GB2526772A

GB2526772A - Cloud point measurement in fuels

Info

Publication number: GB2526772A
Application number: GB1405541.2A
Authority: GB
Inventors: Mark Christopher Turpin; Reuben John Gray
Original assignee: Analytical Technology and Control Ltd
Current assignee: Analytical Technology and Control Ltd
Priority date: 2014-03-27
Filing date: 2014-03-27
Publication date: 2015-12-09
Anticipated expiration: 2034-03-27
Also published as: GB201405541D0; GB2526772B

Abstract

A method and system for the determination of cloud point temperature in fuel oils comprises measuring light scattering from the fuel sample during cooling, measured scattering data is correlated to a numerical model, the next measurement is estimated from the model, the deviation of the next measured datum from the model is determined, and that deviation is amplified subject to discriminatory factors, the amplified deviation is recombined with the original signal, the amplified signal is examined for changes that are indicative of the cloud point temperature, the model parameters are re-evaluated to include the current datum and give improved estimation of the next measured datum.

Description

Cloud Point Measurement in Fuels

Field of the Invention

The present invention relates to the measurement of cloud point temperature in friel oils. The cloud point is one of the essential assessments of petroleum based fuels, particularly diesel fuels, and is the temperature at which waxes in the fuel separate and the fuel becomes cloudy.

Measurement of this parameter is important to producers of fuel oils as it represents the temperature below which the fuel will no longer be operable in typical engines, due to issues with filter clogging.

Back2round to the Invention The American Standards of Testing Materials offers a comprehensive suite of tests for the evaluation of fuels. ASTM D-2500 details a laboratory method for the evaluation of cloud point temperature wherein a jar containing the fuel sample is cooled at a controlled rate and the temperature at which turbidity is first observed is recorded, ASTM D-5771 through D- 5773 detail automated apparatus for the laboratory evaluation of cloud point wherein the turbidity is detected automatically by means of the reduction of light passing through the cell as measured by a photo-electric device. ASTM D-7937 further details an automated laboratory apparatus whereby the turbidity of the sample is evaluated optically in terms of light scattered by the particulates that form in the sample as it is cooled. D-7937 has become the de facto standard method for the automated measurement of cloud point temperature in fuels.

As cloud point is a key specification for fuel oils there is a requirement for measurements to be made during the production of fuels at the refinery. This is particularly important for the producer of fuels, as the lighter, non waxy, constituents of the fuel are typicafly substantially more valuable than the heavier fractions, It therefore makes sense for the producer to try to manufacture fuel with a cloud point as close as possible to the value specified by the customer.

To address the need for continuous evaluation of cloud point temperature during production of fuels, a number of analyser manufacturers offer fully automated process analysers based on the ASTM D-7937 method. One such instrument is the ATAC Cloud Point Analyser. This instrument uses an optical cell wherein the scattering of an incident light beam is measured at right angles to the beam. A cell within which such measurements are made is shown in Figure 1. Light from the LED (1) enters the cell and passes through the fUel (5). The fUel is cooled by the thermoelectric cooler (4) and the scattered light from the fuel is measured by the photo-detector (2), whilst the fuel temperature is monitored by the resistance thermometer (3). As the fUel begins to cloud the inbound light ray (6) is scattered by the particulates forming in the fUel and scattered light (7) is detected by the photo-detector (2).

In a typical measurement cycle the optical cell is flushed with a fresh sample of the fuel to be analysed before being sealed via inlet and outlet valves. The scattered light detected by the photo detector is measured as the sample is cooled, At some point in the cooling cycle the scattered light detected will increase abruptly as the sample becomes turbid, the temperature at this point is recorded as the cloud point temperature. The cell is warmed and flushed with fresh sample for the analysis to begin again.

There are a number of problems with this measurement that can be summarised as: * The incoming light is not collimated and so a high level of background scattering is observed, the change in scattering at the cloud point temperature must be evaluated

against this high background level.

