Xiaonian Lai

US Patent:

62046640, Mar 20, 2001

Filed:

Dec 31, 1998

Appl. No.:

9/224326

Inventors:

Maziar Sardashti - Bartlesville OK
Xiaonian Lai - Burlingame CA

Assignee:

Phillips Petroleum Company - Bartlesville OK

International Classification:

G01V 300

US Classification:

324306

Abstract:

A chemometric technique for predicting styrene content in Butadiene-Styrene resin (K-Resin) using an on-line NMR system comprising the steps of producing a predictive data set and using the predictive data set to obtain unknown concentrations of styrene in samples of K-Resin. The predictive data set is generated by obtaining free induction decays for samples of K-Resin with measured concentrations of styrene to produce a free induction decay data set; analyzing the free induction decay data set using PCA to produce a principle component data set; analyzing the styrene concentrations, the free induction decay data set and the principle component data set using PLS to produce a training data set; and validating the training data set to produce a predictive data set. Using the predictive data set involves the steps of obtaining free induction decays of samples of K-Resin with unknown concentrations of styrene and applying the free induction decays to the predictive data set to predict the unknown concentrations.

US Patent:

62081373, Mar 27, 2001

Filed:

Dec 31, 1998

Appl. No.:

9/224522

Inventors:

Maziar Sardashti - Bartlesville OK
Xiaonian Lai - Burlingame CA

Assignee:

Philips Petroleum Company - Bartlesville OK

International Classification:

G01V3/00

US Classification:

324306

Abstract:

A chemometric method for predicting unknown properties in polymers using an on-line NMR system comprising the steps of producing a predictive data set and using the predictive data set to obtain unknown amounts of properties in a polymer. In a preferred embodiment, the process begins by obtaining free induction decays for samples of polypropylene with measured concentrations of xylene soluble polypropylene from a xylene soluble polypropylene data set to produce a free induction decay data set. The free induction decay data set is analyzed using PCA to produce a principle component data set. The principle component data set, the xylene soluble polypropylene data set, and the free induction decay data set, are analyzed using partial-least squares analysis to produce a training data set and the training data set is subsequently validated to produce a predictive data set. Using the predictive data set involves the steps of obtaining free induction decays of samples of polypropylene with unknown concentrations of xylene soluble polypropylene and applying the free induction decays to the predictive data set to predict the unknown concentrations.