System and method for dynamic multi-objective optimization of machine selection, integration and utilization
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.
1. A device operable in an industrial automation environment, the device comprising:
a processor configured to facilitate execution of computer-executable instructions that perform operations, comprising:
determining, by an artificial intelligence component, a first probability representing a probability of achieving a predicted value associated with a defined capacity for a group of energy generating assets included within a production facility;
determining, by the artificial intelligence component, a second probability representing a probability of sustaining the predicted value for a defined duration of time, wherein the probability of sustaining the predicted value for the defined duration of time is determined based on a reduction of emissions generated by the group of energy generating assets and an interdependency between energy use data representative of energy usage by the production facility, energy cost data representative of costs expended by the production facility, and life cycle data representative of industrial machine life cycle costs associated with a group of industrial machines within the production facility; and
as a function of the first probability and the second probability controlling the group of industrial machines within the production facility.
2. The device of claim 1 , wherein the operations further comprise generating, by the artificial intelligence component, the predicted value based on a quality metric representing a product manufactured by the group of industrial machines.
3. The device of claim 1 , wherein the operations further comprise generating, by the artificial intelligence component, the predicted value based on quality data representing feed stock quality information of feed stock used to produce a product manufactured by the group of industrial machines.
4. The device of claim 1 , wherein the operations further comprise identifying, by the artificial intelligence component, a critical state of a group of critical states to avoid in regard to controlling the group of industrial machines.
5. The device of claim 4 , wherein the critical state of the group of critical states is generated as a function of sensor data received from the group of industrial machines.
6. The device of claim 5 , wherein the sensor data represents temperature data received from a temperature sensor associated with each industrial machine comprising the group of industrial machines.
7. The device of claim 5 , wherein the sensor data represents failure data representing a predicted bearing failure of a bearing associated each industrial machine comprising the group of industrial machines.
8. The device of claim 1 , wherein the operations further comprise identifying, by the artificial intelligence component, a desirable state of a group of desirable states to achieve in regard to controlling the group of industrial machines.
9. A machine readable storage device comprising executable instructions that, in response to execution, cause a system comprising a processor to perform operations, comprising:
employing an artificial intelligence component to determine a first probability representing a probability of achieving a predicted value associated with a defined capacity for a group of energy generating assets included within a production facility;
employing the artificial intelligence component to determine a second probability representing a probability of sustaining the predicted value for a defined duration of time, wherein the probability of sustaining the predicted value for the defined duration of time is determined based on a reduction of emissions generated by the group of energy generating assets and an interdependency between energy use data representative of energy usage by the production facility, energy cost data representative of costs expended by the production facility, and life cycle data representative of industrial machine life cycle costs associated with a group of industrial machines within the production facility; and
based on the first probability and the second probability controlling the group of industrial machines within the production facility.
10. The machine readable storage device of claim 9 , wherein the operations further comprise generating the predicted value based on a data representing a product manufactured by the group of industrial machines.
11. The machine readable storage device of claim 9 , wherein the operations further comprise generating the predicted value based on data representing feed stock quality information associated with a group of feed stock utilized to produce a product manufactured by the group of industrial machines.
12. The machine readable storage device of claim 9 , wherein the operations further comprise identifying, based on use of an implicitly trained classifier, a critical state of a group of critical states to avoid in regard to controlling the group of industrial machines.
13. The machine readable storage device of claim 9 , wherein the operations further comprise identifying, based on use of an explicitly trained classifier, a desirable state of a group of desirable states to achieve in regard to controlling the group of industrial machines.
14. A closed loop monitoring and control system, comprising:
an artificial intelligence component that determines a first probability representing a probability of achieving a predicted value associated with a defined capacity for a group of energy generating assets included within a production facility;
the artificial intelligence component further determines a second probability representing a probability of sustaining the predicted value for a defined duration of time, wherein the probability of sustaining the predicted value for the defined duration of time is determined based on a reduction of emissions generated by the group of energy generating assets and an interdependency between energy use data representative of energy usage by the production facility, energy cost data representative of costs expended by the production facility, and life cycle data representative of industrial machine life cycle costs associated with a group of industrial machines within the production facility; and
a control component that based on the first probability and the second probability controls the group of industrial machines within the production facility.
15. The closed loop monitoring and control system of claim 14 , wherein the artificial intelligence component determines the predicted value based on a quality metric data representing a quality of a product manufactured by the group of industrial machines.
16. The closed loop monitoring and control system of claim 14 , wherein the artificial intelligence component determines the predicted value based on quality data representing feed stock quality information of feed stock used to produce a product manufactured by the group of industrial machines.
17. The closed loop monitoring and control system of claim 14 , wherein the artificial intelligence component identifies a critical state of a group of critical states to avoid in regard to controlling the group of industrial machines.
18. The closed loop monitoring and control system of claim 17 , wherein the critical state of the group of critical states is determined as a function of sensor data received from the group of industrial machines.
19. The closed loop monitoring and control system of claim 18 , wherein the sensor data represents temperature data received from a temperature sensor associated with each industrial machine comprising the group of industrial machines.
20. The closed loop monitoring and control system of claim 14 , wherein the artificial intelligence component identifies a desirable state of a group of desirable states to achieve in regard to controlling the group of industrial machines.