CN112270060B - Method, system and equipment for predicting residual life of furnace tube of heating furnace on line in real time - Google Patents
Method, system and equipment for predicting residual life of furnace tube of heating furnace on line in real time Download PDFInfo
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Abstract
The invention discloses a method, a system and equipment for predicting the residual life of a furnace tube of a heating furnace on line in real time. The method comprises the following steps: acquiring related data of the operation of the furnace tube of the heating furnace, constructing a first fitting function which represents the relation between the temperature and the stress of the furnace tube under different design lives and is marked as sigma=f (T), and a second fitting function which represents the relation between the stress and the design life of the furnace tube under different temperatures and is marked as P i (sigma); collecting the used time length and the temperature T1 of the heating furnace, selecting a corresponding first fitting function to calculate the stress sigma, and selecting a corresponding second fitting function; the Larson-Miller extrapolation relationship T 1(C+logtr)＝Pi (σ) was established to obtain the furnace tube residual life T r. The invention establishes a mathematical model by using field data and a fitting method, calculates the residual life of the furnace tube of the heating furnace, can well overcome the defects of shutdown and production stopping, repeated test, complicated detection, rough off-line evaluation only and the like in the prior art method, and has simple principle and easy realization and popularization.
Description
Technical Field
The invention relates to the technical field of oilfield heating furnaces, in particular to a method, a system and equipment for predicting the residual life of a furnace tube of a heating furnace on line in real time.
Background
The oil field heating furnace is an important production device widely applied in petrochemical enterprises, and is a device for heating flowing media in a furnace tube by taking high-temperature flame and smoke generated by fuel combustion as heat sources. Because the outside of the furnace tube of the heating furnace is directly heated by flame, the working condition of the furnace tube is usually bad, such as working environment with high temperature and high pressure, easy corrosion and easy scaling, which can cause the leakage of the furnace tube, once the furnace tube is leaked, serious accidents such as ignition and explosion are easily caused, the production and personal safety are directly threatened, and huge losses are brought to petrochemical enterprises. Therefore, the residual service life of the furnace tube of the heating furnace in the oil field is reasonably predicted, the damage of the furnace tube is prevented, the safe and stable operation of the furnace tube is ensured, and the method has important significance for normal production of petrochemical enterprises.
In some existing oil fields, the control system of the heating furnace mostly does not have the function of detecting the residual life of the furnace tube, only a small part of heating furnaces establish a short-time lasting strength relation through experiments, the residual life of the furnace tube of the heating furnace is determined by using an extrapolation method, and the method has obvious defects, such as: when forecasting, the heating furnace is required to be shut down and stopped, and the long-lasting test is long in time consumption, complex in detection, difficult to popularize and the like. In addition, some nondestructive detection methods, such as ultrasonic crack length detection, eddy current carburized layer thickness detection and the like, are adopted for a few heating furnaces to estimate the residual service life of the furnace tube, but only rough offline evaluation can be performed, and the calculation result has low referenceability.
Disclosure of Invention
The invention aims to provide a method, a system and equipment for predicting the residual life of a furnace tube of a heating furnace on line in real time.
In order to achieve the above object, a first aspect of the present invention provides a method for predicting the remaining life of a furnace tube of a heating furnace in real time on line, comprising: acquiring related data of the operation of a furnace tube of the heating furnace, wherein the related data comprise the temperature, the design life, the stress and the corresponding relation of the furnace tube; according to the obtained related data, constructing a first fitting function which represents the relation between the temperature and the stress of the furnace tube under different design lives and is marked as sigma=f (T), wherein sigma is the stress, and T is the temperature of the furnace tube; according to the obtained related data, constructing a second fitting function which represents the relation between the stress of the furnace tube and the design life at different temperatures, and marking P i(σ),Pi (sigma) as the design life, wherein i is different in value and corresponds to different temperatures; collecting the used time length of the heating furnace, selecting a corresponding first fitting function according to the used time length, and calculating to obtain a corresponding stress sigma; collecting the temperature T 1 of a furnace tube of the heating furnace, and selecting a corresponding second fitting function according to the temperature T 1 of the furnace tube; establishing a Larson-Miller extrapolation relation T 1(C+log tr)＝Pi (sigma); wherein C is a material constant, and t r is the residual life of the furnace tube; solving to obtain the residual life of the furnace tube according to the extrapolation relational expression
Further, the first fitting function is specifically: σ=a 0×T2+A1×T+A2; wherein A 0,A1,A2 is the regression coefficient of the function.
