CN110923029B - Ash fusion temperature estimation device and ash fusion temperature estimation method - Google Patents
Ash fusion temperature estimation device and ash fusion temperature estimation method Download PDFInfo
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- CN110923029B CN110923029B CN201910870097.4A CN201910870097A CN110923029B CN 110923029 B CN110923029 B CN 110923029B CN 201910870097 A CN201910870097 A CN 201910870097A CN 110923029 B CN110923029 B CN 110923029B
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- C—CHEMISTRY; METALLURGY
- C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
- C10L—FUELS NOT OTHERWISE PROVIDED FOR; NATURAL GAS; SYNTHETIC NATURAL GAS OBTAINED BY PROCESSES NOT COVERED BY SUBCLASSES C10G, C10K; LIQUEFIED PETROLEUM GAS; ADDING MATERIALS TO FUELS OR FIRES TO REDUCE SMOKE OR UNDESIRABLE DEPOSITS OR TO FACILITATE SOOT REMOVAL; FIRELIGHTERS
- C10L5/00—Solid fuels
- C10L5/40—Solid fuels essentially based on materials of non-mineral origin
- C10L5/44—Solid fuels essentially based on materials of non-mineral origin on vegetable substances
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- C—CHEMISTRY; METALLURGY
- C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
- C10L—FUELS NOT OTHERWISE PROVIDED FOR; NATURAL GAS; SYNTHETIC NATURAL GAS OBTAINED BY PROCESSES NOT COVERED BY SUBCLASSES C10G, C10K; LIQUEFIED PETROLEUM GAS; ADDING MATERIALS TO FUELS OR FIRES TO REDUCE SMOKE OR UNDESIRABLE DEPOSITS OR TO FACILITATE SOOT REMOVAL; FIRELIGHTERS
- C10L5/00—Solid fuels
- C10L5/02—Solid fuels such as briquettes consisting mainly of carbonaceous materials of mineral or non-mineral origin
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E50/00—Technologies for the production of fuel of non-fossil origin
- Y02E50/10—Biofuels, e.g. bio-diesel
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E50/00—Technologies for the production of fuel of non-fossil origin
- Y02E50/30—Fuel from waste, e.g. synthetic alcohol or diesel
Abstract
Provided is an ash fusion temperature estimation device capable of quickly estimating the fusion temperature of ash generated by combustion of a plurality of fuels. The device is provided with: a theoretical ash composition calculating unit including a mixing ratio obtaining unit that obtains a mixing ratio of a plurality of types of fuel included in a target fuel, a fuel-corresponding ash composition obtaining unit that obtains a plurality of fuel-corresponding ash compositions each composed of an ash composition of each of the plurality of types of fuel included in the target fuel, and a calculating unit that calculates a theoretical ash composition that is an ash composition relating to a specific ash component of ash when the plurality of types of fuel are mixed at the mixing ratio, based on the mixing ratio and the plurality of fuel-corresponding ash compositions; an estimation model creation unit that creates an estimation model obtained by learning a relationship between a reference ash composition and a reference melting temperature of reference ash generated by combustion of a reference fuel; and a melting temperature calculation unit that calculates an estimated value of the ash melting temperature of the target fuel according to the theoretical ash composition using the estimation model.
Description
Technical Field
The present invention relates to a method for estimating the melting temperature of ash generated by combustion of a plurality of fuels.
Background
There are known coal combustion boilers for co-combustion of two kinds of fuels, such as a boiler for co-combustion of fossil fuels such as coal and heavy oil, biomass fuels such as woody biomass and waste solid fuels (RDF, RPF) produced from waste, and a boiler for co-combustion of high-grade coal and low-grade coal (for example, patent documents 1 to 2). From the viewpoint of environmental problems, resource problems, and the like, it is desirable that the combustion rate of recycled fuel and low-grade coal is high, but if the combustion rate is increased, ash (combustion ash) generated by the combustion tends to adhere to a furnace wall surface (in the furnace), a heat transfer pipe, a flue, and the like of the boiler, and there is a possibility that problems such as heat conduction inhibition, bottom damage due to dropping of bulk slag, and clogging of the burner are caused.
Generally, the adhesion of ash adhering to the inside of a boiler or the like is related to the melting temperature of ash, and therefore, in order to predict the adhesion of ash, the melting temperature of ash is evaluated. For example, JIS M8801 (coal-based test method) is known as a method for testing the fusibility of ash in coal-based and coke-based fuels. Further, examples of the analysis method include JIS M8812 (coal and coke-based industrial analysis method), JIS M8815 (analysis method of coal ash and coke ash), and the like (see patent document 3). In addition, the melting temperature of ash varies depending on the chemical composition of ash (ash composition).
Prior art documents
Patent document
Patent document 1: japanese patent laid-open No. 2012 and 132602
Patent document 2: japanese patent laid-open publication No. 2013-87135
Patent document 3: japanese patent laid-open publication No. 2013-156228
As described above, in order to predict the adhesion of ash generated by the combustion of a plurality of fuels (target fuels described later), when the melting temperature is evaluated by the above-described melting test method or the like, a large amount of time is required until an evaluation result (measurement value) of the melting temperature is obtained. For example, when a plurality of fuels include a fuel having a large variation in properties (moisture content, volume density, heat release amount, and the like) such as a biomass fuel and a waste solid fuel, it is preferable to periodically evaluate the adhesion of ash. In addition, considering the time and cost required until the evaluation result of the adhesion of ash is obtained, a new method capable of quickly determining the melting temperature of ash compared to the conventional method such as the ash fusion test method described above is desired.
