CN113988480B - Gas equipment filter life prediction method and computer readable storage medium - Google Patents
Gas equipment filter life prediction method and computer readable storage medium Download PDFInfo
- Publication number
- CN113988480B CN113988480B CN202111565398.XA CN202111565398A CN113988480B CN 113988480 B CN113988480 B CN 113988480B CN 202111565398 A CN202111565398 A CN 202111565398A CN 113988480 B CN113988480 B CN 113988480B
- Authority
- CN
- China
- Prior art keywords
- filter
- blade
- gas
- performance index
- filtration performance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Quality & Reliability (AREA)
- General Business, Economics & Management (AREA)
- Geometry (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- Tourism & Hospitality (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Computing Systems (AREA)
- Fluid Mechanics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Structures Of Non-Positive Displacement Pumps (AREA)
Abstract
The application relates to the technical field of gas equipment, and provides a gas equipment filter service life prediction method and a computer readable storage medium, which comprise the following steps: obtaining basic parameters of the gas compressor and at least two simulated soot deposition conditions, obtaining reference efficiency based on the basic parameters, and obtaining the predicted service life of the filter based on the first evaluation filtration performance index and the second evaluation filtration performance index. According to the method, a more visual first evaluation filtration performance index is obtained by judgment based on the particulate matter concentration in the gas before and after the filter obtained in real time, and a second evaluation filtration performance index which is closer to the second evaluation filtration performance index formed by long-term actual operation can be obtained by judgment based on the reference efficiency obtained by the geometric dimension of the impeller of the air compressor and by combining the real-time efficiency obtained in real time, so that the actual matching effect of the filter and the air compressor can be effectively distinguished, and the service life of the filter is predicted according to the first evaluation filtration performance index and the second evaluation filtration performance index.
Description
Technical Field
The invention relates to the technical field of gas equipment, in particular to a gas equipment filter service life prediction method and a computer readable storage medium.
Background
The filter of the gas equipment is an important device for guaranteeing the quality of inlet air and the cleanliness of the gas compressor of the gas equipment. The filter has the problems of reduced filtering performance and the like gradually due to the fact that the filter reaches the service life along with the service life, and after the filter reaches the service life, the continuous use of the filter can cause various problems of fouling, abrasion, corrosion, fatigue, foreign object impact and the like of the air compressor.
At present, the evaluation mode of whether the service life of the gas equipment is achieved mainly depends on a laboratory detection means, but the laboratory detection is difficult to estimate the service life of the filter under the real application environment. The accelerated experiment method for generating dust in a short time and in a large amount in a laboratory is greatly different from the real environment faced by an air inlet system, and the components of air inlet particles are often inconsistent with the experimental dust, so that the experimental data and judgment basis have limitations, and the difference between the service life estimated according to the detection result and the actual service life of a filter is large. In addition, as the air quality becomes better, the empirical life obtained by using the air with certain pollution for a long time according to the traditional method may be greatly different from the actual service life of the air with the better air quality. Therefore, it is desirable to provide a gas appliance filter life prediction method and computer readable storage medium to at least partially solve the above problems.
Disclosure of Invention
It is an object of the present invention to provide a gas appliance filter life prediction method and computer readable storage medium which at least partially overcome the deficiencies of the prior art.
According to an aspect of the present invention, there is provided a gas appliance filter life prediction method, including the steps of:
obtaining basic parameters of a compressor, wherein the basic parameters at least comprise the height and the thickness of a blade of the compressor;
acquiring at least two simulated soot deposition conditions, and acquiring the reference efficiency of the gas compressor under each simulated soot deposition condition through flow field analysis based on the basic parameters;
acquiring the original particulate matter concentration of the gas before being filtered by the filter, the filtered particulate matter concentration of the gas after being filtered by the filter and the real-time efficiency of the compressor in real time;
obtaining a first evaluation filtering performance index of the filter based on the original particulate matter concentration and the filtered particulate matter concentration, and obtaining a second evaluation filtering performance index of the filter based on the corresponding degree of the real-time efficiency and the reference efficiency;
obtaining a predicted life of the filter based on the first and second evaluated filtration performance indices.
