IPC분류정보
국가/구분 |
United States(US) Patent
등록
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국제특허분류(IPC7판) |
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출원번호 |
US-0814754
(2001-03-23)
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발명자
/ 주소 |
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출원인 / 주소 |
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대리인 / 주소 |
Gowling Lafleur Henderson LLP
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인용정보 |
피인용 횟수 :
75 인용 특허 :
51 |
초록
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A method and system for providing location based data services. The present invention relates generally to a method and system for generating, storing, manipulating and displaying location-based data and more particularly relates to a method and system for using a unique identifier of spatial locati
A method and system for providing location based data services. The present invention relates generally to a method and system for generating, storing, manipulating and displaying location-based data and more particularly relates to a method and system for using a unique identifier of spatial location to enable a database to become "location smart". The unique identifier comprises an identifier of location such as latitude and longitude as well as one other item of data such as a time-stamp or a sequence number. The unique identifier is used as a key in a database system and can be used to facilitate spatial analysis of data or to provide geographic context to data. The present invention also provides systems and methods that operate in mobile and wireless environments. The present invention allows end users to perform the functions of geographic information systems without having to provide sensitive data to GIS service providers or having to learn to use sophisticated GIS systems.
대표청구항
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A method and system for providing location based data services. The present invention relates generally to a method and system for generating, storing, manipulating and displaying location-based data and more particularly relates to a method and system for using a unique identifier of spatial locati
A method and system for providing location based data services. The present invention relates generally to a method and system for generating, storing, manipulating and displaying location-based data and more particularly relates to a method and system for using a unique identifier of spatial location to enable a database to become "location smart". The unique identifier comprises an identifier of location such as latitude and longitude as well as one other item of data such as a time-stamp or a sequence number. The unique identifier is used as a key in a database system and can be used to facilitate spatial analysis of data or to provide geographic context to data. The present invention also provides systems and methods that operate in mobile and wireless environments. The present invention allows end users to perform the functions of geographic information systems without having to provide sensitive data to GIS service providers or having to learn to use sophisticated GIS systems. n claim 1, wherein the at least two additional properties are viscosity, and aniline point. 7. A method as claimed in claim 1, wherein the at least two additional properties are T10 and T90. 8. A method as claimed in claim 3, wherein the predictive equation is a set of non-linear equations comprising: CN0=-0.07307×T10+0.3080×ANPT-1.152×VISC40+0.4957×D976+0.1836×CLOUD+31.57 CN1000=-0.09374×T10+0.2738×ANPT-0.9512×VISC40+0.5890×D976+0.2069×CLOUD+40.16 CN2500=-0.09681×T10+0.2521×ANPT-0.9890×VISC40+0.6700×D976+0.1943×CLOUD+42.38 CN5000=-0.09009×T10+0.3084×ANPT-1.183×VISC40+0.6461×D976+0.1695×CLOUD+41.30 CN7500=-0.07602×T10+0.3624×ANPT-1.307×VISC40+0.6495×D976+0.1371×CLOUD+35.77 CN10000=-0.06971×T10+0.4190×ANPT-1.257×VISC40+0.6033×D976+0.1086×CLOUD+33.75 wherein CN is cetane number of the product at the noted concentration of cetane improver of from 0 to 10,000 ppmv, and intermediate values are interpolated between points, T10 is the temperature at which 10% of the product boils off, ANPT is the aniline point of the product, VISC40 is the viscosity of the product at 40° C. according to ASTM D445, D976 is the cetane index of the product according to ASTM D976, and CLOUD is the cloud point of the product. 