Method for chemical addition utilizing adaptive optimization

• US 6,535,795 B1
• Filed: 08/09/1999
• Issued: 03/18/2003
• Est. Priority Date: 08/09/1999
• Status: Expired due to Fees

• Current Assignee: Baker Hughes Incorporated (Baker Hughes Company)
• Original Assignee: Baker Hughes Incorporated (Baker Hughes Company)
• Inventors: Fischer, David M., Tacchi, Kenneth J., Zetlmeisl, Michael J., Blaschke, Marilyn W., Schroeder, Myron E.
• Primary Examiner(s): Warden, Jill
• Assistant Examiner(s): BROWN, JENNINE M
• Application Number:
  US09/370,311
• Time in Patent Office: 1,317 Days
• Field of Search: 700/266, 700/265, 700/273, 210/739, 210/709, 210/85, 210/96.1, 210/143, 210/704, 210/705, 210/723, 210/726-728, 210/749, 706/15, 706/23, 706/273
• US Class Current: 700/266
• CPC Class Codes: G05B 13/029 using neural networks and expert systems

Abstract

The present invention provides a method for chemical addition utilizing adaptive process control optimizations having a combination of expert system(s), neural network(s) and genetic algorithm(s).

Claims

1. A method for adaptively controlling the rate of addition of a chemical to a process comprising:

conducting a process which is controlled by a distributed control system;

utilizing an adaptive process control optimization system in a host relationship to the distributed control system wherein the process control optimization system provides (i) a plurality of goal seeking intelligent software objects further comprising sensor software objects providing current data, historical data, and statistical data;

(ii) expert system software objects providing at least one associated rules knowledge base;

(iii) adaptive models software objects providing at least one modeling methodology;

(iv) predictor software objects providing at least one predictor selection criteria;

(v) optimizer software objects providing at least one goal and providing at least one process constraint; and

(vi) communications translator software objects providing one or more data communications protocols for a given sampling delta, comprising the concurrent steps of;

determining, within the optimizer software objects, output data values which achieve the goals without violating the process constraints;

examining, within the expert system software objects, the predictive models that achieve the goals without violating the process constraints, wherein the adaptive process control optimization system changes its models to adapt to current process conditions;

determining, within the expert system software objects, at least one adaptive intervention;

providing the at least one adaptive intervention as an input to a distributed control system; and

utilizing the at least one adaptive intervention input to the distributed control system for controlling the process.

2. The method of claim 1, further comprising a step utilizing an on-line sensor to provide key performance indicator measurements as input to at least one communications translator software object, wherein the expert system provides data validation.

3. The method of claim 1, wherein at least one expert system software object provides an associated rules knowledge base for validation of the current data, wherein the expert system validates control commands.

4. The method of claim 1, wherein at least one adaptive model software object provides one or more modeling methodologies for calculating a key performance indicator based upon the current data, historical data, and statistical data, wherein the adaptive process control optimization system monitors its own performance and modifies its own initial configuration to improve performance according to its initial optimizing goals.

5. The method of claim 4, wherein the key performance indicator indicates residual chemical levels in a process fluid;

conditions conducive to salt removal from hydrocarbons;

conditions conducive to promotion of chemical reactions;

conditions conducive to treatment of waste water, conditions conducive to treatment of finished fuels, indicates product stability;

fouling conditions, corrosive conditions, foaming conditions, azeotropic conditions, emulsifying conditions, conditions conducive to byproduct formation, metal passivation and biological growth produced by the process.

6. The method of claim 1, wherein at least one of the optimizer software object provides a desired chemical addition rate to the process, wherein the adaptive model changes over time and adapts to a designated task.

7. The method of claim 6, wherein at least one of the optimizer software object provides a desired scavenger chemical addition rate to the process.

8. The method of claim 6, wherein at least one of the optimizer software object provides a desired flocculant chemical addition rate to the process.

9. The method of claim 6, wherein at least one of the data communications protocols enables the process control optimization system to output a value to the distributed control system representing the desired chemical addition rate to the process.

10. The method of claim 9, further comprising the steps of utilizing at least one expert system software object to provide an associated rules knowledge base to make a decision to authenticate the desired chemical addition rate to the process and output the value to the distributed control system or to reject the desired chemical rate provided by at least one of the optimizer software objects and to provide a newly calculated desired chemical addition rate.

11. The method of claim 1, further comprising:

alerting a user when a process condition has an integrated effect over time.

12. The method of claim 1, further comprising:

adjusting chemical addition rates at a global optimum.

