NO952957L - A method for predicting the physical properties of hydrocarbon products - Google Patents
A method for predicting the physical properties of hydrocarbon productsInfo
- Publication number
- NO952957L NO952957L NO952957A NO952957A NO952957L NO 952957 L NO952957 L NO 952957L NO 952957 A NO952957 A NO 952957A NO 952957 A NO952957 A NO 952957A NO 952957 L NO952957 L NO 952957L
- Authority
- NO
- Norway
- Prior art keywords
- data
- values
- spectra
- neural network
- input
- Prior art date
Links
- 239000004215 Carbon black (E152) Substances 0.000 title abstract 5
- 229930195733 hydrocarbon Natural products 0.000 title abstract 5
- 150000002430 hydrocarbons Chemical class 0.000 title abstract 5
- 230000000704 physical effect Effects 0.000 title abstract 5
- 238000000034 method Methods 0.000 title abstract 3
- 238000013528 artificial neural network Methods 0.000 abstract 4
- 238000002329 infrared spectrum Methods 0.000 abstract 4
- 238000002835 absorbance Methods 0.000 abstract 2
- 238000010521 absorption reaction Methods 0.000 abstract 2
- 238000004566 IR spectroscopy Methods 0.000 abstract 1
- 230000002596 correlated effect Effects 0.000 abstract 1
- 238000005457 optimization Methods 0.000 abstract 1
- 238000001228 spectrum Methods 0.000 abstract 1
Classifications
-
- 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
- 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
- G01N21/3504—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing gases, e.g. multi-gas analysis
-
- 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
- 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—Mixtures of fuels
-
- 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—Specific substances contained in the oils or fuels
- G01N33/2852—Alcohol in fuels
Landscapes
- Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Chemical Kinetics & Catalysis (AREA)
- General Chemical & Material Sciences (AREA)
- Oil, Petroleum & Natural Gas (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
Fremgangsmåte for dataprosessering og optimalisering av et neuralt nettverk for anvendelse for bestemmelse av fysikalske data for egenskapene av hydrokar- bonprodukter ut fra målte (N)lR-spekter- absorbanser, kjennetegnet ved at man a) måler (N)IR-spektrene for et stort sett av hydrokarbonproduktprøver fra et bredt område av kilder; b) velger et overtoneområde (harmonisk område) blant de således oppnådde (N)IR-spektre, c) velger et antall adskilte bølge- lengder i hvert N(IR)-spektrum, over- fører et antall av disse bølgelenger til absorpsjonsdata og anvender disse ab- sorpsjonsdata som inngangsverdier til et neuralt nettverk, idet antallet lag i det neurale nettverk er fra 2 til 5 og antallet knutepunkter - når det forelig- ger tre eller flere lag - er fra 3 til 10 i inngangslaget, fra l til 10 i det eller de skjulte lag og fra l til 3 i utgangslaget, d) trener det neurale nettverk på hele datasettet ved gjentatt innmating av inngangsverdier og kjente utgangsverdier, dvs. (N)IR-dataene for hydrokarbonproduktet og dets relevante data for fysikalske egenskaper, slik at sammenhengen mellom de to læres, og overvåker ydelsen hva angår dets forut- sigelser mot de aktuelle relevante fysi- kalske egenskapsdata målt ved hjelp av standardmetoder for treningsdataene, slik at absorbansverdiene korreleres med den nevnte relevante fysikalske egen- skap, e) genererer et sett av verdier for det justerte nettverks sammenkob- lingsvektfaktorer og forspenninger etter læringsperioden i trinn d), og f) på-trykker disse justerte verdier, under anvendelse av nettverkalgoritmen, til (N)IR-spektre opptatt under de samme betingelser, for hydrokarbonprodukter med ukjente fysikalske egenskapsdata.Method of data processing and optimization of a neural network for use in determining physical data for the properties of hydrocarbon products from measured (N) IR spectra absorbances, characterized by a) measuring (N) the IR spectra of a large sets of hydrocarbon product samples from a wide range of sources; b) selects an overtone range (harmonic range) from the thus obtained (N) IR spectra; c) selects a number of distinct wavelengths in each N (IR) spectrum, transfers a number of these wavelengths to absorption data, and uses these absorption data as input values