* Many fUel samples are coloured (both naturally and for Inland Revenue control purposes) and this colour may change with temperature thereby altering the light levels seen by the photo-detector in a way that varies from sample to sample, * Whilst conventional paraffinic fliels exhibit a sharply defined cloud point, in more modern naphthalenic fuels obtained by hydrocracking the turbidity seen at the cloud point is very diffuse and difficult to detect reliably.

* Biodiesel fuels as similarly difficult to analyse.

The analysis of these difficult fuels is the basis of this invention.

The electronic amplification and processing of small variations in an otherwise substantial signal poses a particular problem for electronic engineers. For the purpose of this document the substantial signal will be referred to as the background' whilst the small superposed data signal will be referred to as data', the superposed combination of the background and data will be referred to as the signal.

The conventional approach to this problem is to use an offset amplifier, where the background has a constant level, or a known oscillation, the amplification of the data is achieved by subtracting the known background prior to amplification to yield the data. Such a technique is widely used in radio systems where the data is superposed onto the background carrier wave. The carrier wave is of known frequency and can be subtracted from the signal to yield the data in a clean format suitable for amplification.

In the determination of cloud point, the problematic background is the high level of random scattering both from the sample, which may contain particulates and also from the measurement cell. With conventional measurements on refined paraffinic petrochemical products the background is constant during the measurement and the offset amplification outlined above works well. However, more modern fuels often contain naphthalenic or bio-derived products (bio-diesel) and may be coloured for revenue purposes (red diesel for agricultural use for example). For these products changes in the sample during the cooling phase of the cloud point measurement can create a markedly changing background signal and this makes the measurement increasingly difficult, Measurement of the cloud point in such n samples requires dc-convolution of a small signal superposed onto a background that has an unknown variation over time. Simple offset amplification cannot be used in this case, as the background cannot be generated from the signal without compromising the data.

In cloud point measurement on a paraffinic sample the changing temperature causes an abrupt and obvious change in turbidity that is easily detected. This is typically the case with diesel fuels refined from petroleum and an example of the results from a light scattering experiment is shown in Figure 2. The temperature (1) is reduced over the course of the experiment and the scattered light level (2) measured. The abrupt change in scattering is easily discerned at (3) representing the cloud point at -5°C.

In other samples, typically hydrocracking products, biodiesel and coloured diesels the turbidity change is difficult to detect. An example of measurement data from such a sample, a coloured marine diesel, is shown in figure 3. As the temperature (t) is reduced the scattered light detected (2) increases in a significant way. This effect is believed to be due to changes in the absorption frequency of the chromophore, used in the diesel colourant, with temperature. Samples that show both increase and decrease scattering signal as the temperature is reduced have been observed, The cloud point (3) for this sample is very small relative to the changing background scatter and is very difficult to detect experimentally.

The signal analysis is further confounded as offset amplification is not effective due to the changing level of the background scatter. The variations in the background are large by comparison to the cloud point that we wish to detect and so any offset amplification protocol will be swamped by changes in the background and thereby be unable to differentiate the cloud point.

Disclosure of the Invention

This invention comprises a method for the amplification of the cloud point signal by

elimination of the varying background.

The present invention provides a methodology and measurement system for the determination of cloud point in fuels wherein: light scattering from the fuel sample is measured during cooling; measured scattering data is correlated to a numerical model; the next measurement is estimated from the model; the deviation of the next measured datum from the model is determined; that deviation is amplified subject to discriminatory factors; the amplified deviation is recombined with the original signal; the amplified signal is examined for changes that are indicative of the cloud point temperature; the model parameters are re-evaluated to include the current datum and give improved estimation of the I 0 next measured datum.

