CN117404678A - Online coal blending system for relieving slag formation of high-alkali coal burned by boiler - Google Patents
Online coal blending system for relieving slag formation of high-alkali coal burned by boiler Download PDFInfo
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- 239000003245 coal Substances 0.000 title claims abstract description 198
- 238000002156 mixing Methods 0.000 title claims abstract description 88
- 239000003513 alkali Substances 0.000 title claims abstract description 45
- 239000002893 slag Substances 0.000 title claims abstract description 31
- 230000015572 biosynthetic process Effects 0.000 title claims abstract description 25
- 238000002485 combustion reaction Methods 0.000 claims abstract description 50
- 238000013523 data management Methods 0.000 claims abstract description 20
- 238000012544 monitoring process Methods 0.000 claims abstract description 17
- 238000001514 detection method Methods 0.000 claims abstract description 16
- 238000007726 management method Methods 0.000 claims abstract description 13
- 238000012937 correction Methods 0.000 claims abstract description 11
- 238000010801 machine learning Methods 0.000 claims abstract description 11
- 238000013079 data visualisation Methods 0.000 claims abstract description 4
- 239000002956 ash Substances 0.000 claims description 29
- 238000004458 analytical method Methods 0.000 claims description 28
- 239000011734 sodium Substances 0.000 claims description 21
- 238000000034 method Methods 0.000 claims description 13
- 229910018072 Al 2 O 3 Inorganic materials 0.000 claims description 12
- 229910004298 SiO 2 Inorganic materials 0.000 claims description 12
- 229910052708 sodium Inorganic materials 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 9
- 229910010413 TiO 2 Inorganic materials 0.000 claims description 8
- DGAQECJNVWCQMB-PUAWFVPOSA-M Ilexoside XXIX Chemical compound C[C@@H]1CC[C@@]2(CC[C@@]3(C(=CC[C@H]4[C@]3(CC[C@@H]5[C@@]4(CC[C@@H](C5(C)C)OS(=O)(=O)[O-])C)C)[C@@H]2[C@]1(C)O)C)C(=O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)CO)O)O)O.[Na+] DGAQECJNVWCQMB-PUAWFVPOSA-M 0.000 claims description 7
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 7
- 230000008021 deposition Effects 0.000 claims description 7
- 239000011593 sulfur Substances 0.000 claims description 7
- 229910052717 sulfur Inorganic materials 0.000 claims description 7
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 6
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 6
- 239000002253 acid Substances 0.000 claims description 6
- CSDREXVUYHZDNP-UHFFFAOYSA-N alumanylidynesilicon Chemical compound [Al].[Si] CSDREXVUYHZDNP-UHFFFAOYSA-N 0.000 claims description 6
- 229910052799 carbon Inorganic materials 0.000 claims description 6
- 230000008018 melting Effects 0.000 claims description 6
- 238000002844 melting Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 229910052700 potassium Inorganic materials 0.000 claims description 5
- 238000000921 elemental analysis Methods 0.000 claims description 4
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 239000002585 base Substances 0.000 claims description 3
- 239000010883 coal ash Substances 0.000 claims description 3
- 239000003344 environmental pollutant Substances 0.000 claims description 3
- 230000005251 gamma ray Effects 0.000 claims description 3
- 239000007789 gas Substances 0.000 claims description 3
- 238000010438 heat treatment Methods 0.000 claims description 3
- 239000001257 hydrogen Substances 0.000 claims description 3
- 229910052739 hydrogen Inorganic materials 0.000 claims description 3
- 125000004435 hydrogen atom Chemical class [H]* 0.000 claims description 3
- 229910052757 nitrogen Inorganic materials 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- 231100000719 pollutant Toxicity 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 239000003039 volatile agent Substances 0.000 claims description 3
- 238000004876 x-ray fluorescence Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 7
- 229910052783 alkali metal Inorganic materials 0.000 description 4
- 150000001340 alkali metals Chemical class 0.000 description 4
- 238000011161 development Methods 0.000 description 2
- 239000010881 fly ash Substances 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000005496 eutectics Effects 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23K—FEEDING FUEL TO COMBUSTION APPARATUS
- F23K1/00—Preparation of lump or pulverulent fuel in readiness for delivery to combustion apparatus
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23J—REMOVAL OR TREATMENT OF COMBUSTION PRODUCTS OR COMBUSTION RESIDUES; FLUES
- F23J1/00—Removing ash, clinker, or slag from combustion chambers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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Abstract
