EP2257933A1 - Method for determining the suction mass flow of a gas turbine - Google Patents
Method for determining the suction mass flow of a gas turbineInfo
- Publication number
- EP2257933A1 EP2257933A1 EP09725928A EP09725928A EP2257933A1 EP 2257933 A1 EP2257933 A1 EP 2257933A1 EP 09725928 A EP09725928 A EP 09725928A EP 09725928 A EP09725928 A EP 09725928A EP 2257933 A1 EP2257933 A1 EP 2257933A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- gas turbine
- determined
- mass flow
- compressor
- turbine
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C3/00—Registering or indicating the condition or the working of machines or other apparatus, other than vehicles
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01D—NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES
- F01D25/00—Component parts, details, or accessories, not provided for in, or of interest apart from, other groups
- F01D25/002—Cleaning of turbomachines
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/40—Type of control system
- F05D2270/44—Type of control system active, predictive, or anticipative
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/70—Type of control algorithm
- F05D2270/708—Type of control algorithm with comparison tables
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/70—Type of control algorithm
- F05D2270/71—Type of control algorithm synthesized, i.e. parameter computed by a mathematical model
Definitions
- the invention relates to a method for determining the Ansaugmassenstroms a gas turbine. It further relates to a method of diagnosing a multiple-component gas turbine, wherein the additional power by which the operating power of the gas turbine would increase in the case of cleaning one of the components, is automatically predicted.
- the pollution of the gas turbine is caused by the adhesion of particles to the surfaces. Oil and water mist help dust and aerosols settle on the blades. The most common contaminants and deposits are mixtures of water wetting, water-soluble and water-insoluble materials. In the gas turbine, soiling can occur due to ash deposits and unburned solid cleaning preparations. Such air pollutants adhere to the components of the flow path of the gas turbine and react with them as scales. It also causes erosion by particle impact and abrasion, commonly referred to as erosion.
- the aging of the compressor has a negative effect on the gas turbine efficiency, the gas turbine output and the gas turbine exit mass flow.
- regular compressor washing is carried out.
- Compressor blades can be washed in online and offline mode. In the online mode, the turbine system continues to operate during cleaning, the gas turbine load is only slightly lowered. Online washes are mainly used to prevent build up of the dirt layer.
- Online laundry is done once a day with deionized water and every third day with cleansers.
- the system is shut down. To avoid thermal stress, it is cooled for six hours using a shaft rotating device. Offline laundry is usually done about once a month. If the turbine system has not been cleaned for a comparatively long period of time, it is usually necessary to carry out offline scrubbing for typical systems since the online cleaning method can no longer remove the contaminants.
- An offline wash causes a greater performance recovery than an online wash. With the help of an offline wash, performance gains of several percent can be achieved. Online laundry results in lower energy recovery. The most effective bucket cleaning can be achieved with a combination of online and offline washes. A regular online laundry extends the time intervals between the required offline washes.
- the optimal time for off-line laundry is often determined by the operator for purely economic operational considerations, e.g. B. in low load periods. This means that the decision on the time of elimination of contamination of one of the components of the turbine system, for. B. by a wash of the compressor based only on empirical values under economic aspects or under preliminary studies with fixed boundary conditions.
- the determination of the timing of the off-line wash can be made based on an up-to-date prediction of the gas turbine power gain expected by the off-line wash.
- a prognosis is usually created based on the development of the compressor efficiency of the gas turbine, which serves as a parameter for the strength of the contamination of the compressor.
- Such forecasting methods are known, for example, from WO 2005/090764 A or from Schepers et al. : "Optimization of online and offline washing on a 26 MW gas turbine with special consideration of the performance tion enhancement ", VGB Kraftwerkstechnik, Vol. 79 No. 3.
- the measurement data used to determine the compressor efficiency can be provided with comparatively high data uncertainties, which allows an accurate prediction of the expected performance gain by offline scrubbing and thus a determination of the cost-optimal time for the operation of the gas turbine for such Offline laundry makes it difficult.