The numerical model used to fit the data is optimally a third order polynomial function but could equally well be a spline ifinction or a frmnction derived specifically by reference to the mcasurcd data, Thc discriminators uscd arc that thc amplificd data is of thc corrcct polarity as that which would be expected at the cloud point, and that amplification is only applied when the deviation exceeds some threshold value. The amplification applied can be a simple multiplier factor or can be a more complex form of amplification such as an exponential function whereby larger deviations are amplified more.

A data processing algorithm has been developed whereby the real-time incoming light scattering data is subjected to a line or curve fitting routine. The fitting process requires that a sample of the background, before turbidity is present, can be measured and that the background can be modelled mathematically to yield a model with forward predictive capability. Furthermore, it is desirable that the background model be adaptive such that forward prediction is either based on all data collected up to the present time or on a suitably sized sample back in time. The nature of the curve fitting routine used is immaterial and is best determined from an evaluation of the data. In this case a polynomial fitting routine is used but similar results could be obtained from spline fitting or fitting to a formula developed from empirical means that best fits the data.

Once a background model is established the model is used to forward predict the next datum received. On receipt, the datum is compared to that generated from the model and the difference determined. This difference we will refer to as the error term'.

The error term is clearly composed of three elements: modelling error, whereby the background model is inexact; noise in the background; and signal due to turbidity, which may or may not be present. Providing that the background model is unbiased i.e. does not consistently under or over predict the next datum and that the noise is random, the only true bias in the error term is the signal. Amplification of the error term should therefore allow any bias due to the signal to be resolved. Amplification of the error term may take the form of either a simple multiplier or may use an exponential method, whereby the level of amplification increases with the size of the error.

As a further enhancement, amplification might only be applied when the error term exceeds some threshold, This is particularly useful where the background noise level is known and the signal is greater than the background noise, Dy setting an amplification threshold at a level similar to the maximum noise, only signals having tme bias will be amplified and resolution of the signal can be further improved.

In addition, in the case of cloud point determination, the nature of the signal is known, i.e. the signal is known to be a positive variation in the detected scattered light. In this case resolution can be further enhanced by only applying amplification to error terms that are in the direction of the expected signal.

Finally, the nature of the signal analysis is such that the analysis can be applied to a signal in real time. That is to say that, as only signal data collected up to the present time is analysed, the signal processing technique can be applied as the signal is being generated. This is important to the application as the user requires real time estimation of the cloud point temperature for process control purposes.

In order that the invention can be more readily understood the below offers a description of the drawings by way of example only Figure 1 A sectional view of a typical scattering cell for the measurement of cloud point in fUels.

Figure 2 Typical scattering results obtained with regular petroleum based diesel.

Figure 3 Typical scattering results obtained with a coloured biodiesel, Figure 4 The measurement system used in this invention Figure 5 A schematic representation of the data analysis method used in this invention Figure 6 A specific schematic representation of the data analysis method used

in example 1

Figure 7 Data obtained using an ATAC I-Cloud analyser and analysed using the method of example 1, Figure 8 Approximately 7 days of data from the ATAC I-cloud showing the high precision of cloud point measurement with the invention.

Figure 9 A specific schematic representation of the data analysis method used

in example 2

Figure 10 Data obtained using an ATAC I-Cloud analyser and analysed using the method of example 2 A system for analysis according to the present invention is shown in Figure 4. A block diagram of the signal processing system is included in Figure 5, and comprises the following steps: -Initial data is read to develop the model, in this work the initial model is based on the first 50 seconds of scattering data received, where the measured scattering signal, being the photo-current from the photo detector, is measured approximately once every second.

* This is fitted to a third order polynomial equation of the form y = Measured data = A + B.x + C. x2 Equation 1 by the correlator to yield the correlation model. Where y is the scattered light signal and x is the experiment elapsed time or sample temperature.

* The correlation model is used to estimate the value of the next datum to be measured.

* The next datum measured is compared to that generated from the correlation model and the difference, or error term, evaluated, The error term is given by e = Equation 2 Where xm is the measured datum and x0 that estimated from the correlation model and e is the error term.