The invention discloses an online coal blending system for relieving slag formation of high-alkali coal burned by a boiler, which belongs to the technical field of intelligent coal blending and comprises a coal quality data management module, a coal quality online detection module, an online intelligent coal blending module, a combustion condition monitoring module and a user management operation module; the coal quality data management module is used for storing coal quality information data of various coals; the online intelligent coal blending module calls coal quality information data stored by the coal quality data management module, trains a slagging prediction model through a machine learning algorithm, and finally gives a suggested blending burning scheme; the coal quality online detection module and the combustion condition monitoring module are used for carrying out real-time constraint and data correction on the online intelligent coal blending module; the user management operation module is used for realizing data visualization and management of a blending and burning scheme. The online coal blending system for relieving the slagging of the high-alkali coal in the boiler provided by the invention optimizes the blending ratio of coal blending, improves the economical efficiency and the safety of the operation of the boiler, and relieves the slagging condition of the boiler.
Description
Technical Field
The invention belongs to the technical field of intelligent coal blending, and particularly relates to an online coal blending system for relieving slag formation of high-alkali coal during boiler combustion.
Background
The energy is an indispensable material foundation in the development of human civilization, and in the process of the high-speed development of national economy, the power industry provides powerful support, and the thermal power generation still occupies a considerable proportion in the power supply system of China. In recent years, the total consumption of coal, petroleum, natural gas, primary power and other energy sources in China breaks through 40 hundred million tons of standard coal. Along with the increasing tension of the supply chain of the current coal supply market, the supply price is also increased, and the traditional coal power generation enterprises have a plurality of problems to be solved in the management mode and the production flow. Because the utility boilers and industrial boilers in China are mainly coal-fired, actual coal often deviates from designed coal types. The coal for power is generally poor in quality, has high ash content, sulfur content, alkali metal content and the like, is easy to cause the conditions of ash deposition, slag bonding, corrosion and the like on the heating surface of the boiler in the combustion process, and seriously affects the safe operation of the boiler. The problem of slag bonding on the heating surface of the coal-fired boiler is common, and if the problem cannot be solved properly, the efficiency of the boiler is reduced, the combustion is deteriorated, and even accidents such as flameout, shutdown and overhaul of the boiler are caused.
The Xinjiang eastern coal is taken as typical high-alkali coal, and serious slag bonding problem is very easy to occur in the independent combustion and utilization process. How to alleviate the slag bonding phenomenon caused by burning high-alkali coal is a practical engineering problem to be solved at present. The mixed coal blending combustion is one of effective solutions for solving the problems of shortage of fuel supply, complex and changeable coal types of the coal-fired power station boilers in China and improving the operation safety and economy of coal-fired units. In order to solve the problem of easy slagging of high alkali coal, the mineral composition of the mixed coal ash can be changed by mixing and burning with other low sodium coal types, so that the generation of alkali metal low-temperature eutectic in the high alkali coal is reduced, the content of Na, K and other alkali metals in the mixed coal types can be reduced, the probability that the alkali metals (Na, K) form a sticky inner white layer on the heated surface and the surfaces of fly ash particles is reduced, the fly ash particles are not easy to deposit on the heated surface, and further serious slagging is not easy to develop.
In the process of blending coal, how to ensure the economical efficiency and the safety of the operation of the boiler is an important problem. The main problems existing at present are that operators of the existing power plant mainly select different coal types to mix and burn by virtue of manual experience, and the operators have insufficient knowledge of the combustion state in the boiler, so that when the boiler can not run in the optimal combustion state in the face of complex and changeable coal types, the slagging problem of the boiler is more serious. Therefore, a more scientific and reasonable mixed coal blending and burning scheme is needed in the related technical field, and an intelligent online coal blending system is needed correspondingly.
