CN113761476A - Refining pipeline corrosion comprehensive prediction method and device based on local detection - Google Patents
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Abstract
The invention discloses a refining pipeline corrosion comprehensive prediction method and a device based on local detection, wherein the method comprises the following steps: acquiring corrosion depth data of local detection points in the same batch under a spatial distribution rule of corrosion; constructing an extreme value I-type distribution-based maximum corrosion depth model, and calculating the maximum corrosion depth of the pipeline; predicting the residual life based on the maximum corrosion depth according to the maximum corrosion depth and the minimum allowable wall thickness of the pipeline; acquiring corrosion depth data of different batches of local fixed point detection points under a corrosion time distribution rule; constructing a relation model of corrosion depth change and pipeline service time; the remaining life based on variable speed corrosion is predicted in combination with the minimum allowable wall thickness of the pipe. The method carries out residual life prediction from two dimensions of corrosion spatial distribution and time distribution, avoids the problem that the corrosion condition of the pipeline cannot be comprehensively mastered due to local detection, considers the corrosion change of a corrosion key focus point, and realizes the corrosion evaluation full coverage based on the local detection.
Description
Technical Field
The invention relates to the field of pipeline corrosion evaluation, in particular to a refining pipeline corrosion comprehensive prediction method and device based on local detection.
Background
The underground pipelines of the refining enterprises are various in types, and have oil product conveying pipelines between oil depot tanks, crude oil conveying pipelines from the tank area to a refining device, circulating water pipelines, water supply pipelines, oil-containing sewage pipelines of the refining device, purging pipelines of underground sump oil tanks and the like. The underground pipelines are mostly laid among the refining devices, so that the internal and external detection and evaluation work cannot be effectively carried out, and hidden danger is brought to the running of the underground pipelines of refining enterprises. Although in recent years, by transforming underground pipe networks, part of underground pipelines are changed into overground overhead pipelines, so that the detection, the maintenance and the repair are convenient, and the operation risk of the pipelines is reduced. But still some pipelines are laid underground, and because the time span is long, pipeline data cannot be searched, and the pipeline routing trend cannot be clearly identified, which brings great hidden danger to the production operation of refining enterprises. The corrosion evaluation is carried out on the pipeline for conveying oil gas, so that the management level of the integrity of the pipeline can be improved, the running risk of the pipeline is reduced, and the safety production is guaranteed.
The basis of the pipeline corrosion evaluation is to acquire accurate and comprehensive corrosion detection data, however, the underground pipelines of the refining and chemical enterprises cannot be subjected to internal detection at present so as to realize the detection of full-line coverage, and only local characteristics of pipeline corrosion can be reflected. If all pipelines are excavated, the cost required to be invested is extremely high and the operation of the refining and chemical enterprises is influenced.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a refining pipeline corrosion comprehensive prediction method and device based on local detection.
Specifically, the embodiment of the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for comprehensively predicting corrosion of a refinery pipeline based on local detection, including:
s1, acquiring corrosion depth data of local detection points in the same batch under the spatial distribution rule of corrosion;
s2, constructing an extreme value I-type distribution-based maximum corrosion depth model according to the corrosion depth data of the local detection points in the same batch, and calculating the maximum corrosion depth of the pipeline;
s3, predicting the residual life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline by combining the minimum allowable wall thickness of the pipeline;
s4, acquiring corrosion depth data of different batches of local fixed point detection points under the corrosion time distribution rule;
s5, constructing a relation model of corrosion depth change and pipeline service time according to the corrosion depth data of different batches of local fixed point detection points and acquiring model parameters;
and S6, predicting the residual life based on variable-speed corrosion according to the relation model of the corrosion depth change and the service life of the pipeline and by combining the minimum allowable wall thickness of the pipeline.
Further, the S2 constructs an extreme value I-type distribution-based maximum corrosion depth model according to the corrosion depth data of the local detection points in the same batch, and calculates the maximum corrosion depth of the pipeline, specifically including:
locally expressing the probability distribution of the maximum corrosion depth by using an extreme value type I distribution, wherein the probability distribution is expressed as a first relation model:
wherein F (x) is the cumulative probability that the maximum etch depth does not exceed x; x is a random variable of the depth of the etch; lambda is a statistical parameter and represents the depth of the corrosion point with the maximum probability density; alpha is a statistical variable and represents the average value of the depth of the corrosion points; the values of λ and α are obtained by linear fitting, and let (x- λ)/α be y, the first relational model is converted into a second relational model:
F(x)＝exp[-exp(-y)]
and continuously converting the second relation model to obtain a third relation model:
y＝-ln[-lnF(x)]＝(x-λ)/α
if n corrosion points are detected in the same batch, the corrosion depth of the ith point is xiAll the corrosion depths are randomly distributed on the same pipeline, the corrosion depths of n points are sequenced from small to large, and then the sequence of the samples is X ═ X1,x2,…,xi,…,xnH, wherein the statistical probability of the ith point is expressed as a fourth relational model:
introducing the concept of regression phase, i.e. regression phase TnExpressed as achieving the maximum etch depth xmaxThe ratio of the maximum area to be measured to the local measurement area, namely, a fifth relation model:
Tn＝S/s
wherein, S is the area of whole pipeline, and S is the local area of measurement, because the corresponding spatial relationship of refining underground pipeline is the length relation, consequently turns into the sixth relation model with the fifth relation model:
Tn＝L/l
wherein L is the length of the whole corrosion pipeline; l is the local measurement length;
wherein, the regression period TnAlso expressed as the random variable x satisfying the extremum distribution exceeds the maximum value xmaxNumber of samples required, regression period TnAnd cumulative probability F (x)max) Is a seventh relational model:
Tn＝1/[1-F(xmax)]
according to the obtained F (x)max) Value calculation ymaxA value of and will ymaxSubstituting the value into y ═ x- λ)/α to obtain a predicted value x of the maximum corrosion depth of the pipeline section of length LmaxComprises the following steps:
xmax＝α·ymax+λ。
further, the S3 predicts the remaining life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline, in combination with the minimum allowable wall thickness of the pipeline, specifically including:
predicting the residual life SM based on the maximum corrosion depth according to the eighth relational model by combining the maximum allowable wall thickness of the pipeline and the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline and assuming that the corrosion rate is uniform1Wherein the eighth relationship model is:
SM1＝(t-xmax-tmin)/R＝Time·(t-xmax-tmin)/xmax
wherein, the Time is the service Time of the pipeline and the year; t is the wall thickness of the pipe wall in mm; t is tminThe minimum wall thickness allowed by the pipeline is mm; r is the corrosion speed of the pipe wall, mm/s.