Preferably, the simulated soot deposition condition comprises a soot deposition thickness of the blade.
Preferably, the simulated soot deposition conditions comprise the soot deposition thicknesses of the blades being 2 microns, 20 microns and 200 microns, respectively.
Preferably, the obtaining of the reference efficiency of the compressor under the simulated soot deposition condition through flow field analysis includes:
selecting at least two different deposition thicknesses and at least two different deposition distribution schemes, wherein the deposition distribution schemes are used for describing the distribution condition of the deposition along the blade height of the blade;
calculating the reference efficiency of the compressor in different soot deposition distribution schemes based on each soot deposition thickness.
Preferably, the dust deposition distribution scheme includes that the dust deposition is uniformly distributed along the blade height of the impeller on a suction surface, the dust deposition is uniformly distributed along the blade height of the impeller on a pressure surface, the dust deposition is uniformly distributed along the blade height of the impeller on both the pressure surface and the suction surface, the dust deposition is non-uniformly distributed along the blade height of the impeller on the pressure surface, and the dust deposition is non-uniformly distributed along the blade height of the impeller on both the suction surface and the pressure surface.
Preferably, the non-uniform distribution of blade height along the impeller comprises: the deposited ash is respectively located at 15% of the leaf height, 30% of the leaf height, 45% of the leaf height, 60% of the leaf height, 75% of the leaf height, 90% of the leaf height and 100% of the leaf height.
Preferably, the obtaining of the original particulate matter concentration of the gas before filtering by the filter and the filtered particulate matter concentration of the gas after filtering by the filter in real time comprises:
acquiring sampling points before and after the filter through flow field analysis based on the basic parameters;
and acquiring the original particulate matter concentration of the gas before being filtered by the filter and the filtered particulate matter concentration of the gas after being filtered by the filter in real time at the sampling point.
Preferably, the obtaining the predicted life of the filter based on the first evaluation filtration performance index and the second evaluation filtration performance index comprises:
obtaining a difference between the first evaluation filtration performance index and the second evaluation filtration performance index based on the first evaluation filtration performance index and the second evaluation filtration performance index;
and obtaining an index threshold value, and at least taking the rated service life of the filter as the predicted service life of the filter if the difference value is smaller than the index threshold value.
Preferably, if the difference is greater than the exponential threshold, at most 0.8 times the rated life of the filter is taken as the predicted life of the filter.
According to another aspect of the present invention, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a gas appliance filter life prediction method as described above.
According to the embodiment of the invention, a more intuitive first evaluation filtration performance index is obtained by judgment based on the particulate matter concentration in the gas before and after the filter obtained in real time, and a second evaluation filtration performance index which is closer to the formation of long-term actual operation can be obtained by judgment based on the reference efficiency obtained by the geometric dimension of the impeller of the air compressor and by combining the real-time efficiency obtained in real time, so that the actual matching effect of the filter and the air compressor can be effectively distinguished, and the service life of the filter is predicted according to the first evaluation filtration performance index and the second evaluation filtration performance index.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of a method for predicting the life of a gas appliance filter according to an embodiment of the present disclosure;
FIG. 2 is a flow chart for obtaining a reference efficiency in an embodiment of the present application;
fig. 3 is a flowchart of sampling at a sampling point in the embodiment of the present application.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. For convenience of description, only portions related to the invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The gas equipment in the invention, which can be also called as a gas turbine, comprises two devices for filtering air and pressurizing air to improve subsequent combustion efficiency, which are well known to those skilled in the art, namely a filter and a compressor, wherein the two devices are often arranged in sequence at the upstream and downstream of an air inlet passage of the gas equipment.
The efficiency of the compressor in the embodiment of the present application mainly refers to a ratio of working power and consumed power of the compressor, for example, the reference efficiency is a ratio between the working power and the consumed power of the compressor under the condition of analog calculation, and it should be understood by those skilled in the art that the higher the efficiency, for example, the higher the reference efficiency or the real-time efficiency, the better the working state of the compressor is, and the lower the ash deposition degree of the compressor is indirectly reflected.