Disclosure of Invention
In view of the above circumstances, an object of at least one embodiment of the present invention is to provide an ash fusion temperature estimation device capable of quickly estimating the fusion temperature of ash generated by combustion of a plurality of fuels.
Means for solving the problems
(1) An ash fusion temperature estimation device according to at least one embodiment of the present invention estimates the fusion temperature of ash generated by combustion of a target fuel including a plurality of types of fuel,
the ash fusion temperature estimation device is provided with:
a theoretical ash composition calculation unit configured to acquire a theoretical ash composition that is an ash composition relating to a specific ash component of the ash when the plurality of types of fuel included in the target fuel are mixed at a predetermined mixing ratio; and
and a melting temperature calculation unit configured to calculate an estimated value of the ash melting temperature of the target fuel corresponding to the theoretical ash composition, using an estimation model created by learning a relationship between a reference ash composition and a reference melting temperature of reference ash generated by combustion of a reference fuel.
According to the configuration of the above (1), the ash melting temperature of the target fuel is estimated from the theoretical ash composition of the target fuel in which a plurality of fuels are mixed at a mixture ratio, using the estimation model created by learning (machine learning). This enables the melting temperature of the target fuel to be quickly determined.
(2) In some embodiments, based on the structure of (1) above,
the theoretical ash composition calculating section includes:
a mixing ratio obtaining unit configured to obtain the mixing ratio;
a fuel-corresponding ash composition acquisition unit configured to acquire a plurality of fuel-corresponding ash compositions each composed of the ash composition of each of the plurality of types of fuel contained in the target fuel; and
and a calculation unit configured to calculate the theoretical ash composition based on the mixture ratio and the plurality of fuel-corresponding ash compositions.
According to the configuration of the above (2), the theoretical ash composition can be obtained based on the ash compositions corresponding to the respective fuels included in the target fuel and the mixing ratio thereof.
(3) In some embodiments, in addition to the configurations (1) to (2) described above,
the estimation model creation unit is configured to create the estimation model.
According to the configuration of the above (3), the ash fusion temperature estimating device has a function of creating an estimation model. This makes it possible to reliably estimate the ash melting temperature of the target fuel using the estimation model.
(4) In some embodiments, in addition to the structure of the above (3),
the estimation model creation unit includes:
a learning data generation unit configured to generate learning data including data in which the reference ash composition for each of the plurality of reference ashes is associated with the reference melting temperature; and
and a machine learning execution unit configured to create the estimation model by executing machine learning of the learning data.
According to the configuration of the above (4), the estimation model can be created by machine learning.
(5) In some embodiments, in addition to the structure of the above (4),
the learning data generation unit includes a learning data update unit that removes, from the learning data, an estimated value of the melting temperature of the ash calculated based on the estimated model and the reference ash composition associated with any of the data constituting the first learning data and the arbitrary data in which a difference between the reference melting temperature associated with the reference ash composition in the arbitrary data exceeds a predetermined threshold value, thereby updating the first learning data to the second learning data.
According to the configuration of the above (5), the estimation accuracy by the estimation model is improved by filtering data (individual data) that becomes a constituent element of the learning data from the above-described difference and creating the estimation model using the updated learning data.
(6) In some embodiments, in addition to the configurations (1) to (5) described above,
the plurality of fuels includes coal fuels and biomass fuels.
According to the configuration of the above (6), the ash melting temperature of the target fuel including the coal fuel such as high-grade coal and low-grade coal and the biomass fuel such as woody biomass and solid waste fuel (RDF, RPF) can be estimated.
(7) An ash fusion temperature estimation method according to at least one embodiment of the present invention estimates the fusion temperature of ash generated by combustion of a target fuel including a plurality of types of fuel,
the ash fusion temperature estimation method includes:
a theoretical ash composition calculation step of obtaining a theoretical ash composition that is an ash composition relating to a specific ash component of the ash when the plurality of types of fuel included in the target fuel are mixed at a predetermined mixing ratio; and
and a melting temperature calculation step of calculating an estimated value of the ash melting temperature of the target fuel corresponding to the theoretical ash composition, using an estimation model created by learning a relationship between a reference ash composition and a reference melting temperature with respect to reference ash generated by combustion of a reference fuel.
According to the configuration of the above (7), the same effect as the above (1) is achieved.
(8) In some embodiments, in addition to the structure of the above (7),
the theoretical ash composition calculating step comprises:
a mixing ratio obtaining step of obtaining the mixing ratio;
a fuel-corresponding ash composition acquisition step of acquiring a plurality of fuel-corresponding ash compositions each composed of the ash compositions of the plurality of types of fuel included in the target fuel; and
a calculating step of calculating the theoretical ash composition based on the mixture ratio and the plurality of fuel-corresponding ash compositions.
According to the configuration of the above item (8), the same effects as those of the above item (2) are achieved.
(9) In some embodiments, in addition to the configurations (7) to (8) described above,
further comprises an estimation model creation step of creating the estimation model.
