CN112765872A - Method and device for predicting wall thickness of water wall tube in furnace and readable storage medium - Google Patents
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000012423 maintenance Methods 0.000 claims abstract description 13
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims abstract description 9
- 239000003245 coal Substances 0.000 claims abstract description 9
- 238000013178 mathematical model Methods 0.000 claims abstract description 9
- 229910052717 sulfur Inorganic materials 0.000 claims abstract description 9
- 239000011593 sulfur Substances 0.000 claims abstract description 9
- 238000002485 combustion reaction Methods 0.000 claims abstract description 7
- 238000010977 unit operation Methods 0.000 claims abstract description 7
- 238000007664 blowing Methods 0.000 claims abstract 3
- 238000004422 calculation algorithm Methods 0.000 claims description 19
- 230000006870 function Effects 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 6
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 4
- 239000003546 flue gas Substances 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000002068 genetic effect Effects 0.000 claims description 3
- 238000012886 linear function Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000002922 simulated annealing Methods 0.000 claims description 3
- 238000013528 artificial neural network Methods 0.000 claims description 2
- 238000007689 inspection Methods 0.000 abstract description 5
- 230000008859 change Effects 0.000 abstract description 4
- 238000005260 corrosion Methods 0.000 description 3
- 230000007797 corrosion Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910002091 carbon monoxide Inorganic materials 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 238000004868 gas analysis Methods 0.000 description 1
- 239000004047 hole gas Substances 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
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Abstract
The invention provides a method and a device for predicting the wall thickness of a water wall tube in a furnace and a readable storage medium, wherein the method comprises the following steps: acquiring reducing atmosphere parameters c of the wall surface of a water wall, the sulfur content St of coal for combustion and the wall temperature T of the water wall when the boiler is in thermal operation; collecting the pipe wall thickness h and recording the corresponding unit operation time t when blowing out and maintaining are carried out at least twice; establishing a mathematical model, and constructing a functional relation of the wall thickness variation delta h of the pipe: Δ h ═ f (c, St, T); and introducing operation parameters to predict the subsequent pipe wall thickness variation. According to the invention, by collecting the reducing atmosphere parameters, the pipe wall temperature and the like on the water wall in the furnace, analyzing and establishing a model by using the collected data, establishing a function expression of the wall thickness variation of the water wall, predicting the change of the pipe wall thickness of the water wall in the furnace, and further guiding the work of maintenance or pipe replacement, the work efficiency of inspection is improved, and the workload of maintenance is reduced.
Description
Technical Field
The invention relates to a technology for detecting the wall thickness of a water wall tube of a boiler, in particular to a method and a device for predicting the wall thickness of the water wall tube in the boiler and a readable storage medium.
Background
With the continuous push of national ultra-clean emissions, NOxThe emission concentration is reduced by one step, which brings serious high temperature corrosion problem in the furnace, causes serious thinning of the wall of the water wall in the furnace. At present, aiming at the condition of reducing the thickness of the wall of a water-cooled wall pipe, a scaffold is erected or lifted by using the opportunity of unit shutdownThe platform enters the furnace for rows of thickness measurement inspection, so that the cost of maintenance cost and construction period cost are increased, and when the maintenance period is short or the manpower is insufficient, only rows of partial areas or rows of thickness measurement inspection cannot be performed frequently, so that hidden troubles are caused for the safe operation of the unit.
Disclosure of Invention
The invention aims to provide a method for predicting the wall thickness of a water wall tube in a furnace, which can predict the wall thickness change of the tube wall and improve the inspection efficiency.
Another object of the present invention is to provide a device for predicting the wall thickness of water wall tubes in a furnace and a readable storage medium for implementing the method.
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
According to one aspect of the invention, a method for predicting the thickness of a tube wall of a water wall in a furnace is provided, which comprises the following steps: acquiring reducing atmosphere parameters c of the wall surface of a water wall, the sulfur content St of coal for combustion and the wall temperature T of the water wall when the boiler is in thermal operation; collecting the pipe wall thickness h and recording the corresponding unit operation time t when at least two sides are in furnace shutdown for maintenance; establishing a mathematical model, and constructing a functional relation of the wall thickness variation delta h of the pipe: Δ h ═ f (c, St, T); and introducing operation parameters to predict the subsequent pipe wall thickness variation.
