CA2146255A1 - Apparatus for fuel quality monitoring - Google Patents
Apparatus for fuel quality monitoringInfo
- Publication number
- CA2146255A1 CA2146255A1 CA002146255A CA2146255A CA2146255A1 CA 2146255 A1 CA2146255 A1 CA 2146255A1 CA 002146255 A CA002146255 A CA 002146255A CA 2146255 A CA2146255 A CA 2146255A CA 2146255 A1 CA2146255 A1 CA 2146255A1
- Authority
- CA
- Canada
- Prior art keywords
- network
- nodes
- product line
- hydrocarbon product
- spectral
- 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.)
- Abandoned
Links
- 239000000446 fuel Substances 0.000 title description 16
- 238000012544 monitoring process Methods 0.000 title description 3
- 239000004215 Carbon black (E152) Substances 0.000 claims abstract description 21
- 229930195733 hydrocarbon Natural products 0.000 claims abstract description 21
- 150000002430 hydrocarbons Chemical class 0.000 claims abstract description 21
- TVMXDCGIABBOFY-UHFFFAOYSA-N octane Chemical compound CCCCCCCC TVMXDCGIABBOFY-UHFFFAOYSA-N 0.000 claims abstract description 19
- 230000003595 spectral effect Effects 0.000 claims abstract description 16
- 230000003287 optical effect Effects 0.000 claims abstract description 15
- CIWBSHSKHKDKBQ-JLAZNSOCSA-N Ascorbic acid Chemical compound OC[C@H](O)[C@H]1OC(=O)C(O)=C1O CIWBSHSKHKDKBQ-JLAZNSOCSA-N 0.000 claims abstract description 13
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims abstract description 11
- 230000000704 physical effect Effects 0.000 claims abstract description 9
- 230000005855 radiation Effects 0.000 claims abstract description 6
- 238000010183 spectrum analysis Methods 0.000 claims abstract description 6
- 238000013528 artificial neural network Methods 0.000 claims description 22
- 229910000530 Gallium indium arsenide Inorganic materials 0.000 claims description 2
- KXNLCSXBJCPWGL-UHFFFAOYSA-N [Ga].[As].[In] Chemical compound [Ga].[As].[In] KXNLCSXBJCPWGL-UHFFFAOYSA-N 0.000 claims description 2
- 239000013307 optical fiber Substances 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 5
- 238000000034 method Methods 0.000 description 12
- 238000005259 measurement Methods 0.000 description 9
- 238000012549 training Methods 0.000 description 8
- 238000001228 spectrum Methods 0.000 description 7
- 238000002835 absorbance Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 238000004566 IR spectroscopy Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 2
- 210000004556 brain Anatomy 0.000 description 2
- 210000004027 cell Anatomy 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 150000001298 alcohols Chemical class 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000002329 infrared spectrum Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 150000002894 organic compounds Chemical class 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 210000000225 synapse Anatomy 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/26—Oils; viscous liquids; paints; inks
- G01N33/28—Oils, i.e. hydrocarbon liquids
- G01N33/2835—Oils, i.e. hydrocarbon liquids specific substances contained in the oil or fuel
- G01N33/2852—Oils, i.e. hydrocarbon liquids specific substances contained in the oil or fuel alcohol/fuel mixtures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/26—Oils; viscous liquids; paints; inks
- G01N33/28—Oils, i.e. hydrocarbon liquids
- G01N33/2829—Oils, i.e. hydrocarbon liquids mixtures of fuels, e.g. determining the RON-number
Abstract
An apparatus for on-line measuring physical property data of hydrocarbon products such as octane number, cetane num-ber, density, vapour pressure and the like or gasoline/alcohol ratio, comprising means for providing (N)IR radiation in a prede-termined 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 hydrocar-bon products such as octane number, cetane number, density, vapour pressure and the like or gasoline/alcohol ratio.
Description
214625~
W O 94/08226 PCT/EP93/0273~
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 lO 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 l to about 300 ~m) a unique spectral fingerprint.
The potential to obtain correlations between the physical and 15 chemical properties of materials, and their Near Infra Red (NIR) spectra has already been disclosed. (Vide e.g. EP-A-0,304,232 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 25 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
30 technology coupled with an empirical model can be therefore used to predict performance quality of a fuel. The application of these 21462~
techniques to an on-line real-time field instrument is, however, not trivial. This i~ 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 ~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.