* The error term is amplified by the amplifier * The amplified error term is added back to the original signal by the correlator to give the amplified signal * The amplified signal is used for the evaluation of cloud point by the data analyser.

* The correlation model is updated to include the current datum and the sequence repeated.

The analysis can be fUrther enhanced by adding discrimination to the amplification procedure, the two discriminators that have been found to work best for the measurement of cloud point temperature are: * Amplification is only applied when the variation of the datum from the model is in the direction of the expected cloud point effect, i.e. only those variances that show increasing scattering are amplified, in this case corresponding to values of the error term that are positive.

* Amplification is only applied to error terms that exceed the level of background noise in the sample. This can be evaluated as the standard deviation of the error terms, c, and then only applying amplification to variances that exceed N x cIv, where N is a simple number. When N = 2 this approximates to the 95% confidence interval, i.e,95% of the random error will be excluded from amplification. Higher values of N will give correspondingly higher discrimination for random variation but at the risk that the signal may be missed.

The nature of the amplification used also has considerable influence on the efficacy of the method. Two types of amplification have been evaluated * Amplification by multiplication by a simple factor, whereby all variances are amplified to the same degree.

* Amplification by an exponential factor, whereby larger variances are amplified more.

These two forms of amplification are by no means exhaustive and are not intended to preclude the use of other amplification protocols from the scope of this invention.

Example 1

The data shown previously in Figure 3 has been analysed using the amplification technique shown schematically in Figure 6. The measured data is fitted to a quadratic equation of the form y = Measured data =11 -f-B.x+C.x2 Equation3 Where y is the measured scattering current, x is the normalised experiment time, but which could equally be the measured temperature. After a number of points, typically more than 10, have been measured the fit equation is used to forward predict the next measurement datum as a value x0, When the datum is measured, as Xm, the deviation from the model is calculated as the error term e, where e = Xm -Xe..................... Equation 4 Analysis of the early part of the data indicates that the random error in the measured data, estimated as the standard deviation of the error term is 0.00018. On this basis the amplification of the error term is restricted to positive going terms of greater than 0.00036.

The amplification factor is constant at 1000. So that for each measured datum the datum is amplified such that amplified datum = measured datum + e 1000 (where e > 0.00036) Equation 5 Or amplified datum = measured datum (where e «= 0.00036) Equation 6 The resultant data from this process is shown in Figure 7. In this plot the temperature is shown as a dash-dot line and decreases linearly with time, the scattered light level recorded is the solid line showing no obvious discontinuity that would be indicative of the cloud point.

The error term is displayed as the dashed line and the amplified data as the dotted line. The cloud point is indicated by the abrupt, positive deviation in the error term (1) and also in the amplified signal (2). Note, however, that the amplified signal has been offset with respect to the signal for clarity.

The temperature at which the cloud point occurs can easily be determined either from the error term or from the amplified signal.

Efficacy of the Method Figure 8 shows 7 days of data obtained using an ATAC Cloud Point Analyser onto which software that executes the method outlined in example 1 had been installed. The sample is a blue dyed marine biodiesel that had previously been found to be un-measurable on an unmodified machine. The sample was presented to the instrument from an approximately 10 litre tank to which measured samp'e was returned, thereby ensuring the uniformity of the sample throughout the measurement period. Prior to modification to the present invention, the instrument would fail to detect a cloud point on approximately 50% of cycles. Following modification the instrument detected a cloud point on 100% of approximately 1400 measurement cycles and reported the cloud point with a standard error of less than 0.05°C.

Example 2

As previously, the measured data is fitted to a quadratic equation as in equation 1 and the error term, e, determined once the model is established after a number of points, typically at least 10, have been measured, Once the model is established a further number of points, typically at least 10, are measured and the error term is cakulated and the standard deviation of the error term, Ce, estimated.

As the error term is a random deviation from a model that is assumed to be a true fit to the data, the error term average should at all times be zero. Therefore we can say that any error term that is more than twice the standard deviation is likely to be indicative of a data signal.

With this in mind the amplification applied is of the form Amplified data = measured data + e * lO(e/2.ce_l) Equation 7 The effect of this amplification factor is that amplification only occurs when e>2.ce, however, the level of amplification increases exponentially with the value of e to give a strong indication of the presence of the deviation in the data that is indicative of the cloud point. The procedure by which this analysis is carried out is included schematically in Figure 9.

To illustrate this method, the data in Figure 3 has been analysed using the above procedure to yield the plotted results in Figure 10. It can be seen that the error (dashed line) and amplified signal (dotted line) now show a very strong peak as the cloud point occurs and as such the cloud point is very easy to detect.

The particular advantage of example 2 lies in the fact that no assumptions need to be made regarding the noise level of the signal, as this is calculated during the procedure, and also in the fact that once a data signal is detected the level of amplification becomes very high, yielding easy detection.

The use of 2,c discrimination with an exponential amplification factor is a combination that is particularly useful. This is because random errors that fall above the discrimination threshold are typically very small and therefore only subject to a low level of amplification.

Signal data, that is typically several times the random error background, is, by contrast, subject to very high levels of amplification.

Claims

Claims I claim 1. A method of real time data signal processing for the determination of cloud point in fuels, the method comprising: light scattering from the fuel sample is measured during cooling; measured scattering data is correlated to a numerical model; the next measurement is estimated from the model; the deviation of the next measured datum from the model is determined; that deviation is amplified subject to discriminatory factors; the amplified deviation is recombined with the original signal; the amplified signal is examined for changes that are indicative of the cloud point temperature; the model parameters are re-evaluated to include the current datum and give improved estimation of the next measured datum.
2. A method as in claim t wherein only deviations having the polarity of the expected response are amplified.
3. A method as in claim I and 2 wherein the amplification is by multiplication by a simple factor.
4, A method as in claim I and 2 wherein the amplification is by an exponential function such that larger error terms are amplified more.
5. A method as in claims t-4 wherein the data is fitted to a straight line.
6. A method as in claims 1-4 wherein the data is fitted to a third order polynomial.
7. A method as in claims 1-4 wherein the data is fitted to a spline, high order polynomial or other curve function.
8. A method as in claims 1-7 wherein the analysis is carried out in real time as the data is gathered.
9. A measurement system for the determination of cloud point in thels wherein: the sample is contained in an optical cell having an incident light source and a photo in I., detector at right angles; the photo detector is operable to measure light scattering from the the! samp!e during cooling; a correlator to ana!yse the scattering data and forward estimate the next datum, based on historica! data, and then determine the deviation of the next datum from the corre!ation; an amp!ifier to amp!ify the deviation subject to discriminatory factors and recombine the amplified deviation with the original signal; an ana!yser to examine the amplified data for increases in scattering that are indicative of the cloud point temperature.
10. A measurement system as in c!aim 9 wherein the amp!ifier is operab!e to on!y amplify deviations representing an increase in scattering.
11 A measurement system as in claim 9 and 10 wherein the amplifier is operable by mu!tiplication of the signa! by a simp!e factor.
12. A measurement system as in c!aim 9 and 10 wherein the amp!ifier is operab!e using an exponentia! function such that!arger deviations are amplified more.
13. A measurement system as in claims 9-12 wherein the correlator is operab!e to fit the measured data to a straight!ine.
14. A measurement system as in claims 9-12 wherein the correlator is operable to fit the measured data to a third order po!ynomia!.
15, A measurement system as in claims 9-12 wherein the correlator is operab!e to fit the data to a sp!ine, high order po!ynomia! or other curve tlinction.
16. A measurement system as in c!aims 9-t4 wherein the corre!ator is operab!e in rca! time.