- the statistical uncertainties should be minimized. This can be done, for example, by improving the measuring equipment or by increasing the number of measurements. However, such an increase only leads to a reduction in the statistical error, but systematic errors in the forecast of the additional service should also be minimized to a large extent. This can be achieved by additionally using additional parameters to forecast the additional service.
- One such quantity, which is characteristic of the performance of the gas turbine, is the intake mass flow of the gas turbine.
- the Ansaugmassenstrom as a parameter for the operating performance of the gas turbine is usually not measured directly because of the high cost, the large uncertainty and the risk of damage, but indirectly determined by balancing. Very expensive instruments would have to be used for a direct measurement, because first of all there are very high temperatures, and secondly it is absolutely necessary to avoid breaking off the sensors because of the probably high consequential damage to the turbine blading.
- the invention is therefore based on the object to provide a method for determining the Ansaugmassenstroms the above type, which is a particularly reliable prognosis of expected performance gain in a cleaning allows.
- This object is achieved according to the invention by determining the turbine inlet pressure, the combustion chamber pressure loss and / or the pressure loss between ambient and compressor inlet as input parameters for determining the intake mass flow.
- the invention is based on the consideration that for an energy balance of the entire gas turbine on the one hand and the combustion chamber on the other hand as input variables u. a. the operating power, fuel mass flow and fuel calorific value are needed. However, these values are comparatively difficult to determine and involve a very high error. In a combined cycle power plant, in which a gas turbine is operated together with a steam turbine on a shaft, the performance of the gas turbine as a single value only comparatively complex and inaccurate determine, as always only the total power of the entire combined cycle power plant is available. Therefore, to determine the Ansaugmassenstroms the turbine inlet pressure, the combustion chamber pressure drop and / or the pressure loss between the environment and the compressor inlet are determined as entry characteristics.
- the turbine inlet pressure can be converted into a value for the intake mass flow with the aid of the mass pressure equation according to Stodola, while resistance coefficients which can be used to determine the intake mass flow can be determined from the combustion chamber pressure loss or the pressure loss between the environment and the compressor inlet.
- Such determination of the Ansaugmassenstroms without resolution of an energy balance is associated with much lower statistical errors and therefore allows an even more accurate forecast of the additional power by which the operating performance of the gas turbine would increase in the case of cleaning one of the components.
- ansaugmassenstroms In order to further minimize the statistical errors in the determination of the Ansaugmassenstroms, is advantageously determined for determining the Ansaugmassenstroms from a number of input characteristics each a preliminary value for the intake mass flow, wherein for each provisional value by cross-comparison with the respective other provisional values each one validated value is determined.
- a cross-match can be done, for example, based on the VDI2048.
- This is essentially based on the balancing principle according to Gauss, whose basic idea is not only to use the minimum quantity of measured quantities required for a solution, but also to record all achievable measured variables together with the associated variances and covariances. For the present method, this means that all achievable input parameters are used to each determine a preliminary value for the Ansaugmassenstrom.
- the selection of the time of a required to obtain a high operating performance of the gas turbine off-line laundry at particularly low cost can be achieved by a very accurate forecast of the performance gain by such an offline laundry of the gas turbine.
- to determine whether off-line laundering is economically viable at the present time in terms of production stoppage due to the stoppage of the gas turbine be as accurate as possible to know the expected performance recovery through offline laundry. Therefore, in a method for diagnosing a multi-component gas turbine that makes such a prediction using the factory of the intake mass flow, advantageously, the above method of determining the intake mass flow should be used.
- the compressor In a gas turbine, the compressor is on the flow medium side all other components such. B. upstream of the combustion chamber. Accordingly, the compressor is the environmental influences such as incoming dust and dirt particles most exposed component.
- a cleaning of the compressor is therefore carried out in particular, since this has the highest degree of contamination and thus a corresponding cleaning has a particularly positive influence on the recovery of operating performance of the gas turbine.
- the intake mass flow should not be provided as the sole parameter for determining the operating performance of the gas turbine.
- the compressor efficiency of the gas turbine is additionally used as a parameter.