In one embodiment, the reducing atmosphere parameter c of the method comprises O2CO and H2The concentration of S.
In one embodiment, the reducing atmosphere parameter c of the method is obtained by online monitoring of an online flue gas component measuring device or actual measurement of a water-cooled wall hole.
In one embodiment, the method also collects coal-fired parameters during the hot operation of the boiler, wherein the coal parameters comprise the sulfur content St.
In one embodiment, the mathematical model of the method is a monte carlo algorithm, a malaxing-rule algorithm, a graph-theoretic algorithm, a simulated annealing algorithm, a neural network algorithm, or a genetic algorithm.
In one embodiment, the functional relationship of the method is a linear function, an exponential function, a logarithmic function, or a piecewise function formed by any combination.
In one embodiment, the expression of the functional relationship of the method is: Δ h ═ kO2 a·COb·H2Sc·Std·Te·tf(ii) a Wherein k, a, b, c, d, e and f are all weight coefficients.
According to another aspect of the present invention, there is also provided an apparatus for predicting the thickness of a tube wall of a waterwall in a furnace, comprising: the parameter acquisition module is used for acquiring reducing atmosphere parameters c of the wall surface of the water wall, the sulfur content St of the coal for combustion and the wall temperature T of the water wall when the boiler is in thermal operation; the wall thickness detection module is used for collecting the pipe wall thickness h and recording the corresponding unit operation time t during multiple times of furnace shutdown maintenance; the model establishing module is used for establishing a mathematical model and establishing a function relation of the wall thickness variation delta h of the pipe: Δ h ═ f (c, St, T); and the calculation module is used for bringing the operation parameters into the prediction module and predicting the subsequent wall thickness variation of the pipe.
According to another aspect of the invention, a device for predicting the thickness of the tube wall of the water wall in the furnace is also provided, which comprises a memory and a processor; the memory is used for storing a computer program; the processor is configured to, when executing the computer program, implement the method for predicting the wall thickness of the water wall tube in the furnace according to any of the embodiments.
According to still another aspect of the present invention, there is further provided a readable storage medium, having a computer program stored thereon, which when executed by a processor, implements the method for predicting the wall thickness of a water wall tube in a furnace according to any of the above embodiments.
The embodiment of the invention has the beneficial effects that: by collecting reducing atmosphere parameters, pipe wall temperature and the like on the water wall in the furnace, analyzing and establishing a model by using the collected data , establishing a functional relation of parameters such as flue gas components, coal operation parameters and pipe wall temperature of a region of the water wall with non-water wall thickness, predicting the change of the pipe wall thickness of the water wall in the furnace, and guiding the work of maintenance or pipe replacement, the working efficiency of inspection is improved, and the workload of maintenance is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
The above features and advantages of the present disclosure will be better understood upon reading the detailed description of embodiments of the disclosure in conjunction with the following drawings. In the drawings, components are not necessarily drawn to scale, and components having similar relative characteristics or features may have the same or similar reference numerals.
FIG. 1 is a schematic flow diagram of an embodiment of the method of the present invention;
FIG. 2 is a block diagram of an embodiment of the apparatus of the present invention;
wherein: 201-parameter acquisition module; 202-wall thickness detection module; 203-a model building module; 204-calculation module.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is noted that the aspects described below in connection with the figures and the specific embodiments are exemplary only and should not be construed as limiting the scope of protection in any way.
As shown in fig. 1, an embodiment of the present application provides a method for predicting a wall thickness of a water wall tube in a furnace, including the following steps:
s1, preferably selecting a proper position according to a high-temperature corrosion area and the severity of a certain unit by using a blowing-out maintenance opportunity, and additionally installing an online cigaretteThe gas composition monitoring device is used for conveniently knowing the reducing atmosphere condition of the wall surface in a thermal state; on the site without on-line monitoring, a water-cooled wall surface can be used for installing a reducing atmosphere measuring hole, and flue gas analysis is used for manual actual measurement in a hot state; at the same time, the thickness h of the tube wall0 for actual measurement.
