Chevron U.s.a. Inc.

Novvi LLC

Ajit Ramchandra Pradhan

Stephen Joseph Miller

Sven Ivar Hommeltoft

John Michael Rosenbaum

Eduardo J Baralt

Nancy J Bertrand

David C Kramer

Boqin Q Sun

Jason Wells

John A Zakarian

Yalin Hao

Benton Thomas

Jason Charles Rosalli

Liwenny Ho

Michael J Haire

Dengen Zhou

Zheng Yang

Willbe Ho

Allan G Hee

Hussein Alboudwarej

Trevor Miller

Vivek Palekar

John S Zintsmaster

Max I Chang

Cong Yan Chen

Gerald Latorraca

A method for determining the concentration of asphaltenes in a solution is described. A model is first established for estimating the concentration of asphaltenes in a solution based on multiple samples of solutions of asphaltenes in the solvent in which the concentrations are known. The multiple samples have varying concentrations of asphaltenes. The diffusivity and relaxation time are measured for each sample using two-dimensional NMR. The ratio of diffusivity to relaxation time for each sample is then calculated. A linear equation is determined to fit the relationship between the ratio of diffusivity to relaxation time and the asphaltene concentration by weight for the multiple samples, thus creating the model. For a given solution sample for which the concentration of asphaltenes is desired to be determined, diffusivity and relaxation time are determined using two-dimensional NMR, and the ratio of diffusivity to relaxation time is calculated. This ratio is then used with the model, so that the linear equation can be solved for the asphaltene concentration in the given solution sample.

Methods for quantitative characterization of asphaltenes in solutions using two-dimensional low-field NMR measurement

A process for producing a gasoline blending component and a middle distillate, comprising adjusting a level of a halide containing additive provided to an ionic liquid alkylation reactor to shift selectivity towards heavier products, and recovering a low volatility gasoline blending component and the middle distillate.

Process for producing a gasoline blending component and a middle distillate by adjusting a level of a halide containing additive during alkylation

A process for producing a low volatility gasoline blending component and a middle distillate, comprising alkylating a hydrocarbon stream comprising at least one olefin having from 2 to 4 carbon atoms and at least one paraffin having from 4 to 6 carbon atoms with an ionic liquid catalyst and an unsupported halide containing additive, and separating the alkylate into at least the low volatility gasoline blending component and the middle distillate, wherein the middle distillate is a fuel suitable for use as a jet fuel or jet fuel blending component. Also, a process for producing a gasoline blending component and a middle distillate, comprising adjusting a level of a halide containing additive provided to an ionic liquid alkylation reactor to shift selectivity towards heavier products, and recovering a low volatility gasoline blending component and the middle distillate. Also, processes comprising alkylating isobutane with butene over specific chloroaluminate ionic liquids.

Process for producing a low volatility gasoline blending component and a middle distillate using an ionic liquid

A method for predicting properties of lubricant base oil blends, comprising the steps of generating an NMR spectrum, HPLC-UV spectrum, and FIMS spectrum of a sample of a blend of at least two lubricant base oils and determining at least one composite structural molecular parameter of the sample from said spectrums. SIMDIST and HPO analyses of the sample are then generated in order to determine a composite boiling point distribution and molecular weight of the sample from such analysis. A composite structural molecular parameter is applied, and the composite boiling point distribution and the composite molecular weight to a trained neural network is trained to correlate with the composite structural molecular parameter composite boiling point distribution and the composite molecular weight so as to predict composite properties of the sample. The properties comprise Kinematic Viscosity at 40 C, Kinematic Viscosity at 100 C, Viscosity Index, Cloud Point, and Oxidation Performance.

Base oil properties expert system

A multigrade automotive gear lubricant comprising a base oil having a traction coefficient less than 0.021. A method for saving energy using a gear lubricant, comprising blending a multigrade gear lubricant by adding a base oil having a traction coefficient less than 0.021, and using the gear lubricant in an axle or differential. A process for making an energy saving automotive gear lubricant having a kinematic viscosity at 100&#xb0; C. greater than 10 cSt. A gear lubricant comprising a FT derived base oil having a VI greater than 150 and a traction coefficient less than 0.015. A finished lubricant, comprising a FT derived base oil having a traction coefficient less than 0.015. A base oil having a traction coefficient less than 0.011 and a 50 wt % boiling point greater than 582&#xb0; C.

Gear lubricant with a base oil having a low traction coefficient

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Walnut Creek, CA, United States of America

Ajit Ramchandra Pradhan