Disclosure of Invention
The invention aims to provide an online coal blending system for relieving slag bonding of high-alkali coal in boiler combustion, which solves the problems that the prior power plant operators select different coal types to perform blended combustion by experience, the operators know the combustion state in the boiler insufficiently, and the boiler is seriously bonded due to the fact that the operators are difficult to operate in the optimal combustion state in the presence of complicated and changeable coal types.
In order to achieve the above purpose, the invention provides an online coal blending system for relieving slag formation of high alkali coal in boiler combustion, which comprises a coal quality data management module, a coal quality online detection module, an online intelligent coal blending module, a combustion condition monitoring module and a user management operation module;
the coal quality data management module stores coal quality information data of various coals;
the online intelligent coal blending module invokes coal quality information data stored by the coal quality data management module, trains a slagging prediction model through a machine learning algorithm, and finally gives a suggested blending burning scheme;
the coal quality online detection module and the combustion condition monitoring module carry out real-time constraint and data correction on the online intelligent coal blending module;
and the user management operation module is used for inputting, modifying and exporting the coal quality information stored in the coal quality data management module and managing the data visualization and the blending scheme.
Preferably, the coal quality information data includes industrial analysis data, elemental analysis data, calorific value analysis data, ash melting point analysis data, grindability analysis data, and ash composition analysis data; the industrial analytical data includes moisture, volatiles, fixed carbon, and ash.
Preferably, the elemental analysis data includes carbon, hydrogen, oxygen, nitrogen, and sulfur; the calorific value analysis data includes a low-order calorific value; the ash melting point analysis data comprise a deformation temperature DT, a softening temperature ST, a hemispherical temperature HT and a flow temperature FT; the grindability analysis data includes a haar grindability index HGI; the ash composition analysis data includes Fe 2 O 3 、CaO、MgO、Na 2 O、K 2 O、SiO 2 、Al 2 O 3 And TiO 2 。
Preferably, each coal type stored in the coal quality data management module comprises one or more high-alkali coal, one or more non-high-alkali coal types used for blending with the high-alkali coal, and one or more mixed coal types formed after blending the high-alkali coal with the non-high-alkali coal types.
Preferably, the coal quality online detection module comprises a microwave measuring instrument for detecting the moisture of the in-furnace mixed coal, a near infrared spectrometer for detecting the sulfur and volatile matters of the in-furnace mixed coal in real time, a natural gamma ray measuring instrument for detecting the ash content of the in-furnace mixed coal in real time, a multi-energy X ray measuring instrument for detecting the Fe, ca, mg, na, K, si, al and Ti contents of the in-furnace mixed coal in real time, and a Fe component of the in-furnace mixed coal ash in real time 2 O 3 、CaO、MgO、Na 2 O、K 2 O、SiO 2 、Al 2 O 3 And TiO 2 Is an on-line X-ray fluorescence apparatus.
Preferably, a sodium equivalent prediction model M for predicting the slagging tendency of the mixed coal is embedded in the online intelligent coal blending module 1 Alkali-acid ratio prediction model M 2 Prediction model M of silicon-aluminum ratio 3 Prediction model M of Fe-Ca ratio 4 And ash deposition prediction model M 5 。
Preferably, the sodium equivalent predictive model M 1 Is M 1 ＝λ 1 *A/(Na 2 O+0.659K 2 O), the alkali-acid ratio prediction model M 2 Is M 2 ＝λ 2 *(Fe 2 O 3 +CaO+MgO+Na 2 O+K 2 O)/(SiO 2 +Al 2 O 3 +TiO 2 ) The silicon-aluminum ratio prediction model M 3 Is M 3 ＝λ 3 *SiO 2 /Al 2 O 3 The Fe-Ca ratio prediction model M 4 Is M 4 ＝λ 4 *Fe 2 O 3 CaO, said ash deposition prediction model M 5 Is M 5 ＝λ 5 * (2DT+ST+HT+FT)/5, where λ 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 For correction factors, DT is deformation temperature, ST is softening temperature, HT is hemispherical temperature, FT is flow temperature, and A represents the base ash received.
