WO1994017391A1 - Procede de prediction des donnees relatives aux caracteristiques physiques de produits a base d'hydrocarbure - Google Patents

Procede de prediction des donnees relatives aux caracteristiques physiques de produits a base d'hydrocarbure Download PDF

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Publication number
WO1994017391A1
WO1994017391A1 PCT/EP1994/000233 EP9400233W WO9417391A1 WO 1994017391 A1 WO1994017391 A1 WO 1994017391A1 EP 9400233 W EP9400233 W EP 9400233W WO 9417391 A1 WO9417391 A1 WO 9417391A1
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WO
WIPO (PCT)
Prior art keywords
data
physical property
network
neural network
nodes
Prior art date
Application number
PCT/EP1994/000233
Other languages
English (en)
Inventor
John Michael Tolchard
Andrew Boyd
Original Assignee
Shell Internationale Research Maatschappij B.V.
Shell Canada Limited
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Shell Internationale Research Maatschappij B.V., Shell Canada Limited filed Critical Shell Internationale Research Maatschappij B.V.
Priority to KR1019950703127A priority Critical patent/KR960700450A/ko
Priority to JP6516692A priority patent/JPH08505944A/ja
Priority to EP94905722A priority patent/EP0681693A1/fr
Priority to BR9405871A priority patent/BR9405871A/pt
Priority to AU59719/94A priority patent/AU5971994A/en
Publication of WO1994017391A1 publication Critical patent/WO1994017391A1/fr
Priority to FI953579A priority patent/FI953579A/fi
Priority to NO952957A priority patent/NO952957D0/no

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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

Definitions

  • the present invention relates to a method for prediction of physical property data of a hydrocarbon product by correlation of its (near-)infra-red ((N.)I.R.) spectrum to said physical property in question by means of neural networks.
  • physical properties include octane number, vapour pressure, cetane number, density and the like.
  • mid-I.R. (3-14 ⁇ m) is used for the determination of cetane ignition improver concentration.
  • organic compounds have in the infra-red region (about 1 to about 300 ⁇ m) a unique spectral fingerprint.
  • a quality monitoring instrument package which actually measures performance specification of fuels would be advantageous , so degradation to the fuel, cross contamination with other fuels, and changes in the fuels composition could be detected and pinpointed in the distribution chain. This will not only ensure the customer receives the fuel exactly as intended, but problems along the distribution chain may be found and addressed.
  • the most important performance measure of gasoline is octane number (O.N.), this is presently measured by a standard engine at the laboratories of the refineries. This engine is an expensive piece of equipment to install, requires frequent maintenance and calibration, and needs highly trained personnel to operate and analyse results. labour and costs prohibit the installation of such engines along the distribution chain and therefore a cheaper alternative to measure octane number needs to be found.
  • a similar performance measure of Diesel oil is cetane number, which is a quantity reflecting the ignition properties of diesel oil. Engines do exist for cetane number measurement.
  • Reference fuels are used in the same way as for the octane number determination for motor gasoline. These reference fuels are n-cetane and ⁇ -methyl naphthalene.
  • the ignition quality is expressed as the cetane number, which is the percentage by volume of cetane in a blend with ⁇ -methyl naphthalene whose ignition performance matches that of the fuel in the test engine.
  • cetane index is normally calculated as "cetane index" from other measurements e.g. density and distillation. The cetane index, however, may not be suitable for diesel fuels of the future, and an alternative measurement of cetane number is required.
  • the invention therefore provides a method for data processing and optimisation of a neural network for application in the deter- mination of physical property data of hydrocarbon products from measured (N.)I.R. spectral absorbances, characterized by the steps of: a) measuring the (N.)I.R. spectra of a large set of hydrocarbon product samples from a wide range of sources; b) selecting an overtone (harmonic) region of the (N.)I.R. spectra, thus obtained; c) selecting a number of discrete wavelengths in each (N.)I.R.
  • the invention is based upon the principle of correlation of physical properties of hydrocarbon products such as octane number, vapour pressure, density and the like, to near-infra-red spectrum. This principle is known as such (vide e.g. EP-A-0,304,232 and
  • a neural network is applied.
  • the general theory and general operation of neural networks as such is known to those skilled in the art and will therefore not be described in detail.
  • 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.
  • 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.
  • the second overtone (harmonic) region of the (N.)I.R. 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.
  • 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 gasolines 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 number of selected wavelengths is 5 for fuels that do not contain alcohols but may contain oxygenates e.g. MTBE and 6 if the fuels do contain alcohol.
  • 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 network used has a three-layer architecture which, for example, comprises in a first layer four input nodes, 2 hidden nodes in a second layer between the input A and output B, and in a third layer one output node.
  • 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.
  • 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 neural network can also be applied advantageously to provide octane numbers of samples from intermediate process streams e.g. catalytic cracking, reformate, isomerate, alkylate. These can be obtained using a single neural network trained on samples from the process streams.
  • 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 have been 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.