Further, the constructing a first fitting function representing a relationship between a temperature and a stress of the furnace tube under different design lives comprises: according to the design life of 10 ten thousand hours, 6 ten thousand hours, 4 ten thousand hours and 2 ten thousand hours, four groups of regression coefficients are respectively calculated, and four first fitting functions are correspondingly obtained; wherein a 0＝0.003214,A1＝-3.583,A2 =1016 when the design lifetime is 10 ten thousand hours; a 0＝0.003282,A1＝-3.68,A2 =1051 when the design lifetime is 6 ten thousand hours; a 0＝0.003334,A1＝-3.756,A2 =1078 when the design lifetime is 4 ten thousand hours; a 0＝0.003459,A1＝-3.918,A2 =1134 when the design lifetime is 2 ten thousand hours.
Further, the second fitting function is specifically: p i(σ)＝B0+B1logσ+B2(logσ)2, wherein B 0,B1,B2 is a regression coefficient.
Further, 8 sets of P i (σ) functions were obtained from 400 ℃ to 470 ℃ according to a step size of 10 ℃):
Further, the collecting the used time length of the heating furnace, selecting a corresponding first fitting function according to the used time length, including: the used time of the furnace tube of the heating furnace is expressed by year, and the regression coefficient A 0,A1,A2 in the first fitting function is selected according to the range of year, and the rule is as follows: a 0＝0.003214,A1＝-3.583,A2 =1016 when 0< year.ltoreq.2; a 0＝0.003282,A1＝-3.68,A2 =1051 when 2< year < 4; when 4< year is less than or equal to 6, a 0＝0.003334,A1＝-3.756,A2 =1078; when 6< year.ltoreq.10, a 0＝0.003459,A1＝-3.918,A2 =1134.
Further, the collecting the temperature T 1 of the furnace tube of the heating furnace, selecting a corresponding second fitting function according to the temperature T 1 of the furnace tube, includes: the furnace tube temperatures T 1 were collected and one set was selected from the 8 sets of P i (σ) functions according to T 1.
In a second aspect of the present invention, a system for predicting the remaining life of a furnace tube of a heating furnace in real time on line is provided, comprising: the data acquisition module is used for acquiring related data of the operation of the furnace tube of the heating furnace, wherein the related data comprise the temperature, the design life, the stress and the corresponding relation of the furnace tube; the data fitting module is used for constructing a first fitting function which represents the relation between the temperature and the stress of the furnace tube under different design lives according to the acquired related data, wherein the first fitting function is marked as sigma=f (T), sigma is the stress, and T is the temperature of the furnace tube; constructing a second fitting function which shows the relation between the stress and the design life of the furnace tube at different temperatures, and marking P i(σ),Pi (sigma) as the design life of the furnace tube, wherein i is different in value and corresponds to different temperatures; the calculation module is used for collecting the used time length of the heating furnace, selecting a corresponding first fitting function according to the used time length, and calculating to obtain a corresponding stress sigma; collecting the temperature T 1 of a furnace tube of the heating furnace, and selecting a corresponding second fitting function according to the temperature T 1 of the furnace tube; a prediction module for establishing a Larson-Miller extrapolation relationship T 1(C+log tr)＝Pi (σ); wherein C is a material constant, and t r is the residual life of the furnace tube; solving to obtain the residual life of the furnace tube according to the extrapolation relational expression
In a third aspect of the present invention, there is provided a computer device comprising a processor and a memory, the memory having stored therein a program comprising computer-executable instructions, the processor executing the computer-executable instructions stored in the memory when the computer device is in operation, to cause the computer device to perform the method of predicting the remaining life of a furnace tube of a heating furnace in real time on line as described in the first aspect.
In a fourth aspect of the present invention, there is provided a computer readable storage medium storing one or more programs, the one or more programs comprising computer-executable instructions, which when executed by a computer device, cause the computer device to perform the method of predicting the remaining life of a furnace tube of a heating furnace in real time on line as described in the first aspect.
From the above technical solutions, the embodiment of the present invention has the following advantages:
The method provided by the invention establishes a mathematical model by using field data and a fitting method, calculates the residual life of the furnace tube of the heating furnace, can well overcome the defects of shutdown and production stopping, repeated test, complicated detection, rough off-line evaluation only and the like in the prior art method, and has simple principle and easy realization and popularization. Moreover, the method has low computational complexity, is easy to program and realize, and has low requirement on hardware performance.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments and the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for predicting the residual life of a furnace tube of a heating furnace in real time on line according to an embodiment of the present invention;
FIG. 2 is a graphical representation of the function of four first fitting functions in one embodiment of the invention;
FIG. 3 is a schematic diagram of a system for online real-time prediction of remaining life of a furnace tube of a heating furnace according to an embodiment of the present invention;
Fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
The terms first, second, third and the like in the description and in the claims and in the above drawings, are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
The following is a detailed description of specific examples.