- thermodynamic parameters of the gas turbine are dependent on the respective ambient conditions such as air pressure and outside temperature. Nevertheless, in order to be able to compare measured values at different times, the respective parameters should be normalized to reference conditions.
- the standard is the ISO conditions (temperature 15 ° C., pressure 1.013 bar, atmospheric humidity 60%).
- a prognosis of the temporal development of the respective parameter is created. Such a prognosis is possible by several evaluations of the input parameters or measured values at different times.
- a particularly cost-optimal operation of the gas turbine is possible borrowed if a determination of the timing of an offline scrubbing of the gas turbine is taken not only under purely economic aspects, such as in low load times, but based on an accurate forecast of the operating performance of the gas turbine in the future. For this purpose, it is advantageously determined as a function of the value of the determined additional power in consideration of the overall economic expenditure, whether the gas turbine is temporarily stopped to eliminate the contamination and optionally determines an optimal time for the temporary shutdown.
- a determination of a time for such an off-line wash can be made on the basis of a much more precise analysis, in which costs and Benefits of offline laundry can be weighed exactly against each other.
- the method finds application in a gas turbine plant with a gas turbine comprising several components and with a control system which is connected on the data input side to a number of sensors arranged to determine input parameters in the gas turbine, the control system comprising a prognosis module.
- data of a database with comparison variables of structurally identical and / or building-like gas turbines can be read into the prognosis module.
- the forecasting module should have a correspondingly open architecture, which makes such reading possible. This can for example be done using a mobile data carrier or via a permanent data connection to the database, d. H. the database may be stored on a writable memory within the control system or stored on an external server connected to the control system of the gas turbine via a data transmission line.
- the data obtained in the gas turbine can also be used to expand the database by making it available to the database and stored there.
- a prognosis module for use in a gas turbine plant is suitable for carrying out the method.
- the advantages achieved by the invention are in particular that by determining the Ansaugmassenstroms the gas turbine by means of the turbine inlet pressure, the combustion chamber pressure loss and / or the pressure loss between Environment and compressor inlet a comparatively precise analysis of the degree of contamination of the gas turbine, in particular its compressor is possible. As a result, a forward-looking, adapted to the operational and economic circumstances planning the offline washing of the gas turbine is possible, whereby a particularly high efficiency of the gas turbine can be achieved during their term.
- the method described here also allows the determination of the Ansaugmassenstromes without any knowledge of the fuel data and without solving an associated with high uncertainties energy balance. Incidentally, so that the consideration of the Ansaugmassenstroms in relation to the operating performance of the gas turbine for single-shaft systems, in which a gas turbine and a steam turbine are arranged on a common shaft, in the first place possible.
- FIG. 3 shows a schematic representation of a method for forecasting the achieved additional performance in the case of a compressor cleaning.
- the gas turbine 1 has a compressor 2 for combustion air, a combustion chamber 4 and a turbine 6 for driving the compressor 2 and an unspecified generator or a working machine.
- the turbine 6 and the compressor 2 are arranged on a common, also referred to as a turbine rotor turbine shaft 8, with which is also connected to the generator or the working machine and which is rotatably mounted about its central axis 9.
- the combustion chamber arrangement 4 comprises a number of individual burners 10 which are arranged around the turbine shaft 8 in a ring-shaped manner for combustion of a liquid or gaseous fuel.
- the turbine 6 has a number of rotatable blades 12 connected to the turbine shaft 8.
- the blades 12 are arranged in a ring on the turbine shaft 8 and thus form a number of blade rows.
- the turbine 6 comprises a number of fixed vanes 14, which are also fixed in a ring shape with the formation of rows of vanes inside the turbine 6.
- the blades 12 serve to drive the turbine shaft 8 by momentum transfer from the turbine 6 flowing through the working medium M.
- the vanes 14, however, serve to guide the flow of the working medium M between two seen in the flow direction of the working medium M consecutive blade rows or blade rings.
