Method of predicting and optimizing production

• US 6,611,735 B1
• Filed: 11/17/1999
• Issued: 08/26/2003
• Est. Priority Date: 11/17/1999
• Status: Expired due to Fees

• Current Assignee: Afton Chemical Intangibles LLC (Newmarket Corporation)
• Original Assignee: Ethyl Corporation (Newmarket Corporation)
• Inventors: Henly, Timothy J., Stocky, Thomas Patrick
• Primary Examiner(s): Warden, Jill
• Assistant Examiner(s): SINES, BRIAN J
• Application Number:
  US09/441,964
• Time in Patent Office: 1,378 Days
• Field of Search: 700/266, 700/31, 700/272, 259/340, 327/300, 219/494, 702/27, 702/30, 443/24, 208/138
• US Class Current: 700/266
• CPC Class Codes: G05B 21/00 Systems involving sampling of the variable controlled G05B13/00 - G05B19/00 take precedence
• 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

Abstract

A process for the prediction and the optimization of the output of a plant producing products form incoming materials. The incoming materials are classified according to various physical characteristics and costs. The desired or ordered products are also classified according to price and physical requirements. The incoming materials information and the product information is entered into a database which is accessible by a computing device. The computing device then calculates the optimal production process by calculating a plurality of production cycles and selecting the cycle with the optimum profitability. The computing device is programmed with non-linear equations derived from a regressive analysis of data collected from samples of incoming materials and products.

Claims

1. A method of accurately predicting a characteristic of a product to be prepared by a processing plant, comprising

identifying an incoming material, said material having at least one measured property,

selecting at least one product to be prepared from said incoming material by said processing plant, said product having at least one desired characteristic,

selecting a process to be used by the processing plant to produce the product, said process having an effect upon the characteristic of the product,

calculating a predicted value of the characteristic of the product utilizing a predictive equation, wherein the predictive equation is generated by regressive analysis of an accumulation of data relating the measured property of the incoming material, the process, and the characteristic of the product;

wherein the incoming material is a petroleum feedstock, the product is diesel fuel, the process is admixing cetane improver, and the characteristic of the product is cetane number, and

wherein the measured property 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.

2. A method as claimed in claim 1, wherein the measured property is selected from the group consisting of hydrocarbon type, carbon content, hydrogen content, sulfur content, nitrogen content, API gravity, distillation range, cetane number, aniline point, heat content, kinematic viscosity, cloud point, pour point, and flash point.

3. A method as claimed in claim 1, wherein the at least two additional properties are T90, aniline point, viscosity, and cloud point.

4. A method as claimed in claim 1, wherein the at least two additional properties are T90, cloud point, and aromatics content.

5. A method as claimed in claim 1, wherein the at least two additional properties are T10, T90, and cloud point.

6. A method as claimed in 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:

9. A method as claimed in claim 4, wherein the predictive equation is a set of non-linear equations comprising:

10. A method as claimed in claim 5, wherein the predictive equation is a set of non-linear equations comprising:

11. A method as claimed in claim 6, wherein the predictive equation is a set of non-linear equations comprising:

12. A method as claimed in claim 7, wherein the predictive equation is a set of non-linear equations comprising:

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.

20. A method as claimed in claim 1 wherein the predictive equation is a non-linear equation comprising:

21. A method as claimed in claim 1 wherein the predictive equation is a non-linear equation comprising:

22. A method as claimed in claim 1 wherein the predictive equation is a non-linear equation comprising:

23. A method as claimed in claim 1 wherein the predictive equation is a non-linear equation comprising:

24. A method as claimed in claim 1 wherein the predictive equation is a non-linear equation comprising:

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.