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

US9729639B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-9729639-B2
Application numberUS-24252508-A
CountryUS
Kind codeB2
Filing dateSep 30, 2008
Priority dateAug 10, 2001
Publication dateAug 8, 2017
Grant dateAug 8, 2017

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  1. Title

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  2. Abstract

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  3. Assignees and inventors

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  4. Key dates

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  5. First independent claim

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  6. CPC / IPC classifications

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  7. Citations and related patents

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Abstract

Official abstract text for this publication.

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.

First claim

Opening claim text (preview).

What is claimed is: 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 leas

Assignees

Inventors

Classifications

  • in which a parameter or coefficient is automatically adjusted to optimise the performance · CPC title

  • Energy audits or management systems therefor · CPC title

  • H04L67/125Primary

    involving control of end-device applications over a network · CPC title

  • 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 title

  • Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling · CPC title

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Frequently asked questions

Answers are generated from the same data shown on this page.

What does patent US9729639B2 cover?
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 ma…
Who is the assignee on this patent?
Sustaeta Angel, Lin Ka-Hing, Snyder Ric, and 6 more
What technology area does this patent fall under?
Primary CPC classification H04L67/125. Mapped technology areas include Electricity.
When was this patent published?
Publication date Tue Aug 08 2017 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).