Preferably, when the system is just put into use, an operator performs initial setting through a user management operation module, and performs initial setting of constraint conditions on 5 slagging prediction models embedded in the online intelligent coal blending module, so that M is met in the running process of the system 1min ＜M 1 ＜M 1max 、M 2min ＜M 2 ＜M 2max 、M 3min ＜M 3 ＜M 3max 、M 4min ＜M 4 ＜M 4ma x、M 5min ＜M 5 ＜M 5max Is a constraint on (2); when the constraint conditions of the 5 prediction models cannot be satisfied at the same time, the priority level of satisfying the constraint conditions is M 1 ＞M 2 ＞M 3 ＞M 4 ＞M 5 The method comprises the steps of carrying out a first treatment on the surface of the And, correction factor lambda 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The value of (2) is compensated and corrected by a machine learning algorithm after the data are input into an online intelligent coal blending module by a coal quality online detection module and a combustion condition monitoring module, and the corrected interval range is lambda epsilon [0.75,1.25 ]]。
Preferably, the process steps of establishing the five slag formation prediction models embedded in the online intelligent coal blending module are as follows:
s1, dividing initial data into a training set and a testing set;
s2, carrying out normalization processing on the training set data;
s3, setting initial parameters according to manual experience;
s4, training data through a support vector machine;
s5, carrying out normalization processing on the test set data and then carrying out corresponding prediction models;
s6, optimizing parameters of a corresponding prediction model through machine learning;
s7, outputting a prediction result of the corresponding prediction model and giving a blending combustion proportion range;
s8, giving a suggested blending burning scheme.
Preferably, the combustion condition monitoring module monitors boiler efficiency, hearth negative pressure, exhaust gas temperature, heated surface wall temperature and pollutant emission concentration in real time.
Therefore, the online coal blending system for relieving the slag formation of the high-alkali coal in the boiler combustion has the following beneficial effects:
(1) The coal quality data management module can store coal quality information of a large number of coal types, so that the online intelligent coal blending module can train a slagging prediction model by utilizing a machine learning algorithm, a suggested blending combustion scheme is finally provided, the blending combustion proportion of coal blending is scientifically optimized, the economical efficiency and the safety of boiler operation are further improved, and the slagging problem of the boiler is effectively relieved;
(2) The online intelligent coal blending module is subjected to real-time constraint and data correction through the coal quality online detection module and the combustion condition monitoring module, so that a predicted result is more accurate, and a blended combustion proposal scheme is more reasonable.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a schematic diagram of the modular composition of an on-line blending system for reducing slag formation in a boiler combustion process;
FIG. 2 is a schematic diagram of data composition of a coal quality data management module in an on-line coal blending system for alleviating slag formation of high alkali coal burned by a boiler in accordance with the present invention;
FIG. 3 is a schematic diagram showing the composition of an instrument of an on-line coal quality detection module in an on-line coal blending system for relieving slag formation of high alkali coal burned in a boiler according to the present invention;
FIG. 4 is a schematic diagram of a model composition of an online intelligent coal blending module in an online coal blending system for alleviating slag formation of high alkali coal burned by a boiler in accordance with the present invention;
FIG. 5 is a schematic diagram of a predictive model building process of an online intelligent coal blending module in an online coal blending system for relieving slag formation of high alkali coal burned by a boiler;
FIG. 6 is a schematic diagram of the monitoring conditions of the combustion condition monitoring module in the on-line coal blending system for alleviating slag formation of the high alkali coal burned by the boiler according to the present invention;
FIG. 7 is a schematic diagram showing the functional composition of a user management operation module in an on-line coal blending system for alleviating slag formation in burning high alkali coal in a boiler according to the present invention.
Detailed Description
The technical scheme of the invention is further described below through the attached drawings and the embodiments.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.