13. The method of claim 1, wherein the adaptive model is a neural network.

14. The method of claim 1, further comprising:

Using adaptive models produce a variety of competing adaptive models from which a predictor object selects the best fit to actual data for a given sampling delta.

15. The method of claim 1, wherein the predictor objects and adaptive models learn how to model the processes they represent.

16. The method of claim 1, wherein the ISOs provide trained models to other ISOs.

17. The method of claim 1, wherein a large offset causes an optimizer object to procreate a new model for a new operating mode.

18. The method of claim 1, further comprising:

making optimizing control actions in a manner consistent with management objectives and goals.

19. A method for adaptively controlling the rate of addition of a chemical to a process comprising:

conducting a process which is controlled by a distributed control system;

utilizing an adaptive process control optimization system in a host relationship to the distributed control system wherein the process control optimization system provides (i) a plurality of goal seeking intelligent software objects further comprising sensor software objects providing current data, historical data, and statistical data;

(ii) expert system software objects providing at least one associated rules knowledge base;

(iii) adaptive models software objects providing at least one modeling methodology;

(iv) predictor software objects providing at least one predictor selection criteria;

(v) optimizer software objects providing at least one goal and providing at least one process constraint; and

(vi) communications translator software objects providing one or more data communications protocols for a given sampling delta, comprising the concurrent steps of;

determining, within the optimizer software objects, output data values which achieve the goals without violating the process constraints;

examining, within the expert system software objects, the predictive models that achieve the goals without violating the process constraints, wherein the adaptive process control optimization system changes its models to adapt to current process conditions, wherein at least one adaptive model software object provides one or more modeling methodologies for calculating a key performance indicator based upon the current data, historical data, and statistical data, wherein the adaptive process control optimization system monitors its own performance and modifies its own initial configuration to improve performance according to its initial optimizing goals, wherein the key performance indicator indicates residual chemical levels in a process fluid, conditions conducive to salt removal from hydrocarbons;

conditions conducive to promotion of chemical reactions, conditions conducive to treatment of waste water, conditions conducive to treatment of finished fuels, indicates product stability;

fouling conditions, corrosive conditions, foaming conditions, azeotropic conditions, emulsifying conditions, conditions conducive to byproduct formation, metal passivation and biological growth produced by the process, wherein at least one of the optimizer software object provides a desired chemical addition rate to the process, wherein the adaptive model changes over time and adapt to designed task;

determining, within the expert system software objects, at least one adaptive intervention;

providing the at least one adaptive intervention as an input to a distributed control system;

utilizing the at least one adaptive intervention input to the distributed control system for controlling the process;

utilizing an on-line sensor to provide key performance indicator measurements as input to at least one communications translator software object, wherein the expert system provides data validation, wherein at least one expert system software object provides an associated rules knowledge base for validation of the current data, wherein the expert system validates control commands.

20. A method for adaptively controlling the rate of addition of a chemical to a process comprising:

conducting a process which is controlled by a distributed control system;

utilizing an adaptive process control optimization system in a host relationship to the distributed control system wherein the process control optimization system provides (i) a plurality of goal seeking intelligent software objects further comprising sensor software objects providing current data, historical data, and statistical data;

(ii) expert system software objects providing at least one associated rules knowledge base;

(iii) adaptive models software objects providing at least one modeling methodology;

(iv) predictor software objects providing at least one predictor selection criteria;

(v) optimizer software objects providing at least one goal and providing at least one process constraint; and

(vi) communications translator software objects providing one or more data communications protocols for a given sampling delta, comprising the concurrent steps of;

determining, within the optimizer software objects, output data values which achieve the goals without violating the process constraints;

examining, within the expert system software objects, the predictive models that achieve the goals without violating the process constraints, wherein the adaptive process control optimization system changes its models to adapt to current process conditions;

determining, within the expert system software objects, at least one adaptive intervention;

providing the at least one adaptive intervention as an input to a distributed control system;

utilizing the at least one adaptive intervention input to the distributed control system for controlling the process;

alerting a user when a process condition has an integrated effect over time;

adjusting chemical addition rates at a global optimum;

Using adaptive models produce a variety of competing adaptive models from which a predictor object selects the best fit to actual data for a given sampling delta;

wherein the predictor objects and adaptive models learn how to model the processes they represent;

wherein the ISOs provide trained models to other ISOs, wherein a large offset causes an optimizer object to procreate a new model for a new operating mode; and

making optimizing control actions in a manner consistent with management objectives and goals.