As shown in fig. 1, the method for predicting the life of a gas appliance filter provided by the present invention includes the following steps:
s101: obtaining basic parameters of the compressor, wherein the basic parameters at least comprise the height and the thickness of a blade of the compressor;
s102: acquiring at least two simulated soot deposition conditions, and obtaining the reference efficiency of the gas compressor under the simulated soot deposition conditions through flow field analysis based on basic parameters;
s103: acquiring the original particulate matter concentration of gas before being filtered by a filter, the filtered particulate matter concentration of the gas after being filtered by the filter and the real-time efficiency of the compressor in real time;
s104: obtaining a first evaluation filtering performance index of the filter based on the original particulate matter concentration and the filtered particulate matter concentration, and obtaining a second evaluation filtering performance index of the filter based on the corresponding degree of the real-time efficiency and the reference efficiency;
s105: a predicted life of the filter is obtained based on the first evaluation filtration performance index and the second evaluation filtration performance index.
In process S101, as described above, the gas plant in the embodiment of the present application includes at least a compressor, and the compressor includes an impeller or an impeller-like structure. The blade height and the blade thickness of the impeller in the process S101 refer to the extension length and the blade thickness of the blade of the impeller from the root to the tip, which are well known to those skilled in the art, and include various changes of the blade thickness along with the extension of the blade, and the above data can be relatively simply obtained through measurement or directly from an explanatory file of the compressor, such as a specification or a design drawing, so as to provide a calculation basis for the next flow field analysis. Of course, in order to obtain a more accurate flow field analysis result, the basic parameters may further include the size and profile of the impeller shaft, the shape of the air inlet and outlet passages of the compressor, whether other intermediate links exist between the filter and the compressor, and the like, so that the flow field analysis result may be more accurate.
In step S102, the simulated soot deposition condition may be a blade soot deposition thickness, for example, three different simulated soot deposition conditions, that is, a blade soot deposition thickness of 2 microns, a blade soot deposition thickness of 20 microns, and a blade soot deposition thickness of 200 microns, respectively, so as to simulate a situation that the rotation of the blade is blocked at different soot deposition levels after a period of use. In the embodiment of the application, the simulating of the deposition in the deposition condition mainly means that the deposition simulating part is thickened by one thickness, and other aspects such as hardness, adhesion and the like are not considered, so that the calculation difficulty is reduced for flow field calculation. After the condition of simulating soot deposition is added, the basic parameters of the compressor after the simulated soot deposition can be obtained, so that the reference efficiency of the compressor under the condition of simulating soot deposition can be obtained by utilizing common algorithms for calculating fluid dynamics, such as a finite volume method, a finite difference method, a finite element method and the like, which are well known by the technical personnel in the field. This reference efficiency is used to compare with the actual efficiency of the compressor to determine in which state the compressor is actually in.
In the processing S103, the real-time obtaining of the original particulate matter concentration of the gas before being filtered by the filter and the filtered particulate matter concentration of the gas after being filtered by the filter may be respectively setting a sampler before and after the filter, and the samplers are used to collect and detect the particulate matters in the gas before and after passing through the filter, and the particulate matter concentrations in the embodiment of the present application may include the particulate matter quantity concentrations (e.g., PM0.3, PM0.5, PM1, PM2.5, PM5, PM10, etc.) and the particulate matter mass concentrations (e.g., PM1, PM2.5, PM10, etc.), so as to more accurately evaluate and check the filtering efficiency of the filter. The real-time output efficiency of the compressor in the processing S103 can be calculated by a method for measuring the real-time efficiency, which is well known to those skilled in the art, such as comparing the real-time compression power of the compressor with the rated compression power, and will not be described in detail herein.