According to the configuration of the above item (9), the same effects as those of the above item (3) are achieved.
(10) In several embodiments, based on the structure of (9) above,
the estimation model creation step includes:
a learning data generation step of generating learning data configured by data associating the reference ash composition with the reference melting temperature for each of a plurality of the reference ashes; and
a machine learning execution step of creating the estimation model by executing machine learning of the learning data.
According to the configuration of the above item (10), the same effects as those of the above item (4) are achieved.
(11) In some embodiments, in addition to the structure of (10) above,
the learning data generation step includes a learning data update step of removing, from the learning data, an estimated value of the melting temperature of the ash calculated based on the estimated model and the reference ash composition associated with any of the data constituting the first learning data and the arbitrary data in which a difference between the reference melting temperature associated with the reference ash composition in the arbitrary data exceeds a predetermined threshold value, thereby updating the first learning data to the second learning data.
According to the configuration of the above (11), the same effect as the above (5) is achieved.
(12) In some embodiments, in addition to the configurations (7) to (11) described above,
the plurality of fuels includes coal fuels and biomass fuels.
According to the configuration of the above item (12), the same effects as those of the above item (6) are achieved.
Effects of the invention
According to at least one embodiment of the present invention, there is provided an ash fusion temperature estimation device capable of quickly estimating the fusion temperature of ash generated by combustion of a plurality of types of fuel.
Drawings
Fig. 1 is a diagram schematically showing the configuration of an ash fusion temperature estimating apparatus according to an embodiment of the present invention.
Fig. 2 is a diagram schematically showing the configuration of a boiler system using a target fuel as a fuel according to an embodiment of the present invention.
Fig. 3 is a diagram showing melting temperature characteristics of ash generated by combustion of a plurality of types of fuels according to an embodiment of the present invention, and shows a case where fuel a is coal fuel and fuel b is biomass fuel.
Fig. 4 is a flowchart illustrating an ash fusion temperature estimation method according to an embodiment of the present invention.
Fig. 5 is a block diagram showing functions of an estimation model creating unit according to an embodiment of the present invention.
Fig. 6 is a flowchart for creating an estimation model by machine learning based on a neural network according to an embodiment of the present invention.
Description of reference numerals:
an ash fusion temperature estimating device;
a storage portion;
the theoretical ash constitutes a calculating part;
a mixing ratio obtaining section;
a fuel-corresponding ash composition obtaining section;
a calculation section;
a melting temperature calculation section;
estimating a model creation unit;
a learning data generation section;
a learning data update section;
a machine learning execution section;
a display;
a boiler system;
a coal storage facility;
a biomass storage device;
a coal hopper;
a biomass hopper;
a coal supply;
a biomass supply;
a comminution device;
a coal pulverizing device;
a biomass reduction apparatus;
85.. an exhaust gas piping;
86.. a denitration device;
an air heater;
88.. an electric dust collector;
a boiler;
a combustion chamber;
a flue;
a burner;
92.. a heat conduction tube set;
t. melting temperature;
th... threshold;
a subject fuel;
fb.. a biomass fuel;
fc.. coal fuel;
fr.. reference fuel;
r.. mixing ratio;
rh... ratio;
cn.. fuel corresponds to ash composition;
ct.. theoretical ash composition;
cr.. reference ash composition;
tr.. reference melting temperature;
learning data;
estimating a model;
external gas;
air for transportation;
a combustion air;
waste gas;
an air supply tube;
l1.. a conveyance air supply pipe;
l2.. a combustion air supply pipe;
lf.. micropowder tube.
Detailed Description
Hereinafter, several embodiments of the present invention will be described with reference to the drawings. The dimensions, materials, shapes, relative arrangements, and the like of the constituent members described in the embodiments or shown in the drawings are not intended to limit the scope of the present invention to these, but are merely illustrative examples.
For example, expressions such as "in a certain direction", "along a certain direction", "parallel", "orthogonal", "central", "concentric", or "coaxial" indicating relative or absolute arrangements indicate not only such an arrangement strictly, but also a state in which the arrangement is relatively displaced by a tolerance or an angle or a distance to the extent that the same function can be obtained.
For example, expressions indicating states of equality such as "identical", "equal", and "homogeneous" indicate not only states of strict equality but also states of tolerance or difference in degree to obtain the same function.
For example, the expressions indicating the shape such as a rectangular shape and a cylindrical shape indicate not only a shape such as a rectangular shape and a cylindrical shape which are geometrically strict but also a shape including a concave and convex portion, a chamfered portion, and the like within a range where the same effect can be obtained.
On the other hand, expressions such as "including", "provided", "including", or "having" one constituent element are not exclusive expressions which exclude the presence of other constituent elements.
Fig. 1 is a diagram schematically showing the configuration of an ash fusion temperature estimating device 1 according to an embodiment of the present invention. Fig. 2 is a diagram schematically showing the configuration of a boiler system 8 using a target fuel F as a fuel according to an embodiment of the present invention. Fig. 3 is a diagram showing melting temperature characteristics of ash generated by combustion of a plurality of types of fuels according to an embodiment of the present invention, and shows a case where fuel a is coal fuel Fc and fuel b is biomass fuel Fb.