- the compressor 2 is the component of the gas turbine 1 which is closest to the air inlet 16. Accordingly, it is most exposed to dirt deposits and the resulting contamination of the gas turbine 1. To prevent a reduction in the operating performance of the gas turbine 1, therefore, the compressor 2 must be cleaned regularly. In doing so, so-called online washes can be carried out relatively frequently, for example once a day, for which no
- the gas turbine 1 comprises a control system 18 which is connected via a data line 20 to various sensors 22 arranged inside the gas turbine 1.
- a forecasting module 24 which processes the input parameters detected by the sensors 22 and determined on the basis of this data, the degree of contamination of the gas turbine and the expected gain in operating performance at an accomplished offline laundry.
- comparative data of structurally identical or construction-type gas turbines can be read into the forecasting module.
- the control system is connected via a further data line 20 to a database 26 containing such comparison data.
- the database 26 may be located on an external database server, not shown in greater detail.
- the comparison data can also be read in without permanent data connection to the database 26 via a mobile data carrier.
- the line Ll shows the operating performance of the gas turbine 1 at the time of commissioning 30.
- the line L2 shows the theoretical maximum power of the gas turbine over its term, whose waste only by aging and irreversible pollution is generated.
- the line L3 shows the additional influence of the reversible pollution on the operating performance of the gas turbine.
- Section I shows the influence of regular online laundry on the operating performance of the gas turbine. This is carried out at regular intervals 32, for example once a day. This results in a comparatively low increase in performance, but cumulatively contributes to the maintenance of the gas turbine 1 in a not inconsiderable manner via the frequent online washes.
- the time points 34 should be chosen in a forward-looking manner, which can be done on the one hand on the basis of economic criteria such as electricity price or fuel price, on the other hand based on the operational variables of the gas turbine.
- the anticipated performance gain from off-line laundry should be known for optimal determination of the time 34 of offline laundry.
- the turbine inlet pressure 40a, the combustion chamber pressure loss 40b and the pressure loss between the environment and the compressor inlet 40c are first measured as input parameters.
- a preliminary value for the intake mass flow 42a is determined based on the mass pressure equation according to Stodola.
- the pressure loss in the combustion chamber 40b and the pressure loss between the ambient and the compressor inlet 40c are transferred via a constant drag coefficient approach to provisional values for the intake mass flow 42b and 42c, respectively.
- validated values for the intake mass flow 44 are produced from the input characteristics corrected in this way; on the other hand, the validated input parameters can be used as a basis for calculating the compressor efficiency 46.
- Averaging results then comparatively accurate values for the Ansaugmassenstrom 48 and the compressor efficiency 50 at a certain time 52. These measurements are taken at several times 52 and stored. In each case, the recorded measured values are converted to ISO reference conditions (temperature 15 ° C., pressure 1.013 bar, atmospheric humidity 60%) using a mathematical function, for example a polynomial, in order to be able to correlate the values recorded under different environmental conditions.
- the two results 62 are then converted to a gas turbine power with the aid of gas turbine type-specific indices 64.
- the thus obtained forecast of the additional power in the case of a cleaning of the compressor is finally supplied to the output 68.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Structures Of Non-Positive Displacement Pumps (AREA)