In the process S104, the first evaluation filtering performance index of the filter obtained based on the original particulate matter concentration and the filtered particulate matter concentration may be a filtering ratio value calculated based on a ratio of concentrations of the particulate matter in the gas before passing through the filter and the gas after passing through the filter, or a filtering capability value obtained based on a difference between concentrations of the particulate matter in the gas before passing through the filter and the gas after passing through the filter, and the like, and may be set according to specific needs.
The second evaluation filtering performance index of the filter is obtained based on the real-time efficiency and the reference efficiency, the real-time efficiency actually obtained can be compared with different reference efficiencies under various simulated soot deposition conditions calculated above, the corresponding degree between the real-time efficiency and the reference efficiency is calculated according to a distance judgment method or a clustering method, the simulated soot deposition condition with high corresponding degree is used as the actual soot deposition condition of the compressor at the moment, so that the filtering performance of the filter in a past period of time is judged and the second evaluation filtering performance index is obtained, for example, the calculation result shows that the current real-time output efficiency of the compressor is higher than the reference output efficiency under the condition that the simulated soot deposition condition is 20 microns in soot deposition thickness, the ash deposition thickness of the compressor at the moment is judged to be 20 microns, the second evaluation filtering performance index can be obtained according to the working time of the filter at the moment, for example, the historical service time is 6 months, the average ash deposition increase thickness of the compressor in the last 6 months can be judged to be about 3 micrometers, and the content of particulate matters in the air filtered by the filter in the last 6 months can be calculated by combining the basic parameters of the compressor or the average working efficiency of the compressor, so that a second evaluation filtering performance index can be obtained. By judging the actual dust deposition condition of the compressor, the actual filtering function of the filter in the past work of the compressor for a period of time can be judged more clearly.
In step S105, the first evaluated filtering performance index may be understood as an evaluation of the current real-time filtering performance of the filter, the second evaluated filtering performance index may be understood as an evaluation of the filtering performance of the filter since the filter is used, and the combination of the two may eliminate interference such as air quality fluctuation, so as to predict the life of the filter, for example, in the case of good air quality, the first evaluated filtering performance index indicates that the real-time filtering ratio of the filter is 80%, and the second evaluated filtering performance index indicates that the average filtering ratio of the filter since the filter is used is 82%, and the difference between the two is small, which indicates that the filtering performance of the filter decreases less obviously since the filter is used, and 1.2 times or 1.5 times of the rated life of the filter may be used as the predicted life of the filter; similarly, if the first evaluation filtration performance index shows that the concentration difference of the filter before and after the real-time filtration of the particulate matter is 4 micrograms per cubic meter, and the second evaluation filtration performance index shows that the average concentration difference of the filter before and after the filtration of the particulate matter since the use of the filter is 2 micrograms per cubic meter, it indicates that the filtration performance of the filter has significantly decreased since the use of the filter, and 0.8 times or 0.7 times the rated life of the filter can be used as the predicted life of the filter, and the like.
The sequence of the processing S101 to the processing S103 may be alternated or changed according to specific needs, and still achieve the desired result.
According to the embodiment of the invention, a more intuitive first evaluation filtration performance index is obtained by judgment based on the particulate matter concentration in the gas before and after the filter obtained in real time, and a second evaluation filtration performance index which is closer to the formation of long-term actual operation can be obtained by judgment based on the reference efficiency obtained by the geometric dimension of the impeller of the air compressor and by combining the real-time efficiency obtained in real time, so that the actual matching effect of the filter and the air compressor can be effectively distinguished, and the service life of the filter is predicted according to the first evaluation filtration performance index and the second evaluation filtration performance index.
As a preferred implementation, the simulated soot deposition conditions include a blade having a soot deposition thickness of 2 microns, 20 microns, and 200 microns, respectively. The three components are obtained by empirical judgment, and have representative deposition thickness, when the deposition thickness is 2 microns, the efficiency of the gas compressor has small influence, and only has certain influence in non-design working conditions, when the deposition thickness reaches 20 microns, the efficiency of the gas compressor is obviously influenced, and when the deposition thickness reaches 200 microns, the normal work of the gas compressor is seriously interfered, and at this time, the gas compressor is usually required to be disassembled and overhauled. Of course, when the invention is implemented, different deposition thicknesses, such as 10, 100, 400, etc., can be set according to specific basic parameters of the compressor.