Further, the S5 constructs a relation model between corrosion depth change and service time of the pipeline according to the corrosion depth data of different batches of local fixed point detection points, and obtains model parameters, which specifically includes:
according to the corrosion depth data of different batches of local fixed point detection points, expressing the relationship between the corrosion depth change and the service life of the pipeline by using an exponential function as a ninth relationship model:
x＝m·Timen
wherein x is the pipe wall corrosion depth, mm; time is the service Time of the pipeline, year; m and n are constants, and the determination method of the constants m and n is as follows:
taking logarithm of two sides of the above formula, there are:
logx＝logm+n·logTime
setting: logx ═ y; logm is a0；n＝a1(ii) a logTime ═ x, then we get:
y＝a0+a1x
according to the historical detected pipeline corrosion depth data, calculating a by using a linear fitting method0And a1And then the constants m and n are calculated.
Further, the S6 predicts the remaining life based on variable-speed corrosion according to the relation model between the corrosion depth change and the service life of the pipeline, in combination with the minimum allowable wall thickness of the pipeline, specifically including:
according to a relation model of corrosion depth change and service life of the pipeline, a tenth relation model is adopted to predict the residual life SM based on variable-speed corrosion by combining the minimum allowable wall thickness of the pipeline2(ii) a Wherein the tenth relationship model is:
wherein T is the current service time of the pipeline and year.
In a second aspect, an embodiment of the present invention further provides a device for comprehensively predicting corrosion of a refinery pipeline based on local detection, including:
the first acquisition module is used for acquiring corrosion depth data of local detection points in the same batch under the spatial distribution rule of corrosion;
the first construction module is used for constructing an extreme value I-type distribution-based maximum corrosion depth model according to the corrosion depth data of the local detection points in the same batch and calculating the maximum corrosion depth of the pipeline;
the first prediction module is used for predicting the residual life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline by combining the minimum allowable wall thickness of the pipeline;
the second acquisition module is used for acquiring corrosion depth data of different batches of local fixed-point detection points under the corrosion time distribution rule;
the second construction module is used for constructing a relation model of corrosion depth change and pipeline service time according to the corrosion depth data of different batches of local fixed-point detection points and acquiring model parameters;
and the second prediction module is used for predicting the residual life based on variable-speed corrosion by combining the minimum allowable wall thickness of the pipeline according to the relation model of the corrosion depth change and the service life of the pipeline.
Further, the first building module is specifically configured to:
locally expressing the probability distribution of the maximum corrosion depth by using an extreme value type I distribution, wherein the probability distribution is expressed as a first relation model:
wherein F (x) is the cumulative probability that the maximum etch depth does not exceed x; x is a random variable of the depth of the etch; lambda is a statistical parameter and represents the depth of the corrosion point with the maximum probability density; alpha is a statistical variable and represents the average value of the depth of the corrosion points; the values of λ and α are obtained by linear fitting, and let (x- λ)/α be y, the first relational model is converted into a second relational model:
F(x)＝exp[-exp(-y)]
and continuously converting the second relation model to obtain a third relation model:
y＝-ln[-lnF(x)]＝(x-λ)/α
if n corrosion points are detected in the same batch, the corrosion depth of the ith point is xiAll the corrosion depths are randomly distributed on the same pipeline, the corrosion depths of n points are sequenced from small to large, and then the sequence of the samples is X ═ X1,x2,…,xi,…,xnH, wherein the statistical probability of the ith point is expressed as a fourth relational model:
introducing the concept of regression phase, i.e. regression phase TnExpressed as achieving the maximum etch depth xmaxThe ratio of the maximum area to be measured to the local measurement area, namely, a fifth relation model:
Tn＝S/s
wherein, S is the area of whole pipeline, and S is the local area of measurement, because the corresponding spatial relationship of refining underground pipeline is the length relation, consequently turns into the sixth relation model with the fifth relation model:
Tn＝L/l
wherein L is the length of the whole corrosion pipeline; l is the local measurement length;
wherein, the regression period TnAlso expressed as the random variable x satisfying the extremum distribution exceeds the maximum value xmaxNumber of samples required, regression period TnAnd cumulative probability F (x)max) Is a seventh relational model:
Tn＝1/[1-F(xmax)]
according to the obtained F (x)max) Value calculation ymaxA value of and will ymaxSubstituting the value into y ═ x- λ)/α to obtain a predicted value x of the maximum corrosion depth of the pipeline section of length LmaxComprises the following steps:
xmax＝α·ymax+λ。
further, the first prediction module is specifically configured to:
predicting the residual life SM based on the maximum corrosion depth according to the eighth relational model by combining the maximum allowable wall thickness of the pipeline and the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline and assuming that the corrosion rate is uniform1Wherein the eighth relationship model is:
SM1＝(t-xmax-tmin)/R＝Time·(t-xmax-tmin)/xmax
wherein, the Time is the service Time of the pipeline and the year; t is the wall thickness of the pipe wall in mm; t is tminThe minimum wall thickness allowed by the pipeline is mm; r is the corrosion speed of the pipe wall, mm/s.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor, when executing the computer program, implements the method for integrally predicting corrosion of a refinery pipeline based on local detection according to the first aspect.
In a fourth aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for integrated prediction of corrosion of a refinery pipeline based on local detection according to the first aspect.