- Engine Equipment That Uses Special Cycles (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Control Of Positive-Displacement Pumps (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
Claims
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP09725928.7A EP2257933B1 (en) | 2008-03-28 | 2009-03-24 | Method for diagnosing a gas turbine |
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP08005950A EP2105887A1 (en) | 2008-03-28 | 2008-03-28 | Method for diagnosing a gas turbine |
EP09725928.7A EP2257933B1 (en) | 2008-03-28 | 2009-03-24 | Method for diagnosing a gas turbine |
PCT/EP2009/053440 WO2009118311A1 (en) | 2008-03-28 | 2009-03-24 | Method for determining the suction mass flow of a gas turbine |
Publications (2)
Publication Number | Publication Date |
---|---|
EP2257933A1 true EP2257933A1 (en) | 2010-12-08 |
EP2257933B1 EP2257933B1 (en) | 2016-07-27 |
Family
ID=39709247
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP08005950A Withdrawn EP2105887A1 (en) | 2008-03-28 | 2008-03-28 | Method for diagnosing a gas turbine |
EP09725928.7A Not-in-force EP2257933B1 (en) | 2008-03-28 | 2009-03-24 | Method for diagnosing a gas turbine |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP08005950A Withdrawn EP2105887A1 (en) | 2008-03-28 | 2008-03-28 | Method for diagnosing a gas turbine |
Country Status (7)
Country | Link |
---|---|
US (1) | US9466152B2 (en) |
EP (2) | EP2105887A1 (en) |
JP (1) | JP4906977B2 (en) |
CN (1) | CN102099835B (en) |
MX (1) | MX2010010608A (en) |
RU (1) | RU2517416C2 (en) |
WO (1) | WO2009118311A1 (en) |
Families Citing this family (15)
Publication number | Priority date | Publication date | Assignee | Title |
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US9267443B2 (en) | 2009-05-08 | 2016-02-23 | Gas Turbine Efficiency Sweden Ab | Automated tuning of gas turbine combustion systems |
US8437941B2 (en) | 2009-05-08 | 2013-05-07 | Gas Turbine Efficiency Sweden Ab | Automated tuning of gas turbine combustion systems |
US9671797B2 (en) | 2009-05-08 | 2017-06-06 | Gas Turbine Efficiency Sweden Ab | Optimization of gas turbine combustion systems low load performance on simple cycle and heat recovery steam generator applications |
US9354618B2 (en) | 2009-05-08 | 2016-05-31 | Gas Turbine Efficiency Sweden Ab | Automated tuning of multiple fuel gas turbine combustion systems |
US8869603B2 (en) | 2012-02-29 | 2014-10-28 | United Technologies Corporation | Debris detection in turbomachinery and gas turbine engines |
ITCO20120008A1 (en) | 2012-03-01 | 2013-09-02 | Nuovo Pignone Srl | METHOD AND SYSTEM FOR MONITORING THE CONDITION OF A GROUP OF PLANTS |
EP2772742A1 (en) * | 2013-02-27 | 2014-09-03 | Siemens Aktiengesellschaft | Power determination method and turbo engine |
DE102014109711A1 (en) * | 2013-07-22 | 2015-01-22 | General Electric Company | Systems and methods for washing a gas turbine compressor |
EP3091202B1 (en) * | 2015-05-07 | 2019-04-03 | Ansaldo Energia IP UK Limited | Method for counteracting draft through an arrangement including a gas turbine during a stop |
JP6634226B2 (en) * | 2015-06-22 | 2020-01-22 | 株式会社日立製作所 | Plant equipment efficiency analysis system and method |
US20170074173A1 (en) * | 2015-09-11 | 2017-03-16 | United Technologies Corporation | Control system and method of controlling a variable area gas turbine engine |
US11143056B2 (en) | 2016-08-17 | 2021-10-12 | General Electric Company | System and method for gas turbine compressor cleaning |
US11149667B1 (en) | 2020-09-10 | 2021-10-19 | Caterpillar Inc. | Sequential turbocharger diagnostic system and method |
CN112595657A (en) * | 2020-12-11 | 2021-04-02 | 哈尔滨工程大学 | Salt spray corrosion experiment table for turbine of micro gas turbine |
CN112861425A (en) * | 2021-01-13 | 2021-05-28 | 上海交通大学 | Method for detecting performance state of double-shaft gas turbine by combining mechanism and neural network |
