System and method for dynamic multi-objective optimization of machine selection, integration and utilization

• US 9,729,639 B2
• Filed: 09/30/2008
• Issued: 08/08/2017
• Est. Priority Date: 08/10/2001
• Status: Active Grant

• Current Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Original Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Inventors: Discenzo, Frederick M., Sustaeta, Angel, Baier, John J., Lin, Ka-Hing, Snyder, Ric, Theron, John Christopher, Funderburk, Mark, Sugars, Michael Eugene
• Primary Examiner(s): Allen, Akiba
• Application Number:
  US12/242,525
• Publication Number:
  US 20090204267A1
• Time in Patent Office: 3,234 Days
• Field of Search: 705 1443, 705 11, 705 711, 307 77
• 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 processor configured to facilitate execution of computer-executable instructions that at least;

construct an energy-supply model for a production facility based on variable costs associated with a plurality of energy-generating assets included within the production facility;

build an energy-demand model for the production facility based on at least one of a sub-model of production, a user defined time horizon, or a predicted energy demand; and

combine the energy-supply model and the energy-demand model to create an energy optimization model for the production facility, wherein the energy-supply model includes a probability of achieving a predicted value associated with a defined capacity for the plurality of energy-generating assets and a probability of sustaining the predicted value for a specified period of time, wherein the probability of sustaining the predicted value for the specified period of time is determined as a function of a reduction of environmental emissions respectively generated by the plurality of energy-generating assets and an interdependency between energy usage data representing an energy usage of the production facility, energy cost data representing an energy cost incurred by the production facility, revenue data representing a revenue generated by the production facility, throughput data representing a product throughput of the production facility, life cycle data representing a machine life cycle cost, and longevity data representing a machine longevity; and

a memory coupled to the processor that stores at least one of the energy-supply model, the energy demand model or the energy-optimization model; and

control, based on the energy optimization model, at least one industrial machine.

2. The apparatus of claim 1, wherein the processor is further configured to facilitate the execution of the computer-executable instructions to build one or more economic sub-models for the plurality of energy-generating assets included within the production facility.

3. The apparatus of claim 2, wherein, based on first data representing a generating capacity, second data representing an efficiency curve, or third data representing respective operating costs for the plurality of energy-generating assets, the one or more economic sub-models for the plurality of energy-generating assets are utilized to determine respective financial profiles for the plurality of energy-generating assets.

4. The apparatus of claim 2, wherein the one or more economic sub-models for the plurality of energy-generating assets included within the production facility are consolidated to construct the energy-supply model.

5. The apparatus of claim 1, wherein the processor is further configured to facilitate the execution of the computer-executable instructions to utilize at least one of prognostics or optimization to construct the sub-model of production.

6. The apparatus of claim 1, wherein the processor is further configured to facilitate the execution of the computer-executable instructions to utilize a modeling framework to solve for an economic supply optimum or expose a most cost-effective energy-generating asset of the plurality of energy-generating assets included within the production facility.

7. The apparatus of claim 1, wherein the processor is further configured to facilitate the execution of the computer-executable instructions to join the energy-supply model and the energy demand model in one or more of in series, in parallel, nested, or in a networked structure according to a defined function to solve an identified economic problem.

8. A non-transitory machine readable storage medium having stored thereon machine-executable instructions that, in response to execution, cause a system including at least one processor to perform operations, comprising:

constructing an energy-supply model for a production facility based on variable costs associated with a plurality of energy-generating assets included within the production facility, wherein the variable costs include a cost associated with exceeding a threshold environmental emission;

building an energy-demand model for the production facility based on at least one of a sub-model of production, a user defined time horizon, or a predicted energy demand; and

combining the energy-supply model and the energy-demand model to create an energy optimization model for the production facility, wherein the energy-demand model evolves over time without external input and includes a state transition link that indicates a cost, a risk, and a relative probability associated with transitioning from a first state to a second state, and wherein the energy optimization model is created as a function of an interdependency between energy data representing an energy usage of the production facility, energy cost data representing an energy cost incurred by the production facility, revenue data representing a revenue generated by the production facility, product throughput data representing a product throughput of the production facility, machine life cycle cost data representing a machine life cycle cost, and machine longevity data representing a machine longevity; and

controlling, based on the energy optimization model, at least one industrial machine.

9. The non-transitory machine readable storage medium of claim 8, wherein the constructing further comprises building one or more economic sub-models for the plurality of energy-generating assets included within the production facility.

10. The non-transitory machine readable storage medium of claim 9, wherein based on data representing a generating capacity, data representing an efficiency curve, or data representing respective operating costs of the plurality of energy-generating assets the one or more economic sub-models for the plurality of energy-generating assets are utilized to determine respective financial profiles of the plurality of energy-generating assets.

11. The non-transitory machine readable storage medium of claim 9, wherein the one or more economic sub-models for the plurality of energy-generating assets included within the production facility are consolidated to construct the energy-supply model.

12. The non-transitory machine readable storage medium of claim 8, wherein the building further comprises utilizing at least one of prognostics or optimization to construct the sub-model of production.

13. The non-transitory machine readable storage medium of claim 8, wherein the combining further comprises utilizing a modeling process to solve for an economic supply optimum or determine an energy-generating asset included in the plurality of energy-generating assets that satisfies a defined function with respect to cost per energy unit.

14. The non-transitory machine readable storage medium of claim 8, wherein the combining further comprises joining the energy-supply model and the energy demand model in one or more of in series, in parallel, nested, or in a networked structure to solve an economic problem.

15. A method operable in an industrial automation environment, comprising:

constructing, by a system including at least one processor, an energy-supply model as a function of variable costs associated with a plurality of energy-generating assets included within a production facility, wherein the energy-supply model is a prediction of a defined capacity of the plurality of energy-generating assets, the energy-supply model runs in parallel with an actual operation of the plurality of energy-generating assets, and at least one deviation between the defined capacity and the actual operation are used to identify a prospective failure associated with one of the plurality of energy-generating assets;

building, by the system, an energy-demand model as a function of at least one of a sub-model of production, a user defined time horizon, or a predicted energy demand; and

combining, by the system, the energy-supply model and the energy-demand model to create an energy optimization model for the production facility as a function of a reduction of environmental emissions associated with the production facility and a correlation between energy usage data representing an energy usage of the production facility, energy cost data representing an energy cost incurred by the production facility, revenue data representing a revenue generated by the production facility, throughput data representing a product throughput by the production facility, life cycle cost data representing a machine life cycle cost, and longevity data representing a machine longevity; and

controlling, based on the energy optimization model, at least one industrial machine.

16. The method of claim 15, the constructing further comprises building one or more economic sub-models for the plurality of energy-generating assets included within the production facility.

17. The method of claim 16, further comprising utilizing, as a function of data representing a generating capacity, data representing an efficiency capacity, data representing an efficiency curve, or data representing a plurality of operating costs associated with the plurality of energy-generating assets, the one or more economic sub-models of the plurality of energy-generating assets to determine a plurality of financial profiles for the plurality of energy-generating assets.

18. The method of claim 15, wherein the building further comprises utilizing at least one of prognostics or optimization to construct the sub-model of production.

19. The method of claim 15, wherein the combining further comprises utilizing a model to solve a defined function with respect to an economic supply of the plurality of energy-generating assets included within the production facility.

20. The method of claim 15, wherein the combining further comprises utilizing a model to an energy-generating asset included in the plurality of energy-generating assets that satisfies a defined function with respect to cost per energy unit.

21. The method of claim 15, further comprising determining, by the system, an economic return over a planning time period including scheduling a series of different production levels in response to a change that occurs over the planning time period.