According to the technical scheme, the refining pipeline corrosion comprehensive prediction method and device based on local detection, the electronic equipment and the storage medium provided by the embodiment of the invention acquire the corrosion depth data of local detection points in the same batch under the spatial distribution rule of corrosion; constructing an extreme value I-type distribution-based maximum corrosion depth model according to the corrosion depth data of the local detection points in the same batch, and calculating the maximum corrosion depth of the pipeline; predicting the residual life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline by combining the minimum allowable wall thickness of the pipeline; acquiring corrosion depth data of different batches of local fixed point detection points under a corrosion time distribution rule; according to the corrosion depth data of different batches of local fixed point detection points, constructing a relation model of corrosion depth change and pipeline service time and acquiring model parameters; and predicting the residual life based on variable-speed corrosion according to a relation model of the corrosion depth change and the service life of the pipeline and by combining the minimum allowable wall thickness of the pipeline. Therefore, the embodiment of the invention combines the detection data of the same batch and different batches to construct a corrosion spatial distribution and time distribution model, and predicts the residual life of the whole pipeline at a uniform corrosion rate and the residual life of the corrosion-prone point at a non-uniform corrosion rate, thereby forming a comprehensive corrosion evaluation method. The embodiment of the invention takes the pipeline detection data of the refining enterprise as a starting point, carries out data mining deeply, and carries out corrosion evaluation by fully combining the enterprise detection work under the existing condition; from two dimensions of corrosion spatial distribution and time distribution, corrosion change of corrosion key points is considered while the corrosion condition of the pipeline cannot be comprehensively mastered by local detection, and full coverage of corrosion evaluation based on the local detection is realized; through the prediction of the residual service life of two dimensions, a technical support is provided for the formulation of the maintenance and maintenance decision of an enterprise, and the intrinsic safety of the refining pipeline is practically guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for integrated prediction of corrosion of a refinery pipeline based on local detection according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a comprehensive corrosion prediction device for a refinery pipeline based on local detection according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
In the embodiment of the invention, it should be noted that the basis of the evaluation of the pipeline corrosion is to obtain accurate and comprehensive corrosion detection data, however, the underground pipeline of the refinery enterprise cannot be subjected to internal detection to realize the detection of the full-line coverage at present, and only the local characteristics of the pipeline corrosion can be reflected. If excavate all pipelines, detect the operation that the required cost that drops into is high and influence the refining enterprise, compare long defeated pipeline and be difficult to obtain comparatively comprehensive and quantitative detection data. The prior art only aims at the space distribution rule of predicting all corrosion conditions by using local corrosion samples, but cannot track the conditions of easily corroded points in real time, so that the safety condition of a pipeline is difficult to master comprehensively. Therefore, on the basis of the prior art, the embodiment of the invention combines the detection data, analyzes the data obtained by local detection from the spatial distribution rule and the time distribution rule of corrosion, comprehensively evaluates the corrosion condition of the pipeline, and predicts the residual life of the pipeline at the uniform corrosion rate and the non-uniform corrosion rate respectively. The method and the device for comprehensively predicting the corrosion of the refining pipeline based on the local detection provided by the invention are explained in detail by specific embodiments.
Fig. 1 shows a flowchart of a method for comprehensively predicting corrosion of a refinery pipeline based on local detection according to an embodiment of the present invention, and as shown in fig. 1, the method for comprehensively predicting corrosion of a refinery pipeline based on local detection according to an embodiment of the present invention specifically includes the following steps:
step 101: and acquiring corrosion depth data of local detection points in the same batch under the spatial distribution rule of corrosion.
In this embodiment, for an underground pipeline, an abnormal point is determined through trenchless detection of the underground pipeline, then, the abnormal point is verified by trenching, and contact type quantitative detection is carried out by using technologies such as ultrasonic wave, C-scan, ultrasonic phased array, ultrasonic diffraction time difference and the like, so as to obtain local detection data of the pipeline in the same batch. For the overground pipeline, through historical failure data and management experience, easily damaged points are selected, detection is carried out by directly adopting technologies such as ultrasonic waves, C-scanning, ultrasonic phased arrays, ultrasonic diffraction time difference and the like, and the corrosion depth of each detection point is obtained.
In this embodiment, a comprehensive inspection work is performed on an aboveground gasoline pipeline of a certain refinery, the specification of the pipeline is 450mm × 14mm, and an ultrasonic technology is adopted to perform wall thickness detection on 10 selected corrosion-prone points to obtain corrosion depth data of each point: w1: 1.05, W2: 1.26, W3: 0.91, W4: 2.14, W5: 1.56, W6: 1.77, W7: 1.39, W8: 0.62, W9: 1.32, W10: 1.20 in mm.
Step 102: and constructing an extreme value I-type distribution-based maximum corrosion depth model according to the corrosion depth data of the local detection points in the same batch, and calculating the maximum corrosion depth of the pipeline.
In this embodiment, the probability distribution of the maximum erosion depth is locally expressed by an extremum type i distribution, which is expressed as:
wherein F (x) is the cumulative probability that the maximum etch depth does not exceed x; x is a random variable of the depth of the etch; lambda is a statistical parameter and represents the depth of the corrosion point with the maximum probability density; alpha is a statistical variable and represents the average value of the depth of the corrosion points; the values of λ and α can be obtained by linear fitting. Assuming that (x- λ)/α ═ y, the above equation becomes:
F(x)＝exp[-exp(-y)]
the above formula is varied:
y＝-ln[-lnF(x)]＝(x-λ)/α
suppose that n corrosion sites are detected in the same batch, wherein the corrosion depth of the ith site is xiAll the corrosion depths are randomly distributed on the same pipeline, the corrosion depths of n points are sequenced from small to large, and then the sequence of the samples is X ═ X1,x2,…,xi,…,xnH, wherein the statistical probability of the ith measurement is:
introducing the concept of regression phase, i.e. regression phase TnExpressed as achieving the maximum etch depth xmaxThe ratio of the maximum area to be measured to the local measurement area, namely:
Tn＝S/s
wherein S is the area of the whole pipeline; and s is the local measurement area.
Because the corresponding spatial relationship between the small sample and the large sample of the refined underground pipeline is a length relationship, the spatial relationship can be converted into the following relationship:
Tn＝L/l
wherein L is the length of the whole corrosion pipeline; l is the local measurement length.
Regression phase TnIt can also be expressed that the random variable x satisfying the extremum distribution exceeds the maximum value xmaxThe number of samples required. Regression phase TnAnd cumulative probability F (x)max) The relationship of (1) is:
Tn＝1/[1-F(xmax)]
according to the obtained F (x)max) Value calculation ymaxA value; finally, will ymaxThe value is substituted into y ═ x- λ)/α, and thus a tube of length L is obtainedPredicted value x of maximum erosion depth of track sectionmaxComprises the following steps:
xmax＝α·ymax+λ
in this embodiment, the erosion depths of the respective measurement points are sorted in descending order, and the sample sequence is X ═ X1,x2,…,x10Where the statistical probability of the measured value is f (x) {0.091,0.182,0.273,0.364,0.455,0.545,0.636,0.727,0.818,0.909}, and y { -0.874, -0.532, -0.261, -0.011,0.239,0.499,0.793,1.143,1.605,2.350 }. From the relationship between x and y, a value of λ of 1.110 and a value of 0.428 was obtained by linear fitting.
In this embodiment, the length of the pipeline is 1.2km, the length of the detected pipeline section is 20m, and the regression period T is obtainedn0.667, yielding F (x)max)＝0.333，ymax0.367, thus obtaining a predicted value x of the maximum corrosion depth of the pipemax4.086 mm.
Step 103: and predicting the residual life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline and combining the minimum allowable wall thickness of the pipeline.