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  • 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

Procédé de traitement de données et d'optimalisation d'un réseau neuronal permettant de déterminer des données relatives aux caractéristiques physiques de produits à base d'hydrocarbure à partir d'absorbances spectrales dans (le proche) infrarouge (P)IR. Ce procédé consiste à: a) mesurer les spectres (P)IR d'un grand ensemble d'échantillons de produits d'hydrocarbures provenant d'une grande variété de sources; b) choisir une région de l'harmonique supérieure de ces spectres (P)IR ainsi obtenus; c) sélectionner un certain nombre de longueurs d'onde discrètes dans chaque spectre (P)IR, convertir un certain nombre desdites longueurs d'onde en données d'absorption, et utiliser ces dernières comme entrées dans un réseau neuronal; d) effectuer l'apprentissage du réseau neuronal pour tout l'ensemble de données par la présentation répétée d'entrées et de sorties connues, en d'autres termes les données de proche-infrarouge relatives au produit d'hydrocarbure et des données de caractéristiques physiques pertinentes de ce produit, afin que ce réseau apprenne le rapport entre les deux, et surveiller les performances de ses prédictions par rapport aux données de caractéristiques physiques réelles telles que mesurées selon des procédés standards pour les données d'apprentissage, ce qui permet de corréler les valeurs d'absorbances avec lesdites caractéristiques physiques pertinentes; e) générer un ensemble de valeurs pour les distorsions et les pondérations d'interconnexion du réseau, telles qu'ajustées après la période d'apprentissage (d); et f) appliquer ces valeurs ajustées, à l'aide de l'algorithme du réseau, aux spectres du (proche) infrarouge, obtenus dans les mêmes conditions, pour des produits d'hydrocarbure présentant des données de caractéristiques physiques inconnues.
PCT/EP1994/000233 1993-01-28 1994-01-26 Procede de prediction des donnees relatives aux caracteristiques physiques de produits a base d'hydrocarbure WO1994017391A1 (fr)

Priority Applications (7)

Application Number Priority Date Filing Date Title
KR1019950703127A KR960700450A (ko) 1993-01-28 1994-01-26 탄화수소 생성물의 물리적 성질 자료의 예측 방법(method for prediction of physical property data of hydrocarbon products)
JP6516692A JPH08505944A (ja) 1993-01-28 1994-01-26 炭化水素製品の物理特性データの予測方法
EP94905722A EP0681693A1 (fr) 1993-01-28 1994-01-26 Procede de prediction des donnees relatives aux caracteristiques physiques de produits a base d'hydrocarbure
BR9405871A BR9405871A (pt) 1993-01-28 1994-01-26 Processo para o processamento de dados e otimização de uma rede neural para aplicação na determinação de dados de propriedades fisicas de produtos hidrocarbonetos a pertir de absorvâncias espectrais (próximo) infra-vermelho
AU59719/94A AU5971994A (en) 1993-01-28 1994-01-26 Method for prediction of physical property data of hydrocarbon products
FI953579A FI953579A (fi) 1993-01-28 1995-07-26 Menettely hiilivetytuotteiden fysikaalisten ominaisuusarvojen ennustamiseksi
NO952957A NO952957D0 (no) 1993-01-28 1995-07-26 Fremgangsmåte for å forutsi data for hydrokarbonprodukters fysikalske egenskaper

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP93200229 1993-01-28
EP93200229.8 1993-01-28

Publications (1)

Publication Number Publication Date
WO1994017391A1 true WO1994017391A1 (fr) 1994-08-04

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PCT/EP1994/000233 WO1994017391A1 (fr) 1993-01-28 1994-01-26 Procede de prediction des donnees relatives aux caracteristiques physiques de produits a base d'hydrocarbure

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EP (1) EP0681693A1 (fr)
JP (1) JPH08505944A (fr)
KR (1) KR960700450A (fr)
CN (1) CN1116878A (fr)
AU (1) AU5971994A (fr)
BR (1) BR9405871A (fr)
CA (1) CA2154786A1 (fr)
FI (1) FI953579A (fr)
NO (1) NO952957D0 (fr)
NZ (1) NZ261119A (fr)
WO (1) WO1994017391A1 (fr)
ZA (1) ZA94536B (fr)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1996012173A1 (fr) * 1994-10-14 1996-04-25 University Of Washington Systeme de detection de retard de formation de gouttelettes dans un cytometre de flux
WO1996018089A1 (fr) * 1994-12-09 1996-06-13 Foss Electric A/S Procede permettant d'obtenir des informations
GB2312741A (en) * 1996-01-11 1997-11-05 Intevep Sa Determining parameters of hydrocarbons
EP0950934A1 (fr) * 1998-04-17 1999-10-20 United Technologies Corporation Procédé et appareil pour prévoir une caractéristique d'une propriété d'un produit qui est formé par un procédé d'usinage à l'aide d'un modèle du procédé
JP2008509486A (ja) * 2004-08-03 2008-03-27 イー・アイ・デュポン・ドウ・ヌムール・アンド・カンパニー 化学混合物の特性を予測する方法および装置
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
CN105424539A (zh) * 2015-11-06 2016-03-23 中国科学院天津工业生物技术研究所 基于神经网络的预测玉米秸秆水解后产糖量的方法
WO2016177838A1 (fr) 2015-05-05 2016-11-10 IFP Energies Nouvelles Procede de suivi en continu de l'etat d'avancement d'oxydation d'un carburant

