On-line calibration process

• US 20030158680A1
• Filed: 01/21/2003
• Published: 08/21/2003
• Est. Priority Date: 07/19/2000
• Status: Active Grant

• Current Assignee: Shell Oil Company (Shell Plc)
• Original Assignee: Shell Oil Company (Shell Plc)
• Inventors: Viel, Frederic, Hupkes, Wim
• Granted Patent: US 7,359,830 B2
• Time in Patent Office: Days
• US Class Current: 702/85
• CPC Class Codes: G05B 13/02 electric
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G05B 17/02 electric

Abstract

Method for automatic on-line calibration of process models for real-time prediction of process quality from raw process measurements by collecting raw process data, processing data collected through a mathematical model to obtain a prediction of the quality, processing this prediction through two independent dynamic transfers functions thus creating two intermediate signals, storing the two intermediate signals obtained as a function of time in history, retrieving, at the time of a real and validated measurement of the quality, from the history the absolute minimum and maximum values of the two intermediate signals in the time period corresponding to a minimum and maximum specified deadtime, which values define the minimum and maximum prediction possible, calculating the deviation as being the difference between the real and validated measurement and the area encompassed between the minimum and maximum prediction possible as obtained.

Claims

1. A method for automatic on-line calibration of a process model for real-time prediction of process quality from raw process measurements which method comprises

a) collecting raw process data,

b) processing data collected in step a) through the process model to obtain a prediction of the quality,

c) processing this prediction through dynamic transfer functions thus creating two intermediate signals,

d) storing the two intermediate signals obtained in step c) as a function of time in history,

e) retrieving at the time of a real and validated measurement of the quality from the history the absolute minimum and maximum value of the two intermediate signals in the time period corresponding to a minimum and maximum specified deadtime, which values define the minimum and maximum prediction possible,

f) calculating the deviation as being the difference between the real and validated measurement and the area encompassed between the minimum and maximum prediction possible as obtained in step e),

g) proceeding with step i) if the absolute value of the deviation obtained in step f) is zero, or, proceeding with step h) if the absolute value of the deviation obtained in step f) is larger than zero,

h) incorporating the deviation into the process model, and

i) repeating steps a)-h).

2. A method according to claim 1, in which as process model a Multiple Linear Regression model is used.

3. A method according to claim 1, in which as process model a Linear Dynamic Model is used.

4. A method according to claim 1, in which as process model a Radial Basis Function Neural Network is used.

5. A method according to one or more of claims 1-4, in which in step h) a Kalman filter method is used to incorporate the deviation into the process model by adjusting its linear parameters thereby upgrading the prediction and improving the process model by self learning.

6. A method according to claim 5, in which the Kalman filter is used in step h) under non steady-state conditions of the process.

7. A method according to any one of claims 1-6, wherein the prediction in step (c) is processed through two independent dynamic transfer functions.