The embodiment of the invention provides a method for predicting the residual life of a furnace tube of a heating furnace in real time on line, which is characterized in that the furnace tube temperature and stress function relations under different fixed design life time periods are established by combining an industry standard of the petrochemical industry standard SH/T3037-2016 of the people's republic of China and a large amount of statistical data and utilizing Larson-Miller extrapolation and a data fitting modeling method, and meanwhile, the stress and design life function relations under different fixed furnace tube temperatures are established, so that the prediction of the residual life of the furnace tube of the heating furnace in an oil field is realized.
As shown in fig. 1, the specific implementation flow of the method in the embodiment of the present invention mainly includes the following steps:
Step S1: statistics and preparation of data.
Taking an oil field as an example, referring to a data prototype provided by the standard SH/T3037-2016 of petrochemical industry of the people's republic of China, statistics is carried out on relevant data of the oil field, which is operated for about 200 heating furnace tubes for 2 years, wherein the relevant data comprise the temperature, the design life and the stress (allowable stress) of the furnace tubes, and the corresponding relations of different temperatures, different design life and the stress (allowable stress) are shown in a table 1.
TABLE 1 furnace tube temperature, design life, stress data sheet
Step S2: and (6) constructing a fitting function model of the temperature and the stress of the furnace tube.
According to the obtained related data, as shown in table 1, a first fitting function representing the relationship between the temperature and the stress of the furnace tube under different design lives is constructed.
The stress versus temperature in the first fitting function is noted as σ=f (T). In one implementation, the functional relationship may be expressed specifically as:
sigma=a 0×T2+A1×T+A2 -formula (1)
Fitting is carried out according to a formula (1) under the condition of different design lives of the furnace tubes, wherein sigma in the formula (1) is stress, the unit is MPa, T is the temperature of the furnace tubes, the unit is the temperature of the furnace tubes, A 0,A1,A2 is a regression coefficient of a function, a plurality of groups of regression coefficients corresponding to different design lives can be obtained, and therefore a plurality of first fitting functions are obtained.
Alternatively, four sets of regression coefficients may be calculated according to a design lifetime (or referred to as a design working lifetime) of 10 ten thousand hours, 6 ten thousand hours, 4 ten thousand hours, and 2 ten thousand hours, respectively, to correspondingly obtain four first fitting functions, where the corresponding 4 function curves are shown in fig. 2.
The regression coefficients of the functional relationship of the four curves are specifically as follows: a 0＝0.003214,A1＝-3.583,A2 =1016 when the design working life is 10 ten thousand hours; a 0＝0.003282,A1＝-3.68,A2 =1051 when the design working life is 6 ten thousand hours; a 0＝0.003334,A1＝-3.756,A2 =1078 when the design working life is 4 ten thousand hours; a 0＝0.003459,A1＝-3.918,A2 =1134 when the design working life is 2 ten thousand hours.
Step S3: and constructing a fitting function model of the design life and the stress of the furnace tube.
According to the obtained related data, as shown in table 1, a second fitting function representing the relationship between the stress and the design life of the furnace tube at different temperatures is constructed.
Let the second fit function be P (σ), which is a stress-dependent function, typically expressed as a polynomial of the logarithm of the stress, as shown in equation (2), where B 0,B1,B2 is the regression coefficient and σ is the stress.
P (σ) =b 0+B1logσ+B2(logσ)2 -formula (2)
At different temperatures, the second fitting function is different, and can be specifically denoted as P i (sigma), i has different values corresponding to different temperatures, and P i (sigma) is the design life of the furnace tube. Then equation (2) may be rewritten as:
Pi(σ)＝B0+B1logσ+B2(logσ)2
alternatively, the stress and design life function relationship can be fitted and established according to the step length of 10 ℃ and between 400 ℃ and 470 ℃ to obtain 8 groups of P i (sigma) functions, as shown in a formula (3):
wherein i=1 to 7 respectively represent the corresponding temperatures of 400 ℃, 410 ℃, 420 ℃, 430 ℃, 440 ℃, 450 ℃, 460 ℃ and 470 ℃.
Step S4: construction of a model for predicting life by Larson-Miller extrapolation.