The ash fusion temperature estimation device 1 is a device for estimating the fusion temperature T of ash generated by combustion of a target fuel F including a plurality of types of fuels. The target fuel F is used as a fuel for a boiler system 8 capable of operating with a plurality of fuels, as shown in fig. 2, for example. The boiler system 8 is configured to heat a fluid such as water flowing through the inside of the heat exchanger tube group 92 by supplying a plurality of types of fuel to a combustion chamber 9f (furnace) formed inside the boiler 9 via the burners 91(91c, 91r) and burning the fuel in the combustion chamber 9f. The boiler system 8 is used as a hot water supply system that heats the fluid in the heat transfer pipe group 92 and supplies hot water, a power generation system that drives a turbine (not shown) by steam generated by heating the fluid in the heat transfer pipe group 92 to generate power, and the like.
The boiler system 8 of the embodiment shown in fig. 2 performs an operation using fossil fuels such as biomass fuel Fb and coal fuel Fc. The coal fuel Fc is high-grade coal, low-grade coal and the like. The biomass fuel Fb is a renewable biologically-derived organic resource, is a fuel that is produced from a material from which fossil resources have been removed, and is a woody biomass processed into pellets or chips, rdf (reuse DerivedFuel), rpf (reuse Paper and Plastic Fuel), or the like. The plurality of types of fuel used in the boiler system 8 may be two or more, and may contain at least one of the biomass fuels Fb, or may contain at least one of a plurality of types of fuel related to coal such as high-grade coal and low-grade coal. Alternatively, the plurality of fuels used by the boiler system 8 may be all of the plurality of biomass fuels Fb or the plurality of coal fuels Fc.
More specifically, the boiler system 8 of fig. 2 is a system of an individual pulverization method in which the coal fuel Fc and the biomass fuel Fb are pulverized individually. That is, the boiler system 8 includes: a coal pulverizing device 84c that pulverizes the coal fuel Fc to obtain coal powder; a biomass mill 84r that pulverizes the biomass fuel Fb to obtain a biomass powder; and a boiler 9 for supplying coal powder and biomass powder. In the other embodiments, the boiler system 8 may be a mixed-pulverization system in which the coal fuel Fc and the biomass fuel Fb are mixed and pulverized.
The coal fuel Fc stored in the coal storage facility 81c is supplied to the coal pulverizer 84c via the coal hopper 82c and the coal feeder 83c (screw feeder or the like), and the coal pulverizer 84c pulverizes the supplied coal fuel Fc to a desired particle size (for example, about several μm to several hundred μm). On the other hand, the biomass fuel Fb stored in the biomass storage facility 81r is supplied to the biomass mill 84r via the biomass hopper 82r and the biomass supply device 83r (screw feeder or the like), and the biomass mill 84r mills the supplied biomass fuel Fb to a desired particle size (for example, an average particle size of about 0.5mm to 1 mm). The coal pulverizer 84c and the biomass pulverizer 84r are connected to a burner 91 provided in the boiler 9 via a fine powder fuel pipe Lf, respectively, and pulverized fuel is supplied from the pulverizer 84 to the burner 91 by the force of the conveying air a1.
The exhaust gas G generated by the combustion of the target fuel F in the boiler 9 is discharged to the outside through an exhaust gas pipe 85 communicating with the flue 9p of the boiler 9. The exhaust gas pipe 85 is provided with a denitration device 86 for removing nitrogen oxides from the exhaust gas G, an air heater 87 (described later) for raising the temperature of the outside gas a passing through an air supply pipe L (described later) to, for example, a range of 200 to 300 ℃ by the heat of the exhaust gas G, an electrostatic precipitator 88 for removing coal dust contained in the heat-recovered exhaust gas G, a desulfurization device (not shown) for removing sulfur oxides in the dust-removed exhaust gas G, and the like, and the exhaust gas G is treated by these devices and discharged from a stack (not shown) to the outside. The air supply pipe L branches into a conveyance air supply pipe L1 connected to the coal pulverizer 84c and the biomass pulverizer 84r, and a combustion air supply pipe L2 connected to the boiler 9, downstream of the air heater 87. Thus, the outside air a is supplied as the conveyance air a1 (primary air) from the conveyance air supply pipe L1 to the pulverizer (84c, 84r), and is supplied as the combustion air a2 (secondary air) from the combustion air supply pipe L2 to the boiler 9.
When a plurality of types of fuel are simultaneously combusted by combustion or the like in the boiler system 8 or the like having the above-described configuration, the melting temperature T (melting point) of ash generated by the combustion changes depending on the ash composition (chemical composition) (see fig. 3). In the example of fig. 3, fuel a is coal fuel Fc and fuel b is biomass fuel Fb, but if fuel b is added to fuel a, the melting temperature T changes in such a manner that it decreases in a quadratic curve and then increases again as the composition of the ash as a whole changes. As described above, if the proportion of the fuel (fuel b) having relatively poor fuel properties is increased relative to the fuel (fuel a) having relatively excellent fuel properties, the ash melting temperature T of the entire fuel changes nonlinearly with the change in the entire ash composition. When the boiler 9 is used as a fuel, the melting temperature T needs to be higher than a predetermined temperature (threshold value Th), and in fig. 3, the ratio of the fuel a needs to be larger than a mixing ratio R (described later) corresponding to a ratio Rh of a value at which the ash content of the fuel a and the ash content of the fuel b exist, in order to use the boiler 9.
