Method and system for assessing and optimizing crude selection

• US 20050050009A1
• Filed: 08/18/2003
• Published: 03/03/2005
• Est. Priority Date: 08/18/2003
• Status: Active Grant

• Current Assignee: BL Technologies Inc. (Ashland Global Holdings, Inc.)
• Original Assignee: General Electric Company
• Inventors: Dion, Michael, Saha, Angshuman, Winslow, Malcolm Craig, Tyagi, Rajesh, Repoff, Thomas, Gardner, Martha, Sarkar, Abhinanda, Wilson, Roy, Au, Shirley
• Granted Patent: US 7,966,331 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A method and system for assessing and optimizing crude selection are provided. A predictive engine uses data from a database to execute at least one predictive performance model and/or at least one risk assessment model designed to optimize or improve refining operations during a refining process. The predictive engine takes as input key crude information corresponding to a particular crude or crude blend, e.g., at least one crude slate, and refinery operating parameters and/or conditions corresponding to a specific refinery and uses desirability metrics to assess the similarity to data in the database. Based on the resulting output, at least one predictive performance and/or at least one risk assessment model uses the output to predict performance or risk measures of refining the particular crude or crude blend using the specific refinery during the refining process, the probability of problems occurring during the refining process, the distribution of the problems throughout the refining process, etc.

Claims

1. A system for assessing and optimizing crude selection comprising:

a database storing data related to at least one crude or crude blend; and

a predictive engine having programmable instructions configured for execution by at least one processor for accessing the database to obtain data and for executing at least one predictive performance and/or risk assessment model designed to optimize or improve a refining process.

2. The system in accordance with claim 1, wherein the predictive engine takes as input crude information corresponding to at least one crude slate and at least one refinery operating parameter and/or condition and uses desirability metrics to assess similarity of the input to data in the database.

3. The system in accordance with claim 2, wherein the at least one refinery operating parameter and/or condition corresponds to a specific refinery, and wherein the at least one predictive performance or risk assessment model executed by the predictive engine predicts performance or risk measures of refining the at least one crude slate using the specific refinery for running the refining process, probability of problems occurring during the refining process, and distribution of the problems throughout the refining process.

4. The system in accordance with claim 1, wherein the predictive engine accesses treatment options stored within the database suitable for optimizing performance of the refining process.

5. The system in accordance with claim 1, wherein the programmable instructions or a portion thereof are stored on a computer readable medium or included in a computer data signal embodied in a transmission medium.

6. The system in accordance with claim 1, wherein the predictive engine comprises:

a crude search module which takes as input at least one crude name and/or at least one chemical or other characteristic of the at least one crude identifiable by the at least one crude name and outputs information with respect to at least one crude stored in the database, wherein the at least one crude output by the crude search module corresponds to at least one crude identifiable by the at least one crude name, or corresponds to at least one crude having at least one chemical or other property similar to at least one chemical or other property of the at least one crude identifiable by the at least one crude name;

an operating parameters/conditions search module which takes as input at least one refinery operating parameter and/or condition and outputs information stored in the database indicating at least one refinery having at least one identical or similar operating parameter and/or condition compared to the at least one refinery operating parameter and/or condition input; and

a crude slate and chemicals selection module which takes as input the information output by the crude search module and the information output by the operating parameters/conditions search module, and outputs at least one proposed crude slate, chemical treatment and/or performance or risk parameter.

7. The system in accordance with claim 6, wherein the crude slate and chemicals selection module includes a first tier, wherein the first tier identifies at least one crude slate stored in the database which is similar to at least one user-desired crude slate by scoring each crude slate component of the at least one user-desired crude slate based on how well the crude slate component satisfies user criteria, and combines all individual scores of the at least one user-desired crude slate to provide a composite crude slate score;

wherein the first tier further scores each individual operating parameter and/or condition based on how well the individual operating parameter and/or condition satisfies the user criteria for that operating parameter and/or condition and outputs an operational score, and then combines all individual operational scores to provide a composite operational score; and

wherein the first tier further determines a highest total overall score by combining the composite crude slate and composite operational scores.

8. The system in accordance with claim 7, wherein the crude slate and chemicals selection module further includes a second tier, wherein the second tier includes as an input at least information derived by the first tier and obtains predicted response parameters of interest for selected crude slates, operational parameters and/or conditions, and/or chemical treatments using the at least one predictive performance model.

