Method and system for greenhouse gas emissions performance assessment and allocation
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G06Q-010/00
G05B-013/02
출원번호
UP-0075626
(2005-03-08)
등록번호
US-7693725
(2010-05-20)
발명자
/ 주소
Trout, Billy L.
Broadfoot, Robert
Jones, Richard B.
Hileman, Michael
출원인 / 주소
HSB Solomon Associates, LLC
대리인 / 주소
Akin Gump Strauss Bauer & Feld, LLP
인용정보
피인용 횟수 :
3인용 특허 :
8
초록▼
The present invention provides a system and method for determining equivalency factors for use in comparative performance analysis of industrial facilities by determining a target variable and a plurality of characteristics of the target variable. Each of the plurality of characteristics is ranked a
The present invention provides a system and method for determining equivalency factors for use in comparative performance analysis of industrial facilities by determining a target variable and a plurality of characteristics of the target variable. Each of the plurality of characteristics is ranked according to value. Based on ranking value, the characteristics are divided into categories. Based on the sorted and ranked characteristics, a data collection classification system is developed. Data is collected according to the data collection classification system. The data is validated, and based on the data, an analysis model is developed. The analysis model then calculates the equivalency factors for use in one embodiment in performance measurement and equitable benchmarking of green house gas (GHG) emissions from industrial facilities for the purposes of allocating GHG emission allowances for permits, licenses, etc.
대표청구항▼
We claim: 1. A method for determining equivalency factors comprising the steps of: selecting a plurality of first principle characteristics; selecting a target variable for the first principle characteristics, wherein the target variable is a metric for greenhouse gas emissions; selecting a set of
We claim: 1. A method for determining equivalency factors comprising the steps of: selecting a plurality of first principle characteristics; selecting a target variable for the first principle characteristics, wherein the target variable is a metric for greenhouse gas emissions; selecting a set of first principle characteristics having more than a user-specified variation in the target variable; determining a set of developed characteristics; computing a set of equivalency factors from a plurality of selected set of first principle characteristics and the set of developed characteristics, wherein the set of equivalency factors is computed with an equation, {αj, j=1 to n}, using an optimization procedure, wherein n is the number of the selected first principle characteristics and the developed characteristics, and the optimization procedure is a linear optimization or nonlinear optimization executed by a computer processor to minimize a total error, {εi, i=1 to m}, between a set of actual target variable values and a set of predicted target variable values, wherein m is a number of facilities, and the set of predicted target variable values is calculated by: TV i = ∑ j = 1 n α j * C ij + ɛ i , i = 1 , m , wherein Cij=the first principle characteristic or developed characteristic j of the ith facility; and outputting the set of equivalency factors onto a computer data medium. 2. The method of claim 1, further comprising the step of: ranking the plurality of first principle characteristics by their variation of the target variable. 3. The method of claim 1, further comprising the step of: selecting a set of first principle characteristics having less than a user-specified first principle characteristic interdependence. 4. The method of claim 1, further comprising the step of: selecting a set of first principle characteristics with less than a user-specified first principle characteristic co-variance. 5. The method of claim 1, wherein the set of developed characteristics comprises a user-specified function of the plurality of first principle characteristics. 6. The method of claim 1, wherein the plurality of first principle characteristics are comprised of ordinal or rank-based variables. 7. The method of claim 1, wherein the plurality of selected first principle characteristics are selected from one or more of: the set of first principle characteristics with more than a user-specified level of percentage target variable variation influence on the target variable, the set of first principle characteristics with less than a user-specified first principle characteristic interdependence, and the set of first principle characteristics with less than a user-specified first principle characteristic co-variance. 8. The method of claim 1, wherein the total error includes absolute error, wherein absolute error has the form: Absolute Error = [ ∑ i = 1 m A i - P i p ] 1 p , p≧1 where: Ai=an actual value of target variable for facility i; Pi=a predicted value of target variable for facility i; and a selected value. 9. The method of claim 1, wherein the optimization procedure to minimize the total error incorporates range and value constraints on the set of equivalency factors. 10. The method of claim 1, wherein the plurality of first principle characteristics are attributes of refinery facilities. 11. The method of claim 1, wherein the facilities comprise one or more of: a catalytic cracking unit, a catalytic reforming unit, a sulfur recovery unit, a storage vessel, a fluid coking unit, a wastewater unit, a cooling tower, a blowdown system, a vacuum unit, crude unit, a steam boiler, a flare/thermal oxidizer, a pipeline, a turbine, a furnace, a compressor, a vessel loading/unloading unit, and a gasoline rack unit. 12. The method of claim 1, further comprising the step of: removing data outliers. 