The melting temperature T of ash is an evaluation index of adhesion of ash to the inside of the boiler 9, and for example, the biomass fuel Fb is likely to have a change in chemical composition (particularly, ash) depending on the raw material. Therefore, when the behavior of ash generated from the above-mentioned plural kinds of fuels is examined for each kind of fuel by a conventional method such as a fusibility test method (JIS M8801 and the like) before the operation of the boiler 9 or the like, a lot of time and cost are required. Therefore, the ash fusion temperature estimation device 1 described below quickly obtains the ash fusion temperature T generated by the combustion of a plurality of fuels.
Next, the ash fusion temperature estimating apparatus 1 will be described with reference to fig. 1.
As shown in fig. 1, the ash fusion temperature estimating device 1 includes a theoretical ash composition calculating unit 2 and a fusion temperature calculating unit 3. The ash fusion temperature estimating apparatus 1 is constituted by a computer. Specifically, the ash fusion temperature estimating device 1 includes a memory (storage unit m) such as a CPU (processor), ROM, and RAM (not shown). The CPU operates (calculates data, etc.) in accordance with a command (instruction) of a program (ash fusion temperature estimation program) loaded in the main storage device of the storage unit m, thereby realizing the above-described functional units. The program may be recorded in a computer-readable recording medium.
The respective functional parts of the ash fusion temperature estimating apparatus 1 will be described.
The theoretical ash composition calculating unit 2 is a functional unit configured to acquire a theoretical ash composition Ct that is a composition relating to a specific ash component of ash (hereinafter referred to as an ash composition) when a plurality of fuels included in the target fuel F are mixed at a predetermined mixing ratio R. In the specific ash component, SiO may be contained2、Al2O3、TiO2、Fe2O3、CaO、MgO、Na2O、K2O、P2O5、SO3At least one component (ash component) of the ten components of (2). In the embodiment shown in fig. 1, all of the ten components are defined as specific ash components, and the theoretical ash composition Ct is the weight% of the ten components with respect to the weight of the target fuel F. In other embodiments, the specific ash component may contain ash components other than the ten components described above, or may contain all ash components classified into ash.
In the embodiment shown in fig. 1, the theoretical ash composition calculating unit 2 includes a mixing ratio acquiring unit 21, a fuel-corresponding ash composition acquiring unit 22, and a calculating unit 23.
The mixing ratio obtaining section 21 is a functional section configured to obtain the mixing ratio R. The mixing ratio R is a mixing ratio of the plurality of fuels included in the target fuel F. In other words, the mixture ratio R is a composition ratio of each of the plurality of types of fuel constituting the target fuel F. For example, the mixing ratio R may be a weight ratio or a volume ratio of each of the plurality of fuels constituting the target fuel F.
More specifically, in the case of the boiler system 8 of the single pulverizing system shown in fig. 2, the supply amounts of a plurality of types of fuel (coal fuel Fc and biomass fuel Fb in fig. 2) simultaneously supplied per unit time in the boiler 9 may be set to a ratio. On the other hand, in the case of the system of the mixed grinding system, the ratio of the plurality of types of fuel contained per unit amount of the target fuel F obtained by grinding may be used, or the supply ratio before grinding may be used. In the embodiment shown in fig. 1, the mixing ratio acquisition unit 21 acquires the mixing ratio R input from the outside by a user or the like who operates the present apparatus.
The fuel-corresponding ash composition acquisition unit 22 is a functional unit configured to acquire a plurality of fuel-corresponding ash compositions Cn each composed of an ash composition of each of a plurality of fuels included in the target fuel F. That is, each of the plurality of fuel-corresponding ash compositions Cn corresponds to any one of the ash compositions of the plurality of fuels included in the target fuel F. The fuel-corresponding ash composition acquisition unit 22 acquires a fuel-corresponding ash composition Cn obtained by analyzing the ash composition of each of a plurality of types of fuel contained in the target fuel F.
The calculation unit 23 is a functional unit configured to calculate the theoretical ash composition Ct based on the above-described mixture ratio R and the plurality of fuel-corresponding ash compositions Cn. In the embodiment shown in fig. 1, the calculation unit 23 is connected to the mixing ratio acquisition unit 21 and the fuel-corresponding ash composition acquisition unit 22, respectively, and receives the mixing ratio R and the plurality of fuel-corresponding ash compositions Cn as inputs to calculate the theoretical ash composition Ct.
The melting temperature calculation unit 3 is a functional unit configured to calculate an estimated value of the melting temperature T of ash of the target fuel F corresponding to the theoretical ash composition Ct, using an estimation model M created by learning a relationship between an ash composition (hereinafter, referred to as a reference ash composition Cr) and a melting temperature (hereinafter, referred to as a reference melting temperature Tr) with respect to ash (hereinafter, referred to as reference ash) generated by combustion of the reference fuel Fr. Here, the reference fuel Fr is a fuel in which the melting temperature T (i.e., the reference melting temperature Tr) and the ash composition (i.e., the reference ash composition Cr) of the ash (i.e., the reference ash) of the reference fuel Fr are obtained by measurement (analysis) or the like. In other words, the reference fuel Fr is a past fuel (sample) that is burned in the boiler 9, the laboratory, or the like before (in the past) the estimated value of the ash melting temperature T of the target fuel F is calculated. The reference fuel Fr may be composed of a plurality of types of fuel, or may be composed of one type of fuel, as in the case of the target fuel F.