9. A method as claimed in claim 4, wherein the predictive equation is a set of non-linear equations comprising: CN0=0.1700×T50-0.09022×T90-238.4×SPGR-0.1199×FIAAROM+0.2187×CLOUD+237.8 CN1000=0.1802×T50-0.1062×T90-256.1×SPGR-0.1123×FIAAROM+0.2419×CLOUD+259.8 CN2500=0.1861×T50-0.1073×T90-264.7×SPGR-0.1213×FIAAROM+0.2310×CLOUD+269.9 CN5000=0.1928×T50-0.1037×T90-267.9×SPGR-0.1489×FIAAROM+0.2113×CLOUD+273.4 CN7500=0.2079×T50-0.09233×T90-283.8×SPGR-0.1700×FIAAROM+0.1829×CLOUD+282.1 CN10000=0.2163×T50-0.08637×T90-285.5×SPGR-0.1931×FIAAROM+0.1574×CLOUD+281.7 wherein CN is cetane number of the product at the noted concentration of cetane improver of from 0 to 10,000 ppmv, and intermediate values are interpolated between points, T50 is the temperature at which 50% of the product boils off, T90 is the temperature at which 90% of the product boils off, SPGR is the specific (API) gravity of the product, FIAAROM is the aromatics content of the product according to ASTM D 1319, and CLOUD is the cloud point of the product. 10. A method as claimed in claim 5, wherein the predictive equation is a set of non-linear equations comprising: CN0=0.05286×T10+0.1329×T50-0.07308×T90-319.2×SPGR+0.1984×CLOUD+295.1 CN1000=0.05529×T10+0.1391×T50-0.08732×T90-332.5×SPGR+0.2220×CLOUD+313.6 CN2500=0.06523×T10+0.1357×T50-0.08412×T90-348.0×SPGR+0.2085×CLOUD+328.0 CN5000=0.09178×T10+0.1182×T50-0.06947×T90-371.4×SPGR+0.1817×CLOUD+344.9 CN7500=0.1203×T10+0.1059×T50-0.04557×T90-403.8×SPGR+0.1464×CLOUD+363.9 CN1000=0.1377×T10+0.09925×T50-0.03274×T90-422.0×SPGR+0.1158×CLOUD+374.5 wherein CN is cetane number of the product at the noted concentration of cetane improver of from 0 to 10,000 ppmv, and intermediate values are interpolated between points, T10 is the temperature at which 10% of the product boils off, T50 is the temperature at which 50% of the product boils off, T90 is the temperature at which 90% of the product boils off, SPGR is the specific gravity of the product, and CLOUD is the cloud point of the product. 11. A method as claimed in claim 6, wherein the predictive equation is a set of non-linear equations comprising: CN0=0.2811×ANPT-1.030×VISC40+0.6519×D976 CN1000=0.2403×ANPT-0.9091×VISC40+0.7946×D976 CN2500=0.2171×ANPT-1.075×VISC40+0.9147×D976 CN5000=0.2632×ANPT-1.335×VISC40+0.9365×D976 CN7500=0.3048×ANPT-1.322×VISC40+0.9346×D976 CN10000=0.3534×ANPT-1.259×VISC40+0.9055×D976 wherein CN is cetane number of the product at the noted concentration of cetane improver of from 0 to 10,000 ppmv, and intermediate values are interpolated between points, ANPT is the aniline point of the product, VISC40 is the viscosity of th e product at 40° C. according to ASTM D445, and D976 is the cetane index of the product according to ASTM D976. 12. A method as claimed in claim 7, wherein the predictive equation is a set of non-linear equations comprising: CN0=-0.5659×T10+0.000458×T50×T90-0.000992×(T90)2+0.5261×T10/SPGR+0.4263×T90/SPGR-64.40 CN1000=-0.4433×T10+0.000483×T50×T90-0.001215×(T90)2+0.4254×T10/SPGR+0.5364×T90/SPGR-81.53 CN2500=-0.4733×T10+0.000468×T50×T90-0.001226×(T90)2+0.4588×T10/SPGR+0.5420×T90/SPGR-79.54 CN5000=-0.5823×T10+0.000399×T50×T90-0.001130×(T90)2+0.5736×T10/SPGR+0.5086×T90/SPGR-73.52 CN7500=-0.6247×T10+0.000359×T50×T90-0.001125×(T90)2+0.6320×T10/SPGR+0.5245×T90/SPGR-79.91 CN10000=-0.7223×T10+0.000346×T50×T90-0.001046×(T90)2+0.7272×T10/SPGR+0.4876×T90/SPGR-74.34 wherein: CN is cetane number of the product at the noted concentration of cetane improver of from 0 to 10,000 ppmv, and intermediate values are interpolated between points, T10 is the temperature at which 10% of the product boils off, T50 is the temperature at which 50% of the product boils off, T90 is the temperature at which 90% of the product boils off, and SPGR is the specific gravity of the product. 13. A method as claimed in claim 1, wherein the product is diesel fuel, and at least one characteristic of the product is pour point. 14. A method as claimed in claim 1, wherein the calculating is performed on a computing device with appropriate software. 15. A method as claimed in claim 1, wherein the cost of the incoming material and the market price of the product are known, and the step of calculating includes optimizing the profitability of the process. 16. A process for the optimization of diesel fuel production, comprising providing a database of diesel fuel stocks, additives, and products having a set of known properties, providing a non-linear formula for the prediction of diesel fuel properties based upon a regressive analysis of the known properties collected from a series of samples, providing a computing device connected to said at least one database, providing computing instructions incorporating said formula for the prediction of diesel fuel properties, and calculating the diesel fuel properties utilizing said computing device, wherein the set of known properties includes at least API gravity, T50 and at least two additional properties selected from the group consisting of T10, T90, aniline point, viscosity, cloud point, and aromatics content, wherein the computing instructions provide for admixing cetane improver, and wherein the prediction of diesel fuel properties includes cetane number. 