13. The method of claim 1, further comprising the step of: constraining the set of equivalency factors to predetermined ranges. 14. The method of claim 1, further comprising the step of: constraining the plurality of characteristics to predetermined ranges. 15. A system comprising: a server, comprising: a processor, and a storage subsystem; a database stored by the storage subsystem comprising: a plurality of data corresponding to equivalency factors; and a computer program stored by the storage subsystem comprising instructions that, when executed, cause the processor to: select a plurality of first principle characteristics; select a target variable for the plurality of first principle characteristics, wherein the target variable is a metric for greenhouse gas emissions; select a set of first principle characteristics having more than a user-specified variation in the target variable; determine a set of developed characteristics, compute a set of equivalency factors from a plurality of selected first principle characteristics and the set of developed characteristics, wherein the set of equivalency factors is computed with an equation, {αj, j=1 to n}, using an optimization procedure, wherein n is the number of selected first principle characteristics and the developed characteristics, and the optimization procedure is a linear optimization or nonlinear optimization executed by a computer processor to minimize total error, {εi, i=1 to m}, between a set of actual target variable values and a set of predicted target variable values, wherein m is a number of facilities, and the set of predicted target variable values is calculated by: TV i = ∑ j = 1 n α j * C ij + ɛ i , i = 1 , m , wherein Cij=the first principle characteristic or developed characteristic j of the ith facility; and output the equivalency factors onto a computer data medium. 16. The system of claim 15, the computer program further comprising instructions that, when executed, cause the processor to: rank the plurality of first principle characteristics by their variation of the target variable. 17. The system of claim 15, the computer program further comprising instructions that, when executed, cause the processor to: select a set of first principle characteristics having less than a user-specified first principle characteristic interdependence. 18. The system of claim 15, the computer program further comprising instructions that, when executed, cause the processor to: select a set of first principle characteristics with less than a user-specified first principle characteristic co-variance. 19. The system of claim 15, wherein the set of developed characteristics comprises a user-specified function of the plurality of first principle characteristics. 20. The system of claim 15, wherein the plurality of first principle characteristics are comprised of ordinal or rank-based variables. 21. The system of claim 15, wherein the plurality of selected first principle characteristics are selected from one or more of: the set of first principle characteristics with more than a user-specified level of percentage target variable variation influence on the target variable, the set of first principle characteristics with less than a user-specified first principle characteristic interdependence, and the set of first principle characteristics with less than a user-specified first principle characteristic co-variance. 22. The system of claim 15, wherein the total error includes absolute error, wherein absolute error has the form: Absolute Error = [ ∑ i = 1 m A i - P i p ] 1 p , p≧1 where: Ai=an actual value of target variable for facility i; Pi=a predicted value of target variable for facility i; and a selected value. 23. The system of claim 15, wherein the non-linear optimization procedure to minimize the total error incorporates range and value constraints on the set of equivalency factors. 24. The method of claim 15, wherein the plurality of first principle characteristics are attributes of refinery facilities. 25. The system of claim 15, wherein the facilities comprise one or more of: a catalytic cracking unit, a catalytic reforming unit, a sulfur recovery unit, a storage vessel, a fluid coking unit, a wastewater unit, a cooling tower, a blowdown system, a vacuum unit, crude unit, a steam boiler, a flare/thermal oxidizer, a pipeline, a turbine, a furnace, a compressor, a vessel loading/unloading unit, and a gasoline rack unit. 26. The system of claim 15, the computer program further comprising instructions that, when executed, cause the processor to: remove data outliers. 27. The system of claim 15, the computer program further comprising instructions that, when executed, cause the processor to: constrain the set of equivalency factors to predetermined ranges. 28. The system of claim 15, the computer program further comprising instructions that, when executed, cause the processor to: constrain the plurality of characteristics to predetermined ranges. 29. A computer implemented method for optimizing emissions comprising the steps of: determining a target variable for a first facility, wherein the target variable is a metric for greenhouse gas emissions; identifying a plurality of characteristics of the first facility that influence the target variable; optimizing the plurality of characteristics of the first facility that influence the target variable, wherein the optimizing is performed by a computer processor; generating an equivalency factor as a function of at least one of the optimized plurality of characteristics of the first facility that influence the target variable comprising the step of: minimizing an error between an actual value for the target variable and a predicted value for the target variable; and storing the equivalency factor in a computer data medium. 