WO1994008226A1 - An apparatus for fuel quality monitoring - Google Patents
An apparatus for fuel quality monitoring Download PDFInfo
- Publication number
- WO1994008226A1 WO1994008226A1 PCT/EP1993/002735 EP9302735W WO9408226A1 WO 1994008226 A1 WO1994008226 A1 WO 1994008226A1 EP 9302735 W EP9302735 W EP 9302735W WO 9408226 A1 WO9408226 A1 WO 9408226A1
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- WO
- WIPO (PCT)
- Prior art keywords
- network
- light
- spectral
- nodes
- product line
- Prior art date
Links
- 239000000446 fuel Substances 0.000 title description 16
- 238000012544 monitoring process Methods 0.000 title description 3
- 239000004215 Carbon black (E152) Substances 0.000 claims abstract description 20
- 229930195733 hydrocarbon Natural products 0.000 claims abstract description 20
- 150000002430 hydrocarbons Chemical class 0.000 claims abstract description 20
- TVMXDCGIABBOFY-UHFFFAOYSA-N octane Chemical compound CCCCCCCC TVMXDCGIABBOFY-UHFFFAOYSA-N 0.000 claims abstract description 19
- 230000003595 spectral effect Effects 0.000 claims abstract description 18
- 230000003287 optical effect Effects 0.000 claims abstract description 15
- CIWBSHSKHKDKBQ-JLAZNSOCSA-N Ascorbic acid Chemical compound OC[C@H](O)[C@H]1OC(=O)C(O)=C1O CIWBSHSKHKDKBQ-JLAZNSOCSA-N 0.000 claims abstract description 13
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims abstract description 10
- 230000000704 physical effect Effects 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 8
- 238000010183 spectrum analysis Methods 0.000 claims abstract description 6
- 230000005855 radiation Effects 0.000 claims abstract description 5
- 238000013528 artificial neural network Methods 0.000 claims description 20
- 229910000530 Gallium indium arsenide Inorganic materials 0.000 claims description 2
- KXNLCSXBJCPWGL-UHFFFAOYSA-N [Ga].[As].[In] Chemical compound [Ga].[As].[In] KXNLCSXBJCPWGL-UHFFFAOYSA-N 0.000 claims description 2
- 239000013307 optical fiber Substances 0.000 claims description 2
- 238000000034 method Methods 0.000 description 12
- 238000005259 measurement Methods 0.000 description 9
- 238000012549 training Methods 0.000 description 8
- 238000001228 spectrum Methods 0.000 description 7
- 238000002835 absorbance Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 238000004566 IR spectroscopy Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 2
- 210000004556 brain Anatomy 0.000 description 2
- 210000004027 cell Anatomy 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 150000001298 alcohols Chemical class 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000002329 infrared spectrum Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 150000002894 organic compounds Chemical class 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 238000012628 principal component regression Methods 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 210000000225 synapse Anatomy 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/26—Oils; viscous liquids; paints; inks
- G01N33/28—Oils, i.e. hydrocarbon liquids
- G01N33/2835—Oils, i.e. hydrocarbon liquids specific substances contained in the oil or fuel
- G01N33/2852—Oils, i.e. hydrocarbon liquids specific substances contained in the oil or fuel alcohol/fuel mixtures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/26—Oils; viscous liquids; paints; inks
- G01N33/28—Oils, i.e. hydrocarbon liquids
- G01N33/2829—Oils, i.e. hydrocarbon liquids mixtures of fuels, e.g. determining the RON-number
Definitions
- the invention relates to an in-line fuel quality monitor to be used to provide feed forward information on fuel quality for use in the control (e.g. feed-forward control) of an engine management system.
- Such an apparatus is advantageously applied as a small light-weight instrument in cars in order to advise drivers or engine of fuel quality.
- Information obtained will be physical property data of hydrocarbon products such as octane number, cetane number, vapour pressure density and the like of the fuel, and for use in dual-fuelling vehicles, the gasoline/alcohol ratio.
- organic compounds have in the infra-red spectral region (about 1 to about 300 ⁇ m) a unique spectral fingerprint.
- An empirical model can be created by finding the spectral trend in a large set of data known as a training set.
- (N)IR spectroscopy is both rapid and reliable, and could potentially be applied to make on-line real-time measurements.
