Abstract: A process for converting a first hydrocarbon feed stream to one or more liquid transportation fuels in a petroleum refinery where the feed stream is analyzed by at least one analytical method to produce data that is transformed to wavelet coefficients data. A pattern recognition algorithm is trained to recognize subtle features in the wavelet coefficients data that are associated with an attribute of the feed stream. The trained pattern recognition algorithm then rapidly classifies potential hydrocarbon feed streams as a member of either a first group or a second group where the second group comprises hydrocarbon feed streams where the attribute or chemical characteristic at or above a predetermined threshold value. This classification allows rapid decisions to be made regarding utilization of the feedstock in the refinery that may include altering at least one variable in the operation of the refinery.

Abstract: A process for converting a first hydrocarbon feed stream to one or more liquid transportation fuels in a petroleum refinery where the feed stream is analyzed by at least one analytical method to produce data that is transformed to wavelet coefficients data. A pattern recognition algorithm is trained to recognize subtle features in the wavelet coefficients data that are associated with an attribute of the feed stream. The trained pattern recognition algorithm then rapidly classifies potential hydrocarbon feed streams as a member of either a first group or a second group where the second group comprises hydrocarbon feed streams where the attribute or chemical characteristic at or above a predetermined threshold value. This classification allows rapid decisions to be made regarding utilization of the feedstock in the refinery that may include altering at least one variable in the operation of the refinery.

Abstract: A process for producing liquid transportation fuels in a petroleum refinery while preventing or minimizing corrosion of refinery process equipment. Spectral data selected from mid-infrared spectrometry, nuclear magnetic resonance spectrometry, or both is obtained and converted to wavelets coefficients data. A pattern recognition genetic algorithm is then trained to recognize subtle features in the wavelet coefficients data to allow classification of crude samples into one of two groups based on corrosion propensity. One of several actions is taken depending upon the measured corrosion propensity of the potential feed stock in order to prevent or minimize corrosion while producing one or more liquid hydrocarbon fuels.