As a preferred implementation manner, the specific implementation steps of the process S102 may include:
s1021: selecting at least two different deposition thicknesses and at least two different deposition distribution schemes, wherein the deposition distribution schemes are used for describing the distribution condition of the deposition along the blade height of the blade;
s1022: and calculating the reference efficiency of the compressor in different ash deposition distribution schemes based on each ash deposition thickness.
The ash deposition distribution scheme in the process S1021 may include six cases, namely, uniform distribution along the blade height of the blade on the suction surface, uniform distribution along the blade height of the blade on the pressure surface, uniform distribution along the blade height of the blade on both the pressure surface and the suction surface, non-uniform distribution along the blade height of the blade on the pressure surface, and non-uniform distribution along the blade height of the blade on both the suction surface and the pressure surface, which roughly summarize various possibilities of ash deposition distribution along the blade height of the blade. In actual use, the distribution situation of the dust deposit along the blade height of the blade may be more complicated, but the embodiment of the present application is more intended to judge the dust deposit amount on the blade of the compressor through similarity, and various settings are performed on the dust deposit distribution scheme for the purpose of expecting that the coincidence degree of the actual situation and a certain dust deposit distribution scheme is higher, and from this point of view, a person skilled in the art has an incentive to set as many dust deposit distribution schemes as possible.
As a preferred implementation, the non-uniform distribution of the blade height along the blade includes: the deposited ash is respectively located at 15% of the leaf height, 30% of the leaf height, 45% of the leaf height, 60% of the leaf height, 75% of the leaf height, 90% of the leaf height and 100% of the leaf height. This is also the usual way of distributing the ash in practice, i.e. the ash is distributed in sections over the blade, rather than more evenly over the entire blade.
As a preferred implementation manner, the specific implementation step of the process S103 may include:
s1031: based on the basic parameters, acquiring sampling points before and after the filter through flow field analysis;
s1032: and acquiring the original particulate matter concentration of the gas before being filtered by the filter and the filtered particulate matter concentration of the gas after being filtered by the filter in real time at a sampling point.
In the process S1031, based on the basic parameters and in combination with common algorithms for calculating fluid dynamics, such as a finite volume method, a finite difference method, a finite element method, and the like, which are well known to those skilled in the art, parameters such as the thickness of the pressure surface of the blade may be obtained, and then, for example, a sampling point is set at a thickest position of the pressure surface or a farthest position between the pressure surface and the suction surface, so that the representativeness of sampling may be improved.
In the process S1032, the sampling inlet of the sampler may be set to the sampling point obtained in the process S1031, for example, an intake type sampler, or the sensing position of the sampler may be set to the above-mentioned sampling point, for example, a laser type sampler.