In the embodiment, according to the strength analysis result, the minimum allowable wall thickness of the pipeline is obtained, the maximum corrosion depth and the service time of the pipeline are combined, the corrosion rate is assumed to be uniform, and the residual life SM based on the maximum corrosion depth is obtained1：
SM1＝(t-xmax-tmin)/R＝Time·(t-xmax-tmin)/xmax
Wherein, the Time is the service Time of the pipeline and the year; t is the wall thickness of the pipe wall in mm; t is tminThe minimum wall thickness allowed by the pipeline is mm; r is the corrosion speed of the pipe wall, mm/s.
In the embodiment, the service time of the pipeline is 6 years, the allowable minimum wall thickness of the pipeline is 8mm, and the residual life SM based on the maximum corrosion depth is obtained1Was 2.8 a.
It should be noted that the present step 103 predicts the remaining life of the entire pipe at a uniform corrosion rate.
Step 104: and acquiring corrosion depth data of different batches of local fixed point detection points under the corrosion time distribution rule.
In this embodiment, because the medium is various in the refinery enterprise's pipeline, atmospheric environment is complicated for elbow, valve, branch pipe, tee bend and the soil point of cominging in and going out of pipeline are easily influenced by the corruption, become enterprise annual survey, comprehensive inspection's key detected object, and the detection technology of adoption includes: ultrasonic, C-scan, ultrasonic phased array, ultrasonic diffraction moveout, and the like. In the present embodiment, the detection data of the corrosion sites which are mainly focused over the years are collected, and the wall thickness data or the corrosion depth data over the years are mainly used.
In this embodiment, the service time of the pipeline is 6 years, from the 2 nd year of production, the pipeline carries out annual inspection and comprehensive inspection work, and the ultrasonic corrosion detection is carried out year by year on a certain elbow, and the obtained corrosion depth is: 0.23 year 2, 0.41 year 3, 0.76 year 4, 1.29 year 5, 1.77 year 6, in mm.
Step 105: and according to the corrosion depth data of the local fixed-point detection points in different batches, constructing a relation model of the corrosion depth change and the service time of the pipeline and acquiring model parameters.
In the embodiment, an exponential function is utilized to approximately express the relation between the corrosion depth change and the service life of the pipeline, so that an engineering empirical formula is constructed:
x＝m·Timen
wherein x is the pipe wall corrosion depth, mm; time is the service Time of the pipeline, year; m and n are constants, and the determination method of the constants m and n is as follows:
taking logarithm of two sides of the above formula, there are:
logx＝logm+n·logTime
setting: logx ═ y; logm is a0；n＝a1(ii) a logTime ═ x. Thus, the following results were obtained:
y＝a0+a1x
calculating a by a linear fitting method according to the pipeline corrosion depth data detected in the past year0And a1Thus, constants m and n can be calculated.
In this embodiment, the set x of the pipe wall erosion depths is {0.23,0.41,0.76,1.29,1.77}, and the pipe service Time is {2,3,4,5,6}, so a is obtained0Is-1.247, a11.908, giving m 0.057 and n 1.908.
Step 106: and predicting the residual life based on variable-speed corrosion according to a relation model of the corrosion depth change and the service life of the pipeline and by combining the minimum allowable wall thickness of the pipeline.
In the embodiment, according to the relation between the corrosion depth change and the service life of the pipeline, the minimum allowable wall thickness of the pipeline is combined to obtain the residual life SM based on variable-speed corrosion2：
Wherein T is the current service time of the pipeline and year.
In this example, the current service time of the pipeline is 6 years, the minimum allowable wall thickness of the pipeline is 8mm, and therefore the residual life SM based on variable-speed corrosion is obtained2For 5.5 years.
It is noted that step 106 predicts the remaining life of the corrosion point at the non-uniform corrosion rate.
Therefore, the method predicts the residual life of the whole pipeline at the uniform corrosion rate and the residual life of the corrosion-prone points at the non-uniform corrosion rate, and accordingly forms a comprehensive corrosion evaluation method.
Therefore, in the embodiment, from two dimensions of spatial distribution and time distribution of corrosion, the residual life based on the maximum corrosion depth and the residual life based on variable-speed corrosion are obtained simultaneously, the corrosion change of a corrosion key focus point is considered while the corrosion condition of the pipeline cannot be comprehensively mastered due to local detection is avoided, the corrosion evaluation full coverage based on local detection is realized, the technical support is provided for the establishment of maintenance and maintenance decisions of enterprises through the prediction of the residual life of the two dimensions, and the intrinsic safety of the refined pipeline is practically guaranteed.
Therefore, the method establishes a comprehensive refinery pipeline corrosion prediction model based on local detection, predicts the residual service life of the pipeline based on the maximum corrosion depth and the fixed-point variable-speed corrosion based on the distribution rule of the corrosion space and time, breaks through the barriers in the prior art, is beneficial to optimizing the pipeline inspection and maintenance period, and achieves the management goals of cost reduction and efficiency improvement on the basis of ensuring the pipeline safety.
According to the technical scheme, the refining pipeline corrosion comprehensive prediction method based on local detection, provided by the embodiment of the invention, obtains the corrosion depth data of local detection points in the same batch under the spatial distribution rule of corrosion; constructing an extreme value I-type distribution-based maximum corrosion depth model according to the corrosion depth data of the local detection points in the same batch, and calculating the maximum corrosion depth of the pipeline; predicting the residual life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline by combining the minimum allowable wall thickness of the pipeline; acquiring corrosion depth data of different batches of local fixed point detection points under a corrosion time distribution rule; according to the corrosion depth data of different batches of local fixed point detection points, constructing a relation model of corrosion depth change and pipeline service time and acquiring model parameters; and predicting the residual life based on variable-speed corrosion according to a relation model of the corrosion depth change and the service life of the pipeline and by combining the minimum allowable wall thickness of the pipeline. Therefore, the embodiment of the invention combines the detection data of the same batch and different batches to construct a corrosion spatial distribution and time distribution model, and predicts the residual life of the whole pipeline at a uniform corrosion rate and the residual life of the corrosion-prone point at a non-uniform corrosion rate, thereby forming a comprehensive corrosion evaluation method. The embodiment of the invention takes the pipeline detection data of the refining enterprise as a starting point, carries out data mining deeply, and carries out corrosion evaluation by fully combining the enterprise detection work under the existing condition; from two dimensions of corrosion spatial distribution and time distribution, corrosion change of corrosion key points is considered while the corrosion condition of the pipeline cannot be comprehensively mastered by local detection, and full coverage of corrosion evaluation based on the local detection is realized; through the prediction of the residual service life of two dimensions, a technical support is provided for the formulation of the maintenance and maintenance decision of an enterprise, and the intrinsic safety of the refining pipeline is practically guaranteed.
