Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over MUKUND et al. US 2023/0037667 A1 in view of BLEVINS US 2007/168057 A1.
MUKUND teaches:
1. A method comprising:
monitoring, with a processor [processor 2606], a behavior of a distributed control system of an industrial facility [Petroleum Refining equipment 2626] with respect to at least a set of uncontrolled variables, a set of controlled variables, and a set of monitored variables; [data collector 2610 uses historical data from 2622, MV – manipulated variable, CV – controlled variable, and DV – disturbance variable]
training, with the processor [neural network trainer 212], a model [neural network model 216] of the behavior of the distributed control system based on the monitored behavior of the distributed control system, the processor utilizing at least one of artificial intelligence or machine learning to train the model; [para. 0080, “Neural network model 216 can be configured to act as an artificial neural network capable of learning to perform and improve upon tasks relating to controlling oil refinery processes.”]
MUKUND does not teach the following limitation, however, BLEVINS teaches:
identifying, with the processor, based on the model, a subset of controlled variables of the set of controlled variables for optimization; [para. 0099, “The operator may view the step responses of each of the control and auxiliary variables to each of the different manipulated variables and, during the process, select the one control or auxiliary variable that is best responsive to that manipulated variable. Typically, the operator will try to choose the control or manipulated variable that has the best combination of the highest steady state gain and the fastest response time to the manipulated variable.” And para. 0100, “As will be understood, the operator may be enabled to select the subset of M control and auxiliary variables that will be used as inputs the MPC control algorithm which is especially useful when there are numerous ones of these variables.”]
MUKUND further teaches:
monitoring, with the processor, a behavior of the industrial facility without the use of the distributed control system with respect to at least [Fig. 26 sensors, para. 0224, “Sensors 2624 may be configured to provide measurements of environmental data to plant controller 2602 as inputs for making control decisions. In some embodiments, the information from sensors 2624 acts as CV's, MV's, DV's, TVs or any combination thereof for historical data or real-time data of system 100.”]
optimizing, with the processor [optimizer 2618], based on the monitored behavior of the industrial facility without the use of the distributed control system,
a target result [para. 0216, optimizes objective function or cost function to determine optimal values] and
restrict variation of values of the set of monitored values during a change in one or more values of the uncontrolled variables; [constraint generator 3840] and
executing, with a processor, a control system for the industrial facility based on the optimization of [Fig. 26 control signal generator 2620]
It would have been obvious to a person having ordinary skill in the art before the time of filing to combine the teachings of BLEVINS with those of MUKUND. A person having ordinary skill in the art would have been motivated to combine the teachings because Blevins teaches that selecting a subset of input variables for MPC can “reduce conditioning problems” (See para. 0102).
MUKUND teaches:
2. The method of claim 1, wherein optimizing the subset of controlled variables to obtain the target result and restrict variation of values of the set of monitored values during a change in one or more values of the uncontrolled variables comprises: optimizing, with the processor, the subset of controlled variables to obtain the target result while preventing a value of one of the monitored variables from being at least a defined amount from a threshold. [Fig. 38, Fig. 39 and para. 0329 and 0331]
MUKUND teaches:
3. The method of claim 1, wherein optimizing the subset of controlled variables to obtain a target result and restrict variation of values of the set of monitored values during a change in one or more values of the uncontrolled variables comprises: optimizing, with the processor, the subset of controlled variables to obtain the target result while preventing a value of one of the monitored variables from being at least a percentage from a threshold. [Fig. 38, Fig. 39 and para. 0241, 0329 and 0331]
MUKUND teaches:
4. The method of claim 1, wherein optimizing the subset of controlled variables to obtain a target result and restrict variation of values of the set of monitored values during a change in one or more values of the uncontrolled variables comprises: optimizing, with the processor, the subset of controlled variables to obtain the target result and maintain values of the monitored variables during the change in one or more values of the uncontrolled variables. [Fig. 38, Fig. 39 and para. 0241-0242, 0329]
MUKUND teaches:
5. The method of claim 1, wherein the set of uncontrolled variables comprises at least one of an ambient temperature, a condenser pressure, a low-pressure turbine efficiency, a high-pressure turbine efficiency, a steam generator tube opening, or a variable speed pump degradation. [para. 0209]
MUKUND teaches:
6. The method of claim 1, wherein the set of controlled variables comprises at least one of a control rod position, a circulator speed, a feedwater pump speed, or a turbine control valve. [para. 0207]
MUKUND teaches:
7. The method of claim 1, wherein the set of monitored variables comprises at least one of a reactor power level, an outlet reactor temperature, an inlet reactor pressure, an inlet reactor mass flow, a secondary side steam generator outlet temperature, a secondary side steam generator outlet pressure, or a secondary side steam generator inlet mass flow. [para. 0208]
MUKUND teaches:
8. The method of claim 1, wherein training a model, with the processor, based on the monitored behavior of the distributed control system, the processor utilizing at least one of artificial intelligence or machine learning to train the model comprises: determining a multivariate relationship between one or more variables of the set of controlled variables, the set of uncontrolled variables, and the set of monitored variables utilizing at least one of artificial intelligence or machine learning. [paras. 0211, 0213 and 0331-0332]
MUKUND teaches:
9. The method of claim 1, wherein the processor utilizes genetic programming to optimize, based on the monitored behavior of the industrial facility without the use of the distributed control system, the subset of controlled variables to obtain a target result and restrict variation of values of the set of monitored values during a change in one or more values of the uncontrolled variables. [para. 0350]
Blivens teaches:
10. The method of claim 1, wherein: training a model, with the processor, based on the monitored behavior of the distributed control system comprises: training a plurality of models, with the processor, based on the monitored behavior of the distributed control system, the processor utilizing at least one of artificial intelligence or machine learning to train the plurality of models; and identifying, with the processor, based on the model, a subset of controlled variables of the set of controlled variables for optimization comprises: identifying, with the processor, based on the plurality of models, the subset of controlled variables of the set of controlled variables for optimization. [para. 0077, 0086, 0096-0097]
Regarding apparatus claims 11-20, these claims recite the functions for executing the steps of corresponding method steps above and are rejected on the same grounds and rationale as corresponding claims above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zadorojniy US 2021/0350049 A1 – teaches dimensionality reduction for optimizing a control system.
YAN et al. US 2019/0219994 A1 – teaches reducing dimensionality for training normal and abnormal models of gas turbine control system.
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/GARY COLLINS/Primary Examiner, Art Unit 2115
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