As a preferred implementation, the above-mentioned obtaining of the predicted life of the filter may be:
obtaining a difference value between the first evaluation filtration performance index and the second evaluation filtration performance index based on the first evaluation filtration performance index and the second evaluation filtration performance index; and obtaining an index threshold value, and at least taking the rated service life of the filter as the predicted service life of the filter if the difference value is smaller than the index threshold value. Meanwhile, as a preferred implementation, if the difference is greater than the exponential threshold, the rated life of the filter is at most 0.8 times as long as the predicted life of the filter.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The present application also provides a computer readable medium having stored thereon a computer program which, when executed by a processor, implements a gas appliance filter life prediction method as described above. The computer readable media may include both permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (8)
1. A method for predicting the service life of a gas equipment filter is characterized by comprising the following steps:
obtaining basic parameters of a compressor, wherein the basic parameters at least comprise the thickness of a blade of the compressor;
acquiring at least two simulated soot deposition conditions, and acquiring the reference efficiency of the gas compressor under each simulated soot deposition condition through flow field analysis based on the basic parameters;
acquiring the original particulate matter concentration of the gas before being filtered by the filter, the filtered particulate matter concentration of the gas after being filtered by the filter and the real-time efficiency of the compressor in real time;
obtaining a first evaluation filtering performance index of the filter based on the original particulate matter concentration and the filtered particulate matter concentration, and obtaining a second evaluation filtering performance index of the filter based on the corresponding degrees of the real-time efficiency and the reference efficiency;
obtaining a predicted life of the filter based on the first and second evaluated filtration performance indices;
the obtaining a predicted life of the filter based on the first evaluation filtration performance index and the second evaluation filtration performance index comprises:
obtaining a difference between the first evaluation filtration performance index and the second evaluation filtration performance index based on the first evaluation filtration performance index and the second evaluation filtration performance index;
obtaining an index threshold, and at least taking the rated service life of the filter as the predicted service life of the filter if the difference is smaller than the index threshold;
if the difference is greater than the exponential threshold, taking at most 0.8 times of the rated life of the filter as the predicted life of the filter;
specifically, the real-time efficiency is compared with the reference efficiency, the corresponding degree between the real-time efficiency and the reference efficiency is calculated according to a distance judgment method or a clustering method, and a simulated ash deposition condition with high corresponding degree is used as an actual ash deposition condition of the gas compressor at the moment, so that the filtering performance of the filter in a period of time in the past is judged and a second evaluated filtering performance index is obtained.
2. The gas fired unit filter life prediction method of claim 1, wherein said reference efficiency comprises an efficiency of said compressor under at least two different said simulated soot deposition conditions, said simulated soot deposition conditions comprising at least a soot thickness of said blade.
3. The gas fired equipment filter life prediction method of claim 2, wherein the simulated soot deposition conditions comprise soot thicknesses of the vanes of 2 microns, 20 microns and 200 microns, respectively.
4. The gas-fired device filter life prediction method of claim 2, wherein said obtaining a reference efficiency of said compressor under said simulated soot deposition conditions by flow field analysis comprises:
the basic parameters further comprise the blade height of the blade, at least two different deposition thicknesses and at least two different deposition distribution schemes are selected, and the deposition distribution schemes are used for describing the distribution condition of the deposition along the blade height of the blade;
calculating the reference efficiency of the compressor in different soot deposition distribution schemes based on each soot deposition thickness.
5. The gas fired equipment filter life prediction method of claim 4, wherein the ash deposition distribution scheme comprises a uniform distribution of the blade height along the blade at the suction side, a uniform distribution of the blade height along the blade at the pressure side, a uniform distribution of the blade height along the blade at both the pressure side and the suction side, a non-uniform distribution of the blade height along the blade at the pressure side, and a non-uniform distribution of the blade height along the blade at both the suction side and the pressure side.
6. The gas fired equipment filter life prediction method of claim 5, wherein said non-uniform distribution of blade height along said blade comprises: the deposited ash is respectively located at 15% of the leaf height, 30% of the leaf height, 45% of the leaf height, 60% of the leaf height, 75% of the leaf height, 90% of the leaf height and 100% of the leaf height.