Referring to fig. 1, an on-line coal blending system for alleviating slag formation of high alkali coal in boiler combustion comprises a coal quality data management module, a coal quality on-line detection module, an on-line intelligent coal blending module, a combustion condition monitoring module and a user management operation module; the coal quality data management module stores coal quality information data of various coal types, wherein each coal type comprises one or more high-alkali coal, one or more non-high-alkali coal types used for blending with the high-alkali coal, and one or more mixed coal types formed after blending of the high-alkali coal and the non-high-alkali coal types; the online intelligent coal blending module calls coal quality information data stored by the coal quality data management module, trains a slagging prediction model through a machine learning algorithm, and finally gives a suggested blending burning scheme; the on-line detection module and the combustion condition monitoring module are used for carrying out real-time constraint and data correction on the on-line intelligent coal blending module.
Referring to fig. 2, the coal quality information data includes industrial analysis data, elemental analysis data, calorific value analysis data, ash melting point analysis data, grindability analysis data, and ash composition analysis data; industrial analytical data including moisture, volatiles, fixed carbon, and ash, elemental analytical data including carbon, hydrogen, oxygen, nitrogen, and sulfur; the calorific value analysis data includes a low-order calorific value; ash melting point analysis data includes deformation temperature DT, softening temperature ST, hemispherical temperature HT, and flow temperature FT; the grindability analysis data includes the haar grindability index HGI; the ash composition analysis data includes Fe 2 O 3 、CaO、MgO、Na 2 O、K 2 O、SiO 2 、Al 2 O 3 And TiO 2 。
Referring to FIG. 3, the on-line detection module for coal quality includes real-time detection of mixed coal in the furnaceMicrowave measuring instrument for detecting sulfur and volatile matters in mixed coal in real time, natural gamma-ray measuring instrument for detecting ash content in mixed coal in real time, multifunctional X-ray measuring instrument for detecting Fe, ca, mg, na, K, si, al and Ti content in mixed coal in real time, and Fe component in mixed coal in real time 2 O 3 、CaO、MgO、Na 2 O、K 2 O、SiO 2 、Al 2 O 3 And TiO 2 Is an on-line X-ray fluorescence apparatus.
Referring to fig. 4, a sodium equivalent prediction model M for predicting the slagging tendency of mixed coal is embedded in the online intelligent coal blending module 1 Alkali-acid ratio prediction model M 2 Prediction model M of silicon-aluminum ratio 3 Prediction model M of Fe-Ca ratio 4 And ash deposition prediction model M 5 . Sodium equivalent prediction model M 1 Is M 1 ＝λ 1 *A/(Na 2 O+0.659K 2 O) alkali-acid ratio prediction model M 2 Is M 2 ＝λ 2 *(Fe 2 O 3 +CaO+MgO+Na 2 O+K 2 O)/(SiO 2 +Al 2 O 3 +Ti O 2 ) Silicon-aluminum ratio prediction model M 3 Is M 3 ＝λ 3 *SiO 2 /Al 2 O 3 Fe-Ca ratio prediction model M 4 Is M 4 ＝λ 4 *Fe 2 O 3 CaO, ash deposition prediction model M 5 Is M 5 ＝λ 5 * (2DT+ST+HT+FT)/5, where λ 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 As correction factors, DT is deformation temperature, ST is softening temperature, HT is hemispherical temperature, FT is flow temperature, and a is the received base ash.
When the system is just put into use, an operator performs initial setting through a user management operation module, and performs initial setting of constraint conditions on 5 slagging prediction models embedded in an online intelligent coal blending module, so that M is met in the running process of the system 1min ＜M 1 ＜M 1max 、M 2min ＜M 2 ＜M 2max 、M 3min ＜M 3 ＜M 3max 、M 4min ＜M 4 ＜M 4ma x、M 5min ＜M 5 ＜M 5max Is a constraint on (2); when the constraint conditions of the 5 prediction models cannot be satisfied at the same time, the priority level of satisfying the constraint conditions is M 1 ＞M 2 ＞M 3 ＞M 4 ＞M 5 The method comprises the steps of carrying out a first treatment on the surface of the And, correction factor lambda 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The value of (2) is compensated and corrected by a machine learning algorithm after the data are input into an online intelligent coal blending module by a coal quality online detection module and a combustion condition monitoring module, and the corrected interval range is lambda epsilon [0.75,1.25 ]]。
Referring to fig. 5, the process of establishing the 5 slag formation prediction models embedded in the online intelligent coal blending module includes the following steps:
s1, dividing initial data into a training set and a testing set;
s2, carrying out normalization processing on the training set data;
s3, setting initial parameters according to manual experience;
s4, training data through a support vector machine;
s5, carrying out normalization processing on the test set data and then carrying out corresponding prediction models;
s6, optimizing parameters of a corresponding prediction model through machine learning;
s7, outputting a prediction result of the corresponding prediction model and giving a blending combustion proportion range;
s8, giving a suggested blending burning scheme.
