EP0663998A1 - Appareil destine a controler la qualite de carburants - Google Patents

Appareil destine a controler la qualite de carburants

Info

Publication number
EP0663998A1
EP0663998A1 EP93922522A EP93922522A EP0663998A1 EP 0663998 A1 EP0663998 A1 EP 0663998A1 EP 93922522 A EP93922522 A EP 93922522A EP 93922522 A EP93922522 A EP 93922522A EP 0663998 A1 EP0663998 A1 EP 0663998A1
Authority
EP
European Patent Office
Prior art keywords
network
light
spectral
nodes
product line
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.)
Ceased
Application number
EP93922522A
Other languages
German (de)
English (en)
Inventor
Andrew Pool Lane Ince Boyd
John Michael Pool Lane Ince Tolchard
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shell Internationale Research Maatschappij BV
Original Assignee
Shell Internationale Research Maatschappij BV
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 BV filed Critical Shell Internationale Research Maatschappij BV
Priority to EP93922522A priority Critical patent/EP0663998A1/fr
Publication of EP0663998A1 publication Critical patent/EP0663998A1/fr
Ceased legal-status Critical Current

Links

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.

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)
  • Sampling And Sample Adjustment (AREA)

Abstract

Appareil destiné à mesurer en direct les propriétés physiques d'hydrocarbures, tels que l'indice d'octane, l'indice de cétane, la densité, la pression de vapeur et autres ou le rapport essence/alcool, qui comporte un dispositif destiné à produire des rayonnements infrarouges proches (N)IR dans un domaine spectral prédéterminé, un dispositif destiné à transmettre de la lumière à des longueurs d'ondes sélectionnées dans le domaine spectral (N)IR, un dispositif destiné à acheminer la lumière dudit dispositif de transmission à une chaîne de production d'hydrocarbures, un dispositif destiné à permettre une longueur de parcours optique dans une chaîne de production d'hydrocarbures, un dispositif destiné à détecter la lumière transmise à travers ledit parcours optique et un dispositif permettant d'entrer les signaux obtenus dans un équipement de traitement en vue de l'analyse spectrale et d'établir une corrélation entre les données spectrales et les données relatives aux propriétés physiques des hydrocarbures, telles que l'indiced'octane, l'indice de cétane, la densité, la pression de vapeur et autres ou le rapport essence/alcool.
EP93922522A 1992-10-05 1993-10-04 Appareil destine a controler la qualite de carburants Ceased EP0663998A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP93922522A EP0663998A1 (fr) 1992-10-05 1993-10-04 Appareil destine a controler la qualite de carburants

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
EP92309075 1992-10-05
EP92309075 1992-10-05
EP93200229 1993-01-28
EP93200229 1993-01-28
PCT/EP1993/002735 WO1994008226A1 (fr) 1992-10-05 1993-10-04 Appareil destine a controler la qualite de carburants
EP93922522A EP0663998A1 (fr) 1992-10-05 1993-10-04 Appareil destine a controler la qualite de carburants

Publications (1)

Publication Number Publication Date
EP0663998A1 true EP0663998A1 (fr) 1995-07-26

Family

ID=26132219

Family Applications (1)

Application Number Title Priority Date Filing Date
EP93922522A Ceased EP0663998A1 (fr) 1992-10-05 1993-10-04 Appareil destine a controler la qualite de carburants

Country Status (11)

Country Link
EP (1) EP0663998A1 (fr)
JP (1) JPH08501878A (fr)
KR (1) KR950703732A (fr)
AU (1) AU676854B2 (fr)
BR (1) BR9307172A (fr)
CA (1) CA2146255A1 (fr)
FI (1) FI951570A (fr)
MY (1) MY108958A (fr)
NO (1) NO951284L (fr)
NZ (1) NZ256675A (fr)
WO (1) WO1994008226A1 (fr)

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EP0706049A1 (fr) * 1994-10-07 1996-04-10 Bp Chemicals S.N.C. Détermination des caractéristiques de craquage
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EP0706040A1 (fr) * 1994-10-07 1996-04-10 Bp Chemicals S.N.C. Détermination de propriété
EP0706050A1 (fr) * 1994-10-07 1996-04-10 Bp Chemicals S.N.C. Détermination d'une caractéristique d'un lubrifiant
FR2726910B1 (fr) * 1994-11-10 1996-12-27 Piemont Serge Dispositif d'identification de fluides hydrocarbures
CA2168384C (fr) * 1995-02-08 2007-05-15 Bruce Nelson Perry Methode permettant de determiner les caracteristiques des charges utilisees dans un procede de craquage catalytique
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AR003845A1 (es) * 1995-10-18 1998-09-09 Shell Int Research Un método para predecir una propiedad física de un residuo de petróleo crudo, de un petróleo combustible (fuel oil) residual o de un material bituminoso.
DE69733925T2 (de) * 1996-02-21 2006-06-14 Idec Izumi Corp Photoelektrische schaltvorrichtung und schaltverfahren
US5822058A (en) * 1997-01-21 1998-10-13 Spectral Sciences, Inc. Systems and methods for optically measuring properties of hydrocarbon fuel gases
IT1296939B1 (it) * 1997-12-09 1999-08-03 Euron Spa Procedimento per la predizione delle caratteristiche a freddo di gasoli
US6687621B2 (en) * 2000-11-20 2004-02-03 The Procter & Gamble Company Predictive method for polymers
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CN100425975C (zh) * 2004-07-29 2008-10-15 中国石油化工股份有限公司 由近红外光谱测定汽油性质数据的方法
FR2883602B1 (fr) * 2005-03-22 2010-04-16 Alain Lunati Procede d'optimisation des parametres de fonctionnement d'un moteur a combustion
FR2916019B1 (fr) * 2007-05-07 2014-06-27 Sp3H Procede de reglage des parametres d'injection, de combustion et/ou de post-traitement d'un moteur a combustion interne a auto-allumage.
FR2920475B1 (fr) * 2007-08-31 2013-07-05 Sp3H Dispositif de gestion centralisee des mesures et de l'information relative a des flux liquides et gazeux necessaires au fonctionnement d'un moteur thermique
JP4483922B2 (ja) * 2007-09-26 2010-06-16 トヨタ自動車株式会社 内燃機関の燃料劣化検出装置
FR2930598B1 (fr) * 2008-04-24 2012-01-27 Sp3H Procede d'optimisation du fonctionnement d'un moteur thermique par determination de la proportion des composes oxygenes dans le carburant
CN101893560B (zh) * 2010-07-13 2012-04-25 中国人民解放军总后勤部油料研究所 一种汽油锰含量快速测定方法
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GB2520520B (en) * 2013-11-22 2018-05-23 Jaguar Land Rover Ltd Methods and system for determining fuel quality in a vehicle
CN111323387A (zh) * 2020-03-21 2020-06-23 哈尔滨工程大学 甲烷值在线实时监测系统

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Also Published As

Publication number Publication date
AU5149393A (en) 1994-04-26
NZ256675A (en) 1995-11-27
NO951284D0 (no) 1995-04-03
KR950703732A (ko) 1995-09-20
BR9307172A (pt) 1999-03-30
FI951570A0 (fi) 1995-04-03
NO951284L (no) 1995-04-03
JPH08501878A (ja) 1996-02-27
WO1994008226A1 (fr) 1994-04-14
CA2146255A1 (fr) 1994-04-14
MY108958A (en) 1996-11-30
AU676854B2 (en) 1997-03-27
FI951570A (fi) 1995-04-03

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