S2, collecting reducing atmosphere parameters c of the wall surface of the water wall, the sulfur content St of the coal for combustion and the wall temperature T of the water wall when the boiler is in thermal operation. The reducing atmosphere parameter c comprises O2CO and H2The concentration of S is considered because these components react with Fe or FeO in the tube wall to cause corrosion and cause thinning of the tube wall. Wherein CO is not H2The concentration of S is in a linear positive correlation relationship, so that only one of the S concentrations can be taken. The higher the waterwall tube wall temperature T, the faster the reaction rate is generally, so T is also one of the dependent variables.
S3, collecting the pipe wall thickness h when the furnace is stopped again for maintenance1And recording the corresponding unit running time t1；
It will be readily appreciated that data for several more service cycles can be tracked, with the wall thickness h being collected2、h3…, and records the corresponding t2,t3… to improve the fitting accuracy.
S4, establishing a mathematical model, and constructing a function relation of the wall thickness variation delta h: Δ h ═ f (c, St, T).
The mathematical modeling process is to establish a function based on the collected data and to fit lines. Generally, algorithms such as monte carlo algorithm, malaxing algorithm, graph theory algorithm, simulated annealing algorithm, neural net gorge algorithm or genetic algorithm can be adopted, or empirical formulas such as a linear function, an exponential function, a logarithmic function or a piecewise function formed by any combination can be selected firstly, and then each coefficient in the function is deduced by using the least square method.
In a possible embodiment, the expression of the functional relationship is:
Δh＝kO2 a·COb·H2Sc·Std·Te·tf；
wherein k, a, b, c, d, e and f are all weight coefficients, a, b, c, d, e and f are indexes, and the parameters are obtained by modeling. CO is the concentration of carbon monoxide, H, when operating in the hot state2S is the concentration of sulfur dioxide in thermal state operation, St is the sulfur content, T is the wall temperature of water wall in thermal state operation, T is the unit operation time, O2Is the oxygen concentration.
And S5, substituting the relevant parameters into the operation parameters into a functional expression, and predicting the subsequent wall thickness variation. For example, if it is required to predict the amount of change in the wall thickness after one day, and other parameters are kept unchanged, the time t is carried over. Through calculating the wall thickness variation of the pipe, the wall thickness of the pipe can be found in time when the wall thickness is too thin, and rows of maintenance or pipe replacement can be carried out, so that potential safety hazards can be eliminated.
Not corresponding to the above method, an embodiment of the present application further provides a device for predicting a wall thickness of a water wall tube in a furnace, as shown in fig. 2, including:
the parameter acquisition module 201 is used for acquiring a reducing atmosphere parameter c of the wall surface of the water wall, the sulfur content St of coal for combustion and the wall temperature T of the water wall when the boiler is in thermal operation;
the wall thickness detection module 202 is used for collecting the pipe wall thickness h and recording the corresponding unit operation time t during multiple times of furnace shutdown maintenance;
the model establishing module 203 is configured to establish a mathematical model, and construct a functional relation of the wall thickness variation Δ h: Δ h ═ f (c, St, T);
and the calculating module 204 is used for introducing the operation parameters and predicting the subsequent wall thickness variation.
The embodiment of the application also provides a device for predicting the thickness of the tube wall of the water wall in the furnace, which comprises a memory and a processor; the memory is used for storing a computer program; the processor is configured to, when executing the computer program, implement the method for predicting the wall thickness of the water wall tube in the furnace according to any of the embodiments.
In addition, the embodiment of the application provides a readable storage medium, in which computing instructions are stored, and when the computing instructions are executed by a processor, the method for predicting the wall thickness of the water wall tube in the furnace provided in the above embodiment is implemented. Computer-readable storage media described in embodiments herein include Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The embodiments are described in the present specification by way of delivering , each embodiment focuses on the differences of other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The above description is only a preferred example of the present application and should not be taken as limiting the present application, and any modification, equivalent replacement, modification , etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (9)
1. A method for predicting the wall thickness of a water wall tube in a furnace is characterized by comprising the following steps:
acquiring reducing atmosphere parameters c of the wall surface of a water wall, the sulfur content St of coal for combustion and the wall temperature T of the water wall when the boiler is in thermal operation;
collecting the pipe wall thickness h and recording the corresponding unit operation time t when blowing out and maintaining are carried out at least twice;
establishing a mathematical model, and constructing a functional relation of the wall thickness variation delta h of the pipe: Δ h ═ f (c, St, T);
and introducing operation parameters to predict the subsequent pipe wall thickness variation.