The invention therefore provides an apparatus for on-line measuring physical property data of hydrocarbon products such as octane number, cetane number, density, vapoùr pressure and the like or gasoline/alcohol ratio, characterized by means for providing (N)IR radiation in a predetermined spectral range; said means being optically connected to the hydrocarbon product line in such a manner that an optical path length is present 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 a neural network 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, wherein the number of layers of the neural network is 2 to 5, and in case of three or more layers the number of A~llEN~E~) SHEET
W O 94/08226 PCT/EP93/0273~
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 lO 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 l to about 300 ~m) a unique spectral fingerprint.
The potential to obtain correlations between the physical and 15 chemical properties of materials, and their Near Infra Red (NIR) spectra has already been disclosed. (Vide e.g. EP-A-0,304,232 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 25 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
30 technology coupled with an empirical model can be therefore used to predict performance quality of a fuel. The application of these 21462~
techniques to an on-line real-time field instrument is, however, not trivial. This i~ 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 ~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.
The invention therefore provides an apparatus for on-line measuring physical property data of hydrocarbon products such as octane number, cetane number, density, vapoùr pressure and the like or gasoline/alcohol ratio, characterized by means for providing (N)IR radiation in a predetermined spectral range; said means being optically connected to the hydrocarbon product line in such a manner that an optical path length is present 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 a neural network 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, wherein the number of layers of the neural network is 2 to 5, and in case of three or more layers the number of A~llEN~E~) SHEET
2 14 ~255 - 2a -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.
It is remarked that DE-A-3,716,793 discloses an apparatus for on-line measuring physical property data of hydrocarbon products such as gasoline/alcohol ratio.
However, the specific apparatus of the invention applying a specific neural network has not been disclosed.
MCS7/T567lPCT
A~ENDED SHEEJ
~ 21962~
It is remarked that DE-A-3,716,793 discloses an apparatus for on-line measuring physical property data of hydrocarbon products such as gasoline/alcohol ratio.
However, the specific apparatus of the invention applying a specific neural network has not been disclosed.
MCS7/T567lPCT
A~ENDED SHEEJ
~ 21962~
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 mini~i~e 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 35 so the problem of analysis of a set of 100 fuels is very difficult.
-W 0 94/082~ 1 4 ~ 2 5 ~ PCT/EP93/02735 _ - 4 -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.~Principal Component Analysis is used on the training set of gasQ ~ , to generate a 'property spectrum' which represents the re'~ative 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 ~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 re~inin~ 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 ~ 214625~
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).
21~625~ --Further, a photodetector 4 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, l) 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.
21 ~ 62~s W O 94/08226 P ~ /EP93/0~735 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 nm, 1170 nm, 1190 nm and 1219 nm, the normalization wavelength being 1106 nm due to gasoline having minir-l 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 W O 94/08226 21 4 6 2 S 5 PCr/EP93/02735 -description. Such modifications are intended to fall within the scope of the appended claims.
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 mini~i~e 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 35 so the problem of analysis of a set of 100 fuels is very difficult.
-W 0 94/082~ 1 4 ~ 2 5 ~ PCT/EP93/02735 _ - 4 -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.~Principal Component Analysis is used on the training set of gasQ ~ , to generate a 'property spectrum' which represents the re'~ative 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 ~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 re~inin~ 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 ~ 214625~
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).
21~625~ --Further, a photodetector 4 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, l) 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.
21 ~ 62~s W O 94/08226 P ~ /EP93/0~735 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 nm, 1170 nm, 1190 nm and 1219 nm, the normalization wavelength being 1106 nm due to gasoline having minir-l 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 W O 94/08226 21 4 6 2 S 5 PCr/EP93/02735 -description. Such modifications are intended to fall within the scope of the appended claims.
Claims (16)
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, characterized by means (1) for providing (N)IR radiation in a predetermined spectral range; said means (1) being optically connected to the hydrocarbon product line in such a manner that an optical path length is present 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 a neural network 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, wherein the number of layers of the neural network is 2 to 5, and in case of three or more layers 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.
means for detecting the light transmitted through the said optical path;
means for providing the obtained signal to be input to a neural network 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, wherein the number of layers of the neural network is 2 to 5, and in case of three or more layers 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.
2. The apparatus as claimed in claim 1, wherein the spectral range is 0.78-30 µm wavelength.
3. The apparatus as claimed in claim 1 or 2, wherein (N)IR
radiation is provided by light-emitting diodes.
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 said means (1) for providing N(IR) radiation is optically connected to the hydrocarbon product line through an optical fibre bundle.
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 network comprises 4 input nodes, 2 hidden nodes and one output node ((4, 2, 1) network).