Based on the content of the foregoing embodiment, in this embodiment, the S2 constructs, according to the corrosion depth data of the local detection points in the same batch, a maximum corrosion depth model based on extremum I-type distribution, and calculates the maximum corrosion depth of the pipeline, specifically including:
locally expressing the probability distribution of the maximum corrosion depth by using an extreme value type I distribution, wherein the probability distribution is expressed as a first relation model:
wherein F (x) is the cumulative probability that the maximum etch depth does not exceed x; x is a random variable of the depth of the etch; lambda is a statistical parameter and represents the depth of the corrosion point with the maximum probability density; alpha is a statistical variable and represents the average value of the depth of the corrosion points; the values of λ and α are obtained by linear fitting, and let (x- λ)/α be y, the first relational model is converted into a second relational model:
F(x)＝exp[-exp(-y)]
and continuously converting the second relation model to obtain a third relation model:
y＝-ln[-lnF(x)]＝(x-λ)/α
if n corrosion points are detected in the same batch, the corrosion depth of the ith point is xiAll the corrosion depths are randomly distributed on the same pipeline, the corrosion depths of n points are sequenced from small to large, and then the sequence of the samples is X ═ X1,x2,…,xi,…,xnH, wherein the statistical probability of the ith point is expressed as a fourth relational model:
introducing the concept of regression phase, i.e. regression phase TnExpressed as achieving the maximum etch depth xmaxThe ratio of the maximum area to be measured to the locally measured area, i.e. fifthThe relation model is as follows:
Tn＝S/s
wherein, S is the area of whole pipeline, and S is the local area of measurement, because the corresponding spatial relationship of refining underground pipeline is the length relation, consequently turns into the sixth relation model with the fifth relation model:
Tn＝L/l
wherein L is the length of the whole corrosion pipeline; l is the local measurement length;
wherein, the regression period TnAlso expressed as the random variable x satisfying the extremum distribution exceeds the maximum value xmaxNumber of samples required, regression period TnAnd cumulative probability F (x)max) Is a seventh relational model:
Tn＝1/[1-F(xmax)]
according to the obtained F (x)max) Value calculation ymaxA value of and will ymaxSubstituting the value into y ═ x- λ)/α to obtain a predicted value x of the maximum corrosion depth of the pipeline section of length LmaxComprises the following steps:
xmax＝α·ymax+λ。
in this embodiment, a specific implementation manner for calculating the maximum corrosion depth of the pipeline based on the extreme value I-type distribution maximum corrosion depth model is constructed according to the corrosion depth data of the local detection points in the same batch, so that the maximum corrosion depth of the pipeline can be obtained, and the residual life based on the maximum corrosion depth can be conveniently predicted subsequently.
Based on the content of the foregoing embodiment, in this embodiment, the S3 predicts the remaining life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline, in combination with the minimum allowable wall thickness of the pipeline, specifically including:
predicting the residual life SM based on the maximum corrosion depth according to the eighth relational model by combining the maximum allowable wall thickness of the pipeline and the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline and assuming that the corrosion rate is uniform1Wherein the eighth relationship model is:
SM1＝(t-xmax-tmin)/R＝Time·(t-xmax-tmin)/xmax
wherein, the Time is the service Time of the pipeline and the year; t is the wall thickness of the pipe wall in mm; t is tminThe minimum wall thickness allowed by the pipeline is mm; r is the corrosion speed of the pipe wall, mm/s.
In this embodiment, a specific implementation manner for predicting the remaining life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline in combination with the minimum allowable wall thickness of the pipeline is provided, so that the remaining life based on the maximum corrosion depth can be accurately predicted.
Based on the content of the foregoing embodiment, in this embodiment, the S5 constructs a relationship model between corrosion depth change and service time of a pipeline according to corrosion depth data of different batches of local fixed point detection points, and obtains model parameters, which specifically includes:
according to the corrosion depth data of different batches of local fixed point detection points, expressing the relationship between the corrosion depth change and the service life of the pipeline by using an exponential function as a ninth relationship model:
x＝m·Timen
wherein x is the pipe wall corrosion depth, mm; time is the service Time of the pipeline, year; m and n are constants, and the determination method of the constants m and n is as follows:
taking logarithm of two sides of the above formula, there are:
logx＝logm+n·logTime
setting: logx ═ y; logm is a0；n＝a1(ii) a logTime ═ x, then we get:
y＝a0+a1x
according to the historical detected pipeline corrosion depth data, calculating a by using a linear fitting method0And a1And then the constants m and n are calculated.
In this embodiment, a specific implementation process of constructing a relation model between corrosion depth change and pipeline service time and obtaining model parameters according to corrosion depth data of different batches of local fixed-point detection points is provided, so that the relation model between the corrosion depth change and the pipeline service time can be successfully constructed, and the residual life based on variable-speed corrosion can be predicted by combining the minimum allowable wall thickness of the pipeline and the subsequent relation model based on the corrosion depth change and the pipeline service life.
Based on the content of the foregoing embodiment, in this embodiment, the S6 predicts the remaining life based on variable-speed corrosion according to the relation model between the corrosion depth variation and the service life of the pipeline, and by combining the minimum allowable wall thickness of the pipeline, specifically includes:
according to a relation model of corrosion depth change and service life of the pipeline, a tenth relation model is adopted to predict the residual life SM based on variable-speed corrosion by combining the minimum allowable wall thickness of the pipeline2(ii) a Wherein the tenth relationship model is:
wherein T is the current service time of the pipeline and year.
In this embodiment, a specific implementation manner for predicting the remaining life based on variable-speed corrosion according to a relation model between corrosion depth change and service life of a pipeline and by combining the minimum allowable wall thickness of the pipeline is provided, so that the remaining life based on variable-speed corrosion can be accurately predicted.
Therefore, the method establishes a comprehensive refining pipeline corrosion prediction model based on local detection, predicts the residual service life of the pipeline based on the maximum corrosion depth and the fixed-point variable-speed corrosion based on the distribution rule of the corrosion space and time, breaks through the barriers in the prior art, is favorable for optimizing the pipeline inspection and maintenance period, and achieves the management goals of cost reduction and efficiency improvement on the basis of ensuring the pipeline safety.