That is, the estimation model M is a learning model that outputs the melting temperature T corresponding to an arbitrary input ash composition, and the melting temperature T obtained by inputting the theoretical ash composition Ct to the estimation model M becomes an estimated value of the melting temperature T of ash generated when the target fuel F having the theoretical ash composition Ct is combusted. The estimation model M obtains the estimated value of the melting temperature T of the ash of the target fuel F by calculation, and therefore, the melting temperature T of the ash of the target fuel F can be quickly and easily obtained as compared with a case where the melting temperature T is obtained by a professional experiment such as a melting test method.
In the embodiment shown in fig. 1, the ash fusion temperature estimation device 1 further includes an estimation model creation unit 4, and the estimation model creation unit 4 is configured to create the estimation model M described above by using a machine learning method. In the embodiment shown in fig. 1, the estimation model M created by the estimation model creating unit 4 is stored in the storage unit M of the ash fusion temperature estimating device 1 in advance, and the fusion temperature calculating unit 3 is configured to calculate the estimated value of the fusion temperature T using the estimation model M stored in the storage unit M, and the details of the estimation model creating unit 4 will be described later. The calculation result obtained by the melting temperature calculation unit 3 is output to the display 6 and displayed.
In the ash fusion temperature estimating apparatus 1, the theoretical ash composition calculating unit 2 and the fusion temperature calculating unit 3 may be connected via a communication network (not shown). For example, when the user inputs the theoretical ash composition Ct or the mixture ratio R and the fuel-corresponding ash composition Cn via an HMI such as an input screen provided by the ash fusion temperature estimating device 1, the input information may be transmitted to the fusion temperature calculating unit 3 via a communication network, and the calculation result obtained by the fusion temperature calculating unit 3 may be transmitted again via the communication network and displayed on the display 6 viewed by the user.
Next, an ash fusion temperature estimation method corresponding to the processing performed by the ash fusion temperature estimation device 1 will be described with reference to fig. 4. Fig. 4 is a flowchart illustrating an ash fusion temperature estimation method according to an embodiment of the present invention.
The ash fusion temperature estimation method is a method of estimating the fusion temperature T of ash generated by combustion of a target fuel F containing a plurality of fuels. As shown in fig. 4, the ash fusion temperature estimating method includes a theoretical ash composition calculating step (S1) and a fusion temperature calculating step (S2).
The ash fusion temperature estimation method will be described according to the steps of fig. 4. In the case of executing the ash fusion temperature estimating method using a computer, in a computer device including a memory, a processor, and a computer program (ash fusion temperature estimating program) stored in the memory and executed by the processor, when the processor executes the program, the steps described in detail below are implemented.
In step S1 of fig. 4, a theoretical ash composition calculation step is performed. The theoretical ash composition calculation step (S1) is a step of obtaining a theoretical ash composition Ct relating to a specific ash component of the ash when a plurality of fuels included in the target fuel F are mixed at the mixing ratio R. The theoretical gray component calculation step (S1) is the same as the already described processing performed by the theoretical gray component calculation unit 2, and therefore, the details thereof are omitted.
In the embodiment shown in fig. 4, the theoretical ash composition calculating step (S1) includes: a mixing ratio obtaining step (S11) for obtaining the mixing ratio R; a fuel-corresponding ash composition acquisition step (S12) for acquiring a plurality of fuel-corresponding ash compositions Cn each composed of an ash composition of each of a plurality of fuels included in a target fuel F; and a calculation step (S13) of calculating a theoretical ash composition Ct based on the mixture ratio R and the plurality of fuel-corresponding ash compositions Cn. The mixing ratio acquisition step (S11), the fuel-corresponding ash composition acquisition step (S12), and the calculation step (S13) are the same as the processing executed by the mixing ratio acquisition unit 21, the fuel-corresponding ash composition acquisition unit 22, and the calculation unit 23, respectively, and thus the details thereof are omitted. The fuel-corresponding ash composition acquisition step (S12) may be executed before the mixture ratio acquisition step (S11).
In step S2, a melting temperature calculation step is performed. The melting temperature calculation step (S2) is a step of calculating an estimated value of the ash melting temperature T of the target fuel F corresponding to the theoretical ash composition Ct using the above-described estimation model M. The melting temperature calculation step (S2) is the same as the processing executed by the melting temperature calculation unit 3 described above, and therefore, the details thereof are omitted.
According to the above configuration, the estimation model M created by learning (machine learning) is used to estimate the ash melting temperature T of the target fuel F from the theoretical ash composition Ct of the target fuel F in which a plurality of fuels are mixed at a mixture ratio. This enables the melting temperature T of the target fuel F to be quickly obtained.
Next, details of the estimation model creation unit 4 for creating the estimation model M will be described with reference to fig. 5. Fig. 5 is a block diagram showing the function of the estimation model creating unit 4 according to the embodiment of the present invention.