to a neural network, the number of layers in the neural network being from 2 to 5 and the number of nodes - when there are three or more layers - from 3 to 10 in the input layer, from 1 to 10 in the hidden layer (s) and from 1 to 3 in the starting layer; (d) training the neural network of the entire data set by repeated input of input values and known output values, i.e. (N) IR data for the hydrocarbon product and its relevant physical property data, such as that the relationship between the two is learned, and monitors performance in terms of its predictions against the relevant relevant physical property data measured by standard training data methods, so that the absorbance values are correlated with the said relevant physical properties, e) generating a set of values for the adjusted network's interconnection weight factors and biases after the learning period in step d), and f) applying these adjusted values, using the network algorithm, to (N) IR spectra recorded under the same conditions, for hydrocarbon products with unknown physical property data.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP93200229 | 1993-01-28 | ||
| PCT/EP1994/000233 WO1994017391A1 (en) | 1993-01-28 | 1994-01-26 | Method for prediction of physical property data of hydrocarbon products |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| NO952957D0 NO952957D0 (en) | 1995-07-26 |
| NO952957L true NO952957L (en) | 1995-07-26 |
Family
ID=8213596
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| NO952957A NO952957L (en) | 1993-01-28 | 1995-07-26 | A method for predicting the physical properties of hydrocarbon products |
Country Status (12)
| Country | Link |
|---|---|
| EP (1) | EP0681693A1 (en) |
| JP (1) | JPH08505944A (en) |
| KR (1) | KR960700450A (en) |
| CN (1) | CN1116878A (en) |
| AU (1) | AU5971994A (en) |
| BR (1) | BR9405871A (en) |
| CA (1) | CA2154786A1 (en) |
| FI (1) | FI953579A0 (en) |
| NO (1) | NO952957L (en) |
| NZ (1) | NZ261119A (en) |
| WO (1) | WO1994017391A1 (en) |
| ZA (1) | ZA94536B (en) |
Families Citing this family (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5572030A (en) * | 1994-04-22 | 1996-11-05 | Intevep, S.A. | Method for determining parameter of hydrocarbon |
| US5643796A (en) * | 1994-10-14 | 1997-07-01 | University Of Washington | System for sensing droplet formation time delay in a flow cytometer |
| JPH10510355A (en) * | 1994-12-09 | 1998-10-06 | フォス エレクトリック アクティーゼルスカブ | Information acquisition method |
| US6850874B1 (en) | 1998-04-17 | 2005-02-01 | United Technologies Corporation | Method and apparatus for predicting a characteristic of a product attribute formed by a machining process using a model of the process |
| US20060031027A1 (en) * | 2004-08-03 | 2006-02-09 | Alman David H | Method and apparatus for predicting properties of a chemical mixture |
| CN100335885C (en) * | 2004-09-30 | 2007-09-05 | 长沙开元仪器有限公司 | Method for testing hydrogen content in coal |
| EP1744049A3 (en) * | 2005-07-14 | 2012-08-15 | Korea Petroleum Quality Inspection Institute | Vehicle mounted type fuel testing apparatus |
| US7679059B2 (en) * | 2006-04-19 | 2010-03-16 | Spectrasensors, Inc. | Measuring water vapor in hydrocarbons |
| US9244052B2 (en) | 2011-12-22 | 2016-01-26 | Exxonmobil Research And Engineering Company | Global crude oil quality monitoring using direct measurement and advanced analytic techniques for raw material valuation |
| GB2520520B (en) * | 2013-11-22 | 2018-05-23 | Jaguar Land Rover Ltd | Methods and system for determining fuel quality in a vehicle |
| DE102015106881B4 (en) * | 2015-05-04 | 2016-12-29 | Rofa Laboratory & Process Analyzers | Method for determining a characteristic of a fuel that characterizes the knock resistance and corresponding test arrangement |
| FR3035970B1 (en) | 2015-05-05 | 2017-06-02 | Ifp Energies Now | METHOD FOR CONTINUOUSLY MONITORING THE ADVANCED STATE OF OXIDATION OF A FUEL |
| CN105424539A (en) * | 2015-11-06 | 2016-03-23 | 中国科学院天津工业生物技术研究所 | Neural network-based method for predicting sugar yield produced through corn straw hydrolysis |
| CN108369218A (en) * | 2015-12-08 | 2018-08-03 | 国立研究开发法人物质材料研究机构 | Using the particle of alkyl modification as the fuel oil identification sensor of receptive layers and fuel oil recognition methods |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2245401A (en) * | 1989-11-01 | 1992-01-02 | Hughes Aircraft Co | Neural network signal processor |