30. The method of claim 29, wherein the first facility comprises one or more of: a catalytic cracking unit, a catalytic reforming unit, a sulfur recovery unit, a storage vessel, a fluid coking unit, a wastewater unit, a cooling tower, a blowdown system, a vacuum unit, crude unit, a steam boiler, a flare/thermal oxidizer, a pipeline, a turbine, a furnace, a compressor, a vessel loading/unloading unit, and a gasoline rack. 31. The method of claim 29, wherein the step of optimizing the plurality of characteristics of the first facility that influence the target variable is performed using a non-linear optimization method. 32. The method of claim 29, wherein the step of optimizing the plurality of characteristics of the first facility that influence the target variable is performed using a linear optimization method. 33. The method of claim 29, wherein the plurality of characteristics of the first facility that influence the target variable comprises: a plurality of first principle characteristics of the first facility that influence the target variable; and a set of developed characteristics of the first facility, each a function of at least one of the plurality of first principle characteristics of the first facility that influence the target variable. 34. The method of claim 33, wherein the equivalency factor is a function of at least one first principle characteristic of the first facility that influences the target variable and at least one developed characteristic of the first facility. 35. The method of claim 29, wherein each of the plurality of characteristics of the first facility that influence the target variable is a function of at least one first principle characteristic of the first facility that influences the target variable. 36. The method of claim 29, further comprising the step of: classifying the plurality of characteristics of the first facility that influence the target variable responsive to relationships among the plurality of characteristics of the first facility that influence the target variable. 37. The method of claim 36, wherein the classification of the plurality of characteristics of the first facility that influence the target variable classifies the plurality of characteristics of the first facility that influence the target variable into more than two categories. 38. The method of claim 29, further comprising the steps of: determining a percentage variation value for each of the plurality of characteristics of the first facility that influence the target variable; dividing the plurality of characteristics of the first facility that influence the target variable into at least two categories based on the percentage variation value; and grouping each of the plurality of characteristics of the first facility that influence the target variable into the at least two categories responsive to a relationship among the plurality of characteristics of the first facility that influence the target variable. 39. The method of claim 29, further comprising the step of: comparing the first facility and a second facility using the equivalency factor. 40. The method of claim 29, further comprising the steps of: adjusting the value of the target variable for the first facility using the equivalency factor; adjusting the value of the target variable for a second facility using the equivalency factor; and comparing the adjusted value of the target variable for the first facility and the adjusted value of the target variable for the second facility. 41. The method of claim 29, further comprising the step of: selecting a benchmark facility. 42. The method of claim 41, further comprising the step of: calculating a performance gap value between the first facility and the benchmark facility. 43. The method of claim 29, further comprising the step of: calculating a performance gap value between the first facility and a second facility using the equivalency factor. 44. The method of claim 29, further comprising the step of: classifying the first facility into a performance subgroup in accordance with the ratio of an actual target variable from the first facility to the actual target variable from the first facility adjusted using the equivalency factor. 45. The method of claim 29, further comprising the step of: ranking the first facility and a second facility in accordance with an actual value of the target variable from the first facility adjusted using the equivalency factor and an actual value of the target variable from the second facility adjusted using the equivalency factor. 46. The method of claim 29, further comprising the step of: calculating performance gaps using subgroups derived through the use of the equivalency factor. 47. The method of claim 29, wherein the first facility is a refinery. 48. The method of claim 29, wherein the first facility is a power generating plant. 49. The method of claim 29, further comprising the step of: removing data outliers. 50. The method of claim 29, further comprising the step of: constraining the equivalency factor to a predetermined range. 51. The method of claim 29, further comprising the step of: constraining the plurality of characteristics to predetermined ranges. 