2. The method for predicting the wall thickness of the water wall tube in the furnace according to claim 1, wherein: the reducing atmosphere parameter c comprises O2CO and H2The concentration of S.
3. The method for predicting the wall thickness of the water wall tube in the furnace according to claim 1, wherein: and the reducing atmosphere parameter c is obtained by online monitoring through an online flue gas component measuring device or actual measurement through a water-cooled wall measuring hole.
4. The method for predicting the wall thickness of the water wall tube in the furnace according to claim 1, wherein: the mathematical model is established by adopting a Monte Carlo algorithm, an interpolation algorithm, a graph theory algorithm, a simulated annealing algorithm, a neural network algorithm or a genetic algorithm.
5. The method for predicting the wall thickness of the water wall tube in the furnace according to claim 1, wherein: the function relation is a linear function, an exponential function, a logarithmic function or a piecewise function.
6. The method for predicting the wall thickness of the water wall tube in the furnace according to claim 1, wherein: the expression of the functional relationship is:
Δh＝kO2 a·COb·H2Sc·Std·Te·tf；
wherein k, a, b, c, d, e and f are all weight coefficients.
7. A prediction device for the wall thickness of a water wall tube in a furnace is characterized by comprising:
the parameter acquisition module is used for acquiring reducing atmosphere parameters c of the wall surface of the water wall, the sulfur content St of the coal for combustion and the wall temperature T of the water wall when the boiler is in thermal operation;
the wall thickness detection module is used for collecting the wall thickness h of the multiple groups of pipes and recording the corresponding unit operation time t during blowing out for maintenance;
the model establishing module is used for establishing a mathematical model and establishing a function relation of the wall thickness variation delta h of the pipe: Δ h ═ f (c, St, T);
and the calculation module is used for bringing the operation parameters into the prediction module and predicting the subsequent wall thickness variation of the pipe.
8. The device for predicting the wall thickness of the water wall tube in the furnace is characterized by comprising a memory and a processor; the memory is used for storing a computer program;
the processor, when executing the computer program, is configured to implement the method for predicting wall thickness of water wall tubes in a furnace according to any one of claims 1 to 6.
9. A readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of predicting wall thickness of a water wall tube in a furnace of any one of claims 1 to 6.
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| CN113340799A (en) * | 2021-05-31 | 2021-09-03 | 华电渠东发电有限公司 | Anti-abrasion and anti-explosion grid distribution control detection method |
| CN113379072A (en) * | 2021-05-08 | 2021-09-10 | 苏州西热节能环保技术有限公司 | State maintenance method for boiler heating surface of thermal power plant |
| CN113688483A (en) * | 2021-10-09 | 2021-11-23 | 中冶京诚工程技术有限公司 | Method and device for simulating cooling wall of blast furnace |
| CN113701185A (en) * | 2021-08-27 | 2021-11-26 | 国网河北省电力有限公司电力科学研究院 | Monitoring device and method for high-temperature corrosion degree of ultra-low emission power station boiler |
| CN113761785A (en) * | 2021-06-11 | 2021-12-07 | 神华国能宁夏煤电有限公司 | Method and device for determining thinning value of boiler and storage medium |
| CN113836821A (en) * | 2021-10-26 | 2021-12-24 | 华电莱州发电有限公司 | Boiler water wall tension crack online prediction method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113761785A (en) * | 2021-06-11 | 2021-12-07 | 神华国能宁夏煤电有限公司 | Method and device for determining thinning value of boiler and storage medium |
| CN113701185A (en) * | 2021-08-27 | 2021-11-26 | 国网河北省电力有限公司电力科学研究院 | Monitoring device and method for high-temperature corrosion degree of ultra-low emission power station boiler |
| CN113688483A (en) * | 2021-10-09 | 2021-11-23 | 中冶京诚工程技术有限公司 | Method and device for simulating cooling wall of blast furnace |
| CN113836821A (en) * | 2021-10-26 | 2021-12-24 | 华电莱州发电有限公司 | Boiler water wall tension crack online prediction method |
| CN113836821B (en) * | 2021-10-26 | 2023-11-28 | 华电莱州发电有限公司 | Online prediction method for boiler water wall cracking |
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