- a spectrometer can be used to obtain the spectra of a training set of characterized unleaded gasolines.
- complex multivariate statistical techniques such as Principal Component Regression, Reduced Rank Regression and Partial Least Squares to develop the model, the Research Octane Number (RON) of a given fuel may be predicted. These techniques require all of the data points provided by the spectrometer and predict well allowing for the variability of the initial RON measurement.
- non-moving parts instrument uses (near) infra-red techniques (advantageously 0.78-30 ⁇ m wavelength) advantageously coupled with a neural network to measure physical property data of hydrocarbon products such as (research) octane number, cetane number, density, vapour pressure and the like or gasoline/alcohol ratio on-line and in real time and that, in particular, easily can be applied in cars.
- hydrocarbon products such as (research) octane number, cetane number, density, vapour pressure and the like or gasoline/alcohol ratio on-line and in real time and that, in particular, easily can be applied in cars.
- the invention therefore provides an apparatus for on-line measuring physical property data of hydrocarbon products such as octane number, cetane number, density, vapour pressure and the like or gasoline/alcohol ratio, comprising means for providing (N)IR radiation in a predetermined spectral range; means for transmitting light at selected wavelengths in the (N)IR spectral region; means for delivering light from said transmitting means to a hydrocarbon product line; means for allowing an optical path length in the hydrocarbon product line; means for detecting the light transmitted through the said optical path; means for providing the obtained signal to be input to processing equipment for spectral analysis and for correlating the spectral data to the physical property data of hydrocarbon products such as octane number, cetane number, density, vapour pressure and the like or gasoline/alcohol ratio.
- a neural network can be defined as a system, wherein during a learning period a correlation between input- and output variables is searched for. After sufficient examples have been offered in this learning period the neural network is able to produce the relevant output for an arbitrary input.
- Neural networks have found applications e.g. for pattern recognition problems. As those skilled in the art will appreciate, neural networks are built up of layers of processing elements (similar to the brain's neurons) each of which is weighted and connected to elements in other layers (similar to the brain's synapses). A network learns patterns by adjusting weights between the elements whilst it is being trained with accurate qualified data.
- training errors the difference between the actual and predicted result are propagated backwards through the network to the hidden layers which receive no feedback from training patterns.
- the weights of the interconnections are adjusted in small steps in the direction of the error, to minimize the errors, and the training data is run through again. This happens many times till the error reaches an acceptable level, which is usually the repeatability of the initial measurement.
- the invention will particularly be described referring to the prediction of octane number of gasoline, but it will be appreciated by those skilled in the art that the invention is not restricted thereto and could also be used for prediction of vapour pressure, density, cetane number and the like.
- Data analysis on the set of spectra corresponding to the gasolines of the training set is done in the following manner:
- the mean spectrum of the set is generated and the differences between each individual spectrum and the mean are calculated.
- the mean spectrum will be in the order of 5000 data points and so the problem of analysis of a set of 100 fuels is very difficult.
- a technique is required to allow data reduction to a manageable number of problem variables.
- the data reduction is performed by physical reduction in the number of measured wavelengths.
- the data reduction is in the following manner: A multivariate statistical technique such as e.g. Principal Component Analysis is used on the training set of gasoils, to generate a 'property spectrum' which represents the relative importance of each spectral data point to the correlation with octane number.
- the spectral measurement is then simplified to discrete wavelengths, typically numbering between 5 and 10.
- the absorbance values are used as the input to the neural network.
- the second overtone (harmonic) region of the (N)IR spectrum is chosen.
- This region covers 900-1300 nm (wavelength) and is chosen as it is in this region that the best balance between available information from the measurement and component instrumentation stability and sensitivity can be achieved.
- a number of discrete wavelengths is converted to absorption data, which are used as the input to a neural network.
- the number of selected wavelengths is 5 for fuels that do not contain alcohols as oxygenates or do not include cetane ignition improver additions and 6 if the fuels do contain alcohol as oxygenates or do include cetane ignition improver additions.
- a wavelength of 6-7 ⁇ m is chosen in addition to monitor the concentration of cetane ignition improver additive.
- One of the wavelengths is advantageously used as a transmission reference to correct for any instrumental drifts.