The embodiment of the invention takes the pipeline detection data of the refining enterprise as a starting point, carries out data mining deeply, and carries out corrosion evaluation by fully combining the enterprise detection work under the existing condition; from two dimensions of corrosion spatial distribution and time distribution, corrosion change of corrosion key points is considered while the corrosion condition of the pipeline cannot be comprehensively mastered by local detection, and full coverage of corrosion evaluation based on the local detection is realized; through the prediction of the residual service life of two dimensions, a technical support is provided for the formulation of the maintenance and maintenance decision of an enterprise, and the intrinsic safety of the refining pipeline is practically guaranteed.
Fig. 2 is a schematic structural diagram of a refining pipeline corrosion comprehensive prediction device based on local detection according to an embodiment of the present invention, and as shown in fig. 2, the refining pipeline corrosion comprehensive prediction device based on local detection according to an embodiment of the present invention includes: a first obtaining module 21, a first constructing module 22, a first predicting module 23, a second obtaining module 24, a second constructing module 25 and a second predicting module 26, wherein:
the first obtaining module 21 is configured to obtain corrosion depth data of local detection points in the same batch according to a spatial distribution rule of corrosion;
the first construction module 22 is used for constructing an extreme value I-type distribution-based maximum corrosion depth model according to the corrosion depth data of the local detection points in the same batch, and calculating the maximum corrosion depth of the pipeline;
the first prediction module 23 is used for predicting the residual life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline by combining the minimum allowable wall thickness of the pipeline;
the second obtaining module 24 is configured to obtain corrosion depth data of different batches of local fixed-point detection points according to a corrosion time distribution rule;
the second construction module 25 is used for constructing a relation model between corrosion depth change and pipeline service time according to the corrosion depth data of different batches of local fixed-point detection points and acquiring model parameters;
and the second prediction module 26 is used for predicting the residual life based on variable-speed corrosion according to the relation model of the corrosion depth change and the service life of the pipeline and by combining the minimum allowable wall thickness of the pipeline.
Based on the content of the foregoing embodiment, in this embodiment, the first building module is specifically configured to:
locally expressing the probability distribution of the maximum corrosion depth by using an extreme value type I distribution, wherein the probability distribution is expressed as a first relation model:
wherein F (x) is the cumulative probability that the maximum etch depth does not exceed x; x is a random variable of the depth of the etch; lambda is a statistical parameter and represents the depth of the corrosion point with the maximum probability density; alpha is a statistical variable and represents the average value of the depth of the corrosion points; the values of λ and α are obtained by linear fitting, and let (x- λ)/α be y, the first relational model is converted into a second relational model:
F(x)＝exp[-exp(-y)]
and continuously converting the second relation model to obtain a third relation model:
y＝-ln[-lnF(x)]＝(x-λ)/α
if n corrosion points are detected in the same batch, the corrosion depth of the ith point is xiAll the corrosion depths are randomly distributed on the same pipeline, the corrosion depths of n points are sequenced from small to large, and then the sequence of the samples is X ═ X1,x2,…,xi,…,xnH, wherein the statistical probability of the ith point is expressed as a fourth relational model:
introducing the concept of regression phase, i.e. regression phase TnExpressed as achieving the maximum etch depth xmaxThe ratio of the maximum area to be measured to the local measurement area, namely, a fifth relation model:
Tn＝S/s
wherein, S is the area of whole pipeline, and S is the local area of measurement, because the corresponding spatial relationship of refining underground pipeline is the length relation, consequently turns into the sixth relation model with the fifth relation model:
Tn＝L/l
wherein L is the length of the whole corrosion pipeline; l is the local measurement length;
wherein, the regression period TnAlso expressed as the random variable x satisfying the extremum distribution exceeds the maximum value xmaxNumber of samples required, regression period TnAnd cumulative probability F (x)max) Is a seventh relational model:
Tn＝1/[1-F(xmax)]
according to the obtained F (x)max) Value calculation ymaxA value of and will ymaxSubstituting the value into y ═ x- λ)/α to obtain a predicted value x of the maximum corrosion depth of the pipeline section of length LmaxComprises the following steps:
xmax＝α·ymax+λ。
based on the content of the foregoing embodiment, in this embodiment, the first prediction module is specifically configured to:
predicting the residual life SM based on the maximum corrosion depth according to the eighth relational model by combining the maximum allowable wall thickness of the pipeline and the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline and assuming that the corrosion rate is uniform1Wherein the eighth relationship model is:
SM1＝(t-xmax-tmin)/R＝Time·(t-xmax-tmin)/xmax
wherein, the Time is the service Time of the pipeline and the year; t is the wall thickness of the pipe wall in mm; t is tminThe minimum wall thickness allowed by the pipeline is mm; r is the corrosion speed of the pipe wall, mm/s.
The device for comprehensively predicting corrosion of the refining pipeline based on local detection provided by the embodiment can be used for executing the method for comprehensively predicting corrosion of the refining pipeline based on local detection provided by the embodiment, and the working principle and the beneficial effects are similar, so that detailed description is omitted.
Based on the same inventive concept, another embodiment of the present invention provides an electronic device, which specifically includes the following components, with reference to fig. 3: a processor 301, a memory 302, a communication interface 303, and a communication bus 304;
the processor 301, the memory 302 and the communication interface 303 complete mutual communication through the communication bus 304; the communication interface 303 is used for realizing information transmission between the devices;
the processor 301 is configured to call a computer program in the memory 302, and when the processor executes the computer program, the processor implements all the steps of the refining pipeline corrosion comprehensive prediction method based on local detection, for example, when the processor executes the computer program, the processor implements the following steps: s1, acquiring corrosion depth data of local detection points in the same batch under the spatial distribution rule of corrosion; s2, constructing an extreme value I-type distribution-based maximum corrosion depth model according to the corrosion depth data of the local detection points in the same batch, and calculating the maximum corrosion depth of the pipeline; s3, predicting the residual life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline by combining the minimum allowable wall thickness of the pipeline; s4, acquiring corrosion depth data of different batches of local fixed point detection points under the corrosion time distribution rule; s5, constructing a relation model of corrosion depth change and pipeline service time according to the corrosion depth data of different batches of local fixed point detection points and acquiring model parameters; and S6, predicting the residual life based on variable-speed corrosion according to the relation model of the corrosion depth change and the service life of the pipeline and by combining the minimum allowable wall thickness of the pipeline.