7. The gas-fired equipment filter life prediction method of claim 1, wherein the obtaining in real time the original particulate matter concentration of the pre-filter gas and the filtered particulate matter concentration of the post-filter gas comprises:
acquiring sampling points before and after the filter through flow field analysis based on the basic parameters;
and acquiring the original particulate matter concentration of the gas before being filtered by the filter and the filtered particulate matter concentration of the gas after being filtered by the filter in real time at the sampling point.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out a gas appliance filter life prediction method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111565398.XA CN113988480B (en) | 2021-12-21 | 2021-12-21 | Gas equipment filter life prediction method and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111565398.XA CN113988480B (en) | 2021-12-21 | 2021-12-21 | Gas equipment filter life prediction method and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113988480A CN113988480A (en) | 2022-01-28 |
| CN113988480B true CN113988480B (en) | 2022-03-15 |
Family
ID=79734068
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111565398.XA Active CN113988480B (en) | 2021-12-21 | 2021-12-21 | Gas equipment filter life prediction method and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113988480B (en) |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7801660B2 (en) * | 2006-07-31 | 2010-09-21 | General Electric Company | Methods and systems for estimating compressor fouling impact to combined cycle power plants |
| US8685176B2 (en) * | 2006-10-16 | 2014-04-01 | Ecoservices, Llc | System and method for optimized gas turbine compressor cleaning and performance measurement |
| CN201991658U (en) * | 2010-12-20 | 2011-09-28 | 广东惠州天然气发电有限公司 | Device based on improving working efficiency of gas inlet filter of combustion engine and prolonging service life |
| CN107304712A (en) * | 2016-04-22 | 2017-10-31 | 北京澳尔金石油技术开发有限公司 | The apparatus and method of gas turbine blower washing |
| CN113090394B (en) * | 2021-03-19 | 2022-02-15 | 清华大学 | Method for monitoring abnormity of intake air filtering efficiency of gas turbine |
| CN113790102B (en) * | 2021-08-31 | 2022-09-13 | 南京瑞华动力科技有限公司 | Intelligent operation and maintenance system of gas turbine air inlet filter |
| CN113847107A (en) * | 2021-09-26 | 2021-12-28 | 中国联合重型燃气轮机技术有限公司 | Method and device for monitoring accumulated dirt state of gas turbine compressor blade |
| CN113865003B (en) * | 2021-09-28 | 2022-08-19 | 珠海格力电器股份有限公司 | Air purifier, filter screen service life detection method and device thereof, and storage medium |
-
2021
- 2021-12-21 CN CN202111565398.XA patent/CN113988480B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113988480A (en) | 2022-01-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107301617B (en) | Method and equipment for evaluating quality of waste gas monitoring data | |
| JP2018044546A (en) | System and method for condition-based monitoring of compressor | |
| CN109492708B (en) | LS-KNN-based pipeline magnetic flux leakage internal detection missing data interpolation method | |
| CN109029644B (en) | Online monitoring method for metering performance of membrane type gas meter | |
| CN109918364B (en) | Data cleaning method based on two-dimensional probability density estimation and quartile method | |
| CN109740301A (en) | A kind of accounting method of the Gas Generator Set carbon emission amount based on BP neural network | |
| CN113176191B (en) | Gas turbine air inlet system filtering efficiency online testing system and testing method | |
| CN110094251A (en) | SCR catalyst performance deterioration analysis method based on multi-model modeling at times | |
| Vulpio et al. | Analysis of timewise compressor fouling phenomenon on a multistage test compressor: performance losses and particle adhesion | |
| CN111522808A (en) | Abnormal operation data processing method for wind turbine generator | |
| CN110175353A (en) | Elevator operating index trend analysis | |
| CN110837933A (en) | Leakage identification method, device, equipment and storage medium based on neural network | |
| CN113988480B (en) | Gas equipment filter life prediction method and computer readable storage medium | |
| CN116738353A (en) | Pharmaceutical workshop air filter element performance detection method based on data analysis | |
| CN110928248B (en) | Method for determining performance degradation degree of gas turbine | |
| CN116804412B (en) | Monitoring data processing method of hydraulic system | |
| CN116576553B (en) | Data optimization acquisition method and system for air conditioner | |
| CN114517750B (en) | Method and device for determining whether EGR (exhaust gas Recirculation) system is in carbon deposition state | |
| Gregg et al. | Feature selection for monitoring erosive cavitation on a hydroturbine | |
| CN109630901A (en) | A kind of mesolow garden pipeline abnormal flow recognition methods and device | |
| CN116658413B (en) | Hydraulic pump fault detection method | |
| CN112150036B (en) | Method and device for detecting gas theft of boiler gas user based on data driving | |
| CN111289039B (en) | Dust removal filter bag online detection method | |
| Templalexis et al. | Comparative Assessment of Fouling Scenarios in an Axial Flow Compressor | |
| Abdurakipov | Increasing the efficiency of electric submersible pumps by using big data processing and machine learning technologies |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |