Method for predicting hydrocarbon process stream stability using near infrared spectra
View Patent ↗A method of predicting the stability of a hydrocarbon process stream is disclosed and claimed. The method includes obtaining samples from one or more process streams and measuring the actual stability and the near infrared spectral absorbance of those samples. A classification model is initially developed to identify sample subsets and correlation model is created using the stability and absorbance data by inserting that data into a mathematical function. Online or offline measurements are then taken from the hydrocarbon process stream, the classification model is used initially to identify the subset and corresponding correlation model is used to predict the stability of the stream.
1. A method of predicting stability in a hydrocarbon process stream using a near infrared spectrum combined with a correlation model, the method comprising:
(a) measuring a stability value corresponding to each of a plurality of samples from the hydrocarbon process stream to produce a first dataset;
(b) selecting one or more wave numbers in the near infrared spectrum as a first spectrum;
(c) using the first spectrum to determine an absorbance corresponding to each of the plurality of samples from step (a) to produce a second dataset;
(d) incorporating the first dataset and the second dataset into at least one mathematical function to create the correlation model;
(e) selecting one or more wave numbers in the near infrared spectrum as a second spectrum;
(f) using the second spectrum to measure an absorbance corresponding to one or more additional samples;
(g) calculating a predicted stability value corresponding to the additional sample(s) by using the measured absorbance from step (f) as input for the correlation model and receiving as output the predicted stability; and
(h) optionally storing the received output in an electronic storage device and/or displaying the received output on a display device.
2. The method of claim 1 , including classifying the plurality of samples based upon the first dataset and/or the second dataset.
3. The method of claim 1 , wherein the first spectrum and the second spectrum at least partially overlap.
4. The method of claim 1 , wherein the first spectrum and the second spectrum are identical.
5. The method of claim 1 , wherein the first spectrum and/or the second spectrum have wave numbers from about 7,500 cm −1 to about 4,000 cm −1 .
6. The method of claim 1 , wherein the stability and the predicted stability are based upon asphaltenes and/or asphaltene-containing residues in the hydrocarbon process stream.
7. The method of claim 1 , wherein each of the plurality of samples in step (a) is from a same hydrocarbon process stream.
8. The method of claim 1 , wherein one or more of the plurality of samples in step (a) is from a different hydrocarbon process stream, each said stream being a same type.
9. The method of claim 1 , wherein one or more of the plurality of samples in step (a) are derived from a process stream selected from the group consisting of: an uncracked hydrocarbon process stream and a cracked hydrocarbon process stream.
10. The method of claim 1 , including measuring a plurality of samples from a plurality of different hydrocarbon process streams to compile the first dataset and the second dataset.
11. The method of claim 1 , wherein the correlation model is selected from the group consisting of: linear regression analysis; nonlinear regression analysis; and neural network analysis.
12. The method of claim 1 , including operating the method over a network.
13. The method of claim 12 , wherein the network is an Internet.
14. A digital storage medium having computer-executable instructions stored thereon, the instructions operable to execute the method of claim 1 .
15. A method of predicting stability in a hydrocarbon process stream using a near infrared spectrum combined with a correlation model, the method comprising:
(a) categorizing the hydrocarbon process stream into a plurality of different processes;
(b) selecting one of the processes;
(c) measuring a stability value corresponding to each of a plurality of samples from one of the processes to produce a first dataset;
(d) selecting one or more wave numbers in the near infrared spectrum as a first spectrum;
(e) using the first spectrum to determine an absorbance corresponding to each of the plurality of samples from step (a) to produce a second dataset;
(f) incorporating the first dataset and the second dataset into at least one mathematical function to create the correlation model;
(g) selecting one or more wave numbers in the near infrared spectrum as a second spectrum;
(h) using the second spectrum to measure an absorbance corresponding to one or more additional samples;
(i) calculating a predicted stability value corresponding to the additional sample(s) by using the measured absorbance from step (f) as input for the correlation model and receiving as output the predicted intrinsic stability; and
(j) optionally storing the received output in an electronic storage device and/or displaying the received output on a display device.