de soufre à partir du modèle d'apprentissage est calculée, et une table est créée indiquant une quantité introduite de l'agent absorbant et un débit de circulation du liquide d'absorption qui seraient nécessaires pour que la valeur prédite soit inférieure ou égale à une valeur de référence. Sur la base de ladite table, des valeurs cibles de commande sont déterminées pour la quantité introduite de l'agent absorbant et le débit de circulation du liquide d'absorption, qui correspondent à l'état du générateur.
PCT/JP2022/015797 2021-03-31 2022-03-30 Procédé de commande pour dispositif de désulfuration de gaz de fumée humide, dispositif de commande pour dispositif de désulfuration de gaz de fumée humide, système de surveillance à distance comprenant ledit dispositif de commande pour dispositif de désulfuration de gaz de fumée humide, dispositif de traitement d'informations, et système de traitement d'informations WO2022210827A1 (fr)

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