17. A process as claimed in claim 16, wherein the computing device is a computer and the instructions comprise computer software. 18. A process as claimed in claim 16, wherein the database is a spreadsheet of the set of known properties. 19. A process as claimed in claim 16, wherein the database includes the price of the diesel fuel stocks, additives, and products and the cost of processing, and the software includes an optimizer, whereby the maximum profitability of the process may be calculated. 20. A method as claimed in claim 1 wherein the predictive equation is a non-linear equation comprising: CN(X)=T10×(-0.06971 to -0.09681)+ANPT×(0.2521 to 0.4190)+VISC40×(-0.9512 to -1.307)+D976×(0.4957 to 0.6700)+CLOUD×(0.1086 to 0.2069)+(31.57 to 42.38), where (X) equals ppmv of cetane improver ranging from 0 to 10,000 ppmv, CN is cetane number of the product at a concentration of cetane improver ranging from 0 to 10,000 ppmv, T10 is the temperature at which 10% of the product boils off, ANPT is the aniline point of the product, VISC40 is the viscosity of the product at 40° C. according to ASTM D445, D976 is the cetane index of the product according to ASTM D976, and CLOUD is the cloud point of the product. 21. A method as claimed in claim 1 wherein the predictive equation is a non-linear equation comprising: CN(X)=T50×(0.1700 to 0.2163)+T90×(-0.08637 to -0.1073)+SPGR×(-238.4 to -285.5)+FIAAROM×(-0.1123 to -01931)+CLOUD×(0.1574 TO 0.2419)+(237.8 TO 282.1), where (x) equals ppmv of cetane improver ranging from 0 TO 10,000 ppmv, CN is cetane number of the product at the concentration of cetane improver of from 0 to 10,000 ppmv, T50 is the temperature at which 50% of the product boils off, T90 is the temperature at which 90% of the product boils off, SPGR is the specific (API) gravity of the product, FIAAROM is the aromatics content of the product according to ASTM D1319, and CLOUD is the cloud point of the product. 22. A method as claimed in claim 1 wherein the predictive equation is a non-linear equation comprising: CN(X)=T10×(0.05286 to 0.1377)+T50×(0.09925 to 0.1391)+T90(-0.03274 to -0.08732)+SPGR×(-319.2 to -422.0)+CLOUD×(0.1158 TO 0.2220)+(295.1 to 374.5), where (X) equals ppmv of cetane improver ranging from 0 to 10,000 ppmv, CN is cetane number of the product at the concentration of cetane improver of from 0 to 10,000 ppmv, T10 is the temperature at which 10% of the product boils off, T50 is the temperature at which 50% of the product boils off, T90 is the temperature at which 90% of the product boils off, SPGR is the specific gravity of the product, and CLOUD is the cloud point of the product. 23. A method as claimed in claim 1 wherein the predictive equation is a non-linear equation comprising: CN(X)=ANPT×(0.2171 to 0.3534)+VISC40(-0.9091 to -1.335)+D976×(0.6519 to 0.9365), where (X) equals ppmv of cetane improver ranging from 0 to 10,000 ppmv, CN is cetane number of the product at the noted concentration of cetane improver of from 0 to 10,000 ppmv, ANPT is the aniline point of the product, VISC40 is the viscosity of the product at 40° C. according to ASTM D445, and D976 is the cetane index of the product according to ASTM D976. 24. A method as claimed in claim 1 wherein the predictive equation is a non-linear equation comprising: CN(X)=T10×(-0.4433 to -0.7223)+T50×T90×(0.000346 to 0.000483)+(T90)2×(-0.000992 to -0.001226)+T10/SPGR×(0.4254 to 0.7272)+T90/SPGR×(0.4263 to 0.5420)+(-64.40 to -81.53), where (X) equals ppmv of cetane improver ranging from 0 to 10,000 ppmv, CN is cetane number of the product at the noted concentration of cetane improver of from 0 to 10,000 ppmv, T10 is the temperature at which 10% of the product boils off, T50 is the temperature at which 50% of the product boils off, T90 is the temperature at which 90% of the product boils off, and SPGR is the specific gravity of the product. oft; US-5563786, 19961000, Torii; US-5568589, 19961000, Hwang; US-5606850, 19970300, Nakamura; US-5621291, 19970400, Lee; US-5634237, 19970600, Paranjpe; US-5666792, 19970900, Mullins; US-5677927, 19971000, Fullerton et al.; US-5684696, 19971100, Rao et al.; US-5687169, 19971100, Fullerton; US-5757646, 19980500, Talbot et al.; US-5787545, 19980800, Colens; US-5838562, 19981100, Gudat et al.; US-5841259, 19981100, Kim et al., 318/587; US-5867800, 19990200, Leif; US-6031862, 20000200, Fullerton et al.; US-6088644, 20000700, Brandt et al.; US-6112143, 20000800, Allen et al.; US-6128574, 20001000, Diekhans; US-6297773, 20011000, Fullerton et al.; US-6300903, 20011000, Richards et al.; US-6338013, 20020100, Ruffner, 701/023; US-6339735, 20020100, Peless et al., 701/023
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