WO1994008226A1 - An apparatus for fuel quality monitoring - Google Patents

An apparatus for fuel quality monitoring Download PDF

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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
Application number
PCT/EP1993/002735
Other languages
French (fr)
Inventor
Andrew Boyd
John Michael Tolchard
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 AU51493/93A priority Critical patent/AU676854B2/en
Priority to EP93922522A priority patent/EP0663998A1/en
Priority to KR1019950701327A priority patent/KR950703732A/en
Priority to CA002146255A priority patent/CA2146255A1/en
Priority to BR9307172A priority patent/BR9307172A/en
Priority to JP6508731A priority patent/JPH08501878A/en
Publication of WO1994008226A1 publication Critical patent/WO1994008226A1/en
Priority to FI951570A priority patent/FI951570A0/en
Priority to NO951284A priority patent/NO951284L/en

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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

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 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.

Description

AN APPARATUS FOR FUEL QUALITY MONITORING
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. As is known to those skilled in the art, organic compounds have in the infra-red spectral region (about 1 to about 300 μm) a unique spectral fingerprint.
The potential to obtain correlations between the physical and chemical properties of materials, and their Near Infra Red (NIR) spectra has already been disclosed. (Vide e.g. EP-A-O,304,232 and EP-A-2,085,251).
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. By the application of 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. The use of (N)IR technology coupled with an empirical model can be therefore used to predict performance quality of a fuel. The application of these techniques to an on-line real-time field instrument is, however, not trivial. This is because the spectrometers use highly precise optical moving parts and are extremely sensitive to dirty hostile environments such as found in the petrochemical plant or a distribution terminal. Instrument manufacturers are striving to produce more robust spectrometers.
Despite improvements, the spectrometers which are very expensive are non-ideal for on-line real-time monitoring due to their delicate nature, labour costs and the harshness of the environment. A method of simplifying the application of (N)IR techniques as well as the statistical technique to analyse the data is necessary.
Now, a small, robust, cheap and reliable "non-moving parts" instrument has been developed, that uses (near) infra-red techniques (advantageously 0.78-30 μm 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.
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. As already indicated in the foregoing, the principle of the invention is based upon the technique of (near) infra-red ((N)IR) analysis, advantageously coupled with the technology of neural networks. Generally, 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.
According to an advantageous learning algorithm, 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. In the following, 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:
1. The mean spectrum of the set is generated and the differences between each individual spectrum and the mean are calculated.
2. 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.
3. In the case of neural network technology 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.
Advantageously, 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.
Advantageously, 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. Advantageously, for cetane number measurement a wavelength of 6-7 μm 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.
Once the neural network has "learned" the relationship, 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.
The invention will now be described in more detail by way of example by reference to the accompanying drawings, in which: fig. 1 represents schematically an engine based on-line octane analyzer; and fig. 2 represents schematically a neural network advantageously applied in the apparatus of the invention.
Referring to fig. 1. an optical means 1 has been shown. Advantageously, this optical means 1 comprises a plurality of light-emitting diodes (LED), a filter and a lens-holder. For reasons of clarity, mechanical connections of the analyzer to the engine or to the car have not been shown.
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) . Further, a photodetector is present and provides the obtained signal to be input to the processing electronics and neural network for spectral analysis. In fig. 1, there are shown 5 LED's; however, any suitable number can be applied. For reasons of clarity the processing electronics and neural network for spectral analysis are not shown. Advantageously the geometry of the apparatus of the invention is such that it can be applied in cars as an engine-based instrument.
Advantageously, as shown in fig. 2 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. As known to those skilled in the art 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. Thus, for a prediction which utilizes the network algorithm to describe octane number from (N)IR-data, 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.
For multiple outputs, 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.
In addition to unleaded motor gasoline, 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. It will be appreciated by those skilled in the art that 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. Advantageously, 2 to 5 layers are applied. According to the invention advantageously 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, and 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:
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.
1-1.5 microns). Advantageously, for gasoline 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. It will be appreciated that for gasoline/alcohol 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. Advantageously, 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.
Various modifications of the present invention will become apparent to those skilled in the art from the foregoing description. Such modifications are intended to fall within the scope of the appended claims.

Claims

C L A I S
1. 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.
2. The apparatus as claimed in claim 1, wherein the spectral range is 0.78-30 μ wavelength.
3. The apparatus as claimed in claims 1 or 2, wherein (N)IR radiation is provided by light-emitting diodes.
4. The apparatus as claimed in claim 3, wherein the number of light-emitting diodes is at least 5.
5. The apparatus as claimed in any one of claims 1-4, wherein an optical fibre bundle delivers the light to the hydrocarbon product line.
6. The apparatus as claimed in claims 4 or 5, comprising means for selecting the light-emitting diodes.
7. The apparatus as claimed in claim 6, wherein the said selection takes place by electronic pulses.
8. The apparatus as claimed in any one of claims 1-7, wherein at least one optical window is placed in the hydrocarbon product line.
9. The apparatus as claimed in claim 8, wherein the optical path length is 10-30 mm, advantageously 20 mm.
10. The apparatus as claimed in any one of claims 1-9, comprising an indium gallium arsenide detector.
11. The apparatus as claimed in any one of claims 1-10, wherein the hydrocarbon product line comprises an in-line cell.
12. The apparatus as claimed in any one of claims 1-11, wherein its geometry is engine-based.
13. The apparatus as claimed in any one of claims 1-12, wherein the said processing equipment comprises a neural network.
14. The apparatus as claimed in claim 13, wherein the number of layers of the neural network is 2 to 5.
15. The apparatus as claimed in claim 14, wherein the neural network applied has a three-layer or four-layer architecture.
16. The apparatus as claimed in claim 15, wherein the number of nodes of the input layer is from 3 to 10, the number of nodes of the hidden layer(s) is from 1 to 10, and the number of nodes of the output layer is from 1 to 3.
17. The apparatus as claimed in claim 15 or 16, wherein the network comprises 4 input nodes, 2 hidden nodes and one output node ((4, 2, 1) network) .
18. The apparatus as claimed in claim 15 or 16, wherein the network is a (3, 5, 1) network.
19. The apparatus as claimed in claim 15 or 16, wherein the network is a (6, 6, 3) network.
20. The apparatus as claimed in claim 15 or 16, wherein the network is a (6, 6, 6, 3) network.
PCT/EP1993/002735 1992-10-05 1993-10-04 An apparatus for fuel quality monitoring WO1994008226A1 (en)