Referring to fig. 6, the combustion condition monitoring module monitors boiler efficiency, furnace negative pressure, exhaust gas temperature, heated surface wall temperature and pollutant emission concentration in real time.
Referring to fig. 7, the user management operation module inputs, modifies and derives the stored coal quality information in the coal quality data management module, and manages the data visualization and blending scheme.
Therefore, the online coal blending system for relieving the slagging of the high-alkali coal in the boiler combustion adopts the structure, so as to solve the problems that operators of a power plant select different coal types to perform blended combustion through experience, the operators know the combustion state in the boiler insufficiently, and the boiler is seriously slagging caused by the fact that the operators are difficult to operate in the optimal combustion state in the presence of complicated and changeable coal types. The invention has reasonable design structure, can scientifically optimize the blending ratio of the coal, further improves the economical efficiency and the safety of the operation of the boiler, relieves the slag bonding condition of the boiler and is suitable for popularization and application.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting it, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that: the technical scheme of the invention can be modified or replaced by the same, and the modified technical scheme cannot deviate from the spirit and scope of the technical scheme of the invention.
Claims (10)
1. An on-line coal blending system for relieving slag formation of high-alkali coal burned by a boiler is characterized in that: the system comprises a coal quality data management module, a coal quality online detection module, an online intelligent coal blending module, a combustion condition monitoring module and a user management operation module;
the coal quality data management module stores coal quality information data of various coals;
the online intelligent coal blending module invokes coal quality information data stored by the coal quality data management module, trains a slagging prediction model through a machine learning algorithm, and finally gives a suggested blending burning scheme;
the coal quality online detection module and the combustion condition monitoring module carry out real-time constraint and data correction on the online intelligent coal blending module;
and the user management operation module is used for inputting, modifying and exporting the coal quality information stored in the coal quality data management module and managing the data visualization and the blending scheme.
2. An on-line coal blending system for reducing slag formation in boiler combustion of high alkali coal as set forth in claim 1, wherein: the coal quality information data comprises industrial analysis data, element analysis data, heating value analysis data, ash melting point analysis data, grindability analysis data and ash component analysis data; the industrial analytical data includes moisture, volatiles, fixed carbon, and ash.
3. An on-line coal blending system for reducing slag formation in boiler combustion of high alkali coal as set forth in claim 2, wherein: the elemental analysis data includes carbon, hydrogen, oxygen, nitrogen, and sulfur; the calorific value analysis data includes a low-order calorific value; the ash melting point analysis data comprise a deformation temperature DT, a softening temperature ST, a hemispherical temperature HT and a flow temperature FT; the grindability analysis data includes a haar grindability index HGI; the ash composition analysis data includes Fe 2 O 3 、CaO、MgO、Na 2 O、K 2 O、SiO 2 、Al 2 O 3 And TiO 2 。
4. An on-line coal blending system for reducing slag formation in boiler combustion of high alkali coal as set forth in claim 1, wherein: the coal quality data management module is used for storing various coal types including one or more high-alkali coal, one or more non-high-alkali coal types used for blending and burning with the high-alkali coal and one or more mixed coal types formed after blending the high-alkali coal with the non-high-alkali coal types.
