Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Chemical analysis
Reexamination Certificate
2001-11-30
2003-12-09
Shah, Kamini (Department: 2863)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Chemical analysis
C702S027000, C436S029000
Reexamination Certificate
active
06662116
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention is a method for analyzing an unknown material using a multivariate analytical technique such as spectroscopy, or a combination of a multivariate analytical technique and inspections. Such inspections are physical or chemical property measurements that can be made cheaply and easily on the bulk material, and include but are not limited to API or specific gravity and viscosity. The unknown material is analyzed by comparing its multivariate analytical data (e.g. spectrum) or its multivariate analytical data and inspections to a database containing multivariate analytical data or multivariate analytical data and inspection data for reference materials of the same type. The comparison is done so as to calculate a blend of a subset of the reference materials that matches the containing multivariate analytical data or containing multivariate analytical data and inspections of the unknown. The calculated blend of the reference materials is then used to predict additional chemical, physical or performance properties of the unknown using measured chemical, physical and performance properties of the reference materials and known blending relationships.
Within the petrochemical industry, there are many instances where a very detailed analyses of a process feed or product is needed for the purpose of making business decisions, planning, controlling and optimizing operations, and certifying products. Herein below, such a detailed analysis will be referred to as an assay, a crude assay being one example thereof. The methodology used in the detailed analysis may be costly and time consuming to perform, and may not be amenable to real time analysis. It is desirable to have a surrogate methodology that can provide the information of the detailed analysis inexpensively and in a timely fashion. The present invention is one such surrogate methodology.
Infrared spectroscopy, and in particular near-infrared spectroscopy, is widely used for the quantitative analysis of petrochemicals. For most applications, linear regression models are developed that relate the measured spectrum to the chemical, physical and performance properties of the material. Chemical properties include but are not limited to elemental and molecular compositions. Physical properties include but are not limited to density, viscosity, and cold flow properties such as pour, cloud or freeze point. Performance properties include but are not limited to octane and cetane numbers. While such linear regression models have been successfully used for many petrochemical applications, they are of limited utility for the detailed analysis of process feeds and products. The detailed analysis (assay) may involve hundreds of chemical, physical and performance parameters, thereby requiring the development and maintenance of an unmanageably large number of regression models. Further, many of the properties of interest may be complex, nonlinear functions of composition that are not readily predicted using linear regression models. Finally, the detailed analysis (assay) may include composition and property data for subfractions of the whole sample that are not readily predicted using linear regression models based on spectra of the whole sample. The current invention avoids these limitations by using a novel algorithmic approach to represent an unknown material as a blend of known reference materials. The current invention can readily predict large numbers of chemical, physical and performance properties of a material, can predict nonlinear properties providing nonlinear blending rules are known, and can predict chemical, physical and performance properties of subfractions of a material providing such properties were measured on similar subfractions of the reference materials and providing that blending relationships for the properties are known.
Alternative approaches that do not involve linear regression have been applied to spectroscopic data in an attempt to predict chemical, physical and performance properties of petrochemicals. For example, non-linear post-processing methods and neural networks have been employed to improve predictions for properties that are nonlinear functions of composition. Application of these analyses might address non-linearity, but they would only add to the complexity of the unmanageably large number of models needed for prediction of the detailed analysis (assay). Topology based approaches have been applied to spectral data so as to identify reference materials that are sufficiently similar to the material being analyzed to allow properties to be inferred. However, the topology approach requires a much denser database than the current invention to ensure that there are sufficiently similar references to any sample being analyzed. For detailed analyses (assays), the cost of producing a sufficiently dense database to utilize the topological approach is prohibitive. None of the alternative approaches have been shown to be reliably capable of predicting properties of sub-fractions of a sample based on spectra of the whole sample.
While the preferred embodiment of the present invention utilizes extended mid-infrared spectroscopy (7000−400 cm
−1
), similar results could potentially be obtained using other multivariate analytical techniques. Such multivariate analytical techniques include other forms of spectroscopy including but not limited to near-infrared spectroscopy (12500−7000 cm
−1
), UV/visible spectroscopy (200-800 nm), fluorescence and NMR spectroscopy. Similar analyses could also potentially be done using data derived multivariate analytical techniques such as simulated gas chromatographic distillation (GCD) and mass spectrometry or from combined multivariate analytical techniques such as GC/MS. In this context, the use of the word spectra herein below includes any vector or array of analytical data generated by a multivariate analytical measurement such as spectroscopy, chromatography or spectrometry or their combinations.
The present invention is applicable to the prediction of chemical, physical and performance properties of crude oils. Both properties of whole crude, and of any distillate cut of the crude can be predicted. The present invention is also applicable to petrochemical process and product streams. The reference materials used in the analysis and the unknowns that are analyzed can be process feeds, products or both. For example, the reference materials can be gas oil feeds to a catalytic cracking unit for which detailed molecular composition analyses have been performed. The present invention can be used to predict the molecular compositions of unknown gas oils. The present invention is also applicable to the prediction of extraction response data for waxy distillate feeds to lube extraction and dewaxing processes. The extraction response data includes but is not limited to raffinate and dewaxed raffinate yield, raffinate and dewaxed raffinate viscosity and viscosity index, raffinate and dewaxed raffinate saturates content, and raffinate and dewaxed raffinate refractive index as a function of extraction and dewaxing conditions. The reference materials are waxy distillate feed samples for which extraction and dewaxing data was measured. The present invention is used to predict extraction and dewaxing data for unknown waxy distillate feeds.
In the petrochemical industry, extremely detailed analyses of feed and product materials (assays) are often utilized for making business decisions, for planning, controlling and optimizing operations, and for certifying products. Chief among these analyses is the crude assay. When a crude oil is assayed, it is distilled in two steps. A method such as ASTM D2892 (see. Annual Book of ASTM Standards, Volumes 5.01-5.03, American Society for Testing and Materials, Philadelphia, Pa.) is used to isolate distillate cuts boiling below approximately 650° F. (343° C.). The residue from this distillation is further distilled using a method such as ASTM D5236 to produce distillate cuts covering the range
ExxonMobile Research and Engineering Company
Hantman Ronald D.
Shah Kamini
LandOfFree
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