Firstly, collecting the used time length of the heating furnace (from the first installation operation to the present), selecting a corresponding first fitting function according to the used time length, and calculating to obtain a corresponding stress sigma.
The acquired time length h of the heating furnace tube in hours can be converted into a year of use year representation, and the regression coefficient A 0,A1,A2 in the first fitting function represented by the formula (1) is selected according to the range of the year of use year, and the rule of selecting the regression coefficient is as follows: a 0＝0.003214,A1＝-3.583,A2 =1016 when 0< year.ltoreq.2; a 0＝0.003282,A1＝-3.68,A2 =1051 when 2< year < 4; when 4< year is less than or equal to 6, a 0＝0.003334,A1＝-3.756,A2 =1078; when 6< year.ltoreq.10, a 0＝0.003459,A1＝-3.918,A2 =1134.
Taking the heating furnace total life as an example of 12 years, it is easy to understand that when 0< year.ltoreq.2, the design life is still 10 years, so the regression coefficient corresponding to 10 ten thousand hours when the design working life is selected, namely: a 0＝0.003214,A1＝-3.583,A2 =1016. The selection principle of the regression coefficients of other groups is similar and will not be described in detail.
Then, the furnace tube temperature T 1 of the heating furnace is acquired, and a corresponding second fitting function P i (sigma) is selected according to the furnace tube temperature T 1.
Optionally, the furnace tube temperature T 1 is collected, the unit is degrees celsius, the stress σ is calculated according to the regression coefficients of T 1 and the selected a 0,A1,A2, the second fitting function represented by the formula (3) is selected according to the range of T 1, the specific selection method is shown in table 2, and a group is selected from 8 groups of P i (σ) functions according to the furnace tube temperature T 1.
TABLE 2 correspondence between furnace tube operating temperature T 1 and function P i (σ)
Finally, based on the above parameters, a Larson-Miller extrapolation relationship is established, as shown in equation (4). Wherein C is a material constant, and according to different values of C of different materials, most of the current heating furnace tubes adopt 20R carbon steel, so that C can take the value of 20, t r is the residual life of the furnace tubes, and the unit is hours.
T 1(C+log tr)＝Pi (sigma) -formula (4)
Step S5: and predicting the residual life of the furnace tube.
And solving to obtain the residual life t r of the furnace tube of the heating furnace according to the extrapolation relational expression, and substituting each selected parameter into the residual life t r of the furnace tube of the heating furnace as shown in a formula (5).
According to equation (2), equation (5) can be further expressed as:
Substituting the obtained parameters into the formula (5) to obtain the residual life of the furnace tube of the heating furnace.
Above, the embodiment of the invention discloses a method for predicting the residual life of a furnace tube of a heating furnace on line in real time. According to the method, a Larson-Miller extrapolation relational expression is built by establishing a functional relation between furnace tube temperature and stress under different design life conditions and a functional relation between furnace tube stress and design life under different temperatures, so that a prediction formula (5) is obtained, and the prediction of the residual life of the furnace tube of the heating furnace is realized. The method can conveniently and effectively realize online prediction of the residual life of the furnace tube.
Referring to fig. 3, the embodiment of the present invention further provides a system for predicting the remaining life of a furnace tube of a heating furnace in real time, where the system may include:
The data acquisition module 31 is configured to acquire relevant data of the operation of the furnace tube of the heating furnace, where the relevant data includes a temperature, a design life, a stress, and a corresponding relationship of the furnace tube;
the data fitting module 32 is configured to construct a first fitting function representing a relationship between a temperature and a stress of the furnace tube under different design lives according to the acquired related data, and the first fitting function is denoted as σ=f (T), where σ is a stress and T is a temperature of the furnace tube; constructing a second fitting function which shows the relation between the stress and the design life of the furnace tube at different temperatures, and marking P i(σ),Pi (sigma) as the design life of the furnace tube, wherein i is different in value and corresponds to different temperatures;
The calculating module 33 is configured to collect a used time length of the heating furnace, select a corresponding first fitting function according to the used time length, and calculate to obtain a corresponding stress σ; collecting the temperature T 1 of a furnace tube of the heating furnace, and selecting a corresponding second fitting function according to the temperature T 1 of the furnace tube;
A prediction module 34 for establishing a Larson-Miller extrapolation relationship T 1(C+log tr)＝Pi (σ); wherein C is a material constant, and t r is the residual life of the furnace tube; solving to obtain the residual life of the furnace tube according to the extrapolation relational expression
Referring to fig. 4, an embodiment of the present invention further provides a computer device 40, including a processor 41 and a memory 42, where the memory 42 stores a program, and the program includes computer-executable instructions, when the computer device 40 is running, the processor 41 executes the computer-executable instructions stored in the memory 42, so that the computer device 40 performs the method for online real-time predicting the remaining life of a furnace tube of a heating furnace as described above.