5. An on-line coal blending system for reducing slag formation in boiler combustion of high alkali coal as set forth in claim 2, wherein: the on-line detection module for the coal quality comprises a microwave measuring instrument for detecting the moisture of the in-furnace mixed coal in real time, a near infrared spectrometer for detecting the sulfur and volatile matters of the in-furnace mixed coal in real time, a natural gamma ray measuring instrument for detecting the ash content of the in-furnace mixed coal in real time, a multifunctional X ray measuring instrument for detecting the Fe, ca, mg, na, K, si, al and Ti contents of the in-furnace mixed coal in real time, and a Fe component of the in-furnace mixed coal ash in real time 2 O 3 、CaO、MgO、Na 2 O、K 2 O、SiO 2 、Al 2 O 3 And TiO 2 Is an on-line X-ray fluorescence apparatus.
6. An on-line coal blending system for reducing slag formation in boiler combustion of high alkali coal as set forth in claim 1, wherein: sodium equivalent prediction model M for predicting slagging tendency of mixed coal is embedded in the online intelligent coal blending module 1 Alkali-acid ratio prediction model M 2 Prediction model M of silicon-aluminum ratio 3 Prediction model M of Fe-Ca ratio 4 And ash deposition prediction model M 5 。
7. An on-line coal blending system for reducing slag formation in boiler combustion of high alkali coal as set forth in claim 6, wherein: the sodium equivalent predictive model M 1 Is M 1 ＝λ 1 *A/(Na 2 O+0.659K 2 O), the alkali-acid ratio prediction model M 2 Is M 2 ＝λ 2 *(Fe 2 O 3 +CaO+MgO+Na 2 O+K 2 O)/(SiO 2 +Al 2 O 3 +TiO 2 ) The silicon-aluminum ratio prediction model M 3 Is M 3 ＝λ 3 *SiO 2 /Al 2 O 3 The Fe-Ca ratio prediction model M 4 Is M 4 ＝λ 4 *Fe 2 O 3 CaO, said ash deposition prediction model M 5 Is M 5 ＝λ 5 * (2DT+ST+HT+FT)/5, where λ 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 For correction factors, DT is deformation temperature, ST is softening temperature, HT is hemispherical temperature, FT is flow temperature, and A represents the base ash received.
8. An on-line coal blending system for reducing slag formation in boiler combustion of high alkali coal as set forth in claim 7, wherein: when the system is just put into use, an operator performs initial setting through a user management operation module, and performs initial setting of constraint conditions on 5 slagging prediction models embedded in an online intelligent coal blending module, so that M is met in the running process of the system 1min ＜M 1 ＜M 1max 、M 2min ＜M 2 ＜M 2max 、M 3min ＜M 3 ＜M 3max 、M 4min ＜M 4 ＜M 4ma x、M 5min ＜M 5 ＜M 5max Is a constraint on (2); when the constraint conditions of the 5 prediction models cannot be satisfied at the same time, the priority level of satisfying the constraint conditions is M 1 ＞M 2 ＞M 3 ＞M 4 ＞M 5 The method comprises the steps of carrying out a first treatment on the surface of the And, correction factor lambda 1 、λ 2 、λ 3 、λ 4 And lambda (lambda) 5 The value of (2) is compensated and corrected by a machine learning algorithm after the data are input into an online intelligent coal blending module by a coal quality online detection module and a combustion condition monitoring module, and the corrected interval range is lambda epsilon [0.75,1.25 ]]。
9. The online coal blending system for relieving boiler combustion high alkali coal slagging according to claim 8, wherein the process steps of establishing the five slagging prediction models embedded in the online intelligent coal blending module are as follows:
s1, dividing initial data into a training set and a testing set;
s2, carrying out normalization processing on the training set data;
s3, setting initial parameters according to manual experience;
s4, training data through a support vector machine;
s5, carrying out normalization processing on the test set data and then carrying out corresponding prediction models;
s6, optimizing parameters of a corresponding prediction model through machine learning;
s7, outputting a prediction result of the corresponding prediction model and giving a blending combustion proportion range;
s8, giving a suggested blending burning scheme.
10. An on-line coal blending system for reducing slag formation in boiler combustion of high alkali coal as set forth in claim 1, wherein: the combustion condition monitoring module monitors boiler efficiency, hearth negative pressure, exhaust gas temperature, heated surface wall temperature and pollutant emission concentration in real time.
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