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JP2855620B2 (en) * | 1988-07-27 | 1999-02-10 | トヨタ自動車株式会社 | Control device for twin-shaft gas turbine engine |
US5113691A (en) * | 1989-02-26 | 1992-05-19 | Westinghouse Electric Corp. | Turbine-medium flow monitor |
US5267277A (en) * | 1989-11-02 | 1993-11-30 | Combustion Engineering, Inc. | Indicator system for advanced nuclear plant control complex |
US5048285A (en) * | 1990-03-26 | 1991-09-17 | Untied Technologies Corporation | Control system for gas turbine engines providing extended engine life |
RU2146012C1 (en) * | 1992-05-29 | 2000-02-27 | Нэшнл Пауэр П.Л.С. | Gas turbine plant |
RU2123610C1 (en) * | 1992-11-09 | 1998-12-20 | ОРМАТ, Инк. | Process increasing energy produced by gas turbine |
JPH06331781A (en) * | 1993-05-26 | 1994-12-02 | Toshiba Corp | Plant state display |
WO1995016296A1 (en) * | 1993-12-09 | 1995-06-15 | B + H Ingenieur-Software Gmbh | Control process for interconnected power plants generating electrical and/or thermal energy |
JPH09228853A (en) * | 1996-02-27 | 1997-09-02 | Hitachi Ltd | Gas turbine combustor |
DE19736384A1 (en) * | 1997-08-21 | 1999-02-25 | Man Nutzfahrzeuge Ag | Method for metering a reducing agent into nitrogen oxide-containing exhaust gas from an internal combustion engine |
EP0921292B1 (en) * | 1997-12-08 | 2003-09-10 | ALSTOM (Switzerland) Ltd | Method for controlling a gas turbine group |
DE59905874D1 (en) * | 1998-09-24 | 2003-07-10 | Siemens Ag | FUEL PREHEATING IN A GAS TURBINE |
DE10001997A1 (en) * | 2000-01-19 | 2001-07-26 | Alstom Power Schweiz Ag Baden | Composite power plant and method for operating such a composite power plant |
US6574584B2 (en) * | 2000-12-11 | 2003-06-03 | General Electric Company | Method for evaluating compressor stall/surge margin requirements |
US7591150B2 (en) * | 2001-05-04 | 2009-09-22 | Battelle Energy Alliance, Llc | Apparatus for the liquefaction of natural gas and methods relating to same |
JP2004169667A (en) * | 2002-11-22 | 2004-06-17 | Mitsubishi Heavy Ind Ltd | Monitoring device of multi-stage filter |
US6976351B2 (en) * | 2003-04-04 | 2005-12-20 | General Electric Company | Methods and apparatus for monitoring gas turbine combustion dynamics |
US7231305B2 (en) * | 2003-08-07 | 2007-06-12 | Schlumberger Technology Corporation | Flow rate determination |
CA2437264C (en) * | 2003-08-12 | 2013-12-03 | Brian Wilson Varney | Heat exchanger optimization process and apparatus |
WO2005090764A1 (en) * | 2004-02-23 | 2005-09-29 | Siemens Aktiengesellschaft | Method and device for diagnosing a turbine plant |
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-
2008
- 2008-03-28 EP EP08005950A patent/EP2105887A1/en not_active Withdrawn
-
2009
- 2009-03-24 RU RU2010144075/08A patent/RU2517416C2/en not_active IP Right Cessation
- 2009-03-24 JP JP2011501199A patent/JP4906977B2/en not_active Expired - Fee Related
- 2009-03-24 US US12/934,358 patent/US9466152B2/en not_active Expired - Fee Related
- 2009-03-24 EP EP09725928.7A patent/EP2257933B1/en not_active Not-in-force
- 2009-03-24 WO PCT/EP2009/053440 patent/WO2009118311A1/en active Application Filing
- 2009-03-24 CN CN200980111286.8A patent/CN102099835B/en not_active Expired - Fee Related
- 2009-03-24 MX MX2010010608A patent/MX2010010608A/en not_active Application Discontinuation
Non-Patent Citations (1)
Title |
---|
See references of WO2009118311A1 * |
Also Published As
Publication number | Publication date |
---|---|
RU2010144075A (en) | 2012-05-10 |
EP2257933B1 (en) | 2016-07-27 |
JP4906977B2 (en) | 2012-03-28 |
CN102099835B (en) | 2014-12-17 |
EP2105887A1 (en) | 2009-09-30 |
CN102099835A (en) | 2011-06-15 |
MX2010010608A (en) | 2010-11-09 |
WO2009118311A1 (en) | 2009-10-01 |
RU2517416C2 (en) | 2014-05-27 |
US9466152B2 (en) | 2016-10-11 |
JP2011515620A (en) | 2011-05-19 |
US20110247406A1 (en) | 2011-10-13 |
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