Based on the same inventive concept, another embodiment of the present invention provides a non-transitory computer-readable storage medium, having a computer program stored thereon, where the computer program is executed by a processor to implement all the steps of the above-mentioned refining pipeline corrosion comprehensive prediction method based on local detection, for example, when the processor executes the computer program, the processor implements the following steps: s1, acquiring corrosion depth data of local detection points in the same batch under the spatial distribution rule of corrosion; s2, constructing an extreme value I-type distribution-based maximum corrosion depth model according to the corrosion depth data of the local detection points in the same batch, and calculating the maximum corrosion depth of the pipeline; s3, predicting the residual life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline by combining the minimum allowable wall thickness of the pipeline; s4, acquiring corrosion depth data of different batches of local fixed point detection points under the corrosion time distribution rule; s5, constructing a relation model of corrosion depth change and pipeline service time according to the corrosion depth data of different batches of local fixed point detection points and acquiring model parameters; and S6, predicting the residual life based on variable-speed corrosion according to the relation model of the corrosion depth change and the service life of the pipeline and by combining the minimum allowable wall thickness of the pipeline.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on such understanding, the technical solutions mentioned above may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method for comprehensive prediction of corrosion of a refinery pipeline based on local detection according to various embodiments or some parts of embodiments.
In addition, in the present invention, terms such as "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Moreover, in the present invention, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Furthermore, in the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A smelting pipeline corrosion comprehensive prediction method based on local detection is characterized by comprising the following steps:
s1, acquiring corrosion depth data of local detection points in the same batch under the spatial distribution rule of corrosion;
s2, constructing an extreme value I-type distribution-based maximum corrosion depth model according to the corrosion depth data of the local detection points in the same batch, and calculating the maximum corrosion depth of the pipeline;
s3, predicting the residual life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline by combining the minimum allowable wall thickness of the pipeline;
s4, acquiring corrosion depth data of different batches of local fixed point detection points under the corrosion time distribution rule;
s5, constructing a relation model of corrosion depth change and pipeline service time according to the corrosion depth data of different batches of local fixed point detection points and acquiring model parameters;
and S6, predicting the residual life based on variable-speed corrosion according to the relation model of the corrosion depth change and the service life of the pipeline and by combining the minimum allowable wall thickness of the pipeline.
2. The integrated refining pipeline corrosion prediction method based on local detection as claimed in claim 1, wherein the S2 is configured to construct an extreme value I-type distribution-based maximum corrosion depth model according to the corrosion depth data of the local detection points in the same batch, and calculate the maximum corrosion depth of the pipeline, specifically including:
locally expressing the probability distribution of the maximum corrosion depth by using an extreme value type I distribution, wherein the probability distribution is expressed as a first relation model:
wherein F (x) is the cumulative probability that the maximum etch depth does not exceed x; x is a random variable of the depth of the etch; lambda is a statistical parameter and represents the depth of the corrosion point with the maximum probability density; alpha is a statistical variable and represents the average value of the depth of the corrosion points; the values of λ and α are obtained by linear fitting, and let (x- λ)/α be y, the first relational model is converted into a second relational model:
F(x)＝exp[-exp(-y)]
and continuously converting the second relation model to obtain a third relation model:
y＝-ln[-lnF(x)]＝(x-λ)/α
if n corrosion points are detected in the same batch, the corrosion depth of the ith point is xiAll the corrosion depths are randomly distributed on the same pipeline, the corrosion depths of n points are sequenced from small to large, and then the sequence of the samples is X ═ X1,x2,…,xi,…,xnH, wherein the statistical probability of the ith point is expressed as a fourth relational model:
introducing the concept of regression phase, i.e. regression phase TnExpressed as achieving the maximum etch depth xmaxThe ratio of the maximum area to be measured to the local measurement area, namely, a fifth relation model:
Tn＝S/s
wherein, S is the area of whole pipeline, and S is the local area of measurement, because the corresponding spatial relationship of refining underground pipeline is the length relation, consequently turns into the sixth relation model with the fifth relation model:
Tn＝L/l
wherein L is the length of the whole corrosion pipeline; l is the local measurement length;
wherein, the regression period TnAlso expressed as the random variable x satisfying the extremum distribution exceeds the maximum value xmaxNumber of samples required, regression period TnAnd cumulative probability F (x)max) Is a seventh relational model:
Tn＝1/[1-F(xmax)]
according to the obtained F (x)max) Value calculation ymaxA value of and will ymaxSubstituting the value into y ═ x- λ)/α to obtain a predicted value x of the maximum corrosion depth of the pipeline section of length LmaxComprises the following steps:
xmax＝α·ymax+λ。
3. the method for comprehensively predicting the corrosion of the refinery pipeline based on the local detection as claimed in claim 2, wherein the step S3 is implemented for predicting the residual life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline in combination with the minimum allowable wall thickness of the pipeline, and specifically comprises the following steps:
predicting the residual life SM based on the maximum corrosion depth according to the eighth relational model by combining the maximum allowable wall thickness of the pipeline and the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline and assuming that the corrosion rate is uniform1Wherein the eighth relationship model is:
SM1＝(t-xmax-tmin)/R＝Time·(t-xmax-tmin)/xmax
wherein, the Time is the service Time of the pipeline and the year; t is the wall thickness of the pipe wall in mm; t is tminThe minimum wall thickness allowed by the pipeline is mm; r is the corrosion speed of the pipe wall, mm/s.
4. The integrated refining pipeline corrosion prediction method based on local detection as claimed in claim 3, wherein the S5 constructs a relation model between corrosion depth change and pipeline service time and obtains model parameters according to corrosion depth data of different batches of local fixed point detection points, specifically comprising:
according to the corrosion depth data of different batches of local fixed point detection points, expressing the relationship between the corrosion depth change and the service life of the pipeline by using an exponential function as a ninth relationship model:
x＝m·Timen
wherein x is the pipe wall corrosion depth, mm; time is the service Time of the pipeline, year; m and n are constants, and the determination method of the constants m and n is as follows:
taking logarithm of two sides of the above formula, there are:
log x＝log m+n·log Time
setting: log x ═ y; log m ═ a0；n＝a1(ii) a log Time ═ x, then we get:
y＝a0+a1x
according to the historical detected pipeline corrosion depth data, calculating a by using a linear fitting method0And a1And then the constants m and n are calculated.
5. The method for comprehensively predicting the corrosion of the refinery pipeline based on the local detection as claimed in claim 4, wherein the step S6 is implemented by combining the minimum allowable wall thickness of the pipeline and predicting the residual life based on the variable-speed corrosion according to a relation model between the corrosion depth change and the service life of the pipeline, and specifically comprises the following steps:
according to a relation model of corrosion depth change and service life of the pipeline, a tenth relation model is adopted to predict the residual life SM based on variable-speed corrosion by combining the minimum allowable wall thickness of the pipeline2(ii) a Wherein the tenth relationship model is:
wherein T is the current service time of the pipeline and year.