Embodiments of the present invention also provide a computer-readable storage medium storing one or more programs, the one or more programs comprising computer-executable instructions, which when executed by a computer device, cause the computer device to perform a method of online real-time predicting remaining life of a furnace tube of a heating furnace as described above.
In the foregoing embodiments, the descriptions of the embodiments are each focused, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; those of ordinary skill in the art will appreciate that: the technical scheme described in the above embodiments can be modified or some technical features thereof can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. A method for predicting the residual life of a furnace tube of a heating furnace on line in real time, comprising the steps of:
Acquiring related data of the operation of a furnace tube of the heating furnace, wherein the related data comprise the temperature, the design life, the stress and the corresponding relation of the furnace tube;
According to the obtained related data, constructing a first fitting function which represents the relation between the temperature and the stress of the furnace tube under different design lives and is marked as sigma=f (T), wherein sigma is the stress, and T is the temperature of the furnace tube;
According to the obtained related data, constructing a second fitting function which represents the relation between the stress of the furnace tube and the design life at different temperatures, and marking P i(σ),Pi (sigma) as the design life, wherein i is different in value and corresponds to different temperatures;
Collecting the used time length of the heating furnace, selecting a corresponding first fitting function according to the used time length, and calculating to obtain a corresponding stress sigma;
Collecting the temperature T 1 of a furnace tube of the heating furnace, and selecting a corresponding second fitting function according to the temperature T 1 of the furnace tube;
Establishing a Larson-Miller extrapolation relational expression T 1(C+log tr)＝Pi (sigma), wherein C is a material constant, and T r is the residual life of the furnace tube;
solving to obtain the residual life of the furnace tube according to the extrapolation relational expression
The first fitting function specifically comprises: σ=a 0×Τ2+A1×Τ+A2; wherein A 0,A1,A2 is the regression coefficient of the function;
the construction of a first fitting function representing the relationship between the temperature and the stress of the furnace tube under different design lives comprises the following steps:
according to the design life of 10 ten thousand hours, 6 ten thousand hours, 4 ten thousand hours and 2 ten thousand hours, four groups of regression coefficients are respectively calculated, and four first fitting functions are correspondingly obtained;
Wherein a 0＝0.003214,A1＝-3.583,A2 =1016 when the design lifetime is 10 ten thousand hours; a 0＝0.003282,A1＝-3.68,A2 =1051 when the design lifetime is 6 ten thousand hours; a 0＝0.003334,A1＝-3.756,A2 =1078 when the design lifetime is 4 ten thousand hours; a 0＝0.003459,A1＝-3.918,A2 =1134 when the design lifetime is 2 ten thousand hours;
The second fitting function is specifically: p i(σ)＝B0+B1logσ+B2(logσ)2, wherein B 0,B1,B2 is a regression coefficient;
8 sets of P i (σ) functions were obtained from 400℃to 470℃in steps of 10 ℃):
2. The method of claim 1, wherein the collecting the used time period of the heating furnace, and selecting the corresponding first fitting function according to the used time period, comprises:
The used time of the furnace tube of the heating furnace is expressed by year, and the regression coefficient A 0,A1,A2 in the first fitting function is selected according to the range of year, and the rule is as follows: a 0＝0.003214,A1＝-3.583,A2 =1016 when 0< year.ltoreq.2; a 0＝0.003282,A1＝-3.68,A2 =1051 when 2< year < 4; when 4< year is less than or equal to 6, a 0＝0.003334,A1＝-3.756,A2 =1078; when 6< year.ltoreq.10, a 0＝0.003459,A1＝-3.918,A2 =1134.
3. The method of claim 1, wherein the acquiring the furnace tube temperature T 1, selecting a corresponding second fitting function based on the furnace tube temperature T 1, comprises:
The furnace tube temperatures T 1 were collected and one set was selected from the 8 sets of P i (σ) functions according to T 1.