- the remaining wavelengths, corrected by the reference, are converted to absorption data. This may be done logarithmically, and the data can be mathematically scaled within predetermined bounds for each wavelength. That is, extreme values expected for either fuels, or more likely, process streams are used to provide the range of acceptable absorbances at each wavelength against which the scaling can be done for the fuel to be tested.
- the neural network is trained on the entire data set by repeated presentation of input and known outputs i.e. the infra-red data for a gasoline and its octane number, to learn the relationship between the two and the performance of its predictions against the actual octane number data as measured by standard engine methods is monitored.
- the data set should be split into a further training set and a validation set that will not be used in the "learning” phase.
- the instrument of the invention advantageously collects (N)IR absorbances at five discrete wavelengths, selected to yield information from the C-H bond vibrations structure known to influence the octane rating of a gasoline.
- the measured absorbances are normalized to one of the wavelengths which is chosen to provide a baseline and does not contain hydrocarbon information. This allows for changing ambient conditions (temperature, (N)IR source, electronic drift etc.) and the remaining four measurements are applied to the neural network.
- fig. 1 represents schematically an engine based on-line octane analyzer
- fig. 2 represents schematically a neural network advantageously applied in the apparatus of the invention.
- this optical means 1 comprises a plurality of light-emitting diodes (LED), a filter and a lens-holder.
- LED light-emitting diodes
- filter a filter
- lens-holder a lens-holder
- the means 1 is connected through any suitable optical connecting means 2 (advantageously a multi-way fibre bundle) to an in-line gasoline cell 3 fitted in any suitable manner in a hydrocarbon product line (not shown) .
- a photodetector is present and provides the obtained signal to be input to the processing electronics and neural network for spectral analysis.
- FIG. 1 there are shown 5 LED's; however, any suitable number can be applied.
- the geometry of the apparatus of the invention is such that it can be applied in cars as an engine-based instrument.
- the network used has a three-layer architecture which, for example, comprises four input nodes, 2 hidden nodes in a layer between the input A and output B, and one output node.
- This is called a (4, 2, 1) network.
- the spectral data are presented as inputs A to the input nodes, wherein the product quality information B is the output.
- the nodes possess certain weights of interconnections, and may be biased.
- the weights and biases of the network can be stored and used to analyze input data comprising the measured infra-red absorbances and correlate the pattern to the octane number of a gasoline.
- important parameters having been trained and successfully tested against the validation set, are the weights of interconnection between the nodes and the biases at the hidden and output nodes. These can be interrogated and then implemented in the network algorithm for the octane number analysis of future fuel samples.
- a neural network algorithm is implemented for each output.
- the implementation is by software code on a microprocessor chip, and is therefore flexible to any changes in network parameters which can be easily re-programmed.
- the instrument can produce results for leaded fuels, provided that the lead content is known.
- a simple numerical correction can be added to the octane number predicted.
- the network architectures applied may vary in the precise number of nodes that'are present in each layer, or even in the number of actual layers.
- 2 to 5 layers are applied.
- the number of nodes of the input layer ranges from 3-10
- the number of nodes of the hidden layer(s) ranges from 1-10
- the number of nodes of the output layer ranges from 1-3. More in particular, (3, 5, 1), (6, 6, 3) and (6, 6, 6, 3) networks could be applied.
- the operation of the apparatus of the invention is as follows:
- LED's Five light emitting diodes (LED's) provide the near infra-red radiation e.g. in the spectral range of 1-2.0 microns.
- the light from the LED's is collimated and passed through interference filters (one for each LED) which transmit light at selected wavelengths in the near-infra-red spectral region (e.g.
- the five wavelengths are 1106 nm, 1150 run, 1170 nm, 1190 nm and 1219 nm, the normalization wavelength being 1106 nm due to gasoline having minimal absorbance at this wavelength, thus giving a good baseline measurement.
- the normalization wavelength being 1106 nm due to gasoline having minimal absorbance at this wavelength, thus giving a good baseline measurement.
- other wavelengths are needed: advantageously 1766 nm and 1730 nm. These may be required in addition to the others.
- An optical fibre bundle (five into one) collects the filtered light through the filters and delivers the light, from the selected LED, to the hydrocarbon product line.
- the LED selection can be achieved by electronic pulses, to allow rapid measurements ( ⁇ 1 second) achieved by pulsing the LED's one by one.