52. A computer implemented method for optimizing emissions comprising the steps of: selecting a first facility, wherein the first facility is either a refinery or a power generation plant, and wherein the first facility comprises one or more of: a catalytic cracking unit, a catalytic reforming unit, a sulfur recovery unit, a storage vessel, a fluid coking unit, a wastewater unit, a cooling tower, a blowdown system, a vacuum unit, a crude unit, a steam boiler, a flare/thermal oxidizer, a pipeline, a turbine, a furnace, a compressor, a vessel unloading/loading unit, and a gasoline rack; determining a target variable for the first facility, wherein the target variable is a metric for greenhouse gas emissions; identifying a plurality of first principle characteristics of the first facility; determining a percentage variation value for each of the plurality of first principle characteristics of the first facility; dividing the plurality of first principle characteristics of the first facility into at least two categories based on the percentage variation value; grouping the plurality of first principle characteristics of the first facility into the at least two categories responsive to a relationship among the plurality of first principle characteristics of the first facility; selecting a plurality of first principle characteristics of the first facility that influence the target variable; collecting data with respect to the set of first principle characteristics of the first facility that influence the target variable; creating a set of developed characteristics of the first facility, wherein the set of developed characteristics of the first facility is a function of one or more of the set of first principle characteristics of the first facility that influence the target variable; optimizing the plurality of first principle characteristics of the first facility that influence the target variable and the set of developed characteristics of the first facility, using a computer processor and at least one of: a linear optimization method and a non-linear optimization method; generating an equivalency factor based on at least one member of the optimized plurality of first principle characteristics of the first facility that influence the target variable and the optimized set of developed characteristics of the first facility comprising the steps of: minimizing an error between an actual value for the target variable and a predicted value for the target variable; and removing data outliers; constraining the equivalency factor to a predetermined range; storing the equivalency factor in a computer data medium; selecting a second facility; and calculating a performance gap value between the first facility and the second facility using the equivalency factor. 53. A system comprising: a server, comprising: a processor, and a storage subsystem; a computer program stored by the storage subsystem comprising instructions that, when executed, cause the processor to: determine a target variable for a first facility, wherein the target variable is a metric for greenhouse gas emissions; identify a plurality of characteristics of the first facility that influence the target variable; optimize the plurality of characteristics of the first facility that influence the target variable; generate an equivalency factor as a function of at least one of the optimized plurality of characteristics of the first facility that influence the target variable, the computer program further comprising instructions that, when executed, cause the processor to: minimize an error between an actual value for the target variable and a predicted value for the target variable; and store the equivalency factor in the storage subsystem. 54. The system of claim 53, wherein the first facility comprises one or more of: a catalytic cracking unit, a catalytic reforming unit, a sulfur recovery unit, a storage vessel, a fluid coking unit, a wastewater unit, a cooling tower, a blowdown system, a vacuum unit, crude unit, a steam boiler, a flare/thermal oxidizer, a pipeline, a turbine, a furnace, a compressor, a vessel loading/unloading unit, and a gasoline rack. 55. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to optimize the plurality of characteristics of the first facility that influence the target variable using a non-linear optimization method. 56. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to optimize the plurality of characteristics of the first facility that influence the target variable using a linear optimization method. 57. The system of claim 53, wherein the plurality of characteristics of the first facility that influence the target variable comprise: a plurality of first principle characteristics of the first facility that influence the target variable; and a set of developed characteristics of the first facility, each a function of at least one of the plurality of first principle characteristics of the first facility that influence the target variable. 58. The system of claim 57, wherein the equivalency factor is a function of at least one first principle characteristic of the first facility that influences the target variable and at least one developed characteristic of the first facility. 59. The system of claim 53, wherein each of the plurality of characteristics of the first facility that influence the target variable is a function of at least one first principle characteristic of the first facility that influences the target variable. 60. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to: classify the plurality of characteristics of the first facility that influence the target variable responsive to relationships among the plurality of characteristics of the first facility that influence the target variable. 61. The system of claim 60, wherein the classification of the plurality of characteristics of the first facility that influence the target variable classifies the plurality of characteristics of the first facility that influence the target variable into more than two categories. 62. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to: determine a percentage variation value for each of the plurality of characteristics of the first facility that influence the target variable; divide the plurality of characteristics of the first facility that influence the target variable into at least two categories based on the percentage variation value; and group each of the plurality of characteristics of the first facility that influence the target variable into the at least two categories responsive to a relationship among the plurality of the characteristics of the first facility that influence the target variable. 63. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to: use the equivalency factor to compare the first facility and a second facility. 64. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to: adjust the value of the target variable for the first facility using the equivalency factor; adjust the value of target variable for a second facility using the equivalency factor; and compare the adjusted value of the target variable for the first facility and the adjusted value of the target variable for the second facility. 65. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to: select a benchmark facility. 66. The system of claim 65, the computer program further comprising instructions that, when executed, cause the processor to: calculate a performance gap value between the first facility and the benchmark facility. 67. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to: calculate a performance gap value between the first facility and a second facility using the equivalency factor. 68. The system of claim 53, the computer program further comprising instruction that, when executed, cause the processor to: classify the first facility into a performance subgroup in accordance with the ratio of an actual target variable from the first facility to the actual target variable from the first facility adjusted using the equivalency factor. 69. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to: rank the first facility and a second facility in accordance with an actual value of the target variable from the first facility adjusted using the equivalency factor and an actual value of the target variable from the second facility adjusted using the equivalency factor. 70. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to: calculate performance gaps using subgroups derived through the use of the equivalency factor. 71. The system of claim 53, wherein the first facility is a refinery. 72. The system of claim 53, wherein the first facility is a power generating plant. 73. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to remove data outliers. 74. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to: constrain the equivalency factor to a predetermined range. 75. The system of claim 53, the computer program further comprising instructions that, when executed, cause the processor to: constrain the plurality of characteristics to predetermined ranges. 76. A system comprising: a server, comprising: a processor, and a storage subsystem; a database stored by the storage subsystem comprising: a plurality of data corresponding to equivalency factors; and a computer program stored by the storage subsystem comprising instructions that, when executed, cause the processor to: select a first facility, wherein the first facility is either a refinery or a power generation plant, and wherein the first facility comprises one or more of: a catalytic cracking unit, a catalytic reforming unit, a sulfur recovery unit, a storage vessel, a fluid coking unit, a wastewater unit, a cooling tower, a blowdown system, a vacuum unit, a crude unit, a steam boiler, a flare/thermal oxidizer, a pipeline, a turbine, a furnace, a compressor, a vessel unloading/loading unit, and a gasoline rack; determine a target variable for the first facility, wherein the target variable is a metric for greenhouse gas emissions; identify a plurality of first principle characteristics of the first facility; determine a percentage variation value for each of the plurality of first principle characteristics of the first facility; divide the plurality of first principle characteristics of the first facility into at least two categories based on the percentage variation value; group the plurality of first principle characteristics of the first facility into the at least two categories responsive to a relationship among the plurality of first principle characteristics of the first facility; select a plurality of first principle characteristics of the first facility that influence the target variable; collect data with respect to the plurality of first principle characteristics of the first facility that influence the target variable; create a set of developed characteristics of the first facility, wherein the set of developed characteristics of the first facility is a function of one or more of the plurality of first principle characteristics of the first facility that influence the target variable; optimize the plurality of first principle characteristics of the first facility that influence the target variable and the set of developed characteristics of the first facility, wherein optimization is performed using at least one of: a linear optimization method and a non-linear optimization method; generate the equivalency factor based on at least one member of the optimized plurality of first principle characteristics of the first facility that influence the target variable and the optimized set of developed characteristics of the first facility comprising the steps of: minimizing an error between an actual value for the target variable and a predicted value for the target variable; and removing data outliers; constrain the equivalency factor to a predetermined range; store the equivalency factor in the storage subsystem; select a second facility; and calculate a performance gap value between the first facility and the second facility using the equivalency factor.
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