-
1994
- 1994-01-26 NZ NZ261119A patent/NZ261119A/en unknown
- 1994-01-26 CN CN94191022A patent/CN1116878A/en active Pending
- 1994-01-26 ZA ZA94536A patent/ZA94536B/en unknown
- 1994-01-26 BR BR9405871A patent/BR9405871A/en not_active Application Discontinuation
- 1994-01-26 KR KR1019950703127A patent/KR960700450A/en not_active Application Discontinuation
- 1994-01-26 EP EP94905722A patent/EP0681693A1/en not_active Ceased
- 1994-01-26 JP JP6516692A patent/JPH08505944A/en active Pending
- 1994-01-26 WO PCT/EP1994/000233 patent/WO1994017391A1/en not_active Application Discontinuation
- 1994-01-26 CA CA002154786A patent/CA2154786A1/en not_active Abandoned
- 1994-01-26 AU AU59719/94A patent/AU5971994A/en not_active Abandoned
-
1995
- 1995-07-26 FI FI953579A patent/FI953579A0/en not_active Application Discontinuation
- 1995-07-26 NO NO952957A patent/NO952957L/en unknown
Also Published As
| Publication number | Publication date |
|---|---|
| CA2154786A1 (en) | 1994-08-04 |
| NO952957D0 (en) | 1995-07-26 |
| JPH08505944A (en) | 1996-06-25 |
| EP0681693A1 (en) | 1995-11-15 |
| NZ261119A (en) | 1997-08-22 |
| FI953579A (en) | 1995-07-26 |
| BR9405871A (en) | 1995-12-12 |
| AU5971994A (en) | 1994-08-15 |
| CN1116878A (en) | 1996-02-14 |
| FI953579A0 (en) | 1995-07-26 |
| ZA94536B (en) | 1994-09-09 |
| WO1994017391A1 (en) | 1994-08-04 |
| KR960700450A (en) | 1996-01-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| NO952957L (en) | A method for predicting the physical properties of hydrocarbon products | |
| Wang et al. | Uncertainties of inherent optical properties obtained from semianalytical inversions of ocean color | |
| CN107491784A (en) | Tobacco leaf near infrared spectrum quantitative modeling method and application based on deep learning algorithm | |
| US5015856A (en) | Automated process for permeability determinations of barrier resins | |
| AU2095388A (en) | Method for the direct determination of physical properties of hydrocarbon products | |
| CN107273633B (en) | Varying delay method of estimation and flow time lag method of estimation is hydrocracked between multiple operation | |
| Fatemi et al. | Identification of informative spectral ranges for predicting major chemical constituents in corn using NIR spectroscopy | |
| Wang | Research on the prediction of per capita coal consumption based on the ARIMA–BP combined model | |
| Kose | Modelling of colour perception of different age groups using artificial neural networks | |
| Aires et al. | The" weight smoothing" regularization of MLP for Jacobian stabilization | |
| CN102184452B (en) | The flexible measurement method of the component gas of " Texaco syngas " | |
| US6052194A (en) | Method and apparatus for controlling the spectral reflectance of a material | |
| GR3022931T3 (en) | Method for prediction of cetane numbers of gasoils. | |
| Seemann et al. | Classical and modern algorithms for the evaluation of data from sensor-arrays | |
| Zhang et al. | Neural network-molecular descriptors approach to the prediction of properties of alkenes | |
| CN114935565B (en) | Method for detecting rhodamine B content in grape juice based on laser-induced fluorescence technology | |
| Zhu et al. | Discrimination of Brands of Strong Aroma Type Liquors Using Synchronous Fluorescence Spectroscopy and Chemometrics Methods | |
| Billock et al. | Sensory recoding via neural synchronization: integrating hue and luminance into chromatic brightness and saturation | |
| Cen et al. | Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network | |
| Mandal et al. | To Predict the Lightfastness of Prints on Foil Applying Artificial Neural Network | |
| Walczak et al. | Calibration of non-linear analytical systems by a neuro fuzzy approach | |
| Shao et al. | Optical system for tablet variety discrimination using visible/near-infrared spectroscopy | |
| REN et al. | PROGRESS AND PROSPECT OF OCEAN COLOR REMOTE SENSING IN CASE 2 WATERS | |
| Sodre | The connection between continuum emission and emission lines in galaxy spectra | |
| EP0922953A1 (en) | Process for predicting the cold characteristics of gasoils |