- optical windows are placed in the in-line cell of the fuel line, to allow a 10-30 mm, advantageously 20 mm optical path length.
- An indium gallium arsenide detector is mounted to detect the light transmitted through the optical path, and provide the obtained signal to be input to the processing electronics and neural network for spectral analysis.
Abstract
Description
Claims
Priority Applications (8)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CA002146255A CA2146255A1 (en) | 1992-10-05 | 1993-10-04 | Apparatus for fuel quality monitoring |
BR9307172A BR9307172A (en) | 1992-10-05 | 1993-10-04 | Apparatus for measuring physical property data of hydrocarbon products online |
JP6508731A JPH08501878A (en) | 1992-10-05 | 1993-10-04 | Fuel quality monitoring device |
EP93922522A EP0663998A1 (en) | 1992-10-05 | 1993-10-04 | An apparatus for fuel quality monitoring |
AU51493/93A AU676854B2 (en) | 1992-10-05 | 1993-10-04 | An apparatus for fuel quality monitoring |
KR1019950701327A KR950703732A (en) | 1992-10-05 | 1993-10-04 | AN APPARATUS FOR FUEL QUALITY MONITORING |
NO951284A NO951284L (en) | 1992-10-05 | 1995-04-03 | Device for measuring fuel |
FI951570A FI951570A (en) | 1992-10-05 | 1995-04-03 | Fuel quality monitoring device |
Applications Claiming Priority (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP92309075.7 | 1992-10-05 | ||
EP92309075 | 1992-10-05 | ||
EP93200229 | 1993-01-28 | ||
EP93200229.8 | 1993-01-28 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO1994008226A1 true WO1994008226A1 (en) | 1994-04-14 |
Family
ID=26132219
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/EP1993/002735 WO1994008226A1 (en) | 1992-10-05 | 1993-10-04 | An apparatus for fuel quality monitoring |
Country Status (11)
Country | Link |
---|---|
EP (1) | EP0663998A1 (en) |
JP (1) | JPH08501878A (en) |
KR (1) | KR950703732A (en) |
AU (1) | AU676854B2 (en) |
BR (1) | BR9307172A (en) |
CA (1) | CA2146255A1 (en) |
FI (1) | FI951570A (en) |
MY (1) | MY108958A (en) |
NO (1) | NO951284L (en) |
NZ (1) | NZ256675A (en) |
WO (1) | WO1994008226A1 (en) |
Cited By (22)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
FR2726910A1 (en) * | 1994-11-10 | 1996-05-15 | Piemont Serge | DEVICE FOR IDENTIFYING HYDROCARBON FLUIDS |
WO1997014951A1 (en) * | 1995-10-18 | 1997-04-24 | Shell Internationale Research Maatschappij B.V. | Transmission cell for measuring near infrared spectra of a hydrocarbonaceous material |
WO1997014953A1 (en) * | 1995-10-18 | 1997-04-24 | Shell Internationale Research Maatschappij B.V. | Method for predicting a physical property of a residual hydrocarbonaceous material |
WO1997031384A1 (en) * | 1996-02-21 | 1997-08-28 | Idec Izumi Corporation | Photoelectric switching device and switching method |
GB2312741A (en) * | 1996-01-11 | 1997-11-05 | Intevep Sa | Determining parameters of hydrocarbons |
NL1003058C2 (en) * | 1996-01-11 | 1997-11-10 | Intevep Sa | Evaluation of hydrocarbon fuels by near=I.R. spectroscopy |
US5712797A (en) * | 1994-10-07 | 1998-01-27 | Bp Chemicals Limited | Property determination |
US5740073A (en) * | 1994-10-07 | 1998-04-14 | Bp Chemicals Limited | Lubricant property determination |
WO1998032003A1 (en) * | 1997-01-21 | 1998-07-23 | Spectral Sciences, Inc. | Systems and methods for optically measuring properties of hydrocarbon fuel gases |
US5817517A (en) * | 1995-02-08 | 1998-10-06 | Exxon Research And Engineering Company | Method of characterizing feeds to catalytic cracking process units |
US5861228A (en) * | 1994-10-07 | 1999-01-19 | Bp Chemicals Limited | Cracking property determination |
EP0922953A1 (en) * | 1997-12-09 | 1999-06-16 | AGIP PETROLI S.p.A. | Process for predicting the cold characteristics of gasoils |