9. The system in accordance with claim 1, wherein the predictive engine executes at least one optimization algorithm for the refining process.

10. The system in accordance with claim 9, wherein the at least one predictive performance model and/or the at least one risk assessment model is a type of model selected from the group consisting of linear regression models;

logistic regression models;

non-linear regression models;

classification and regression trees and extensions thereof;

multiple additive regression splines and extensions thereof;

partial least squares regression models;

generalized additive models;

neural networks and extensions thereof, such as projection pursuit regression;

simulation models;

expert system-based models, such as Bayesian Belief Networks;

theoretical calculation models;

engineering economic models;

financial risk models;

decision analytic models; and

engineering process models based on chemistry, physics and engineering principles, such as reaction kinetics and thermodynamics, mass transfer, energy transfer, separation processes, and fluid dynamics.

11. A method for assessing and optimizing crude selection comprising the steps of:

accessing a database for obtaining data related to at least one stored crude or crude blend; and

executing at least one predictive performance and/or risk assessment model designed to optimize or improve a refining process for at least one crude or crude blend.

12. The method in accordance with claim 11, further comprising the steps of:

taking as input crude information corresponding to the at least one crude or crude blend and at least one refinery operating parameter and/or condition; and

using desirability metrics to assess similarity of the input to data in the database, including the at least one stored crude or crude blend.

13. The method in accordance with claim 12, wherein the at least one refinery operating parameter and/or condition corresponds to a specific refinery, and wherein the at least one predictive performance or risk assessment model predicts performance or risk measures of refining the at least one crude or crude blend using the specific refinery for running the refining process, probability of problems occurring during the refining process, and distribution of the problems throughout the refining process.

14. The method in accordance with claim 11, further comprising the step of accessing treatment options stored within the database suitable for improving or optimizing performance of the refining process.

15. The method in accordance with claim 11, further comprising the step of executing at least one optimization algorithm for the refining process.

16. The method in accordance with claim 15, wherein the at least one predictive performance model and/or the at least one risk assessment model is a type of model selected from the group consisting of linear regression models;

logistic regression models;

non-linear regression models;

classification and regression trees and extensions thereof;

multiple additive regression splines and extensions thereof;

partial least squares regression models;

generalized additive models;

neural networks and extensions thereof, such as projection pursuit regression;

simulation models;

expert system-based models, such as Bayesian Belief Networks;

theoretical calculation models;

engineering economic models;

financial risk models;

decision analytic models; and

engineering process models based on chemistry, physics and engineering principles, such as reaction kinetics and thermodynamics, mass transfer, energy transfer, separation processes, and fluid dynamics.

17. A computer readable medium storing a set of instructions configured for execution by at least one processor for performing the steps of:

accessing a database for obtaining data related to at least one stored crude or crude blend; and

executing at least one predictive performance or risk assessment model designed to optimize or improve a refining process for at least one crude or crude blend.

18. The computer readable medium in accordance with claim 17, further performing the steps of:

taking as input crude information corresponding to the at least one crude or crude blend and at least one refinery operating parameter and/or condition; and

using desirability metrics to assess similarity of the input to data in the database, including the at least one stored crude or crude blend.

19. The computer readable medium in accordance with claim 18, wherein the at least one refinery operating parameter and/or condition corresponds to a specific refinery, and wherein the at least one predictive performance and/or risk assessment model predicts performance or risk measures of refining the at least one crude or crude blend using the specific refinery for running the refining process, probability of problems occurring during the refining process, and distribution of the problems throughout the refining process.

20. The computer readable medium in accordance with claim 17, further performing the step of accessing treatment options stored within the database suitable for optimizing performance of the refining process.

21. The computer readable medium in accordance with claim 17, further performing the step of executing at least one optimization algorithm for the refining process.

22. The computer readable medium in accordance with claim 21, wherein the at least one predictive performance model and/or the at least one risk assessment model is a type of model selected from the group consisting of linear regression models;

logistic regression models;

non-linear regression models;

classification and regression trees and extensions thereof;

multiple additive regression splines and extensions thereof;

partial least squares regression models;

generalized additive models;

neural networks and extensions thereof, such as projection pursuit regression;

simulation models;

expert system-based models, such as Bayesian Belief Networks;

theoretical calculation models;

engineering economic models;

financial risk models;

decision analytic models; and

engineering process models based on chemistry, physics and engineering principles, such as reaction kinetics and thermodynamics, mass transfer, energy transfer, separation processes, and fluid dynamics.