6. A comprehensive corrosion prediction device for a refining pipeline based on local detection is characterized by comprising:
the first acquisition module is used for acquiring corrosion depth data of local detection points in the same batch under the spatial distribution rule of corrosion;
the first construction module is used for constructing an extreme value I-type distribution-based maximum corrosion depth model according to the corrosion depth data of the local detection points in the same batch and calculating the maximum corrosion depth of the pipeline;
the first prediction module is used for predicting the residual life based on the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline by combining the minimum allowable wall thickness of the pipeline;
the second acquisition module is used for acquiring corrosion depth data of different batches of local fixed-point detection points under the corrosion time distribution rule;
the second construction module is used for constructing a relation model of corrosion depth change and pipeline service time according to the corrosion depth data of different batches of local fixed-point detection points and acquiring model parameters;
and the second prediction module is used for predicting the residual life based on variable-speed corrosion by combining the minimum allowable wall thickness of the pipeline according to the relation model of the corrosion depth change and the service life of the pipeline.
7. A refinery pipeline corrosion comprehensive prediction device based on local detection according to claim 6, wherein the first construction module is specifically configured to:
locally expressing the probability distribution of the maximum corrosion depth by using an extreme value type I distribution, wherein the probability distribution is expressed as a first relation model:
wherein F (x) is the cumulative probability that the maximum etch depth does not exceed x; x is a random variable of the depth of the etch; lambda is a statistical parameter and represents the depth of the corrosion point with the maximum probability density; alpha is a statistical variable and represents the average value of the depth of the corrosion points; the values of λ and α are obtained by linear fitting, and let (x- λ)/α be y, the first relational model is converted into a second relational model:
F(x)＝exp[-exp(-y)]
and continuously converting the second relation model to obtain a third relation model:
y＝-ln[-lnF(x)]＝(x-λ)/α
if n corrosion points are detected in the same batch, the corrosion depth of the ith point is xiAll the corrosion depths are randomly distributed on the same pipeline, the corrosion depths of n points are sequenced from small to large, and then the sequence of the samples is X ═ X1,x2,…,xi,…,xnH, wherein the statistical probability of the ith point is expressed as a fourth relational model:
introducing the concept of regression phase, i.e. regression phase TnExpressed as achieving the maximum etch depth xmaxThe ratio of the maximum area to be measured to the local measurement area, namely, a fifth relation model:
Tn＝S/s
wherein, S is the area of whole pipeline, and S is the local area of measurement, because the corresponding spatial relationship of refining underground pipeline is the length relation, consequently turns into the sixth relation model with the fifth relation model:
Tn＝L/l
wherein L is the length of the whole corrosion pipeline; l is the local measurement length;
wherein, the regression period TnAlso expressed as the random variable x satisfying the extremum distribution exceeds the maximum value xmaxNumber of samples required, regression period TnAnd cumulative probability F (x)max) Is a seventh relational model:
Tn＝1/[1-F(xmax)]
according to the obtained F (x)max) Value calculation ymaxA value of and will ymaxSubstituting the value into y ═ x- λ)/α to obtain a predicted value x of the maximum corrosion depth of the pipeline section of length LmaxComprises the following steps:
xmax＝α·ymax+λ。
8. a refinery pipeline corrosion comprehensive prediction device based on local detection as claimed in claim 7, wherein the first prediction module is specifically configured to:
predicting the residual life SM based on the maximum corrosion depth according to the eighth relational model by combining the maximum allowable wall thickness of the pipeline and the maximum corrosion depth according to the maximum corrosion depth and the service time of the pipeline and assuming that the corrosion rate is uniform1Wherein the eighth relationship model is:
SM1＝(t-xmax-tmin)/R＝Time·(t-xmax-tmin)/xmax
wherein, the Time is the service Time of the pipeline and the year; t is the wall thickness of the pipe wall in mm; t is tminThe minimum wall thickness allowed by the pipeline is mm; r is the corrosion speed of the pipe wall, mm/s.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program when executed by the processor implements the method for integrated prediction of corrosion of a refinery pipeline based on local detection according to any one of claims 1 to 5.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method for integrated prediction of corrosion of a refinery pipeline based on local detection according to any one of claims 1 to 5.
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| CN202010483481.1A CN113761476A (en) | 2020-06-01 | 2020-06-01 | Refining pipeline corrosion comprehensive prediction method and device based on local detection |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115618601A (en) * | 2022-10-13 | 2023-01-17 | 新疆敦华绿碳技术股份有限公司 | Gathering and transportation pipeline safety assessment method and system based on detection results |
| CN115879267A (en) * | 2022-10-13 | 2023-03-31 | 新疆敦华绿碳技术股份有限公司 | Method and system for predicting corrosion defects of pipeline |
| CN116336400A (en) * | 2023-05-30 | 2023-06-27 | 克拉玛依市百事达技术开发有限公司 | Baseline detection method for oil and gas gathering and transportation pipeline |
| CN117497074A (en) * | 2023-10-30 | 2024-02-02 | 南智（重庆）能源技术有限公司 | Corrosion analysis method, device and terminal for pipe column pipeline system of ultra-high sulfur-containing gas field |
| CN115618601B (en) * | 2022-10-13 | 2024-05-31 | 新疆敦华绿碳技术股份有限公司 | Gathering pipeline safety assessment method and system based on detection result |
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2020
- 2020-06-01 CN CN202010483481.1A patent/CN113761476A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115618601A (en) * | 2022-10-13 | 2023-01-17 | 新疆敦华绿碳技术股份有限公司 | Gathering and transportation pipeline safety assessment method and system based on detection results |
| CN115879267A (en) * | 2022-10-13 | 2023-03-31 | 新疆敦华绿碳技术股份有限公司 | Method and system for predicting corrosion defects of pipeline |
| CN115618601B (en) * | 2022-10-13 | 2024-05-31 | 新疆敦华绿碳技术股份有限公司 | Gathering pipeline safety assessment method and system based on detection result |
| CN116336400A (en) * | 2023-05-30 | 2023-06-27 | 克拉玛依市百事达技术开发有限公司 | Baseline detection method for oil and gas gathering and transportation pipeline |
| CN116336400B (en) * | 2023-05-30 | 2023-08-04 | 克拉玛依市百事达技术开发有限公司 | Baseline detection method for oil and gas gathering and transportation pipeline |
| CN117497074A (en) * | 2023-10-30 | 2024-02-02 | 南智（重庆）能源技术有限公司 | Corrosion analysis method, device and terminal for pipe column pipeline system of ultra-high sulfur-containing gas field |
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