US5935863A (en) * | 1994-10-07 | 1999-08-10 | Bp Chemicals Limited | Cracking property determination and process control |
WO2003046522A3 (en) * | 2001-11-30 | 2004-06-10 | Air Liquide | Apparatus and methods for launching and receiving a broad wavelength range source |
WO2006100377A1 (en) * | 2005-03-22 | 2006-09-28 | Sp3H | Method for optimizing operating parameters of a combustion engine |
CN100425975C (en) * | 2004-07-29 | 2008-10-15 | 中国石油化工股份有限公司 | Method for measuring character data of gasoline from near infrared light spectrum |
WO2009040635A1 (en) * | 2007-09-26 | 2009-04-02 | Toyota Jidosha Kabushiki Kaisha | Device and method for detecting degradation of fuel for internal combustion engine |
FR2930598A1 (en) * | 2008-04-24 | 2009-10-30 | Sp3H Soc Par Actions Simplifie | METHOD FOR OPTIMIZING THE OPERATION OF A THERMAL ENGINE BY DETERMINING THE PROPORTION OF OXYGEN COMPOUNDS IN THE FUEL |
RU2478809C2 (en) * | 2007-05-07 | 2013-04-10 | Сп3Х | Control method of injection, combustion and cleaning parameters of internal combustion engine with self-ignition; equipment for implementation of above described method, and engine system |
FR2985316A1 (en) * | 2012-01-04 | 2013-07-05 | Rhodia Operations | Method for external diagnosis of malfunction of e.g. lubricant additive, additivation device in vehicle's diesel engine, involves analyzing variation between measured and theoretical additive contents with respect to maximum variation |
WO2015075244A1 (en) * | 2013-11-22 | 2015-05-28 | Jaguar Land Rover Limited | Methods and system for determining fuel quality in a vehicle |
CN111323387A (en) * | 2020-03-21 | 2020-06-23 | 哈尔滨工程大学 | Methane number on-line real-time monitoring system |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
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US6687621B2 (en) * | 2000-11-20 | 2004-02-03 | The Procter & Gamble Company | Predictive method for polymers |
FR2920475B1 (en) * | 2007-08-31 | 2013-07-05 | Sp3H | DEVICE FOR CENTRALIZED MANAGEMENT OF MEASUREMENTS AND INFORMATION RELATING TO LIQUID AND GASEOUS FLOWS NECESSARY FOR THE OPERATION OF A THERMAL ENGINE |
CN101893560B (en) * | 2010-07-13 | 2012-04-25 | 中国人民解放军总后勤部油料研究所 | Method for quickly determining manganese content in gasoline |
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- 1993-09-30 MY MYPI93001987A patent/MY108958A/en unknown
- 1993-10-04 BR BR9307172A patent/BR9307172A/en not_active Application Discontinuation
- 1993-10-04 EP EP93922522A patent/EP0663998A1/en not_active Ceased
- 1993-10-04 NZ NZ256675A patent/NZ256675A/en unknown
- 1993-10-04 WO PCT/EP1993/002735 patent/WO1994008226A1/en not_active Application Discontinuation
- 1993-10-04 CA CA002146255A patent/CA2146255A1/en not_active Abandoned
- 1993-10-04 JP JP6508731A patent/JPH08501878A/en active Pending
- 1993-10-04 AU AU51493/93A patent/AU676854B2/en not_active Ceased
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- 1995-04-03 FI FI951570A patent/FI951570A/en unknown
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Cited By (37)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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Also Published As
Publication number | Publication date |
---|---|
AU5149393A (en) | 1994-04-26 |
NZ256675A (en) | 1995-11-27 |
NO951284D0 (en) | 1995-04-03 |
KR950703732A (en) | 1995-09-20 |
BR9307172A (en) | 1999-03-30 |
FI951570A0 (en) | 1995-04-03 |
NO951284L (en) | 1995-04-03 |
JPH08501878A (en) | 1996-02-27 |
CA2146255A1 (en) | 1994-04-14 |
MY108958A (en) | 1996-11-30 |
AU676854B2 (en) | 1997-03-27 |
FI951570A (en) | 1995-04-03 |
EP0663998A1 (en) | 1995-07-26 |
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