In some embodiments, as shown in fig. 1, the ash fusion temperature estimating device 1 may further include an estimation model creating unit 4, and the estimation model creating unit 4 may be configured to create the estimation model M. More specifically, as shown in fig. 5, the estimation model creation unit 4 may further include: a learning data generation unit 41 configured to generate learning data D (teaching data) including data (hereinafter, referred to as individual data) in which reference ash compositions Cr for the respective reference ashes are associated with a reference melting temperature Tr; and a machine learning execution unit 42 that creates the estimation model M by executing machine learning of the learning data D.
More specifically, each individual piece of data constituting the learning data D is an information set (one record) including the reference gray composition Cr and the reference melting temperature Tr associated with each reference gray. The learning data generation unit 41 may generate the learning data D by acquiring a plurality of individual data from the storage unit m or the like. The machine learning execution unit 42 performs machine learning by any of known machine learning methods (algorithms) such as a neural network. In the embodiment shown in fig. 5, the estimation model creation unit 4 is configured to perform machine learning based on a Neural Network (NN).
In the embodiment shown in fig. 5, the learning data generating unit 41 includes a learning data updating unit 41a that updates the first learning data Da to the second learning data Db by removing, from the learning data D, any individual data in which a difference between an estimated value of the melting temperature T calculated based on the reference gray composition Cra associated with the first learning data Da created at a relatively earlier stage in the composition time and the reference melting temperature Tra associated with the reference gray composition Cra exceeds a predetermined threshold value among the any individual data, and the learning data updating unit 41a removes the estimated value of the melting temperature T from the learning data D. In this way, by filtering individual data that become components of the learning data D from the above-described difference values and creating the estimation model M using the updated learning data D, the estimation accuracy by the estimation model M can be improved (see below). The predetermined threshold value may be a value corresponding to a predetermined ratio such as a difference of ± 10%, for example.
An example of a method corresponding to the processing executed by the estimation model creating unit 4 will be described with reference to fig. 6, taking a case where the estimation model creating unit 4 executes machine learning by a neural network as an example. Fig. 6 is a flowchart for creating an estimation model by machine learning based on a neural network according to an embodiment of the present invention.
As shown in fig. 6, in some embodiments, the ash fusion temperature estimation method may further include an estimation model creation step (S6) of creating the estimation model M described above. More specifically, as shown in fig. 6, the estimation model creation step (S6) further includes a learning data generation step (S61) of generating the learning data D, and a machine learning execution step (S62 to S67) of creating the estimation model M by executing machine learning of the learning data D.
The learning data generation step (S61) and the machine learning execution steps (S62 to S67) described above are the same as the processing contents executed by the learning data generation unit 41 and the machine learning execution unit 42, respectively, which have already been described, and therefore, the details thereof are omitted. As shown in fig. 6, the estimation model creation step (S6) may further include a learning data update step (S63 to S66) which is the same processing content as the learning data update unit 41a described above.
The flow of creating the estimation model M shown in fig. 6 will be described step by step.
In step S61 of fig. 6, the learning data generation step is executed to generate the learning data D, and then in steps S62 to S67, the machine learning execution step is executed. Specifically, in step S61, a plurality of necessary individual data items are acquired from the storage unit M or the like, learning data D (first learning data Da) is generated, and then in step S62, an estimation model M (provisional estimation model) is created using the learning data D generated in step S61.
Next, in step S63, any individual data is selected from the plurality of individual data constituting the learning data D using the provisional estimation model generated in step S62, and the estimated value of the melting temperature T corresponding to the reference ash composition Cr included in the selected individual data is calculated. In step S64, the estimated value of the melting temperature T calculated in step S63 described above is compared with the reference melting temperature Tr possessed by the same individual data used in this step (S63). As a result, if the difference between the two is not within ± α% in step S65, the single data is removed (deleted) from the learning data D in step S66, and the process returns to step S62. That is, a new provisional estimation model is created again using the learning data D (second learning data Db) updated in step S66.
In contrast, if the difference between the two is within ± α% in step S65, the provisional estimation model at this time is regarded as the completed version of the estimation model M in step S67. Note that, when steps S63 to S65 are executed on one or more individual data items, for example, all individual data items constituting the learning data D, the process may proceed to step S67.
When the reason for sorting individual data based on the difference between the melting temperature T calculated using the estimation model M and the reference melting temperature Tr is explained as described above, it has been found that the melting temperature T assumed based on experience or the like based on the ratio of at least one constituent component (one of the above ten components) in the reference ash composition Cr greatly differs from the reference melting temperature Tr as a result of the examination of individual data in which the difference between the two exceeds ± α%. More specifically, for example, when a predetermined ash component is focused on, the tendency of the melting temperature to the ratio can be known by comparing a plurality of individual data, but the tendency of individual data having a difference of more than ± α% between them is opposite, for example, differs from the tendency that it should be.
That is, such individual data can be regarded as unreliable data with reference to the melting temperature Tr (measured value), and as described above, the learning data D can be configured from the difference between the two data, and such unreliable individual data can be found. In this case, since the single data is removed from the learning data D without reliability (the single data is inaccurate), the learning data D composed of the single data with reliability is machine-learned, whereby the estimation accuracy by the estimation model M can be improved.
In other embodiments, the ash fusion temperature estimation device 1 may not include the estimation model creation unit 4. In this case, the estimation model M created by a device (such as an estimation model creation device) different from the ash fusion temperature estimation device 1 may be attached to the storage unit M of the ash fusion temperature estimation device 1 using a portable storage medium such as a communication network or a USB memory.