14. The apparatus as claimed in any one of claims 1-12, wherein the network is a (3, 5, 1) network.
15. The apparatus as claimed in any one of claims 1-12, wherein the network is a (6, 6, 3) network.
16. The apparatus as claimed in any one of claims 1-12, wherein the network is a (6, 6, 6, 3) network.
Applications Claiming Priority (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP92309075 | 1992-10-05 | ||
| EP92309075.7 | 1992-10-05 | ||
| EP93200229.8 | 1993-01-28 | ||
| EP93200229 | 1993-01-28 | ||
| PCT/EP1993/002735 WO1994008226A1 (en) | 1992-10-05 | 1993-10-04 | An apparatus for fuel quality monitoring |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2146255A1 true CA2146255A1 (en) | 1994-04-14 |
Family
ID=26132219
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA002146255A Abandoned CA2146255A1 (en) | 1992-10-05 | 1993-10-04 | Apparatus for fuel quality monitoring |
Country Status (11)
| Country | Link |
|---|---|
| EP (1) | EP0663998A1 (en) |
| JP (1) | JPH08501878A (en) |
| KR (1) | KR950703732A (en) |
| AU (1) | AU676854B2 (en) |
| BR (1) | BR9307172A (en) |
| CA (1) | CA2146255A1 (en) |
| FI (1) | FI951570A0 (en) |
| MY (1) | MY108958A (en) |
| NO (1) | NO951284L (en) |
| NZ (1) | NZ256675A (en) |
| WO (1) | WO1994008226A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101893560A (en) * | 2010-07-13 | 2010-11-24 | 中国人民解放军总后勤部油料研究所 | Method for quickly determining manganese content in gasoline |
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|---|---|---|---|---|
| US5572030A (en) * | 1994-04-22 | 1996-11-05 | Intevep, S.A. | Method for determining parameter of hydrocarbon |
| NL1003058C2 (en) * | 1996-01-11 | 1997-11-10 | Intevep Sa | Evaluation of hydrocarbon fuels by near=I.R. spectroscopy |
| US5935863A (en) * | 1994-10-07 | 1999-08-10 | Bp Chemicals Limited | Cracking property determination and process control |
| EP0706050A1 (en) * | 1994-10-07 | 1996-04-10 | Bp Chemicals S.N.C. | Lubricant property determination |
| EP0706040A1 (en) * | 1994-10-07 | 1996-04-10 | Bp Chemicals S.N.C. | Property determination |
| EP0706049A1 (en) * | 1994-10-07 | 1996-04-10 | Bp Chemicals S.N.C. | Cracking property determination |
| FR2726910B1 (en) * | 1994-11-10 | 1996-12-27 | Piemont Serge | HYDROCARBON FLUID IDENTIFICATION DEVICE |
| CA2168384C (en) * | 1995-02-08 | 2007-05-15 | Bruce Nelson Perry | Method for characterizing feeds to catalytic cracking process units |
| AR003845A1 (en) * | 1995-10-18 | 1998-09-09 | Shell Int Research | A METHOD FOR PREDICTING A PHYSICAL PROPERTY OF A RESIDUAL OIL RESIDUE, A RESIDUAL FUEL OIL OR A BITUMINOUS MATERIAL. |
| AR003846A1 (en) * | 1995-10-18 | 1998-09-09 | Shell Int Research | A TRANSMISSION CELL SUITABLE FOR USE IN A DEVICE TO MEASURE INFRARED (NEARBY) SPECTRUMS OF A HYDROCARBONACEOUS MATERIAL, A SPECTROMETER THAT UNDERSTANDS IT, USE OF THE SAME, A PHYSICAL PROPERTY OF SUCH MATERIAL PROCEDURE TO PREPARE A BETUM COMPOSITION USING SUCH METHOD WITH SUCH SPECTOMETER |
| WO1997031384A1 (en) * | 1996-02-21 | 1997-08-28 | Idec Izumi Corporation | Photoelectric switching device and switching method |
| US5822058A (en) * | 1997-01-21 | 1998-10-13 | Spectral Sciences, Inc. | Systems and methods for optically measuring properties of hydrocarbon fuel gases |
| IT1296939B1 (en) * | 1997-12-09 | 1999-08-03 | Euron Spa | PROCEDURE FOR THE PREDICTION OF GASOLI COLD CHARACTERISTICS |
| EP1339820A2 (en) * | 2000-11-20 | 2003-09-03 | The Procter & Gamble Company | Fabric softening compositions and methods |
| 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 |
| FR2883602B1 (en) * | 2005-03-22 | 2010-04-16 | Alain Lunati | METHOD FOR OPTIMIZING THE OPERATING PARAMETERS OF A COMBUSTION ENGINE |