Families Citing this family (6)

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CN100335885C (zh) * 2004-09-30 2007-09-05 长沙开元仪器有限公司 一种煤中氢含量的测试方法
EP1744049A3 (fr) * 2005-07-14 2012-08-15 Korea Petroleum Quality Inspection Institute Appareil de test de carburant monté dans un véhicule
US7679059B2 (en) * 2006-04-19 2010-03-16 Spectrasensors, Inc. Measuring water vapor in hydrocarbons
GB2520520B (en) * 2013-11-22 2018-05-23 Jaguar Land Rover Ltd Methods and system for determining fuel quality in a vehicle
DE102015106881B4 (de) * 2015-05-04 2016-12-29 Rofa Laboratory & Process Analyzers Verfahren zur Bestimmung einer die Klopffestigkeit charakterisierenden Kenngröße eines Kraftstoffs sowie entsprechende Prüfanordnung
US10877016B2 (en) * 2015-12-08 2020-12-29 National Institute For Materials Science Fuel oil identification sensor equipped with receptor layer composed of hydrocarbon-group-modified particles, and fuel oil identification method

Citations (1)

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DE3938645C1 (fr) * 1989-11-01 1992-05-21 Hughes Aircraft Co., Los Angeles, Calif., Us

Patent Citations (1)

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Publication number Priority date Publication date Assignee Title
DE3938645C1 (fr) * 1989-11-01 1992-05-21 Hughes Aircraft Co., Los Angeles, Calif., Us

Non-Patent Citations (5)

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Title
"DETERMINATION OF FUEL PROPERTIES", RESEARCH DISCLOSURE, 1 July 1991 (1991-07-01), pages 571 - 572, XP000258800 *
GEMPERLINE: "NONLINEAR MULTIVARIATE CALIBRATION,ETC.", ANALYTICAL CHEMISTRY, vol. 63, no. 20, 15 October 1991 (1991-10-15), pages 2313 - 2323 *
KELLY: "PREDICTION OF GASOLINE OCTANE NUMBERS,ETC.", ANALYTICAL CHEMISTRY, vol. 61, no. 4, 15 February 1989 (1989-02-15), pages 313 - 320, XP000307462 *
LONG: "SPECTROSCOPIC CALIBRATION,ETC.", ANALYTICAL CHEMISTRY, vol. 62, no. 17, 1 September 1990 (1990-09-01), pages 1791 - 1797, XP000165499 *
MORRIS: "DEVELOPMENT OF EXPERT SYSTEM,ETC.", INTELLIGENT INSTRUMENTS & COMPUTERS, vol. 9, no. 5, 1 May 1991 (1991-05-01), pages 167 - 176 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1996012173A1 (fr) * 1994-10-14 1996-04-25 University Of Washington Systeme de detection de retard de formation de gouttelettes dans un cytometre de flux
WO1996018089A1 (fr) * 1994-12-09 1996-06-13 Foss Electric A/S Procede permettant d'obtenir des informations
US5771096A (en) * 1994-12-09 1998-06-23 Foss Electric A/S Method of obtaining information
GB2312741A (en) * 1996-01-11 1997-11-05 Intevep Sa Determining parameters of hydrocarbons
EP0950934A1 (fr) * 1998-04-17 1999-10-20 United Technologies Corporation Procédé et appareil pour prévoir une caractéristique d'une propriété d'un produit qui est formé par un procédé d'usinage à l'aide d'un modèle du procédé
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
JP2008509486A (ja) * 2004-08-03 2008-03-27 イー・アイ・デュポン・ドウ・ヌムール・アンド・カンパニー 化学混合物の特性を予測する方法および装置
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
WO2016177838A1 (fr) 2015-05-05 2016-11-10 IFP Energies Nouvelles Procede de suivi en continu de l'etat d'avancement d'oxydation d'un carburant
US10564141B2 (en) 2015-05-05 2020-02-18 IFP Energies Nouvelles Method for continuously monitoring the degree of progress of oxidation of a fuel
CN105424539A (zh) * 2015-11-06 2016-03-23 中国科学院天津工业生物技术研究所 基于神经网络的预测玉米秸秆水解后产糖量的方法

Also Published As

Publication number Publication date
ZA94536B (en) 1994-09-09
CA2154786A1 (fr) 1994-08-04
JPH08505944A (ja) 1996-06-25
CN1116878A (zh) 1996-02-14
NO952957L (no) 1995-07-26
FI953579A0 (fi) 1995-07-26
FI953579A (fi) 1995-07-26
NO952957D0 (no) 1995-07-26
KR960700450A (ko) 1996-01-20
BR9405871A (pt) 1995-12-12
AU5971994A (en) 1994-08-15
NZ261119A (en) 1997-08-22
EP0681693A1 (fr) 1995-11-15

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