The present invention is not limited to the above-described embodiments, and includes modifications of the above-described embodiments and combinations of these embodiments as appropriate.
Claims (9)
1. An ash fusion temperature estimation device that estimates the fusion temperature of ash generated by combustion of a target fuel including a plurality of types of fuels,
the ash fusion temperature estimation device is provided with:
a theoretical ash composition calculating unit including a mixing ratio obtaining unit configured to obtain a mixing ratio of the plurality of types of fuel included in the target fuel, a fuel-corresponding ash composition obtaining unit configured to obtain a plurality of fuel-corresponding ash compositions each composed of an ash composition of each of the plurality of types of fuel included in the target fuel, and a calculating unit configured to calculate a theoretical ash composition based on the mixing ratio and the plurality of fuel-corresponding ash compositions, the theoretical ash composition being the ash composition relating to a specific ash component of the ash when the plurality of types of fuel are mixed at the mixing ratio;
an estimation model creation unit configured to create an estimation model obtained by learning a relationship between a reference ash composition and a reference melting temperature of reference ash generated by combustion of a reference fuel; and
and a melting temperature calculation unit configured to calculate an estimated value of the ash melting temperature of the target fuel according to the theoretical ash composition using the estimation model.
2. The ash fusion temperature estimation apparatus according to claim 1,
the estimation model creation unit includes:
a learning data generation unit configured to generate learning data including data in which the reference ash composition for each of the plurality of reference ashes is associated with the reference melting temperature; and
and a machine learning execution unit configured to create the estimation model by executing machine learning of the learning data.
3. The ash fusion temperature estimation apparatus according to claim 2,
the learning data generation unit includes a learning data update unit that removes, from learning data, an estimated value of the melting temperature of the ash calculated based on the estimated model and the reference ash composition associated with arbitrary data as the data constituting first learning data as the learning data, and the arbitrary data in which a difference between the reference melting temperature associated with the reference ash composition in the arbitrary data exceeds a predetermined threshold value, thereby updating the first learning data to second learning data as the learning data.
4. The ash fusion temperature estimation device according to any one of claims 1 to 3,
the plurality of fuels includes coal fuels and biomass fuels.
5. An ash fusion temperature estimation method for estimating the fusion temperature of ash generated by combustion of a target fuel including a plurality of fuels,
the ash fusion temperature estimation method includes:
a theoretical ash composition calculation step including a mixing ratio acquisition step of acquiring a mixing ratio of the plurality of types of fuel included in the target fuel, a fuel-corresponding ash composition acquisition step of acquiring a plurality of fuel-corresponding ash compositions each composed of an ash composition of each of the plurality of types of fuel included in the target fuel, and a calculation step of calculating a theoretical ash composition that is the ash composition relating to a specific ash component of the ash when the plurality of types of fuel are mixed at the mixing ratio, based on the mixing ratio and the plurality of fuel-corresponding ash compositions;
an estimation model creation step of creating an estimation model in which a relationship between a reference ash composition and a reference melting temperature of reference ash generated by combustion of reference fuel is learned; and
a melting temperature calculation step of calculating an estimated value of the ash melting temperature of the target fuel corresponding to the theoretical ash composition using the estimated model.
6. The ash fusion temperature estimation method according to claim 5,
the estimation model creation step includes:
a learning data generation step of generating learning data configured by data associating the reference ash composition with the reference melting temperature for each of a plurality of the reference ashes; and
a machine learning execution step of creating the estimation model by executing machine learning of the learning data.
7. The ash fusion temperature estimation method according to claim 6,
the learning data generation step includes a learning data update step of removing, from learning data, an estimated value of the melting temperature of the ash calculated based on the estimated model and the reference ash composition associated with arbitrary data as the data constituting first learning data as the learning data, and the arbitrary data in which a difference between the reference melting temperature associated with the reference ash composition in the arbitrary data exceeds a predetermined threshold value, thereby updating the first learning data to second learning data as the learning data.
8. The ash fusion temperature estimation method according to any one of claims 5 to 7,
the plurality of fuels includes coal fuels and biomass fuels.
9. A computer-readable storage medium storing a computer program for estimating a melting temperature of ash generated by combustion of a target fuel including a plurality of fuels,
when the program is executed by a processor, the following steps are performed:
a theoretical ash composition calculation step including a mixing ratio acquisition step of acquiring a mixing ratio of the plurality of types of fuel included in the target fuel, a fuel-corresponding ash composition acquisition step of acquiring a plurality of fuel-corresponding ash compositions each composed of an ash composition of each of the plurality of types of fuel included in the target fuel, and a calculation step of calculating a theoretical ash composition that is the ash composition relating to a specific ash component of the ash when the plurality of types of fuel are mixed at the mixing ratio, based on the mixing ratio and the plurality of fuel-corresponding ash compositions;
an estimation model creation step of creating an estimation model in which a relationship between a reference ash composition and a reference melting temperature of reference ash generated by combustion of reference fuel is learned; and
a melting temperature calculation step of calculating an estimated value of the ash melting temperature of the target fuel corresponding to the theoretical ash composition using the estimated model.
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