4. A system for predicting the remaining life of a furnace tube of a heating furnace on-line in real time, comprising:
The data acquisition module is used for acquiring related data of the operation of the furnace tube of the heating furnace, wherein the related data comprise the temperature, the design life, the stress and the corresponding relation of the furnace tube;
The data fitting module is used for constructing a first fitting function which represents the relation between the temperature and the stress of the furnace tube under different design lives according to the acquired related data, wherein the first fitting function is marked as sigma=f (T), sigma is the stress, and T is the temperature of the furnace tube; constructing a second fitting function which shows the relation between the stress and the design life of the furnace tube at different temperatures, and marking P i(σ),Pi (sigma) as the design life of the furnace tube, wherein i is different in value and corresponds to different temperatures;
The calculation module is used for collecting the used time length of the heating furnace, selecting a corresponding first fitting function according to the used time length, and calculating to obtain a corresponding stress sigma; collecting the temperature T 1 of a furnace tube of the heating furnace, and selecting a corresponding second fitting function according to the temperature T 1 of the furnace tube;
A prediction module for establishing a Larson-Miller extrapolation relationship T 1(C+log tr)＝Pi (σ); wherein C is a material constant, and t r is the residual life of the furnace tube; solving to obtain the residual life of the furnace tube according to the extrapolation relational expression
The first fitting function specifically comprises: σ=a 0×Τ2+A1×Τ+A2; wherein A 0,A1,A2 is the regression coefficient of the function;
the construction of a first fitting function representing the relationship between the temperature and the stress of the furnace tube under different design lives comprises the following steps:
according to the design life of 10 ten thousand hours, 6 ten thousand hours, 4 ten thousand hours and 2 ten thousand hours, four groups of regression coefficients are respectively calculated, and four first fitting functions are correspondingly obtained;
Wherein a 0＝0.003214,A1＝-3.583,A2 =1016 when the design lifetime is 10 ten thousand hours; a 0＝0.003282,A1＝-3.68,A2 =1051 when the design lifetime is 6 ten thousand hours; a 0＝0.003334,A1＝-3.756,A2 =1078 when the design lifetime is 4 ten thousand hours; a 0＝0.003459,A1＝-3.918,A2 =1134 when the design lifetime is 2 ten thousand hours;
The second fitting function is specifically: p i(σ)＝B0+B1logσ+B2(logσ)2, wherein B 0,B1,B2 is a regression coefficient;
8 sets of P i (σ) functions were obtained from 400℃to 470℃in steps of 10 ℃):
5. A computer device comprising a processor and a memory, the memory having stored therein a program comprising computer-executable instructions that, when the computer device is in operation, cause the computer device to perform the method of predicting the remaining life of a furnace tube of a heating furnace in real time on-line as claimed in claim 1.
6. A computer readable storage medium storing one or more programs, the one or more programs comprising computer-executable instructions, which when executed by a computer device, cause the computer device to perform the method of online real-time prediction of remaining life of a heating furnace tube of claim 1.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101578133A (en) * | 2007-01-09 | 2009-11-11 | 乔治洛德方法研究和开发液化空气有限公司 | Method of replacing the catalyst tubes of a hydrocarbon reformer |
| CN103267683A (en) * | 2013-04-28 | 2013-08-28 | 扬州大学 | Method for determining remaining life of heat-resisting metal material |
| CN109856039A (en) * | 2019-04-08 | 2019-06-07 | 大连理工大学 | Inner screw channel type ethane cracking furnace pipe residue lifetime estimation method based on L-M parametric method |
-
2020
- 2020-11-16 CN CN202011279008.8A patent/CN112270060B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101578133A (en) * | 2007-01-09 | 2009-11-11 | 乔治洛德方法研究和开发液化空气有限公司 | Method of replacing the catalyst tubes of a hydrocarbon reformer |
| CN103267683A (en) * | 2013-04-28 | 2013-08-28 | 扬州大学 | Method for determining remaining life of heat-resisting metal material |
| CN109856039A (en) * | 2019-04-08 | 2019-06-07 | 大连理工大学 | Inner screw channel type ethane cracking furnace pipe residue lifetime estimation method based on L-M parametric method |
Non-Patent Citations (3)
| Title |
|---|
| 基于Larson-Miller参数锅炉管壁剩余寿命预测方法研究;熊定标;陶国强;袁岑颉;金杰;汤云峰;;电力设备管理;20200925(09);全文 * |
| 大型合成氨装置一段转化炉炉管的剩余寿命预测;付仕勇;;云南化工;20070225(01);全文 * |
| 锅炉高温承压部件剩余寿命的评估及应用;郑晓红, 赵翔, 曹欣玉, 周俊虎, 王如竹;锅炉技术;20030830(04);全文 * |
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