| FR2916019B1 (en) * | 2007-05-07 | 2014-06-27 | Sp3H | METHOD FOR ADJUSTING THE PARAMETERS OF INJECTION, COMBUSTION AND / OR POST-PROCESSING OF A SELF-IGNITION INTERNAL COMBUSTION ENGINE. |
| FR2920475B1 (en) * | 2007-08-31 | 2013-07-05 | Sp3H | DEVICE FOR CENTRALIZED MANAGEMENT OF MEASUREMENTS AND INFORMATION RELATING TO LIQUID AND GASEOUS FLOWS NECESSARY FOR THE OPERATION OF A THERMAL ENGINE |
| JP4483922B2 (en) | 2007-09-26 | 2010-06-16 | トヨタ自動車株式会社 | Fuel deterioration detection device for internal combustion engine |
| FR2930598B1 (en) * | 2008-04-24 | 2012-01-27 | Sp3H | METHOD FOR OPTIMIZING THE OPERATION OF A THERMAL ENGINE BY DETERMINING THE PROPORTION OF OXYGEN COMPOUNDS IN THE FUEL |
| FR2985316B1 (en) * | 2012-01-04 | 2015-08-07 | Rhodia Operations | METHOD FOR THE EXTERNAL DIAGNOSIS OF THE DYSFUNCTION OF A DEVICE ADDITIVE DEVICE IN A FUEL FOR A VEHICLE |
| GB2520520B (en) * | 2013-11-22 | 2018-05-23 | Jaguar Land Rover Ltd | 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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| US3716793A (en) * | 1971-12-01 | 1973-02-13 | L Parker | Amplitude modulation detector for radio receivers |
| US4404642A (en) * | 1981-05-15 | 1983-09-13 | Trebor Industries, Inc. | Apparatus for near infrared quantitative analysis with temperature variation correction |
| JPS5912323A (en) * | 1982-07-14 | 1984-01-23 | Asahi Chem Ind Co Ltd | Wavelength analyzer |
| JPS62192633A (en) * | 1986-02-19 | 1987-08-24 | Ngk Spark Plug Co Ltd | Mixing ratio sensor for alcohol mixed fuel |
| FR2611911B1 (en) * | 1987-02-27 | 1989-06-23 | Bp France | METHOD OF DIRECT DETERMINATION OF AN OCTANE INDEX |
| DE3855720T2 (en) * | 1987-08-18 | 1997-05-22 | Bp Oil Int | Method for the direct determination of the physical properties of hydrocarbon products |
| DE3926881C2 (en) * | 1989-08-16 | 1994-04-21 | Ulrich Dr Schreiber | Spectrophotometer for measuring rapid changes over time of absorption difference spectra |
| US4963745A (en) * | 1989-09-01 | 1990-10-16 | Ashland Oil, Inc. | Octane measuring process and device |
-
1993
- 1993-09-30 MY MYPI93001987A patent/MY108958A/en unknown
- 1993-10-04 WO PCT/EP1993/002735 patent/WO1994008226A1/en not_active Application Discontinuation
- 1993-10-04 KR KR1019950701327A patent/KR950703732A/en not_active Application Discontinuation
- 1993-10-04 CA CA002146255A patent/CA2146255A1/en not_active Abandoned
- 1993-10-04 BR BR9307172A patent/BR9307172A/en not_active Application Discontinuation
- 1993-10-04 JP JP6508731A patent/JPH08501878A/en active Pending
- 1993-10-04 NZ NZ256675A patent/NZ256675A/en unknown
- 1993-10-04 AU AU51493/93A patent/AU676854B2/en not_active Ceased
- 1993-10-04 EP EP93922522A patent/EP0663998A1/en not_active Ceased
-
1995
- 1995-04-03 FI FI951570A patent/FI951570A0/en unknown
- 1995-04-03 NO NO951284A patent/NO951284L/en unknown
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101893560A (en) * | 2010-07-13 | 2010-11-24 | 中国人民解放军总后勤部油料研究所 | Method for quickly determining manganese content in gasoline |
Also Published As
| Publication number | Publication date |
|---|---|
| WO1994008226A1 (en) | 1994-04-14 |
| NO951284D0 (en) | 1995-04-03 |
| AU676854B2 (en) | 1997-03-27 |
| NO951284L (en) | 1995-04-03 |
| AU5149393A (en) | 1994-04-26 |
| JPH08501878A (en) | 1996-02-27 |
| FI951570A (en) | 1995-04-03 |
| NZ256675A (en) | 1995-11-27 |
| BR9307172A (en) | 1999-03-30 |
| MY108958A (en) | 1996-11-30 |
| EP0663998A1 (en) | 1995-07-26 |
| KR950703732A (en) | 1995-09-20 |
| FI951570A0 (en) | 1995-04-03 |
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