Patent ID: 11929153
Assignee: PHILLIPS 66 COMPANY
Field: Measurement (Instruments)
Classification: CPC G  C | IPC C  G

Claim 10:
11. A process for producing one or more liquid transportation fuels in a commercial petroleum refinery, comprising:
a) analyzing a sample of a hydrocarbon stream selected from a member of the group consisting of crude petroleum, a crude petroleum fraction, a refinery intermediate stream and a refinery hydrocarbon product by an analytical method selected from midinfrared spectrometry, near infrared spectrometry, Ramen spectroscopy and nuclear magnetic resonance spectroscopy to produce spectral data comprising a set of discrete digitized data points;
b) transforming a subset of the spectral data to produce sample wavelet coefficients data according to wavelet theory by applying a mother wavelet that is a selected from a group consisting of Symlet, Haar and the Coiflets families of mother wavelets, wherein each discrete digitized data point within the subset of the spectral data is converted to a wavelet coefficients data point;
c) training a genetic algorithm to classify the sample wavelet coefficients data into one of two groups consisting of a first group and a second group to produce a trained genetic algorithm,
wherein the first group comprises multiple training hydrocarbon streams of distinct origin, wherein each training hydrocarbon stream is selected from the same member of the group as the first hydrocarbon stream of a), wherein each member of the training hydrocarbon stream has at least one attribute in common that is distinct from the second group,
wherein the training comprises performing the analyzing of part a) on each of the multiple training hydrocarbon streams from both the first group and the second group to produce training wavelets coefficients data and presenting the training wavelets coefficients data obtained from each training hydrocarbon stream to an untrained genetic algorithm that recognizes subtle collective differences within the training wavelet coefficients data obtained from each member of the first group compared to the training wavelet coefficients data obtained from each member of the second group to produce a trained genetic algorithm that successfully associates the collective differences with the presence of the at least one attribute at or above a predetermined threshold level, wherein the training further comprises an iterative process wherein each iteration places increasing emphasis on wavelet coefficient data points that increase the probability of accurately classifying the sample into either the first group or the second group;

d) classifying the sample of a) as a member of either the first group or the second group by presenting the sample wavelets coefficients data of b) to the trained genetic algorithm, wherein the trained genetic algorithm performs the classifying by examining one or more identifying spectral features that collectively identify the sample as a member of the first group or the second group;
e) performing an action selected from:
converting the hydrocarbon stream in the petroleum refinery to one or more liquid transportation fuels when the sample of a) is classified as a member of the first group,
altering at least one variable in operation of the petroleum refinery that increases the yield of one or more liquid transportation fuels when the sample of a) is classified as a member of the second group, and converting the hydrocarbon stream to one or more liquid transportation fuels in the petroleum refinery,
rejecting the hydrocarbon stream for conversion to one or more liquid transportation fuels in the petroleum refinery when the sample of a) is classified as a member of the second group.