SYSTEM AND METHOD FOR DYNAMIC MULTI-OBJECTIVE OPTIMIZATION OF MACHINE SELECTION, INTEGRATION AND UTILIZATION

• US 20090210081A1
• Filed: 09/30/2008
• Published: 08/20/2009
• Est. Priority Date: 08/10/2001
• Status: Abandoned Application

• Current Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Original Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Inventors: Baier, John J., Sustaeta, Angel, Discenzo, Frederick M., Funderburk, Mark, Lin, Ka-Hing, Sugars, Michael Eugene, Snyder, Ric, Theron, John Christopher
• Application Number:
  US12/242,529
• Publication Number:
  US 20090210081A1
• Time in Patent Office: Days
• US Class Current: 700/99
• CPC Class Codes: G05B 13/024 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 13/0285 using neural networks and fuzzy logic
• 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 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: H04L 67/125 involving control of end-device applications over a network
• CPC Class Codes: Y02P 90/82 Energy audits or management systems therefor

Abstract

The invention provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation over a time horizon.

Claims

1. An apparatus operable in an industrial automation environment, the apparatus comprising:

a memory that retains instructions related to;

determining a current production rate of an industrial process;

predicting a theoretical capacity of the industrial process; and

creating a visualization of the current production rate or the theoretical capacity of the industrial process, the visualization employed to drive the industrial process from the current production rate to the theoretical capacity; and

a processor, coupled to the memory, configured to execute the instructions retained in the memory.

2. The apparatus of claim 1, the memory further retains instructions related to:

identifying a current bottleneck or historical bottleneck to achieving the theoretical capacity of the industrial process; and

based at least in part on the identifying, mitigating the current bottleneck or the historical bottleneck to drive the industrial process from the current production rate to the theoretical capacity, the identifying includes at least one of determining a cost-benefit of removing the current bottleneck or the historical bottleneck or is driven by a financial return.

3. The apparatus of claim 1, the visualization permits tactile interaction by a human intermediary to manipulate, in real-time, the visualization in order to adjust at least one of the current production rate or the theoretical capacity of the industrial process.

4. The apparatus of claim 3, the tactile interaction of the visualization by the human intermediary includes varying at least one factor of production to a target capacity associated with the at least one factor of production.

5. The apparatus of claim 1, the memory further retains instructions related to:

performing dynamic constraint profiling based at least in part on at least one of a current operating condition or a predicted operating condition; and

linking to a corporate financial business system and automatically quantifying a potential gain associated with increased capacity affiliated with driving up the industrial process from the current production rate to the theoretical capacity, the linking further including associating with at least one of an external database or web site to obtain one or more of current or predicted energy or commodity costs.

6. The apparatus of claim 1, the memory further retains instructions related to:

utilizing a built-in framework for instantaneous analysis of potential scenarios associated with an optimal capacity by one or more of product, shift, or disparate production site; and

based at least in part on the utilizing, analyzing tradeoffs associated with a plurality of choices available to achieve the optimal capacity of the one or more of product, shift, or disparate production site.

7. The apparatus of claim 6, the analyzing tradeoffs further comprising at least one of proactively capturing a profitable opportunity or proactively shedding a non-profitable opportunity.

8. The apparatus of claim 1, the memory further retains instructions related to:

ascertaining by production site the production site'"'"'s top constraints;

quantifying a latent capacity available across the top constraints; and

based at least in part on the quantifying, generating financial profiles of production opportunities restricted by the top constraints.

9. A method utilized in an industrial automation environment, comprising:

determining a current production rate of an industrial process;

predicting a theoretical capacity of the industrial process; and

creating a visualization of the current production rate or the theoretical capacity of the industrial process, the visualization employed to drive the industrial process from the current production rate to the theoretical capacity.

10. The method of claim 9, further comprising:

identifying a current bottleneck or historical bottleneck to achieving the theoretical capacity of the industrial process; and

based at least in part on the identifying, mitigating the current bottleneck or the historical bottleneck to drive the industrial process from the current production rate to the theoretical capacity.

11. The method of claim 9, the visualization permits tactile interaction by a human intermediary to manipulate, in real-time, the visualization in order to adjust at least one of the current production rate or the theoretical capacity of the industrial process.

12. The method of claim 11, the tactile interaction of the visualization by the human intermediary includes varying at least one factor of production to a target capacity associated with the at least one factor of production.

13. The method of claim 9, further comprising:

performing dynamic constraint profiling based at least in part on at least one of a current operating condition or a predicted operating condition; and

linking to a corporate financial business system and automatically quantifying a potential gain associated with increased capacity affiliated with driving up the industrial process from the current production rate to the theoretical capacity.

14. The method of claim 9, further comprising:

employing a built-in framework for instantaneous analysis of potential scenarios associated with an optimal capacity by one or more of product, shift, or disparate production site; and

based at least in part on the employing, analyzing tradeoffs associated with a plurality of choices available to achieve the optimal capacity of the one or more of product, shift, or disparate production site.

15. The method of claim 9, further comprising:

ascertaining by production site the production site'"'"'s top constraints;

quantifying a latent capacity available across the top constraints; and

based at least in part on the quantifying, generating financial profiles of production opportunities restricted by the top constraints.

16. A system operable in an industrial automation environment, comprising:

means for determining a current production rate of an industrial process;

means for predicting a theoretical capacity of the industrial process; and

means for creating a visualization of the current production rate or the theoretical capacity of the industrial process, the visualization employed to drive the industrial process from the current production rate to the theoretical capacity.

17. The system of claim 16, further comprising:

means for identifying a current bottleneck or historical bottleneck to achieving the theoretical capacity of the industrial process; and

based at least in part on the means for identifying, means for mitigating the current bottleneck or the historical bottleneck to drive the industrial process from the current production rate to the theoretical capacity.

18. The system of claim 16, further comprising:

means for performing dynamic constraint profiling based at least in part on at least one of a current operating condition or a predicted operating condition; and

means for linking to a corporate financial business system and automatically quantifying a potential gain associated with increased capacity affiliated with driving up the industrial process from the current production rate to the theoretical capacity.

19. The system of claim 16, further comprising:

means for utilizing a built-in framework for instantaneous analysis of potential scenarios associated with an optimal capacity by one or more of product, shift, or disparate production site; and

based at least in part on the means for utilizing, means for analyzing tradeoffs associated with a plurality of choices available to achieve the optimal capacity of the one or more of product, shift, or disparate production site.

20. The system of claim 16, further comprising:

means for ascertaining by production site the production site'"'"'s top constraints;

means for quantifying a latent capacity available across the top constraints; and

based at least in part on the means for quantifying, means for generating financial profiles of production opportunities restricted by the top constraints.

21. The system of claim 16, the means for visualization further comprising:

means for generating one or more plausible scenario;

means for data mining information from one or more product process database; and

means for assigning at least one of a probabilistic number to a predicted operating condition or a probabilistic number to a potential gain, a most likely gain, or a least likely gain.