Priority Applications (8)

Application Number Priority Date Filing Date Title
AU51493/93A AU676854B2 (en) 1992-10-05 1993-10-04 An apparatus for fuel quality monitoring
EP93922522A EP0663998A1 (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
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
FI951570A FI951570A0 (en) 1992-10-05 1995-04-03 Device for monitoring fuel quality
NO951284A NO951284L (en) 1992-10-05 1995-04-03 Device for measuring fuel

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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
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
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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
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US5861228A (en) * 1994-10-07 1999-01-19 Bp Chemicals Limited Cracking property determination
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
US5935863A (en) * 1994-10-07 1999-08-10 Bp Chemicals Limited Cracking property determination and process control
WO1996015064A1 (en) * 1994-11-10 1996-05-23 Serge Piemont Device for identifying hydrocarbon fluids
FR2726910A1 (en) * 1994-11-10 1996-05-15 Piemont Serge DEVICE FOR IDENTIFYING HYDROCARBON FLUIDS
US5817517A (en) * 1995-02-08 1998-10-06 Exxon Research And Engineering Company Method of characterizing feeds to catalytic cracking process units
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
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
AU694896B2 (en) * 1995-10-18 1998-07-30 Shell Internationale Research Maatschappij B.V. Method for predicting a physical property of a residual hydrocarbonaceous material
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
WO1997031384A1 (en) * 1996-02-21 1997-08-28 Idec Izumi Corporation Photoelectric switching device and switching method
US6043504A (en) * 1996-02-21 2000-03-28 Idec Izumi Corporation Apparatus and method for detecting transparent substances
USRE37926E1 (en) * 1996-02-21 2002-12-10 Idec Izumi Corporation Apparatus and method for detecting transparent substances
US5822058A (en) * 1997-01-21 1998-10-13 Spectral Sciences, Inc. Systems and methods for optically measuring properties of hydrocarbon fuel gases
WO1998032003A1 (en) * 1997-01-21 1998-07-23 Spectral Sciences, Inc. Systems and methods for optically measuring properties of hydrocarbon fuel gases
EP0922953A1 (en) * 1997-12-09 1999-06-16 AGIP PETROLI S.p.A. Process for predicting the cold characteristics of gasoils
WO2003046522A3 (en) * 2001-11-30 2004-06-10 Air Liquide Apparatus and methods for launching and receiving a broad wavelength range source
US7005645B2 (en) 2001-11-30 2006-02-28 Air Liquide America L.P. Apparatus and methods for launching and receiving a broad wavelength range source
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
US7676316B2 (en) 2005-03-22 2010-03-09 Sp3H Methods for optimizing the operation parameters of a combustion engine
WO2006100377A1 (en) * 2005-03-22 2006-09-28 Sp3H Method for optimizing operating parameters of a combustion engine
FR2883602A1 (en) * 2005-03-22 2006-09-29 Alain Lunati METHOD FOR OPTIMIZING THE OPERATING PARAMETERS OF A COMBUSTION ENGINE
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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
WO2009040635A1 (en) * 2007-09-26 2009-04-02 Toyota Jidosha Kabushiki Kaisha Device and method for detecting degradation of fuel for internal combustion engine
CN101809442A (en) * 2007-09-26 2010-08-18 丰田自动车株式会社 Device and method for detecting degradation of fuel for internal combustion engine
US8347828B2 (en) 2007-09-26 2013-01-08 Toyota Jidosha Kabushiki Kaisha Device and method for detecting degradation of fuel for internal combustion engine
WO2009138585A1 (en) * 2008-04-24 2009-11-19 Sp3H Method for optimising the operation of a thermal engine by determining the proportion of oxygenated compounds in the fuel
JP2011518984A (en) * 2008-04-24 2011-06-30 エスペートロワアッシュ A method for optimizing the operation of a heat engine by determining the proportion of oxygenates in the fuel.
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
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US9234477B2 (en) 2008-04-24 2016-01-12 Sp3H Method for optimizing the operation of a thermal engine by determining the proportion of oxygenated compounds in the fuel
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

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MY108958A (en) 1996-11-30
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NO951284D0 (en) 1995-04-03
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BR9307172A (en) 1999-03-30
NO951284L (en) 1995-04-03
JPH08501878A (en) 1996-02-27
CA2146255A1 (en) 1994-04-14
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EP0663998A1 (en) 1995-07-26
FI951570A0 (en) 1995-04-03

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